Strategic Planning towards Electric Mobility in the Automotive Industry

Strategic Planning towards Electric Mobility in the Automotive Industry Der Carl-Friedrich-Gauß-Fakultät der Technischen Universität Carolo-Wilhelmina zu Braunschweig

zur Erlangung der venia legendi im Fach Betriebswirtschaftslehre angenommene Habilitationsschrift (kumulative Schrift)

von Dr. rer. pol. Karsten Kieckhäfer

2018

Preface

In this habilitation thesis, topics related to the strategic planning towards electric mobility in the automotive industry are discussed from a management perspective. The thesis is cumulative in nature, consisting of ten scientific papers. Nine out of the ten papers have already been published in renowned, peer-reviewed international journals and conference proceedings. One further manuscript is currently in the second round of revision with good prospects of being published. This thesis points out the connections between the different articles and summarizes their essential findings. The following articles form the backbone of the cumulative habitation thesis: 

Kieckhäfer, K.; Wachter, K.; Spengler, T. S. (2017): Analyzing manufacturers' impact on green products' market diffusion: The case of electric vehicles, in: Journal of Cleaner Production, Vol. 162, pp. S11–S25.



Lukas, E.; Spengler, T. S.; Kupfer, S.; Kieckhäfer, K. (2017): When and how much to invest? Investment and capacity choice under product life cycle uncertainty, in: European Journal of Operational Research, Vol. 260, pp. 1105–1114.



Thies, C.; Kieckhäfer, K.; Spengler, T. S.; Sodhi, M. S. (2017): Operations Research for sustainability assessment of products: A review, submitted to: European Journal of Operational Research (2nd revision).

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Thies, C.; Kieckhäfer, K.; Spengler, T. S. (2016): Market introduction strategies for alternative powertrains in long-range passenger cars under competition, in: Transportation Research Part D: Transport and Environment, Vol. 45, pp. 4–27.

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Hoyer, C.; Kieckhäfer, K.; Spengler, T. S. (2015): Technology and capacity planning for the recycling of lithium-ion electric vehicle batteries in Germany, in: Journal of Business Economics, Vol. 85, pp. 505–544.

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Huth, C.; Kieckhäfer, K.; Spengler, T. S. (2015): Make-or-buy strategies for electric vehicle batteries: A simulation-based analysis, in: Technological Forecasting & Social Change, Vol. 99, pp. 22–34.

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Kieckhäfer, K.; Feld, V.; Jochem, P.; Wachter, K.; Spengler, T. S.; Walther, G.; Fichtner, W. (2015): Prospects for regulating the CO2 emissions from passenger cars within the European Union after 2023, in: Zeitschrift für Umweltpolitik und Umweltrecht, Vol. 38 (4), pp. 425–450.

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Kieckhäfer, K.; Volling, T.; Spengler, T. S. (2014): A hybrid simulation approach for estimating the market share evolution of electric vehicles, in: Transportation Science, Vol. 48, pp. 651–670.

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Hoyer, C.; Kieckhäfer, K.; Spengler, T. S. (2013): Impact of mandatory rates on the recycling of lithium-ion batteries from electric vehicles in Germany, in: Nee, A. Y. C.; Song, B; Ong, S. (eds.): Reengineering manufacturing for sustainability, Proceedings of the 20th CIRP International Conference on Life Cycle Engineering, Springer, Singapore, pp. 543–548.

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Walther, G.; Wansart, J.; Kieckhäfer, K.; Schnieder, E.; Spengler, T. S. (2010): Impact assessment in the automotive industry: Mandatory market introduction of alternative powertrain technologies, in: System Dynamics Review, Vol. 26, pp. 239–261.

II

Preface

The papers reflect the results of the research I have conducted at the Chair of Production and Logistics (Prof. Dr. Thomas S. Spengler) as a subdivision of the Institute of Automotive Management and Industrial Production at Technische Universität Braunschweig as well as the Automotive Research Centre Niedersachsen as an interdisciplinary and interdepartmental research association of Technische Universität Braunschweig. All research activities have been carried out in close cooperation with different disciplines and stakeholders from science, policy, and industry, typically in long-term research projects funded by public funding bodies or industrial project partners.

Contents

Preface .................................................................................................................................................................... I Contents .............................................................................................................................................................. III List of Figures ...................................................................................................................................................... V List of Tables .....................................................................................................................................................VII List of Abbreviations ......................................................................................................................................... IX 1

Introduction ................................................................................................................................................ 1

2

Fundamentals of Electric Vehicles and Traction Batteries ............................................................... 5 2.1

Classification of Electric Vehicles ..................................................................................................................5

2.2

Design and Variants of Lithium-Ion Electric Vehicle Batteries ........................................................... 7

2.3

Production and Recycling of Lithium-Ion Electric Vehicle Batteries................................................. 8

2.4

Challenges related to the Transition towards Electric Vehicles .......................................................... 11

3

Planning Framework towards Electric Mobility ................................................................................ 13 3.1

Development of the Framework ................................................................................................................... 13

3.2

Planning Tasks of Car Manufacturers and Suppliers ............................................................................. 15

3.3

Planning Tasks of Fleet and Infrastructure Operators ......................................................................... 18

3.4

Planning Tasks of Policy-Makers................................................................................................................ 19

3.5

Research Domains of Strategic Planning towards Electric Mobility in the Automotive Industry .............................................................................................................................................................. 21

4

Planning Issues related to the Transformation of the Technology and Product Portfolio ..................................................................................................................................................... 23 4.1

Estimating the Market Share Evolution of Electric Vehicles............................................................... 23

4.2

Analyzing Product and Product Portfolio Decisions ..............................................................................25

4.3

Analyzing Market Introduction Strategies ............................................................................................... 27

4.4

Assessing the Sustainability of Products ................................................................................................... 29

5

Planning Issues related to the Transformation of the Supply Chain ............................................ 31 5.1

Analyzing Make-or-Buy Strategies for Electric Vehicle Batteries ....................................................... 31

5.2

Planning Investments in the Production of Electric Vehicle Batteries ............................................. 33

IV

Contents 5.3

6

Planning Technologies and Capacities for the Recycling of Electric Vehicle Batteries ................ 35 Impact Assessment of Policy Measures ............................................................................................... 39

6.1

Analyzing Policy Measures for Regulating the CO2 Emissions from Passenger Cars .................. 39

6.2

Assessing the Impact of Policy Measures on the Market Diffusion of Electric Vehicles ............. 41

6.3

Assessing the Impact of Mandatory Rates on the Recycling of Electric Vehicle Batteries ...........43

7

Conclusion ................................................................................................................................................ 45 7.1

Discussion and Recommendations .............................................................................................................45

7.2

Summary ............................................................................................................................................................ 49

Literature ............................................................................................................................................................. 53 Appendix .............................................................................................................................................................. 65

List of Figures

Figure 1:

Overview of different types of electric vehicles ....................................................................................... 6

Figure 2:

Production process of battery cells and systems ..................................................................................... 9

Figure 3:

Possible combinations of recycling processes and recovered materials......................................... 10

Figure 4:

Development of the number of scientific publications addressing planning problems in the context of electric mobility between 2008 and 2017 ................................................................. 12

Figure 5:

Framework of planning tasks in the context of electric mobility from a production and logistics management perspective .............................................................................................................14

Figure 6:

Concept of the hybrid modeling approach to estimate the market share evolution of electric vehicles ...............................................................................................................................................25

Figure 7:

Concept of the system dynamics model to analyze market introduction strategies ................... 28

Figure 8:

Concept of the simulation model to evaluate make-or-buy strategies for electric vehicle batteries .............................................................................................................................................................32

Figure 9:

Concept of the optimization model for technology and capacity planning in recycling networks ........................................................................................................................................................... 36

Figure 10: Simulated development of the electric vehicle stock in Germany between 2016 and 2020 for alternative designs of a purchase premium under consideration of two market scenarios........................................................................................................................................................... 42

List of Tables

Table 1:

Evaluated policy measures for regulating the CO2 emissions from passenger cars in the EU ...................................................................................................................................................................... 40

List of Abbreviations

AMaSi

Automotive Market Simulator

BAFA

Federal Office of Economic Affairs and Export Control

BEV

Battery Electric Vehicle

BMU

Federal Ministry for the Environment, Nature Conservation, and Nuclear Safety

BMWi

Federal Ministry for Economic Affairs and Energy

BOP

Balance of Plant

BSM

Braunschweig City Marketing

CCR

California Code of Regulations

CO

Carbon Monoxide

CO2

Carbon Dioxide

DEA

Data Envelopment Analysis

EC

European Commission

ETS

Emission Trading System

EU

European Union

EV

Electric Vehicle

EVI

Electric Vehicle Initiative

EREV

Extended-Range Electric Vehicle

FCEV

Fuel Cell Electric Vehicle

GHG

Greenhouse Gas

HEV

Hybrid Electric Vehicle

ICCT

International Council on Clean Transportation

ICE

Internal Combustion Engine

ICEV

Internal Combustion Engine Vehicle

IEA

International Energy Agency

IPCC

Intergovernmental Panel on Climate Change

LCA

(Environmental) Life Cycle Assessment

LCC

Life Cycle Costing

LCO

Lithium Cobalt Oxide

LCSA

Life Cycle Sustainability Assessment

X

List of Abbreviations

LFP

Lithium Iron Phosphate

MADM

Multi-Attribute Decision Making

MODM

Multi-Objective Decision Making

NCA

Nickel Cobalt Aluminum

NGV

Natural Gas Vehicle

NMC

Nickel Manganese Cobalt

NOx

Nitrogen Oxide

NPE

German National Platform for Electric Mobility

OICA

International Organization of Motor Vehicle Manufacturers

OPEC

Organization of the Petroleum Exporting Countries

PHEV

Plug-in Hybrid Electric Vehicle

PM

Particulate Matter

PZEV

Partial Zero-Emission Vehicle

SLCA

Social Life Cylcle Assessment

UBA

German Environmental Protection Agency

USA

United States of America

VDA

German Association of the Automotive Industry

VHB

German Academic Association for Business Research

ZEV

Zero-Emission Vehicle

1

Introduction

Worldwide, the automotive industry is one of the most important economic sectors with 91.6 million new motor vehicles produced globally in 2016 out of which 77.7 million units were passenger cars. 29% of the passenger car production took place in Greater China1, 24% in Europe, 18% in North America, and 15% in Japan/Korea.2 Focusing on the European Union (EU), 230 automobile assembly and engine production plants were operated in 2016.3 Thereby, a trade surplus of about €90 billion was generated due to the export of more than 6.3 million vehicles.4 Moreover, 12.6 million jobs and thus almost 6% of the total employment in the EU could be attributed directly or indirectly to the automotive industry in 2015.5 For Germany, the importance of the automotive industry is even higher. In 2015, German car manufacturers produced about 15 million passenger cars globally and thereof 9.4 million units in foreign locations. 77% of the passenger cars produced in Germany were exported.6 The value of these exports amounted to €190 billion. Overall, revenues of €400 billion were generated by about 800.000 direct employees of the automotive industry in Germany.7 New passenger car registrations amounted to 77.3 million units worldwide in 2016. With a share of 46.0% Asia was the major selling region, directly followed by North America (22.4%) and the EU (18.9%). The most important markets in the different regions were China (registration of about 23.0 million new passenger cars, market share of 29.8%), the United States of America (USA) (about 14.4 million units, 18.6%), and Germany (about 3.4 million units, 4.3%).8 Globally, motorization rates have been rocketing over the last decades, resulting in almost one billion passenger cars in use in 2015.9 The majority of the newly registered passenger cars and thus also the majority of the vehicles in use are still fueled by petrol or diesel. For instance, the 2016 registration shares by fuel type in the EU15 were 49.9% for diesel, 45.8% for petrol, 1.9% for hybrid electric vehicles (HEV), 1.2% for natural gas vehicles (NGV), LPG-fueled vehicles, and ethanol (E85) vehicles, and 1.1% for battery electric vehicles (BEV), extended-range electric vehicles (EREV), plug-in hybrid electric vehicles (PHEV), and fuel cell electric vehicles (FCEV). Thus, in total just 4.2% alternative fuel vehicles were sold.10 Similar numbers can be obtained for the vehicles in use. Here, 5.6% of all passenger cars operated in the EU were alternatively fueled in 2015.11 The globally increasing numbers of vehicle registrations and vehicles in use come along with severe ecological consequences, especially due to the combustion of fossil fuels. Overall, the complete transportation sector contributes to approximately 23% of the worldwide, anthropogenic carbon dioxide (CO2)

1 2 3 4 5 6 7 8 9 10 11

Mainland China, Hong Kong, Macau, and Taiwan. ACEA (2017), pp. 19–20. ACEA (2017), p. 26. ACEA (2017), pp. 49–50. ACEA (2017), p. 13. VDA (2016), pp. 23–25. VDA (2016), p. 42. ACEA (2017), pp. 30–32. OICA (2017). ACEA (2017), p. 38. ACEA (2017), p. 46.

2

Introduction

emissions and to more than 50% of the global oil demand.12 Within the transportation sector, road transportation is the most important contributor.13 While other economic sectors have been able to almost cap or even reduce CO2 emissions over the last years, emissions in the transport sector have still been rising.14 On a local level, the combustion of fossil fuels leads to exhaust emissions such as carbon monoxide (CO), nitrogen oxide (NOx), and particulate matter (PM) as well as noise emissions. For instance, vehicle traffic contributed to 64% of all NO2 emissions and 30% of all PM emissions in German cities between 2002 and 2012.15 These emissions have a negative impact on health and quality of life.16 Since 2015, especially NOx emissions of diesel vehicles have received increasing attention due to the uncovering that real-world emissions of several vehicle models are much higher than in test cycles during type-approval.17 Accordingly, the automotive industry has been in the center of manifold environmental discussions and efforts that are directed towards limiting global climate change and resource scarcity as well as improving health and quality of life. Most often, electric mobility is seen as a promising solution in this context. Electric cars allow for a substitution of fossil fuels and thus a significant reduction of CO2 emissions, especially when powered by renewable energy. Moreover, local exhaust and noise emissions can be diminished considerably. Consequently, governments all over the world have announced objectives related to the desired market development of electric vehicles. In Germany, for instance, one million electric vehicles with charging equipment (i.e., PHEV, EREV, BEV, and FCEV) should be on the road until 2020.18 In order to support the desired market development, regulations have been applied in all major automotive markets. First and foremost, these regulations concern the average CO2 or greenhouse gas (GHG) emissions of the vehicle fleets sold by car manufacturers that must not exceed a mandatory threshold. Up to now, car manufacturers have been mainly able to meet the thresholds by simply reducing the fuel consumption of petrol and diesel cars. Since the thresholds are intended to get stricter and stricter over time, car manufactures have to sell an increasing number of electric vehicles in the future. Typically, financial penalties apply if the threshold is exceeded. These penalties can threaten the competitiveness of single manufacturers. For instance, the average fleet emissions of new cars sold by a manufacturer in the EU must not exceed 95 grams of CO2 per kilometer by 2021, phased in from 2020. Otherwise, the manufacturers would have to pay a penalty of €95 for every gram of exceedance and each car registered.19 Due to this regulation, penalties of Volkswagen are expected to amount to €1.7 billion in 2020 alone.20 The State of California and several other states in the USA additionally require that a certain percentage of the vehicles produced and delivered for sale by large manufacturers must be (partial) zero-emission vehicles ((P)ZEV) and thus electric vehicles.21 Also, China decided to roll out an electric car quota in

12 13 14 15 16 17 18 19

20 21

IEA (2017), p. 12; OPEC (2016), p. 110. IEA (2017), p. 73; OPEC (2016), p. 110. IEA (2017), pp. 12–14. Diegmann et al. (2014), pp. 34, 44. Further information can be found, for example, in WHO (2006). Further information can be found, for example, in Baldino et al. (2017). Die Bundesregierung (2009), p. 18. European Parliament and the Council 3/11/2014. According to a legislative proposal for post-2020 targets, the average fleet emissions should be further reduced by 15% until 2025 and by 30% until 2030, European Commission 11/8/2017 . Eisert (2017). California Air Resources Board 3/22/2012; ZEV Program Implementation Task Force (2014).

Introduction

3

2019.22 In Europe and Germany, such kind of regulation is at least subject of political discussions.23 Some governments even plan a sales ban for petrol and diesel vehicles, starting between 2025 and 2040.24 The same holds true for a ban on driving petrol and diesel cars in certain city centers.25 These developments require a transition of the automotive industry towards electric mobility26, which especially challenges the business models of incumbent car manufacturers. In particular, the two following transformation processes can be identified. 

Transformation of the technology and product portfolio: In order to meet regulatory requirements, car manufacturers are under increasing pressure to produce and offer competitive electric vehicles, i.e., EREV, PHEV, and BEV. Thereby, most of the currently offered electric vehicles show severe disadvantages with regard to price, cruising range, infrastructure availability, and recharging times when compared to their conventional counterparts. This is especially due to the use of lithium-ion batteries for energy storage. Moreover, the present generation of electric vehicles is mainly based on conversion design, i.e., introducing an electrified version of an existing conventional car model (e.g., Volkswagen e-Golf). Only a few electric cars are already based on purpose design, i.e., developing completely new electric car models (e.g., BMW i3).27 As a consequence, market shares of electric cars fall short of expectations in most countries and there exist high uncertainties regarding their future market development, even though worldwide registrations of electric cars hit a new record in 2016 (approximately 750,000 registrations).28 At the same time, new competitors such as Tesla showcase how to successfully offer electric cars. As of August 2017, the newly introduced Tesla Model 3 reached more than 450,000 reservations.29 Despite the prevailing uncertainties, incumbent car manufacturers have thus to invest heavily in research and development of new electric vehicles to ensure long-term competitiveness. Volkswagen, for example, recently announced to invest more than €20 billion in the deployment of electric mobility until 2030. This is even more challenging as Volkswagen plans to simultaneously offer conventional and electric cars until then.30 Hence, they must manage the balancing act between electric and conventional cars for a long time. Similar strategies can be identified for all other incumbent car manufacturers.

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Transformation of the supply chain: In accordance with the transition towards electric cars, components of the conventional powertrain (e.g., internal combustion engine, gearbox) have to be replaced by new components such as the traction battery, the electric motor, and power electronics.31 Thereby, the share of value added in battery production alone is 30% to 40% of which 60% to 70% can be attributed to the production of battery cells.32 To maintain competitiveness, it is thus not only

22 23 24 25 26

27 28 29 30 31 32

McDonald (2017). Hoppe/Kersting (2017). Petroff (2017). Beale (2017). A further measure would be to deploy sustainable bio- and/or electrofuels to a large extent. However, this option plays currently a minor role and are out of scope of the thesis. A detailed discussion can be found, for example, in Kampker (2014), pp. 14–18. EVI (2017), p. 12. Ohnsmann (2017). Volkswagen 9/11/2017. Huth et al. (2015). VDA (2016), p. 118.

4

Introduction of utmost importance to transform the automotive industry’s technology and product portfolio but also its supply chain. The corresponding challenges are mainly twofold: First, new skills and knowledge related to the production and the recycling of electric and electro-chemical components have to be acquired and integrated into the supply chain. Second, high investments in the deployment of the necessary production and recycling capacities have to be made, taking into account the outlined uncertainties regarding the market development of electric cars as well as uncertainties regarding the prevailing battery technology in future. For instance, Volkswagen has recently started to put out a tender for the provision of battery cells and related technology worth over €50 billion,33 and Daimler announced to invest more than €1 billion in the establishment of global production network for traction batteries.34

Shaping the two outlined transformation processes is mainly a strategic planning issue. Decisions on the design of future technology and product portfolios as well as the design of future supply chains in the automotive industry have to be made. Given the complexity of these decisions, the manifold existing uncertainties, and the importance of a successful transformation of the automotive industry, expedient decision support is required. This is where this thesis seeks to make a contribution. The objective is to develop an integrated planning framework as well as simulation and optimization models for the strategic planning towards electric mobility in the automotive industry. While a rich strand of scientific literature focuses on planning issues and quantitative decision support related to the use phase of electric vehicles, models for strategic planning issues of the automotive industry have received little attention so far. This thesis covers the most important strategic planning tasks of the automotive industry, outlines their characteristics and the accompanying challenges, and showcases how simulation and optimization techniques can be used to support decision making. To this end, the thesis is structured in seven chapters. After the introduction to the topic in Chapter 1, fundamentals of electric vehicles and traction batteries are summarized in Chapter 2. The chapter includes a classification of electric vehicles, followed by aspects concerning the design, production, and recycling of traction batteries that are most relevant for the planning problems discussed in this thesis. On that basis, a planning framework towards electric mobility is developed in Chapter 3. The framework structures relevant planning tasks, which are briefly described subsequently. For those tasks that are identified to be important for the automotive industry on a strategic planning level, specific approaches are developed in the following chapters. Chapter 4 concentrates on planning issues related to the transformation of the technology and product portfolio. Planning issues related to the transformation of the supply chain are subject of Chapter 5. In Chapter 6, it is shown how the models introduced in the fourth and fifth chapter can be used for an impact assessment of policy measures from a policy as well as an industry perspective. The thesis concludes with a discussion of its contribution, recommendations for political and industrial decision makers, and a brief summary in Chapter 7.

33 34

Volkswagen 9/11/2017. Daimler 10/9/2017.

2

Fundamentals of Electric Vehicles and Traction Batteries

This chapter summarizes the fundamentals of electric vehicles and traction batteries, which serve as a basis for the discussion of planning tasks and approaches in the subsequent chapters. In Section 2.1, a classification of electric vehicles is given. Afterwards, the focus is on the design (Section 2.2) as well as the production and recycling (Section 2.3) of traction batteries. From this, challenges related to the transition towards electric vehicles are derived in Section 2.4.

2.1

Classification of Electric Vehicles

Electric vehicles can be classified by their level of electrification, the type of energy storage system used, and the possibility to use grid-supplied energy. From this, five different types of electric vehicles can be derived that are most relevant for the automotive industry (cf. Figure 1): hybrid electric vehicles (HEV), plug-in hybrid electric vehicles (PHEV), extended-range electric vehicles (EREV), battery electric vehicles (BEV), and fuel cell electric vehicles (FCEV).35 In HEV, PHEV, and EREV, two energy converters (internal combustion engine (ICE) and electric motor) as well as two energy storage systems (fuel tank and battery) are used. Thus, they are hybrid forms of conventional and innovative, electrified powertrain structures. Serial, parallel, and power-split hybrid powertrains are available. HEV are designed in a way that pure electric driving is limited to a few kilometers. The electric motor is mainly used to complement the internal combustion engine. In contrast to PHEV and EREV, HEV do not allow for external charging. Instead, electricity is fully generated on board by making use of the ICE or regenerative breaks. PHEV and EREV especially differ in the design of the powertrain. If the propulsion is fully based on a serial powertrain structure in which the internal combustion engine is only used to recharge the battery, the car is considered to be an EREV. BEV and FCEV are fully electric vehicles without any internal combustion engine. In BEV, electrical energy is stored in the traction battery, which can be recharged by plugging the vehicle into a household power outlet or into any specifically designed charging device. In FCEV, the electrical energy is produced by means of a fuel cell based on a chemical reaction of hydrogen and oxygen. To this end, FCEV need a hydrogen tank. If the produced energy is not directly used for propulsion but initially buffered instead, which is typically the case, an additional battery is integrated in FCEV.

35

Detailed information can be found, for example, in Achleitner et al. (2013) or Wallentowitz/Freialdenhoven (2011), pp. 58–89.

6

Fundamentals of Electric Vehicles and Traction Batteries (Plug-in) hybrid electric vehicle (HEV/PHEV)

Tank ICE Generator

Transmission Electric motor Power electronics Power battery

Extended-range electric vehicle (EREV)

Tank ICE Generator

(opt.) Plug-in ICE powertrain

Transmission

Battery electric vehicle (BEV)

Transmission Electric motor Power electronics Energy battery

Transmission Electric motor Power electronics Energy battery

Plug-in charger

Plug-in charger

Electric powertrain

Battery

Fuel-cell electric vehicle (FCEV)

Transmission Electric motor Power electronics Power battery BOP Fuel cell stack Tank Fuel cell powertrain

Characteristics Parallel hybrid configuration of electric and ICE drive; optional with plug-in (PHEV) ICE is primary mover of the vehicle with support from small electric motor Small battery charged by the ICE Fully electric driving only at low speed for smaller

ICE: Internal combustion engine

Series hybrid configuration of electric and ICE drive Sometimes smaller battery capacity than BEV Medium range electric driving Vehicle can be pluggedin to charge from the grid Small ICE-based generator for larger range as compared to BEV (range extender)

Purely electric drive Large battery capacity, lithium-ion technology Short to medium range Only charging of battery from the grid while stationary

Series configuration of fuel cell system and electric drive Fuel cell stack based on polymer electrolyte membrane technology Hydrogen tank pressure typically 350 bar or 700 bar Medium to high range

BOP: Balance of plant (various required support components, e.g., humidifier, pumps, valves, and compressor)

Figure 1: Overview of different types of electric vehicles 36 In the following, the focus of this chapter is set on the traction battery of electric vehicles since the battery can be considered to be the single most important component for the success of electric vehicles, especially of PHEV, EREV, and BEV. The choice of the battery technology and the storage capacity has a significant influence on the electric driving range, the production costs, and the selling price of electric vehicles.37 For instance, the most recent version of the Volkswagen e-Golf includes a lithium-ion electric vehicle battery with a capacity of 35.8 kWh. This comes along with a cruising range of 300 km in the test cycle and a selling price of €35,900 for the base model, which is about twice as high as the entry-level price for the petrol Golf.38 The price difference is mainly due to the total costs of the battery system of approximately €8,000, assuming production costs of €220 per kWh.39 The Tesla Model S is currently offered in two versions with an energy storage capacity of 75 kWh (selling price > €69,000) and 100 kWh (selling price > €100,000), respectively. This allows for a cruising range of up to 630 km in the test cy-

36 37 38 39

Adapted from Amsterdam Roundtables Foundation (2014), p. 21. Detailed information can be found, for example, in Gerssen-Gondelach/Faaij (2012) or Young et al. (2013). Bauman (2017a). According to a study by Horváth & Partners, automotive manufacturers had to pay €225 per kWh on average in 2016, Horváth & Partners 7/3/2017.

Design and Variants of Lithium-Ion Electric Vehicle Batteries

7

cle.40 At the same time, total costs for the battery system amount to up to €22,000 given the current level of production costs per kWh.

2.2

Design and Variants of Lithium-Ion Electric Vehicle Batteries

Electric vehicle batteries can be subdivided into three different levels: battery cell level, battery module level, and battery system level. Several identical cells are connected in series to a battery module in order to raise voltage. Multiple battery modules are then connected in series or in parallel to raise voltage or capacity, respectively. In addition to the modules, a battery system comprises components for thermal management, monitoring, protection, charge/discharge balancing, and car integration.41 The performance and cost characteristics of a battery system are mainly determined by the battery cell technology. For the application in an electric vehicle, different technologies are generally suitable. These comprise, amongst others, lead-acid batteries, nickel-cadmium batteries, nickel-metal hydride batteries, sodium-nickel chloride batteries, and lithium-ion batteries. Over the last years, the main focus of the automotive industry has been on lithium-ion batteries, which is especially due to their high specific power and energy density as well as their good durability compared to the other technologies.42 Lithium-ion battery cells are composed of two electrodes (an anode and a cathode), a separator, an electrolyte, and a casing. Most often, the cathode conductor is made out of aluminum foil and the anode conductor out of copper foil. Due to different cell designs (cylindrical cells, prismatic cells, and pouch cells) and the use of different materials for the cathode and anode coating as well as the electrolyte (liquid-type, gel-type, and solid-type), several variants of lithium-ion cells exist. These variants exhibit different performance characteristics.43 The most important aspect for differentiating lithium-ion battery cells is the active material of the cathode. Cathodes for electric vehicle battery cells are typically coated with a mix of lithium and other metals, such as cobalt, nickel, manganese, and aluminum. From this mix, variants like lithium cobalt oxide (LCO) batteries, lithium nickel manganese cobalt oxide (NMC) batteries, or lithium nickel cobalt aluminum oxide (NCA) batteries can be derived. A further important variant is the lithium iron phosphate (LFP) battery. All cathode materials have specific advantages and disadvantages related to power, energy, lifetime, safety, and costs of the battery cell and system.44 Currently, especially LFP, NCA, and NMC cells with amorphous carbon as anode coating material are used in electric cars. For the next years, NMC batteries with higher shares of nickel and a carbon-silicon anode to increase the energy density will play a major role in the automotive industry. In the mediumterm, all-solid state lithium-ion battery cells with a solid electrolyte are expected to gain momentum. In the long term, “post” lithium-ion technologies such as lithium-air or lithium-sulfur batteries are on the

40 41 42 43 44

Bauman (2017b). Detailed information can be found, for example, in Köhler (2013) or Wallentowitz/Freialdenhoven (2011), p. 104. Detailed information can be found, for example, in Wallentowitz/Freialdenhoven (2011), pp. 105–109. Hoyer et al. (2011); Hoyer (2015), pp. 20–21. Hoyer et al. (2011). Detailed information can be found, for example, in Graf (2013).

8


rise. All these new technologies promise significant improvements of performance and cost characteristics. Up to now, however, high uncertainties exist with regard to their market entry.45 With respect to the intended use, lithium-ion electric vehicle battery cells can be distinguished in highenergy cells for PHEV, EREV, and BEV and high-power cells for HEV and FCEV (cf. Figure 1). On the one hand, these characteristics can be defined by the appropriate choice and combination of anode and cathode coating material. On the other hand, the relation between power and energy can be influenced by the coating thickness.46 As a consequence, high-energy cells and high-power cells differ significantly in their material composition with high-energy cells having a much higher share of cathode and anode active material in order to increase the cells’ capacity.47 Given a certain battery cell, the capacity of the battery system is especially defined by the number of cells and modules deployed. Thus, high-power HEV and FCEV batteries are typically smaller and weigh less than high-energy PHEV, EREV, and BEV batteries. The higher the desired electric driving range the higher the required battery capacity. For instance, the battery of the 35.8 kWh Volkswagen e-Golf weighs approximately 345 kg.48 Based on this value, it can be assumed that the battery system integrated in the Tesla Model S has a weight of more than 500 kg.

2.3

Production and Recycling of Lithium-Ion Electric Vehicle Batteries

On an aggregate level, the production of lithium-ion electric vehicle batteries can be subdivided into two process steps: cell manufacturing and battery packaging. Cell manufacturing includes the subprocesses electrode manufacturing, cell assembly, and electrical formation (cf. Figure 2). In battery packaging, cells are first assembled to modules. The modules are then assembled to a battery system. Thereby, further components such as the battery management and the thermal system are integrated. From a technology perspective, cell manufacturing is considered as the most challenging step, which, amongst others, requires sophisticated electro-chemical knowledge and skills in the design of highly automated processes. The processes are comparable to the manufacturing of solar panels and flat screens. In contrast, battery packaging is very similar to existing assembly processes in the automotive industry, even though specific requirements exist due to the handling of high-voltage components.49 From a financial point of view, significant investments in battery cell and system production facilities are required to deploy the necessary production capacities. Investments are much higher for cell manufacturing than for battery packaging due to the highly automated manufacturing processes.50 For Germany, a deployment scenario for a battery cell production facility was developed by the German National Platform for Electric Mobility (NPE). Here, production capacity is increased in five steps from 2.3 million battery cells per year (0.3 GWh/a) in 2020 to 90.2 million cells per year (13 GWh/a) in 2025. Total invest-

45 46 47 48 49 50

NPE (2016), pp. 19–20. Detailed information can be found, for example, in Thielmann et al. (2013). Hoyer (2015), pp. 25–26. Gaines/Cuenca (2000), pp. 6–7. Schmidt (2016). Huth et al. (2015). A detailed description of the production process can be found, for example, in Huth (2014), pp. 21–27. Huth (2014), pp. 28–29.

Production and Recycling of Lithium-Ion Electric Vehicle Batteries

9

ments are estimated to amount to almost €1 billion.51 In 2014, Tesla and Panasonic started to invest an estimated amount of $5 billion in the so far largest battery production facility worldwide. The so-called “Gigafactory” should reach an annual capacity 0f traction batteries for 500.000 electric vehicles (cell output of 35 GWh/a) by 2018.52 Overall, investments and also production costs of batteries are expected to significantly drop in future due to economies of scale, learning effects, higher capacity utilization, as well as technology and process improvements.53 Besides the production of battery cells and systems, recycling plays a major role for the transition towards electric mobility in the automotive industry. This is especially due to regulatory requirements as well as economic and ecological considerations. According to the EU directive 2006/66/EC and its translation into German law, for instance, at least 50% of the spent battery mass must be recovered in Germany.54 Moreover, recovering raw materials might become an additional source of revenues. The metals included in a 300 kg NMC electric vehicle battery have a value of more than €700 based on market prices from 2016.55 Securing raw material supply, counteracting price volatility, and lowering environmental impacts are further benefits of providing a secondary feedstock.56 Cell manufacturing Electrode manufacturing

Cell assembly

Battery packaging

Electrical formation

-

Coating, compressing, and slitting of electrodes (cathode and anode)

Assembly of electrode package; insertion in cell case; infection of electrolyte; cell closing

Pack assembly

Integration of battery management and thermal system

+

Electrochemical activation of cells

Electro-chemical knowledge and skills related to coating, separation, and joining processes required

Module assembly

Selection of identical cells and assembly to modules; integration of cell-attached components of battery management and thermal system

Inserting of modules in pack-level housing; integration of main battery management and thermal system; testing of battery pack

Easily scalable assembly process of highvoltage components

Figure 2: Production process of battery cells and systems57

51 52 53

54 55 56 57

NPE (2016), pp. 36–37. Elon Musk’s gigafactory (2014); Randall (2017). Further information on the expected development of costs and investments can be found, for example, in Huth (2014), pp. 27– 34, and NPE (2016). BMU 12.11.2009; European Parliament and the Council 9/6/2006. Thies et al. (2018). Hoyer (2015), pp. 2–3; Hoyer et al. (2015). Adapted from Huth et al. (2013), p. 79.

10


In general, a recycling process for lithium-ion electric vehicle batteries consists of a sequence of collection, transport, sortation, treatment, and disposal activities. After removing the batteries from the cars at service stations or end-of-life vehicle treatment facilities, batteries should be collected immediately to prevent mistreatment and to allow for proper storage. The batteries must then be inspected, sorted, and stored. Subsequently, batteries are recycled with the aim of recovering valuable materials (e.g., cobalt, nickel, or lithium) and disposing hazardous substances (e.g., electrolyte with conductive salts). For this, several technologies are available and need to be combined appropriately (cf. Figure 3), typically resulting in multi-stage co-production processes. Thereby, the choice and combination of specific technologies is mainly influenced by the battery types to be treated (cf. Section 2.2) and the components and materials to be recovered in a certain quality.58 Technologies for the treatment of the batteries can be classified into disassembly, mechanical conditioning, pyrometallurgical conditioning, and hydrometallurgical conditioning technologies. In disassembly, battery systems are discharged and disassembled to module or cell level in order to recover the battery casing, cables, and electronics. Mechanical conditioning aims at separating different materials such as copper, aluminum, and the cathode coating with the help of crushing, shredding, grinding, screening, sortation, and classification technologies. The active materials of the electrode coating can be recovered by making use of pyrometallurgical and hydrometallurgical conditioning technologies. In pyrometallurgical conditioning, the materials are treated thermally at high temperatures. In hydrometallurgical conditioning, flotation, extraction, concentration, precipitation, and electrolysis technologies are used to recover the metals.59 Batteries

Recycling processes Mechanical conditioning

Pyrometallurgy

Hydrometallurgy

Disassembly

Mechanical conditioning

Hydrometallurgy

Hydrometallurgy

Pyrometallurgy

Hydrometallurgy

Pyrometallurgy

Hydrometallurgy

Decoupling point 

Disassembly

Casing, cable, electronics

Recovered materials

Disassembly

Copper, aluminum

Pyrometallurgy

Cobalt, nickel

Lithium

Figure 3: Possible combinations of recycling processes and recovered materials 60 The deployment of the recycling processes requires significant investments. For instance, Umicore invested €25 million in a pyrometallurgical recycling facility for batteries, which was opened with an initial

58 59 60

Hoyer et al. (2011); Hoyer et al. (2013); Hoyer et al. (2015). Hoyer et al. (2011); Hoyer et al. (2013); Hoyer et al. (2015). Cf. Hoyer et al. (2011), p. 81.

Challenges related to the Transition towards Electric Vehicles

11

annual capacity of 7,000 tons in 2011.61 Most of the recycling processes for lithium-ion electric vehicle batteries are still under development, especially those aiming at recovering almost all of the valuable materials.62 Thus, investments in the processes can only be estimated with some degree of uncertainty. For the so-called LithoRec recycling process, which combines disassembly, mechanical conditioning, and hydrometallurgical conditioning, investments in a hydrometallurgical treatment facility allowing to process 33,000 tons of electrode coating per year are projected to range between €40 and €44 million, for example.63

2.4

Challenges related to the Transition towards Electric Vehicles

In summary, the transition towards electric mobility in the automotive industry is characterized by a very high level of complexity and uncertainty. The complexity particularly results from the different types of electric vehicles offered to the market and the manifold battery technologies that are currently under research and development in order to improve performance and to cut costs. The electric vehicles differ among each other and also from their conventional counterparts with regard to production costs and purchase price, infrastructure availability, refueling time, and cruising range. This is mainly due to alternative powertrain structures with the traction battery being the most important component in this regard. For the traction battery and also for the further components of the electric powertrain, a multitude of production and recycling processes is available or currently under research and development.64 These processes substantially deviate from the production and recycling processes for conventional vehicles. Moreover, interdependencies exist between a specific variant of a traction battery integrated into an electric vehicle and the production and recycling technologies to be deployed. Uncertainties mainly relate to the market evolution of electric vehicles. So far, conventional cars typically can be produced and purchased at lower cost, are compatible with the established infrastructure network, and have much shorter refueling times as well as much higher cruising ranges. For that reason, still a significant amount of research and development is necessary in order to make electric vehicles an attractive alternative and ensure their market diffusion. Especially with regard to the electric vehicle battery, it is highly uncertain which of the battery technologies will prevail in future and in what year a market entry will take place. In turn, the evolution of electric vehicle characteristics is associated with high uncertainties. Further uncertainties are, amongst others, related to the consumer behavior, the application of policy measures, the evolution of the recharging infrastructure, the development of prices and availability of important raw materials, and the evolution of production and recycling technologies.65

61 62

63 64

65

Umicore 9/7/2011. An overview of different recycling processes for lithium-ion electric vehicle batteries is given, for example, in Hoyer (2015), pp. 55–65. Hoyer (2015), p. 133. A description of production processes for the electric motor and the power electronics can be found, for example, in Kampker (2014), pp. 115–211. Information on recycling processes for the electric motor is given, for example, in Hörnig (2015) and for the power electronics, for example, in Bulach et al. (2017). A detailed analysis of the uncertainties, for example, is given in Huth (2014), pp. 34–47. Regarding prices and availability of the raw materials, it has to be noted that extraction and production of metals like cobalt, nickel, and lithium is geologically concentrated and global resources are partially scarce, cf. Hoyer (2015), pp. 28–39.

12


All of these factors have a direct or indirect influence on the attractiveness of electric vehicles and thus on their market diffusion as well as on the production and recycling technologies to be deployed. 140

Number of publications

120 100 80 60 40 20 0 2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

Figure 4: Development of the number of scientific publications addressing planning problems in the context of electric mobility between 2008 and 201766 The described aspects of complexity and uncertainty challenge the planning towards electric mobility. As a consequence, planning problems that are related to the development, production, use, and recycling of electric vehicles have been addressed in scientific publications over the last decade to an increasing extent (Figure 4). For these problems, planning approaches have been developed based on methods and models from Operations Research to provide decision support, taking into account the aforementioned characteristics. In the following chapter, a framework is introduced in order to structure the planning tasks from the perspective of production and logistics management. On the one hand, this allows for a thematic classification. On the other hand, it underscores the necessity to develop simulation and optimization models for the strategic planning towards sustainable mobility in the automotive industry based on the current state of scientific literature.

66

Kieckhäfer (2018). Based on a Scopus query on September 26th, 2017, with TITLE-ABS-KEY (("electric car*" OR "electric vehicle*") AND ("optim*" OR "simul*" OR "Operations Research" OR "plan*" OR "model*")) in journals that are ranked category A+, A, and B amongst the disciplines “General Business Economics”, “Logistics”, “Marketing”, “Sustainability Management”, “Operations Research”, “Production Management”, “Technology, Innovation, and Entrepreneurship”, “Business Information Systems”, and “Service and Trade Management” according to the VHB-JOURQUAL3, VHB 2/27/2015.

3

Planning Framework towards Electric Mobility

This chapter starts with the development of a planning framework towards electric mobility in Section 3.1. The framework structures relevant planning tasks, which are briefly described subsequently. Section 3.2 concentrates on planning tasks of car manufacturers and suppliers, Section 3.3 on planning tasks of fleet and infrastructure providers, and Section 3.4 on planning tasks of policy-makers. The chapter closes with a summary of research domains that are most relevant for the strategic planning towards electric mobility in the automotive industry (Section 3.5).

3.1

Development of the Framework

From the perspective of production and logistics management, the planning problems discussed in the literature can be classified into those tasks that are related to the development, production, and recycling/disposal of electric vehicles and into those that are related to the use phase of electric vehicles (cf. Figure 5). The former particularly concern automotive manufacturers and suppliers, the latter fleet and infrastructure operators.67 For automotive manufactures and their suppliers, product design, portfolio design, and network design tasks are most relevant. In product and portfolio design, a competitive market offer has to be developed, taking into account all available conventional and electric powertrain technologies as well as substitution effects between the powertrains. Network design is mainly concerned with the transformation of the supply chains in the automotive industry towards the production and recycling of traction batteries, electric motors, and power electronics. First and foremost, make-or-buy decisions as well as technology and capacity choice decisions are discussed in the literature. These planning tasks will be introduced in Section 3.2. With regard to the use phase of electric vehicles, infrastructure/location planning, fleet planning, vehicle routing, and scheduling tasks are in the center of attention of fleet and infrastructure operators. Thereby, especially the drawbacks of electric vehicles with regard to price, range, and infrastructure are considered. On a strategic level, the focus is on the planning of charging sites (especially locations and capacities) in order to develop expedient infrastructural conditions for the operation of electric vehicles. Next, it is the task of fleet planning to integrate electric vehicles into commercial vehicle fleets under economic and environmental considerations. In vehicle routing and scheduling, questions on the optimal use of electric vehicles and the infrastructure are addressed. Scheduling concerns the scheduling of electric vehicles (task of fleet operators) as well as the scheduling of charging sites (task of infrastructure operators). For the latter, a direct interface to planning tasks relevant to energy economics exist, such as the integration of electric vehicles into smart grids or the design of electricity tariffs in terms of demand

67

The description of the planning framework and the single planning tasks closely follows Kieckhäfer (2018).

14


management.68 Such kind of planning tasks are out of scope of this thesis. All other tasks related to the use phase of electric vehicles will be described in Section 3.3.

Figure 5: Framework of planning tasks in the context of electric mobility from a production and logistics management perspective69 Besides the planning tasks mentioned so far, impact assessment plays an important role in the context of electric mobility. Following initiatives on better regulation, impact assessment basically strives for an ex-ante analysis of short-, medium-, and long-term consequences that are related to the application of regulations and policy measures from an economic, ecological, and social point of view.70 Accordingly, an important task of policy-makers is to design alternative (environmental) policy measures in order to support the market success of electric vehicles and evaluate their effectiveness and efficiency. These policy measures can comprise, amongst other, fleet emission thresholds, technology mandates or bans (cf. Chapter 1), as well as subsidies, tax regulations, and specific using restrictions or allowances.71 Likewise, impact assessments should also be carried out by the industry during the planning process in order to evaluate the most relevant consequences of policy measures as well as possible corporate reactions from an industry perspective. A brief introduction to the planning tasks of policy-makers focusing on the support of the market diffusion of electric vehicles is part of Section 3.4.

68 69 70 71

Examples can be found in Broneske/Wozabal (2017); Flath et al. (2014); Kaschub et al. (2016). Adapted from Kieckhäfer (2018). Böhret/Konzendorf (2001), p. 1; European Commission 1/15/2009, pp. 3–6. An overview of policy measures can be found, for example, in Kieckhäfer et al. (2015).

Planning Tasks of Car Manufacturers and Suppliers

3.2

15


With regard to the product (portfolio) design, decisions have to be taken on the content, structure, and scope of the portfolio as well as on the technical, functional, and aesthetical characteristics of single products. The content is defined by the products that are included in the portfolio. Structure and scope result from the breadth (number of alternative product lines) and the depth (number of products within one product line; typically referred to as variants) of the portfolio.72 General options for the design of the product portfolio are product innovation, product variation, product differentiation, and product elimination. The first three options cause the development of new products in order to implement requirements from portfolio planning. For this, the core and additional features of a product need to be determined. This is especially important for the functionality of the product and the differentiation from competitors. Moreover, decisions have to be made on the timing of market introduction, advertising, and pricing.73 Product and product portfolio planning in the automotive industry addresses the question which vehicle model should be offered to which market segment with which properties. Thereby, interdependencies between the vehicle models have to be considered, for instance with regard to cannibalization effects or the potential of using common parts. Typically, the product-related characteristics car body type (e.g., sedan) and car size (e.g., compact car) have been used in the automotive industry do define vehicle models and market segments on a strategic level. Due to the introduction of electric vehicles, additional consideration has to be given to the powertrain to be deployed in the vehicles since a higher number of alternative powertrains has become available that substantially differ in their characteristics (cf. Section 2.4).74 In the automotive industry, portfolio decisions are usually based on alternative cycle plans. A cycle plan includes all current and future vehicle models and the key lifecycle milestones over a planning period of about ten years. The alternative cycle plans are evaluated with regard to particular financial and marketrelated performance indicators (e.g., return on investment and sales volume of single vehicle models). Additionally, factors such as utilization of development and production capacities and compliance with regulatory requirements are taken into account. For this, manifold and mostly uncertain data and information must be gathered, for instance on expected sales and investment volumes, product costs, as well as technical and regulatory restrictions. Thereby, the interdependencies between the vehicle models described earlier need to be considered.75 With regard to the product design, the construction of batteries plays a major role. On the one hand, a sufficient electric driving range of electric vehicles must be balanced with the resulting battery production costs. On the other hand, environmental impacts of electric vehicles are mainly influenced by the design of the battery. In order to make optimal decisions on the battery design, factors such as driving profiles, battery technology and costs, electricity prices and mix, as well as infrastructure availability have

72 73 74 75

Meffert et al. (2015), pp. 361–365. Detailed information can be found, for example, in Homburg (2017), pp. 556–609. Kieckhäfer et al. (2012); Kieckhäfer (2013), pp. 43–46. Raasch et al. (2007).

16


to be taken into account.76 Based on an integrated planning of product design and infrastructure (cf. Section3.3), synergy potentials can potentially be realized: The higher (lower) the cruising range of an electric vehicle, the lower (higher) the need for a dense infrastructure network.77 The second important planning task, strategic network planning, basically comprises decisions related to the definition of storage and production capacities, site selection, production system configuration, and design of procurement and distribution structures. Additionally, the degree of vertical integration, the selection of suppliers, and the formation of cooperation are determined. The objective is to configure a network that includes the most important suppliers and customers, own production facilities and distribution centers, as well as fundamental material flows from the suppliers to the customers.78 Network planning is also of utmost relevance in the automotive industry. Thereby, manifold industryspecific characteristics have to be regarded.79 Because of the diversity of planning tasks, the focus will be on challenges that are directly related to the field of electric mobility and especially to the production and recycling of lithium-ion electric vehicle batteries in the following (cf. Section 2.3). With regard to the production of lithium-ion batteries, automotive manufacturers have to decide in which production steps and in which technologies to invest. Moreover, production capacities need to be fixed. In principle, the following options are available: in-house production of battery cells and systems, sourcing of battery cells and in-house assembly of the battery systems, and sourcing of the complete battery systems. Moreover, a joint venture and partial sourcing of battery cells and/or systems is conceivable.80 For automotive manufacturers, in-house production offers the chance to differentiate from competition as well as to secure a high share of value added and maintain jobs. In contrast, risks are associated with the high investments in the production facilities, the partially missing skills in cell manufacturing, and the prevailing uncertainties. With regard to procurement, uncertainties are mainly related to price development and availability of raw materials like lithium or cobalt. Accordingly, suitable suppliers must be selected and long-term contracts need to be negotiated to secure raw material supply if car manufacturers decide to engage in the internal production of battery cells. Demand uncertainties refer to the expected sales volume, which have a direct influence on the required production capacities. Moreover, technology uncertainties exist with regard to the development of battery cell and production technologies (cf. Section 2.4). The sourcing of complete battery systems or at least of battery cells from specialized suppliers therefore allows car manufacturers to decrease the investment risk significantly. Additionally, car manufacturers can fall back on the suppliers’ skills in the manufacturing of battery cells. Again, main planning tasks for this option are the selection of suitable suppliers and a hedging of procurement quantities and prices by means of long-term contracts and cooperation. As a consequence, the share of value added by the car manufacturers would substantially drop, threatening existing jobs the more electric vehicles are sold.

76 77 78 79 80

Garcia et al. (2017); Lin (2014); Redelbach et al. (2014). . Nie/Ghamami (2013); Traut et al. (2012). Fleischmann et al. (2015); Rohde et al. (2001). Detailed information can be found, for example, in Meyr (2004); Volling et al. (2013); Wittek (2013), pp. 9–74. Huth (2014), pp. 88–91; Huth et al. (2015).


17

From the perspective of the suppliers, there is a potential to realize economies of scale and reduce demand and technology uncertainties by aggregating material requirements of several car manufacturers. For the establishment of a recycling network for end-of-life electric vehicle batteries, the planning tasks of technology and capacity choice are most relevant. Technology choice is challenged by the manifold alternative technologies that are available for every single recycling process and the technology and market uncertainties described earlier (cf. Section 2.4). On the one hand, interdependencies between the single processes exist since certain technologies may rely on previous treatment steps. On the other hand, technology choice has an influence on type, quantity, and quality of materials that can be recovered. Thereby, the alternative technologies come along with different investment levels and operating costs, which in turn are influenced by capacity choice. This planning task is particularly challenging due to the dynamic and uncertain development of battery returns, affecting decisions on the deployment of capacities over time. For these decisions, the realization of economies of scale needs to be traded-off with the minimization of investment risks that are induced by the market and technology uncertainties. In order to realize economies of scale, an early deployment of large recycling facilities would be beneficial. In contrast, sequentially installing small facilities would counteract investment risks. Closely connected with capacity choice are decisions on the centralization or decentralization of the network, which are part of location planning and especially important for the collection of batteries. The transport of the heavy and large battery systems for which certain safety requirements apply is very expensive. In order to cap transportation costs, transportation distances to a first treatment facility should be kept as short as possible by decentralizing the collection of battery systems.81 Similar considerations hold true for the further components of the electric powertrain, i.e., the electric motor and the power electronics. For these components, make-or-buy decisions are also of importance, taking into account financial performance indicators, differentiation potentials, manufacturing skills, as well as impacts on the share of value added and the job situation. Subsequently, the focus in network planning must be on the selection of suppliers, the formation of cooperation, and the choice of technologies and capacities, if necessary. Additionally, the design of a recycling network for electric motors can be considered to be an important planning task. Electric motors often contain rare earth metals that are currently almost completely sourced from China.82 Thus, an uptake of electric mobility might come along with supply risks and an increase in prices. Due to the manifold interdependencies between the individual decisions, an integrated planning of sites, technologies, and capacities in a (closed-loop) supply chain for conventional and electric vehicles might be promising, taking into account economic, ecological, and social criteria. This allows, for instance, to analyze which decisions have to be made in order to minimize costs and emissions among all supply chain partners, as well as the job effects of these decisions.83

81 82 83

Hoyer (2015), pp. 74–75; Hoyer et al. (2015). Hörnig (2015). Günther et al. (2015); Kannegiesser et al. (2014).

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3.3

Planning Tasks of Fleet and Infrastructure Operators

The strategic planning of the infrastructure concerns infrastructure operators as well as fleet operators. Infrastructure operators are responsible for the planning of public charging stations.84 In contrast, the planning of private charging stations is of relevance for operators of commercial fleets such as car sharing or taxi fleets.85 On a design level, the planning task especially comprises site selection and capacity choice in order to allow for a smooth operation of BEV despite their limited cruising ranges and long charging times. For this, factors like the uncertain and dynamic market development of electric vehicles, the limited battery and transmission capacity, and the energy consumption of the vehicles have to be taken into account. All these factors have a substantial influence on the energy demand and the occupancy time of a service station and thus on the necessary capacity do be deployed.86 Typically, charging stations for conductive charging by means of a cable and plug connection are in the center of attention. However, also inductive charging stations87 or battery swapping stations88 are options under consideration. A similar planning problem is given for PHEV and EREV. Essentially, these vehicles do not rely on a charging infrastructure network. However, the electric driving range of PHEV and EREV can be increased by an appropriate deployment of charging stations. This might have a positive influence on the operating costs since electricity prices are typically much lower than fuel prices.89 In strategic fleet planning, fleet operators such as logistics companies, car sharing providers, or taxi businesses must decide on the size and composition of the vehicle fleet over time. In this context, purchasing and selling decisions as well as leasing decisions play an important role.90 Decisions on the integration of electric vehicles into the vehicle fleet particularly concern the optimal fleet composition. Here, it has to be decided to what extent conventional vehicles should be replaced by electric vehicles under consideration of purchase prices, operating costs, fueling/charging times, cruising ranges, driving/transport distances, transport volumes, and potential environmental regulations. Because of the differences in cruising ranges and availability between the different vehicle types, this decision is also pivotal for the required fleet size. As explained earlier, most of the listed influencing factors are subject to uncertainties, which need to be addressed in the decision process.91 Building on the fleet decisions, especially logistics companies face the planning task of vehicle routing. Basically, the optimal set of routes in order to serve a given set of customers with a given vehicle fleet has to be determined. Most often, the objective is to minimize transportation costs or sometimes even emissions. Typically, a route starts and ends in the same depot and comprises one or more customer orders.92

84 85 86 87 88 89 90 91 92

For instance, Jochem et al. (2016). For instance, Brandstätter et al. (2017). Chung/Kwon (2015); Vries/Duijzer (2017); Wu/Sioshansi (2017). For instance, Liu/Wang (2017). For instance, Mak et al. (2013). Arslan/Karaşan (2016). Neboian/Spinler (2015). Hiermann et al. (2016); Kleindorfer et al. (2012); Kuppusamy et al. (2017). Günther/Tempelmeier (2016), pp. 272–273.

Planning Tasks of Policy-Makers

19

With regard to electric mobility, the main challenges of vehicle routing are again related to the limited cruising range of the vehicles. As a consequence, the availability of the charging stations and charging times need to be considered.93 In vehicle scheduling, fleet operators have to assign vehicles to a given set of timetabled trips in order to minimize total costs. Thereby, every vehicle must perform a feasible sequence of trips. This planning task is especially relevant for public transport companies.94 Similar to electric vehicle routing, electric vehicle scheduling is challenged by the limited availability of the vehicles due to range and charging restrictions.95 Also operators of charging infrastructure can face scheduling problems. In case of high utilization of a charging station, decisions on the timing of charging single vehicles and the power transmission level might become relevant. This is especially due to long charging times of electric vehicles and a low number of service points compared to the refueling network for conventional vehicles as well as potential capacity limitations with regard to the transmission of electrical power. Therefore, the objective of scheduling the charging infrastructure is to ensure an efficient operation of the charging stations, for instance, by minimizing the waiting time or the difference between desired and actual charging quantity within a given time window.96 For battery swapping stations, a similar planning task exists. While battery swapping takes much less time than battery charging, only a limited number of batteries is available. In order to fulfill the customer request, the batteries must be recharged in due time.97 Certain interdependencies can be identified between the individual decisions to be taken with respect to the use of electric vehicles. For instance, vehicle routing is influenced by the location of charging infrastructure. In turn, site selection depends on the energy demand of the vehicles, which, amongst others, is determined by the selected route.98 Moreover, infrastructure planning has an influence on fleet planning since the adoption of electric vehicles is directly supported by a high availability of charging stations. At the same time, more charging stations are required the higher the share of electric vehicles adopted.99 Regarding fleet planning and vehicle routing, interdependencies exist between the size and composition of the vehicle fleet and the number and design of routes.100 These interdependencies need to be addressed appropriately in planning.

3.4

Planning Tasks of Policy-Makers

Due to the manifold barriers to adoption, policy-makers need to design and apply policy measures in order to support the market diffusion of electric vehicles. The objective is to determine a basket of efficient and effective measures that increases the attractiveness of electric vehicles to consumers, boosts the

93 94 95 96 97 98 99 100

Desaulniers et al. (2016); Schneider et al. (2014). Bunte/Kliewer (2009). Sassi/Oulamara (2017); Wen et al. (2016). Kim et al. (2017). Raviv (2012). Schiffer/Walther (2017). Kuppusamy et al. (2017); Li et al. (2016). Hiermann et al. (2016).

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manufacturers’ willingness to deploy competitive electric vehicles on a large scale, and reduces the risk for potential investors in businesses related to electric mobility.101 Thus, policy measures should not only steer consumer behavior,102 but affect the decisions made by the automotive industry as well as operators of vehicle fleets and infrastructure. For instance, product and product portfolio decisions can be influenced by applying fleet emission thresholds and/or technology mandates/bans.103 Overall, the selection and design of appropriate policy measures is a complex and challenging planning task due to several reasons. First, decisions can be made on very different levels, ranging from the establishment of international agreements to the application of local regulations. Second, plenty of policy measures are available. These can be generally assigned to the classes of regulatory instruments, marketbased instruments, and suasive instruments.104 Regulatory instruments aim to directly influence the behavior of market participants based on commands, prohibitions, or obligations. In contrast, marketbased instruments are designed to have an indirect influence on the behavior in terms of financial incentives. Suasive instruments are intended to change the moral concepts of the market participants by providing information. Third, the concrete mechanisms must be defined for every chosen policy measure. Fourth, interdependencies between individual measures must be taken into account when combining them. Otherwise, they cannot unfold their full potential. Fifth, the impact of the policy measures has to be assessed ex-ante based on multiple criteria in order to ensure their effectiveness and efficiency and avoid unintended side-effects. For that reason, impact assessment of policy measures is a very important task of policy-makers, too (cf. Section 3.1). The complexity of the decision situation can be illustrated by current policy actions associated with the reduction of emissions from passenger cars. On an international level, 170 parties of the United Nations’ Framework Convention on Climate Change have agreed to limit climate change by undertaking ambitious efforts based on the Paris Agreement from 2015. Every party is required to define nationally determined contributions and to decide on corresponding mitigation measures on a national or community level.105 As one example, the EU declared to strive for a reduction of CO2 emissions by 80% to 95% in 2050 compared to 1990-levels already before the Paris Agreement. From this, specific reduction levels for transportation were derived, i.e., –54% to –67% in 2050 compared to 1990. This reduction is intended to be realized by higher vehicle efficiency, cleaner energy use, as well as safer and more secure operation.106 Amongst others, the regulation on reducing the average CO2 emissions of the vehicle fleets sold by car manufacturers was established in the EU in order to achieve these objectives. As a consequence, car manufacturers are forced to sell an increasing number of electric vehicles (cf. Chapter 1). In turn, the German government decided to apply a purchase premium for electric vehicles to support their market diffusion on national level. Further measures applied in Germany are tax incentives, funding of research and development projects, the appointment of the NPE, the implementation of the Electric Mobility Act,

101

EVI (2017), p. 13. A comprehensive overview of policy measures applied worldwide can be found Lieven (2015), for example. Analyses on the impact of selected policy measures on consumer behavior can be found in Helveston et al. (2015) or Langbroek et al. (2016), for example. 103 Analyses on the impact of selected policy measures on manufacturer behavior can be found in Whitefoot/Skerlos (2012) or Zhang et al. (2011), for example. 104 For more information on the instruments of environmental policy, please refer to Michaelis (1996), pp. 25–58, for example. 105 United Nations 12/12/2015. 106 European Commission 3/8/2011. 102

Research Domains of Strategic Planning towards Electric Mobility in the Automotive Industry

21

a public procurement program for electric vehicles, and public investments in the charging infrastructure.107 On the level of federal states, municipalities, and cities, this support can be accompanied in several ways. The city of Braunschweig, for instance, deployed 17 quick charging stations in the city area. At these stations, vehicles can be charged for free. The same holds true for three hours of parking at metered parking lots.108 For the future, the establishment of using allowances for electric vehicles or using restrictions for conventional vehicles in city centers is conceivable, as explained in Chapter 1. These measures, however, are not only aimed at reducing CO2 emissions from passenger cars but rather at cutting down exhaust emissions on a local level.

3.5

Research Domains of Strategic Planning towards Electric Mobility in the Automotive Industry

Summarizing the current state of scientific literature, it must be noted that manifold models and methods from Operations Research have been applied in order to support decision making in the field of electric mobility. The majority of approaches concentrates on planning tasks related to the use phase of electric vehicles. In contrast, strategic planning issues of the automotive industry have received little attention so far. This is all the more surprising, given the great challenges faced by the automotive industry with regard to the transformation of the technology and product portfolio and the transformation of the supply chain. For that reason, selected contributions to the research domains on market simulation to support the transformation of the technology and product portfolio, network planning to support the transformation of the supply chain, and impact assessment to analyze the consequences of policy measures for the transformation processes in the automotive industry are summarized in the subsequent chapters. These comprise: 

Transformation of the technology and product portfolio (Chapter 4): Estimating the market share evolution of electric vehicles (Kieckhäfer et al. 2014), analyzing the impact of product and product portfolio decisions on the market diffusion of electric vehicles (Kieckhäfer et al. 2017), analyzing market introduction strategies for electric vehicles (Thies et al. 2016), and assessing the sustainability of products (Thies et al. 2017a) .



Transformation of the supply chain (Chapter 5): Analyzing make-or-buy strategies for electric vehicle batteries (Huth et al. 2015), planning investments in the production of electric vehicle batteries (Lukas et al. 2017), and planning technologies and capacities for the recycling of electric vehicle batteries (Hoyer et al. 2015).



Impact assessment (Chapter 6): Analyzing policy measures for regulating the CO2 emissions from passenger cars (Kieckhäfer et al. 2015), assessing the impact of policy measures on the market diffusion of electric vehicles (Walther et al. 2010), and assessing the impact of mandatory rates on the recycling of electric vehicle batteries (Hoyer et al. 2013).

107 108

BMWI (n.d.). BSM (n.d.).

22


In eight out of the ten contributions, specific planning approaches are developed and applied in terms of simulation and optimization models. In contrast, Kieckhäfer et al. (2015) and Thies et al. (2017a) draw their conclusions from comprehensive literature reviews.

4

Planning Issues related to the Transformation of the Technology and Product Portfolio

In this chapter, selected contributions related to the transformation of the technology and product portfolio are summarized. First, a hybrid simulation model for estimating the market share evolution of electric vehicles is introduced in Section 4.1. The model is then applied in Section 4.2 in order to analyze the impact of product and product portfolio decisions on the market diffusion of electric vehicles. In Section 4.3, market introduction strategies for electric vehicles are analyzed by means of a system dynamics model of the automotive market. Finally, key findings from a literature review on the use of methods from Operations Research in order to facilitate sustainability assessment of products are condensed in Section 4.4.

4.1

Estimating the Market Share Evolution of Electric Vehicles Kieckhäfer, K.; Volling, T.; Spengler, T. S. (2014): A hybrid simulation approach for estimating the market share evolution of electric vehicles, in: Transportation Science, Vol. 48, pp. 651–670.

For product and product portfolio decisions, information on the expected evolution of market shares of single vehicle models is of utmost importance in order to evaluate alternative cycle plans (cf. Section 3.2). From this information, the development of sales volumes can directly be forecasted, given a certain overall market development. In turn, the expected sales volumes have a substantial influence on financial performance indicators.109 Estimating the market share evolution of electric vehicles comes along with manifold uncertainties. Amongst others, this is due to the long forecasting horizon, the innovative character of electric vehicles, and the manifold interdependencies between the vehicle models (cf. Section 2.4). Moreover, the automotive market can be described as a socio-technical/economic system. Thereby, the system behavior is mainly determined by the behavior of the single market participants, which especially comprise manufacturers, customers, policy-makers, and infrastructure operators. Despite the high uncertainties, forecasts are inevitable to support product (portfolio) design and determine the long-term range of products to be offered by the car manufacturers. In fact, the uncertainties must be addressed appropriately by modeling and analyzing causal links and interdependencies as well as the consequences of system interventions explicitly, for example in terms of product portfolio decisions. For this, market simulation is a suitable method,110 which can be defined as follows:

“[Market simulation is] an approach to simulate the consumer response to companies' marketing mix decisions as well as to the competitive and environmental influences. Thereby, the response is based on a mathematical measurement model that comprises sub-models of supply, demand and the market environment. By evaluating the impact of different product portfolios 109 110

Kieckhäfer et al. (2012); Kieckhäfer (2013), p. 49. Kieckhäfer et al. (2012); Kieckhäfer (2013), pp. 52–55.

24


on ‘demand’ and other company objectives recommendations concerning strategic product portfolio decisions can be derived. This way, the market simulation can be used as a decision support for strategic product portfolio planning.”111 In Kieckhäfer et al. (2014), a hybrid market simulation approach is developed allowing for the estimation of the market share evolution of electric vehicles from an industry perspective.112 The approach integrates system dynamics and agent-based modeling (cf. Figure 6).113 In the system dynamics model, the interdependencies between consumer choice, consumer awareness, evolution of powertrain technologies, and service station availability are modeled with the help of non-linear ordinary differential equations. The model parts of consumer choice and consumer awareness are refined by integrating an agentbased model, allowing for the simulation of individual consumer choices from a wide range of vehicle models as a two-staged decision process on a micro level. In the model, vehicle models can be defined as combinations of vehicle size classes (subcompact, compact, mid-size, and full-size) and electric (HEV, PHEV, BEV) as well as conventional (diesel, petrol) powertrains. Consumer choices depend on the range of products offered, heterogeneous consumer preferences, the previous vehicle model, and dynamically changing vehicle characteristics. The actual purchase decisions are modeled with the help of discrete choice theory.114 For integration purposes, the two sub-models exchange information on the characteristics of the vehicles offered to the market and the aggregated sales and stock figures for these vehicle models. The contribution of the paper is twofold. On the one hand, existing modeling approaches are expanded by simultaneously considering individual consumer choice and aggregated system behavior to estimate the market share evolution of electric vehicles. On the other hand, conditions are derived under which it is appropriate to take into account individual consumer choices in aggregated system dynamics models when estimating the evolution of market shares. Thereby, multiple stages of the adoption process of the individual agents are regarded and elaborated, namely consideration, choice, purchase, and repeated purchase. For the computational experiments, the model is applied to the established and saturated German car market, where replacement purchases dominate. To this end, real-world data is used. Based on the data, the model is also thoroughly validated following a procedure suggested by Rand/Rust (2011) in order to build confidence in the appropriateness of the model for gaining new insights. The simulation results indicate that neglecting individual consumer choices in aggregated system models may lead to systematically wrong estimations of the evolution of the market shares of electric vehicles. On the one hand, it is important to consider the characteristics of individual replacement purchases appropriately. Otherwise, the diffusion speed and level of electric vehicles might be overestimated, which is mainly related to long turnover rates and the up to now low willingness of consumers to replace conventionally powered vehicles by electric vehicles. On the other hand, heterogeneous consumer behavior should be considered in aggregated system models. If consumer heterogeneity is high, the evolution of market shares varies between consumer segments with regard to diffusion speed and level, prop-

111 112 113

114

Cf. Kieckhäfer et al. (2012), p. 131. Further details are also given in Kieckhäfer (2013). Details on system dynamics can be found, for example, in Sterman (2000) and on agents.-based simulation, for example, in Gilbert (2008). Details on discrete choice modelling can be found, for example, in Train (2009).

Analyzing Product and Product Portfolio Decisions

25

agating into the aggregated evolution of market shares. The application of pure macro-level models is thus limited to situations where consumer heterogeneity is low. System dynamics model Agent-based model Purchase decision Market shares [powertrain, size class]

Vehicle model  Powertrain  Size class  Characteristics

Consumer segment  Consumer choice  Characteristics

Vehicle stock [powertrain]

Service stations [powertrain]

Portfolio definition  Vehicle models offered (powertrain, size class)  Time of introduction

Market definition  Region  Consumer segments  Purchase decision rule

Technology [powertrain]

Definition of general settings  Energy prices  Regulatory measures  …

Figure 6: Concept of the hybrid modeling approach to estimate the market share evolution of electric vehicles115 In the presence of high consumer heterogeneity and a large share of replacement purchases in most of the major automotive markets, taking into account the aggregated system behavior and individual consumer choices simultaneously is thus of high relevance when modeling the diffusion of electric vehicles. The proposed modeling approach can be considered appropriate for this purpose. By embedding an agent-based simulation model of individual consumer choices in an aggregated system dynamics model, it allows for a better understanding of the causal links supporting or impeding the market success of electric vehicles. Moreover, the model supports the exogenous definition and analysis of a manufacturer’s vehicle portfolio decision as to which specific powertrain should be employed in which vehicle size class and introduced to or withdrawn from the market at which point in time. It can thus be considered to be a powerful tool for product portfolio planning.

4.2

Analyzing Product and Product Portfolio Decisions Kieckhäfer, K.; Wachter, K.; Spengler, T. S. (2017): Analyzing manufacturers' impact on green products' market diffusion: The case of electric vehicles, in: Journal of Cleaner Production, Vol. 162, pp. S11–S25.

In Kieckhäfer et al. (2017), the hybrid modeling approach is extended in four ways, resulting in a simulation tool called automotive market simulator (AMaSi).116 The extensions are as follows: First, FCEV are 115

Cf. Kieckhäfer et al. (2014), p. 654.

26


incorporated. Second, a second competing manufacturer is modeled. Third, it is taken into account that consumer’s attitude can change due to communication between the agents and advertising. Fourth, the impact of electric vehicles on the reduction of fossil fuel consumption is estimated. Besides the model extension, the paper’s primary contribution lies in the comprehensive investigation of a car manufacturer’s product portfolio decisions on the market diffusion of electric vehicles, taking into account a competitive setting. Moreover, the potential of electric vehicles to contribute to overall fuel savings is studied for different scenarios of their market diffusion. To this end, the AMaSi model is parameterized for the German car market and different product portfolio options are simulated. In a first step, an aggregated industry perspective is taken. For this setting, the influence of different strategies regarding the timing of the introduction and of the withdrawal of specific vehicle models is analyzed. The strategies range from rather moderate options (e.g., early introduction of PHEV) to rather radical options (e.g., partial elimination of conventional powertrains). Subsequently, the same product portfolio decisions are simulated for the case of two incumbent and competing full-range manufacturers. One of the manufacturers is on the verge of introducing electric vehicles to the market striving for a competitive advantage. In the simulation experiments, it is studied whether portfolio decisions that seem beneficial for the overall market development may also be favorable from an individual manufacturer's point of view. This way, new insights are achieved into how particularly incumbent full-range manufacturers can influence the market diffusion of electric vehicles by shaping their future product and powertrain portfolio. These insights are also of importance to policy-makers when designing efficient and effective policy instruments in order to facilitate market diffusion. The results of the simulation experiments indicate that manufacturers can use portfolio decisions as a leverage to support the market diffusion of electric vehicles. The more electric vehicles are introduced to the market in the simulation runs, the higher the likelihood that consumers are attracted, change their attitude towards electric vehicles, actually purchase one, and positively report their experience to other consumers in terms of word of mouth. From this, it can be concluded that electric vehicles are currently falling short of expectations in most major markets because of the low number of electric vehicle models offered and not just due to the perceived drawbacks related to price, range, and infrastructure. Moreover, the results suggest that competition can prove helpful for the market diffusion of electric vehicles, if all competitors proactively strive to shape the electric vehicle market instead of adopting a waiting position. This could also include the elimination of selected conventional vehicles from the portfolio, which reduces competition for electric vehicles and thus has a positive effect on their market diffusion as well as on overall savings of fossil fuels. However, such decisions might come along with unintended consequences, especially in the short run. First, demand may just shift from the eliminated conventional vehicle models to those conventional vehicle models that are still offered to the market. Second, demand may shift from one manufacturer to the other, if one competitor behaves rather conservatively and offers a rather conventional vehicle portfolio. For policy-makers, the findings imply that measures to steer manufacturers' behavior towards improving their offer of electric vehicles are just as important as those targeted at consumers. These measures

116

Further details on the model and also its full potential to analyze competitive strategies can be found in Wachter (2016).

Analyzing Market Introduction Strategies

27

can range from subsidies in research and development to technology mandates or a ban on certain technologies. The application of the AMaSi model to analyze the impact of such kind of policy measures will be part of the discussion in Section 6.2. Overall, the paper broadens the discussion on the levers for the market diffusion of electric vehicles and their contribution to the efficient and effective use of energy. This is of high relevance as more and more incumbent car manufacturers are currently designing their future powertrain and product portfolio. Moreover, political discussions point in the direction to apply technology mandates and restrictions to an increasing extent (cf. Chapter 1), having a direct and severe influence on product portfolio planning in the automotive industry. Beyond electric vehicles, the findings presented in the paper can generally help to shape the successful market introduction of green products. Most importantly, it is shown that reducing the competition from the conventional incumbent products is a substantial lever to facilitate the adaption of green innovations.

4.3

Analyzing Market Introduction Strategies Thies, C.; Kieckhäfer, K.; Spengler, T. S. (2016): Market introduction strategies for alternative powertrains in long-range passenger cars under competition, in: Transportation Research Part D: Transport and Environment, Vol. 45, pp. 4–27.

A further approach to analyze the manufacturers leverage to support the market diffusion of electric vehicles is introduced in Thies et al. (2016). Here, a system dynamics model is developed that allows for the evaluation of strategies for the market introduction of electric vehicles (PHEV, FCEV) under competition. PHEV and FCEV allow for similar cruising ranges (≥ 400 km) and refueling times as conventional vehicles, thus offering a similar convenience for customers. The model considers two competing manufacturers; one first-mover and one follower (cf. Figure 7). Each of the manufacturers offers a portfolio of vehicles that can be distinguished by size class and powertrain. Conventional vehicles (ICEV) and NGV are offered to the market from the beginning on. In contrast, the introduction of PHEV and FCEV depends on market introduction strategies of the competing manufacturers, which have to be defined before the start of the simulation. These strategies comprise decisions on timing, pricing, as well as targets for the fuel efficiency improvement of ICEV. During a simulation run, one manufacturer may still react to the actions of its competitor due to an endogenous adjustment of vehicle prices based on an anchoring-and-adjustment heuristic.117 This heuristic adjusts the target price for a certain vehicle model as a response to deviations from a target market share and a target profit margin. Moreover, the manufacturers can learn from each other due to technology spillover, leading to cost reductions of the powertrains. The same holds true with regard to learning by doing. On the demand side, market development is determined by the customers’ purchase decisions. The purchase decisions are modeled by combining a Bass diffusion model, which describes the customers’ awareness of the powertrains over time, and a discrete choice model, which considers the influence of the vehicle and

117

Details on anchoring and adjustment are given, for example, in Sterman et al. (2007).

28


customer attributes on the purchase decision.118 Additionally, the co-evolution of a complementary service station infrastructure and external influences from the market environment are modeled. Alternative market introduction strategies can directly be evaluated by comparing market shares, profits, and CO2 fleet emissions. Module “Infrastructure and external conditions”

Market environment OEM A

CO2 fleet emissions Fuel and energy thresholds prices

 Strategy − Timing − Pricing − Improvements ICEV  Experience  Production cost  Profit

Infrastructure

OEM B

Module “Manufacturer behavior”

 Gasoline filling stations  Natural gas filling stations  Electric charging stations (private, public)  Hydrogen filling stations

Vehicle models

Spillover

 Strategy − Timing − Pricing − Improvements ICEV  Experience  Production cost  Profit

Infrastructure subsidies

 Powertrain: ICEV, NGV, PHEV, FCEV  Segment: small, medium, large  Technical attributes  Purchase price

Potential customers

Purchase decisions

Customers  Population  Preferences  Awareness for powertrains  Vehicle service life

Market development  Vehicle sales  Vehicle stock  CO2 emissions

Module “Customer behavior”

ICEV: Internal combustion engine vehicle; NGV: Natural gas vehicle; PHEV: Plug-in hybrid electric vehicle; FCEV: Fuel cell electric vehicle

Figure 7: Concept of the system dynamics model to analyze market introduction strategies119 For model validation and the simulation experiments, real-world data related to the German market is used. The market introduction strategy of one manufacturer is kept fixed while different strategies are tested for its competitor. Overall, the results point in the same direction as in Kieckhäfer et al. (2017). In general, more competition leads to higher market shares of electric vehicles and thus allows for a higher reduction of fossil fuel consumption and fleet emissions. Thereby, aggressive pricing strategies can substantially facilitate the electric vehicle sales of PHEV and FCEV, but cause decreasing profits for both manufacturers. Since this is in conflict with the manufacturers’ interest, policy-makers may strive for subsidizing the purchase price of electric vehicles. Moreover, it is shown that technology spillover (e.g., reverse engineering, joint venture, and learning on sub-supplier level) has a positive effect on the market diffusion of electric vehicles, in particular if technology experience is low and high cost reductions can be achieved. Due to this technology spillover, waiting seems to be a reasonable strategy. Moreover, a follower can benefit from its competitors’ actions to prepare the market (e.g., deployment of hydrogen filling stations) as well as reduced demand and technology uncertainty.

118

119

Details on the Bass diffusion model can be found, for example, in Bass (1969). Similar approaches are described in Struben/Sterman (2008) and Walther et al. (2010). Adapted from Thies et al. (2016), p. 7.

Assessing the Sustainability of Products

29

In summary, the proposed model differs from existing system dynamics models as it explicitly considers market introduction strategies of individual manufacturers and competition between the manufacturers. This way, the paper contributes to a better understanding of the manufacturers’ influence on the market success of alternative powertrains in a competitive environment as well as of the underlying mechanisms. Along with the AMaSi model paper, this facilitates the formulation of suitable market introduction strategies, which is essential for the successful transformation of the product and powertrain portfolio of automotive manufactures.

4.4

Assessing the Sustainability of Products Thies, C.; Kieckhäfer, K.; Spengler, T. S.; Sodhi, M. S. (2017): Operations Research for sustainability assessment of products: A review, submitted to: European Journal of Operational Research (2nd revision).

The introduced simulation models focus on fuel and CO2 emission savings in order to analyze the influence of electric vehicles on achieving sustainable mobility. While this can be considered to be an important first step, it is inadequate from the point of a comprehensive sustainability assessment. Instead, all relevant ecological, economic, and social sustainability impacts over the entire life cycle have to be integrated into the assessment. For electric vehicles, amongst others, this would mean to additionally take into account the procurement of raw materials for battery cell manufacturing as well as the production and recycling of battery cells and systems, which currently comes along with severe ecological and social consequences.120 Besides gaining significant market shares, a second prerequisite of electric vehicles to substantially contribute to sustainable mobility is thus to improve their sustainability impacts over the entire life cycle. Over the last decades, much attention has been directed to the development of suitable methods to trace and estimate the sustainability of products. A very promising approach is given by the framework of life cycle sustainability assessment (LCSA), incorporating (environmental) life cycle assessment (LCA), (economic) life cycle costing (LCC), and social life cycle assessment (SLCA).121 While the different assessments are highly recognized and quite common in the scientific community and in industry, still major unresolved challenges exist. These comprise the selection of relevant indicators and impact categories for the assessment of a specific product, the normalization and weighting of multiple and typically incommensurable assessment criteria, the handling of uncertain and incomplete data, as well as the spatial differentiation to account for the heterogeneity in technologies, the environment, and decision-makers’ preferences across different regions. These challenges can be facilitated by adopting advanced analytical methods from Operations Research. For that reason, Thies et al. (2017a) survey how Operations Research methods are applied to facilitate sustainability assessment of products. Overall, 120 articles from the peer-reviewed scientific literature that use Operations Research methods for product-related sustainability assessments are systematically reviewed. The paper contributes to the scientific body of knowledge by providing a structured overview 120 121

Onat et al. (2014). For a discussion on the framework of life cycle sustainability assessment please refer to Kloepffer (2008), for example.

30


of the literature published in this field and identifying important research gaps. While manifold articles review the integration of sustainability aspects into operations and supply chain management models, Thies et al. (2017a) are the first to set the focus on product-related sustainability assessment issues and the application of methods from Operations Research. The review illustrates that most often methods from multi-attribute decision making (MADM), data envelopment analysis (DEA), and multi-objective decision making (MODM) are used to address challenges in sustainability assessment. With the application of these methods, the articles particularly strive for increasing the comprehensiveness of the assessment and providing new ways of communicating results. Mostly, environmental indicators are taken into account. Social and economic indicators are integrated to an increasing extent. Fuzzy logic, stochastic models, or sensitivity analysis are applied to address uncertainties in the data. This holds especially true with regard to the decision maker’s preferences and the inventory data, which forms the basis of any sustainability assessment. Challenges related to the spatial differentiation are not frequently addressed in the reviewed papers. The analysis of the articles reveals important research needs. These comprise the integration of qualitative and semi-quantitative indicators to address requirements on the consideration of social indicators, the simultaneous consideration of global and local sustainability objectives to allow for a spatial differentiation, and the application of advanced approaches to address uncertainty appropriately. Given that all these issues are relevant for the sustainability assessment of electric vehicles, promising avenues for future research open up. Motivated by this finding, a first concept for a spatially differentiated sustainability assessment of products with global supply chains is presented in Thies et al. (2017b).

5

Planning Issues related to the Transformation of the Supply Chain

This chapter includes selected contributions related to the transformation of the supply chain. In Section 5.1, an approach for the financial evaluation of make-or-buy strategies for electric vehicle batteries by means of Monte Carlo simulation is presented. This is followed by the description of a real option model that allows to support investment decisions in facilities for the production of electric vehicle batteries, taking into account product life cycle uncertainty (Section 5.2). Finally, a mathematical optimization model for technology and capacity choice for the recycling of electric vehicle batteries is summarized in Section 5.3.

5.1

Analyzing Make-or-Buy Strategies for Electric Vehicle Batteries Huth, C.; Kieckhäfer, K.; Spengler, T. S. (2015): Make-or-buy strategies for electric vehicle batteries: A simulation-based analysis, in: Technological Forecasting & Social Change, Vol. 99, pp. 22– 34.

With regard to the transformation of the supply chain in the automotive industry, one important task of network planning is the definition of make-or-buy strategies (cf. Section 3.2). In order to support decision-making and to offer electric vehicles at competitive prices, the financial consequences of alternative make-or-buy strategies need to be evaluated ex-ante. The evaluation is challenged by the uncertain market development and thus uncertain production volumes as well as the uncertain evolution of the battery technology, leading to high investment risks. Moreover, manifold forms of vertical integration are available, including partial integration and quasi-integration by means of a joint venture. Thereby, a dynamic adoption of the degree of vertical integration is possible according to the development of the electric vehicle volumes and battery technology. An approach for the financial evaluation of make-or-buy strategies for electric vehicle batteries in the face of uncertainties is presented and applied in Huth et al. (2015).122 The approach is based on a Monte Carlo simulation and considers the following aspects: (1) impact of volume uncertainty on economies of scale, (2) financial consequences of a technology leap, (3) joint ventures as a form of quasi-integration, and (4) the option to adjust the degree of vertical integration dynamically over time. This way, the article also generally contributes to the field of make-or-buy decisions. While simulation-based approaches are quite common to evaluate make-or-buy decisions, these features are typically not taken into account. The approach allows for simulating the net present value of alternative make-or-buy strategies for electric vehicle batteries a car manufacturer can pursue (cf. Figure 8). Overall, 14 different strategies are considered in the model. These are derived from six sourcing/manufacturing options, ranging from the sourcing of complete battery systems to the full in-house manufacturing of battery cells and systems, two forms of partial integration (i.e., yes/no) for cell manufacturing as well as battery assembly, and three

122

Further details are also given in Huth (2014).

32


different market phases (i.e., introduction, growth, consolidation) for which the decisions on the manufacturing option and the form of partial integration can be changed. For each strategy, battery volumes are distributed across the possible sourcing options in each period. In the case of in-house production, necessary manufacturing capacities are deployed. The battery volumes are derived from the market development for electric vehicles described by a Pearl curve.123 Afterwards, the relevant cash flows are estimated embracing investments in machines and facilities as well as cash-effective costs, which differ amongst the strategies. The cash flows are modeled as a function of the battery volume due to static (i.e., degression of fixed costs) as well as dynamic (i.e., learning effects) economies of scale. From the cash flows, the net present value of the alternative strategies is derived. To account for existing uncertainties, Monte Carlo simulation is applied.124 The uncertainties considered in the model comprise the maximum market potential of electric vehicles, the time of a technology leap, as well as the cost of conversion and the new manufacturing costs associated with a technology leap. Based on the Monte Carlo simulation, probability distributions of the net present value of the alternative make-or-buy strategies are computed. Make-or-buy strategy (example) Introduction Manufacturing option

EX

Growth

JV

JV

Partial integration



JV

EX

JV

IN

EX

EX

Volume distribution

Random variables 

Consolidation

Volume of electric vehicles

Joint venture

Manufacturer

Supplier

Output Net present value

Technology leap −

Point of time





Financial implications

Manufacturing capacity



−





Manufacturing volume









Investments, cash-effective costs



Investments, cash-effective costs



Cash-effective costs

Parameter 

Investment in machines and facilities



Initial cash-effective costs of manufacturing (e.g., material costs)



Learning rate



….

Cash flows EX: External sourcing; JV: Joint venture; IN: In-house production

Figure 8: Concept of the simulation model to evaluate make-or-buy strategies for electric vehicle batteries125 For the analysis, seven different archetypes of make-or-buy strategies for electric vehicle batteries are considered. These strategies are applied to two types of car manufacturers with varying manufacturing

123 124 125

For details on the Pearl curve please refer to Pearl (1926). Details on Monte Carlo simulation can be found, for example, in Kohlas (1972). Adapted from Huth et al. (2015), p. 27.

Planning Investments in the Production of Electric Vehicle Batteries

33

volumes (i.e., volume manufacturer, premium manufacturer) based on real-world data. Overall, the results of the simulation study show that the net present values of the alternative make-or-buy strategies differ fundamentally. From a financial perspective, complete external sourcing performs best and complete in-house production performs worst. If car manufacturers strive for deploying own production capacities due to strategic considerations, they should collaborate with specialized companies in joint ventures, especially in the introduction phase. By making use of partial integration, the manufacturers can broaden their technology base without negative financial consequences. This way, they can reduce the risk of a technology leap. For premium manufacturers with lower sales volumes, the in-house manufacturing of cells comes along with financial disadvantages compared to a volume manufacturer due to lower manufacturing volumes and thus higher fixed costs. For that reason, in-house manufacturing of battery cells is a less attractive option for premium manufacturers. As discussed in Section 3.2, make-or-buy decisions should not only rely on financial considerations but also on further aspects such as the dependency on suppliers and the possibility of technological differentiation. This calls for a multi-criteria approach, which is out of scope of the article. Yet, the presented model can be integrated in such an approach and can be used to validate strategic make-or-buy strategies for electric vehicle batteries from a financial point of view. For instance, a typical experts’ recommendation is that car manufacturers should substantially engage in the battery production to build up new competences in the short- and medium-term, while outsourcing to suppliers is preferable in the long-term due to economies of scale. By applying the simulation model, it can be shown that the net present value of such a strategy is high compared to other strategies. However, a similar NPV can be achieved if the degree of vertical integration is increased considerably in the growth and consolidation phase. In doing so, this strategy provides the additional long-term benefit of securing a high share of value added.

5.2

Planning Investments in the Production of Electric Vehicle Batteries Lukas, E.; Spengler, T. S.; Kupfer, S.; Kieckhäfer, K. (2017): When and how much to invest? Investment and capacity choice under product life cycle uncertainty, in: European Journal of Operational Research, Vol. 260, pp. 1105–1114.

Provided that a car manufacturer has decided for an in-house production of battery cells and/or systems, decisions related to investment and capacity planning need to be addressed. For this task of strategic capacity planning, models have been introduced in the scientific literature for more than 50 years. Often, methods from Operations Research such as linear optimization are applied. More and more models aim at maximizing the net present value in order to take into account the dynamic development of cash flows and the time value of money.126 In contrast, the flexibility of a decision maker with regard to the timing of the investment is typically not taken into account. This flexibility can be considered a real option that has a value for the manufacturer because of the investment size, the market and technology uncertain-

126

An overview of mathematical programming models for strategic capacity planning in manufacturing is given, for example, in Martínez-Costa et al. (2014).

34


ties, as well as the scalable capacities. Being a research area of corporate finance, such kind of problems is addressed by real options theory with the help of methods from Operations Research.127 Based on these considerations, a real option model that allows to evaluate investment and capacity choice under product life cycle uncertainty is developed in Lukas et al. (2017). The model solves for both the optimal timing of an investment and the optimal capacity choice simultaneously. Based on the assumptions, it is limited to a situation where a company has to decide whether or not to invest once only in own production facilities in order to (partially) fulfill the demand for a specific product that would be otherwise sourced from a supplier. Since the demand for the product can also be fulfilled with the help of a supplier, it is assumed to be independent of the company’s investment decision. The proposed model contributes to the existing literature by taking into account the characteristics of the product life cycle due to consumers’ adoption decision and new product diffusion over time. Based on these characteristics, sales might suddenly drop drastically. Moreover, demand for a product typically follows a bell-shaped curve, increasing throughout the introduction and growth phase and then decreasing in the maturity and decline phase. As a consequence for investment decisions, cash flows are supposed to follow the typical product life cycle and are of uncertain nature due to demand uncertainties.128 This is in contrast to most papers guiding investment decisions under uncertainty, which assume endless cash flow growth. In order to take into account demand and cash flow uncertainties over the life cycle, a stochastic version of the bass model is integrated in the real options model. Uncertainty is covered by introducing a Brownian motion resulting in a stochastic differential equation for the expected cash flows. Using Ito’s Lemma, the value of the investment option is modeled as a parabolic partial differential equation, which can be solved with the Crank–Nicolson finite difference approach.129 From the option values, optimal investment thresholds and optimal production capacities can be determined. The optimal timing of the investment directly depends on the investment threshold: Provided that the observed cash flows exceed the investment threshold, an investor should immediately invest in the project. Overall, the model assumptions apply to the situation in the automotive industry quite well. For that reason, Lukas et al. (2017) apply the model to a numerical example referring to investment decisions in facilities for the production of electric vehicle batteries. Most notably, the bass model is parameterized based on the simulated market diffusion for the German car market given in Kieckhäfer et al. (2014) by making use of regression analysis. This illustrates the close links between the different strategic planning tasks, as well as between the introduced planning approaches. With regard to the investment and capacity planning for the production of electric vehicle batteries, comparative static analysis indicates, amongst others, that the optimal threshold values follow an sshaped curve, monotonically increasing over the product life cycle. The threshold is segmented according to the optimal capacity to be installed. In the introduction phase of electric vehicles, an investment in small- or medium-sized facilities seems reasonable provided that the cash flows exceed the threshold.

127 128 129

Kupfer et al. (2015); details on real options theory are given in Dixit/Pindyck (1994), for example. Kupfer et al. (2015). For more information on the Crank-Nicolson finite difference approach, please refer to Hull (2012), pp. 579–580, for example.

Planning Technologies and Capacities for the Recycling of Electric Vehicle Batteries

35

The longer a manufacturer delays the decision, the more electric vehicles have to be sold to justify an investment in a production facility. In this situation, large capacities should be deployed directly. Moreover, investment decisions in small-sized facilities are optimal for a longer period of time the lower the manufacturer’s expectations on the rate of return. The optimal investment decision is highly sensitive to the expected technology diffusion: Higher demand uncertainties cause investment in larger capacities to be optimal at any point in time. In turn, this means that investments in larger capacities become optimal at earlier points in time given a certain threshold level. Moreover, the optimal investment threshold and capacity choice may be ambiguous over time if market diffusion is influenced by just few innovators and many imitators. In this situation, sales are expected to take off relatively late. Thus, it may be optimal to invest in facilities with high capacity during the introduction phase already if the market signals a positive outlook. The analysis reveals that ignoring the product life cycle in models to support investment and capacity decisions under uncertainty may result in misled decisions. If the product life cycle is not considered, investments may take place too late in the early technology diffusion phase while for mature products investments may be done too early. These insights are not only helpful to support investment decisions in production facilities for electric vehicle batteries but also for other innovative products that are characterized by uncertain cash flows following the well-known product life cycle.

5.3

Planning Technologies and Capacities for the Recycling of Electric Vehicle Batteries Hoyer, C.; Kieckhäfer, K.; Spengler, T. S. (2015): Technology and capacity planning for the recycling of lithium-ion electric vehicle batteries in Germany, in: Journal of Business Economics, Vol. 85, pp. 505–544.

In the paper by Hoyer et al. (2015), the focus is on the deployment of an appropriate recycling network for electric vehicle batteries in Germany.130 To this end, a mathematical optimization model for technology and capacity choice is developed, adopting the viewpoint of a central decision-maker (“potential investor”). The model depicts both strategic decisions on the investment plan (i.e., number of recycling facilities of specific technology and capacity, time of installation and liquidation) and tactical decisions on the recycling program plan (i.e., volume and mix of products and intermediates to be treated in each module and period, supplies required, recyclables to be sold, and residues to be disposed of). It maximizes the net present value that results from the discounted cash flows comprising investment expenses, liquidation revenues, fixed facility-operation expenses, variable operation expenses, as well as expenses for transportation, purchase, and disposal, and revenues from the sale of materials and energy. Thereby, economies of scale with regard to the deployment and operation of recycling facilities as well as decentralization effects in the collection of spent batteries are taken into account. To allow for the consideration of multi-level co-production processes with alternative technologies as well as different product variants, intermediates, supplies, recyclables, and residues, the production-related processes and the

130

Further details on the model and also its full potential to analyze decisions related to the recycling of lithium-ion electric vehicle batteries are given in Hoyer (2015).

36


transformation of materials are modeled by means of linear activity analysis.131 Additional constraints of the optimization model are illustrated in Figure 9. Data Factors Products available Mass of the factors Purchase, sale, and disposal prices Collection expenses Delivery expenses Modules and activities Specific net present values Minimum time of operation Physical life Capacity Capacity coefficients Activity vectors Variable expense per execution of activity Others Target interest rate Mandatory collection rate Mandatory min. recycling rate

Optimization model Objective function of the potential investor Maximization of the net present value of the cash-flows embracing Collection of products Module investments, operation (fixed/variable), and liquidations Delivery between modules Purchase of supplies, sale of recyclables, disposal of residues

Constraints Limited product availability Material transformations in and material flow between modules Capacity restrictions Collection cost level selection Minimum time of operation Achievement of mandatory collection rates and minimum recycling rates

Decisions Investment plan Number of modules of specific technology & capacity operated, time of start-up and liquidation

Recycling program plan Volume and mix of products to be treated, supplies required, recyclables to be sold, residues to be disposed of

Figure 9: Concept of the optimization model for technology and capacity planning in recycling networks132 Compared to other models for reverse supply chain planning, the model features two distinct characteristics, i.e. the consideration of dynamic cash flows and the modelling of production processes and material transformations by means of linear activity analysis. On the one hand, existing models typically concentrate on cost-based optimization of a single, representative period, which neglects the heterogeneity of cash flows over time. On the other hand, details of processes and products are usually neglected to reduce model complexity and focus on location decisions instead. This, however, impedes to include decisions on the decoupling points between the processes and the recycling depth of products depending on their value to use plant capacities efficiently. Based on real-world data, the model is applied to the LithoRec process (cf. Section 2.3) in order to analyze which recycling technologies to deploy when and in what capacity for the recycling of lithium-ion electric vehicle batteries in Germany. For the analysis, the three LithoRec technologies disassembly, mechanical conditioning, and hydrometallurgical conditioning are considered in two different capacity classes. Uncertainties with regard to the market and technology development are addressed by taking into account five different scenarios and conducting subsequent sensitivity analyses. The scenarios differ in the spent battery volume and the mix of battery variants available, the factor prices, as well as the initial investments and fixed operating expenses for the recycling facilities. The resulting investment plans for an optimal deployment of recycling facilities are evaluated by means of the net present value, the internal rate of return, and the payback period. While the analysis is conducted from the perspective of a 131 132

Details on activity analysis can be found, for example, in Debreu (1959) and Koopmanns (1951). Adapted from Hoyer et al. (2015), p. 515.

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central decision-maker in particular, results are transferred to the level of individual actors additionally in order to discuss consequences for the organization of the network. The main findings of the analysis can be summarized as follows. From an aggregated perspective, the recycling network can be operated highly economically in four out of the five scenarios. Thereby, the profitability is mainly dependent on the available volume and mix of battery returns as well as the factor prices. The higher the volume of spent batteries, the better the utilization of decentralization effects to reduce collection costs and economies of scale in hydrometallurgical conditioning. Only for the case that the share of the LFP battery returns, which include less valuable materials compared to NMC batteries, is rather high (i.e., 80% at the end of the planning horizon in 2030) and factor prices stagnate at 2015 levels, expenses outweigh the revenues. Moreover, it is shown that an investment plan exists that is very suitable for different levels of factor prices, investments and shares of the rather unattractive LFP batteries as long as the volume of battery returns is moderate (i.e., 0.8 million batteries to be recycled in the planning horizon). For the initial decisions, even a robust plan exists despite the prevailing uncertainties. With regard to the individual actors participating in the recycling network, the analysis reveals that profits, risks, and risk premiums are very heterogeneous, calling for co-operation models (e.g., joint ventures) and/or allocation mechanisms (e.g., compensation prices, two-part tariffs, etc.) in order to successfully deploy the recycling process. Beyond that, long payoff periods of up to 8 years or even more may impede the installation of the recycling network. This is mainly due to the slow uptake of the electric vehicle market in Germany and thus a low amount of battery returns that have to be expected for the next several years. The results emphasize what important insights can be derived from the application of the model. Thereby, it is not limited to the optimization of recycling networks or single recycling processes for lithium-ion electric vehicle batteries, but may also be applied to similar reverse supply chain planning problems. The model unfolds its full potential whenever multi-level co-production processes with alternative technologies in different capacities as well as different product variants are in the center of attention, cash flows are highly dynamic, and the planning of locations is rather unimportant compared to the technology and capacity choice.

6

Impact Assessment of Policy Measures

This chapter introduces contributions that are related to an impact assessment of policy measures. First, key findings from a literature study on alternative policy measures for regulating the CO2 emissions from passenger cars are summarized in Section 6.1. In Section 6.2, it is then illustrated how the market simulation models presented in Chapter 4 can help to analyze the impact of policy measures on the market diffusion of electric vehicles. The chapter closes with an assessment of the impact of mandatory rates on the recycling of electric vehicle batteries for which the optimization model from Section 5.3 is applied.

6.1

Analyzing Policy Measures for Regulating the CO2 Emissions from Passenger Cars Kieckhäfer, K.; Feld, V.; Jochem, P.; Wachter, K.; Spengler, T. S.; Walther, G.; Fichtner, W. (2015): Prospects for regulating the CO2 emissions from passenger cars within the European Union after 2023, in: Zeitschrift für Umweltpolitik und Umweltrecht, Vol. 38 (4), pp. 425–450.

Given the challenges related to the transition of the automotive industry towards electric mobility and the need to reduce CO2 emissions from passenger cars significantly, environmental and climate policies are of utmost importance to push the development in the right direction (cf. Section 3.4).133 Thereby, fleet emission standards as introduced in Chapter 1 can be considered to be the most important policy measure that is applied in all major automotive markets worldwide.134 Contributing to the discussion with regard to legislative proposals for post-2020 targets in the EU, alternative policy measures to the currently applied fleet emission standard are comprehensively evaluated in Kieckhäfer et al. (2015).135 The evaluation is mainly based on an extensive literature review. Additionally, real-world effects of already implemented policies are taken into account. The objective of the study is to produce insights on the application of alternative policy measures as a basis for thorough political discussions. For the evaluation, the focus is set on design alternatives for an emission standard as a well-established regulatory instrument and an emission trading system (ETS) as a well-established market-based instrument in the EU. This way, it is taken into account that the current situation in the EU is important for the acceptance of any new regulation in the future. The single design alternatives considered in the study are summarized in Table 1. With regard to the design of an ETS, upstream (i.e., fuel providers acquire emission rights for every liter of fuel sold), midstream (i.e., car manufacturers must prove emission rights for the life-cycle emissions of every car sold), and downstream approaches (i.e., car users are re133

134 135

Focusing on the USA, a recent discussion on climate actions for reducing CO2 emissions in the automotive industry to be consistent with the long-term reduction targets for GHG emissions according to the Intergovernmental Panel on Climate Change (IPCC) can be found in Supekar/Skerlos (2017), for example. Details are given in Yang/Bandivadekar (2017), for example. This research project was partially funded by VDA as a reaction to the political discussions in 2013. All results, opinions, and conclusions of this contribution are solely those of the authors. The results were discussed with experts from politics, industry, and science, for instance with ACEA and the Office of the Federal Chancellor. In November 2017, a final legislative proposal was communicated by the European Commission, calling for a further reduction of the average fleet emissions by 15% until 2025 and by 30% until 2030, European Commission 11/8/2017.

40


quired to hold emission rights for every gram of CO2 emitted) as well as open (i.e., trading occurs between companies of different industry sectors) and closed systems (i.e., trading occurs between companies of one industry sector only) are analyzed.136 The analysis of the design alternatives is based on multiple criteria in five categories, namely neutrality, implementation, achievement of objectives (effectiveness and efficiency), economy, and applicability. Moreover, prospects for combining a regulatory and a market-based instrument are discussed. Table 1: Evaluated policy measures for regulating the CO 2 emissions from passenger cars in the EU137 Base

Regulations (EC) No. 443/2009 and (EU) No. 333/2014

Market-based instruments

Regulatory instruments

Design alternatives Change single parameter

Switch to footprint as utility parameter Switch to energy consumption as measured value Consider tank-to-wheel emissions during complete use phase

Change system boundary

Consider well-to-tank and tank-to-wheel emissions Consider emissions during whole vehicle life-cycle Implement upstream ETS (open/closed)

Change regulatory system

Implement midstream ETS (open/closed) Implement downstream ETS (open/closed)

The study comes to the conclusion that there is a need to think about new regulatory options besides simply tightening the currently applied emission standard. One reason for that is the expected growth in market shares of electric vehicles for which most life cycle emissions are not covered by a regulation that only accounts for direct tailpipe CO2 emissions.138 The main challenge of setting up a new policy is to find a balance between maintaining the pressure on car manufacturers to utilize technical mitigation potentials, fostering the de-carbonization potential of the energy carriers, and preventing the automotive industry from exceptionally high financial burdens compared to other sectors. In this context, combinations of an emission standard and an upstream or midstream ETS are particularly worth considering. The potential of such a regulation would lie in the higher flexibility and thus lower abatement costs of the automotive industry to comply with the regulation due to the participation in an ETS while technical innovations to reduce CO2 emissions from passenger cars would be driven by the standard.

136 137 138

Detail can be found, for example, in Mock et al. (2014). Cf. Kieckhäfer et al. (2015), p. 429. For details, please refer to Jochem et al. (2015).

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Overall, Kieckhäfer et al. (2015) contribute to the political discussions on the reduction of CO2 emissions from passenger cars in the EU by proposing alternatives to the current regulation and giving first insights in their general applicability and operability. For the concrete design of a specific policy measure and the underlying mechanisms, qualitative analyses should be backed by a detailed model-based impact assessment (cf. Section 3.1). This is out of the scope of the paper but will be discussed in the following.

6.2

Assessing the Impact of Policy Measures on the Market Diffusion of Electric Vehicles Walther, G.; Wansart, J.; Kieckhäfer, K.; Schnieder, E.; Spengler, T. S. (2010): Impact assessment in the automotive industry: Mandatory market introduction of alternative powertrain technologies, in: System Dynamics Review, Vol. 26, pp. 239–261.

With regard to a model-based impact assessment of policy measures to support the market diffusion of electric vehicles, the simulation approaches introduced in Chapter 4 can be used. One example is the purchase premium for PHEV, EREV, BEV, and FCEV that was introduced in Germany in July 2016.139 Right before the introduction, the impact of the purchase premium on the market diffusion of electric vehicles was assessed with help of the AMaSi model.140 At that point in time, a proposal was under discussion to pay a premium for purchasing an electric vehicle between July 2016 and 2020, which should amount to €5,000 for private consumers and €3,000 for commercial consumers. The premium was supposed to decrease by €500 per year with an overall budget of €1.3 billion.141 In the simulation experiment, the impact of the proposed premium was compared to the status quo without any purchase premium and the option to constantly subsidize purchase decisions by €10,000 per electric vehicle until 2020. Budget and list price restrictions were not taken into account. Moreover, uncertainties were considered by making use of two alternative market development scenarios (cf. Figure 10): In the base scenario, it is assumed that electric car sales follow the trend of the preceding years, resulting in a stock of 366,000 electric vehicles at the end 2020 if the premium is not applied. The optimistic scenario assumes electric vehicles to be much more considered in the choice set of the customers due to advertising and word of mouth, resulting in a stock of 628,000 electric vehicles at the end of 2020 without subsidizing the purchase decisions. If no purchase premium applies, it can be seen that the German government’s aim of having one million electric vehicles on the road until 2020 (cf. Chapter 1) is missed clearly even in the optimistic market scenario. The simulation results illustrate that the purchase premium stimulates additional consumers 139

140

141

The description of the impact assessment related to the application of the purchase premium in Germany closely follows Kieckhäfer (2018). The results of the study were communicated in the press release Technische Universität Braunschweig 3/9/2016.This press release was then taken up by the German Press Agency and several national newspapers and magazines. Eventually, the German government decided to apply a purchase premium that slightly differs from the proposal. Since July 2016, purchasing a BEV or FCEV is subsidized by €4,000 per car and purchasing a PHEV or EREV by € 3,000 per car, provided the list price of the car does not exceed €60,000. The total budget is €1.2 billion, equally shared between the public purse and the car makers. Further information can be found in BAFA (2017a). Given the similarity between the actual and the proposed purchase premium, similar impacts can be assumed. Additionally, this can be deduced from the number of applications submitted between July 2016 and October 2017. With almost 38,000 applications, the purchase premium has fallen short of expectations so far, BAFA (2017b).

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to buy a new electric vehicle. However, the effect is limited. In the base scenario (optimistic scenario), up to 93,000 (171,000) additional electric vehicles are sold if a premium is paid. Total cost of the purchase premium range between €830 million (base scenario, purchase premium 0f €5,000 diminishing over time) and €7.2 billion (optimistic scenario, purchase premium of €10,000). Thereby, the premium has to be paid to every buyer of an electric vehicle and thus also to those who would have purchased an electric car anyways. 1,000,000

Electric vehicle stock

Without purchase premium 800,000

€5,000 (diminishing) Optimistic sceanrio

€10,000 600,000

Base scenario

400,000

200,000

0 2010

2012

2014

2016

2018

2020

Figure 10: Simulated development of the electric vehicle stock in Germany between 2016 and 2020 for alternative designs of a purchase premium under consideration of two market scenarios 142 Given the limited impact of the purchase premium and the high costs, other policy measures might be considered to be more promising to facilitate the market diffusion of electric vehicles. These include, a sales ban on diesel vehicles as can be shown with help of the AMaSi model. For instance, by eliminating all diesel vehicles from the product portfolio in 2016, a stock of approximately one million electric vehicles in 2020 and thus the aim of the German government can be reached already in the base scenario. Nevertheless, technology bans constitute a very forceful and profound intervention in market mechanisms, which should be applied very carefully and only if no other option is available. A further example how market simulation can be used to support an impact assessment of policy measures is given in Walther et al. (2010).143 Here, a system dynamics model is developed and applied to analyze compliance strategies of the automotive industry as a reaction to California’s low emission vehicle regulations related to the reduction of GHG fleet emissions and a mandatory ZEV sales quota (cf. Chapter 1). The model is quite similar to the model developed in Thies et al. (2016). The main differences can be summarized as follows: First, competition is neglected as the model takes on an industry perspective. Second, California’s complex GHG and ZEV regulation is incorporated in the model. Third, besides ICEV, manufacturers can introduce HEV, PHEV, and BEV in the size classes extra-small, small, medium,

142 143

Adapted from Kieckhäfer (2018). Further details on the model and also its full potential to analyze compliance strategies are given in Wansart (2012).

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and large to define the compliance strategies. Fourth, these strategies are not only evaluated in terms of sales figures but also with respect to regulatory compliance. The paper is particularly motivated by the fact that California’s low emission vehicle regulations have a leading position worldwide and the complex interdependencies between the GHG and the ZEV regulation. Both regulations force manufacturers to define appropriate powertrain strategies to comply with the regulation. A reduction of GHG emissions can be achieved by reducing the fuel consumption of ICEV and the introduction of HEV, PHEV, and BEV. The latter must also be introduced to comply with the ZEV regulation. Interdependencies between the two regulations exist as the reduction of fuel consumption of ICEV in order to meet GHG requirements leads to a higher attractiveness of ICEV compared to electric vehicles. Thus, meeting the ZEV requirements becomes more challenging. In contrast, the more electric vehicles are introduced and sold, the less are manufacturers forced to reduce the fuel consumption of ICEV. Since the sales quota for electric vehicles depends on the number of ICEV sold in the preceding years, these interdependencies have a direct influence on the future ZEV requirements: Higher (lower) sales of conventional vehicles result in a more (less) stringent sales quota for ZEV. For the impact assessment, various strategies for the compliance with the GHG and ZEV regulations are defined and analyzed. Compliance strategies related to the GHG regulation comprise measures to reduce the fuel consumption of ICEV and adjust the product portfolio by introducing an ICEV in the size class “extra small”. The strategies to comply with the ZEV regulation differ with respect to the type of electric vehicle introduced and the year of market introduction. The results of the simulation experiment point out that California’s low emission vehicle regulation requires an early introduction of electric vehicles in all size classes. Even in this case, meeting the challenging ZEV sales quota is hardly possible. Moreover, the best GHG compliance strategy (i.e., simultaneous reduction of fuel consumption and introduction of an extra small ICEV) makes meeting ZEV requirements more difficult. Thus, joint compliance strategies have to be developed by the automotive industry due to the interdependencies between GHG and ZEV regulations. Overall, the two examples illustrate the high potential of using market simulation models to support the impact assessment of policy measures from both the perspective of policy and of industry. This way, the complex structure of the socio-technical/economic system “automotive market” can be taken into account when analyzing the efficiency and effectiveness of alternative policy measures on the market diffusion of electric vehicles and their consequences for the automotive industry.

6.3

Assessing the Impact of Mandatory Rates on the Recycling of Electric Vehicle Batteries Hoyer, C.; Kieckhäfer, K.; Spengler, T. S. (2013): Impact of mandatory rates on the recycling of lithium-ion batteries from electric vehicles in Germany, in: Nee, A. Y. C.; Song, B; Ong, S. (eds.): Re-engineering manufacturing for sustainability, Proceedings of the 20th CIRP International Conference on Life Cycle Engineering, Springer, Singapore, pp. 543–548.

Policy measures can also be applied to facilitate the deployment of production and recycling facilities for electric vehicles or single components of the electric powertrain, respectively. This is illustrated in

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Hoyer et al. (2013).144 Here, the impact of mandatory recycling rates on the recycling of lithium-ion batteries from electric vehicles in Germany is analyzed. These rates have a substantial influence on the planning of recycling technologies and capacities as compliance can only be guaranteed by a very early deployment of the recycling network. The assessment is carried out by means of the optimization model introduced in Section 5.3 and relies on the same data base. It follows the idea to compare optimal investment plans with and without consideration of a minimum recycling rate of 50% as it applies in Germany (Cf. Section 2.3). The comparison is based on the categories antedated investments, change in the financial key figures, change in achieved recycling rates, and differences in the mass and type of materials disposed. The analysis reveals that the mandatory recycling rate causes an earlier installation of facilities and partly a considerable decrease of the profitability and an increase in the investor’s risk. At the same time, the achieved recycling rates can be improved to a minor extent only. Yet, the minimum recycling rate has a very positive effect on the avoidance of hazardous waste if low-value batteries such as LFP batteries prevail. In order to allow for both the recovery of a large mass of recyclables and a high financial attractiveness for potential investors, political decision makers should thus allow for the temporal storage of intermediates, e.g., cells and cathode coating, and alter the associated calculation method of the recycling rate. By allowing for a compensation of recycling deficits from years with a low volume of battery returns in later years when substantial battery return flows are reached, the freedom of action with respect to the deployment of the recycling network could be increased considerably.

144

Additional analyses are carried out in Hoyer et al. (2015).

7

7.1

Conclusion

Discussion and Recommendations

The transition towards electric mobility challenges the current business models of incumbent car manufacturers, requiring a successful transformation of the technology and product portfolio as well as the supply chain in order to maintain competitiveness. Motivated by this, tailor-made simulation and optimization models are developed in this thesis that allow for guiding decision-making towards electric mobility on a strategic planning level. Even though application-oriented, the presented approaches also make substantial scientific contributions in terms of problem structuring, model building, validation, evaluation, and production of knowledge. Moreover, the models bear the potential to be applied to similar and generalized problem settings related to the transformation of technology and product portfolios and supply chains. In-depth discussions with regard to the scientific contribution, relevance of the results for industry and society, transferability, and avenues for future research can be found in the single articles. In the following, these aspects will be summarized on an aggregated level according to the three research domains covered in this thesis (cf. Section 3.5). With regard to the research domain on market simulation as a means to support the transformation of the technology and product portfolio, the primary contribution of the articles lies in the modeling and investigation of car manufacturers’ levers to support the market diffusion of electric vehicles. While the impact of vehicle characteristics such as price and cruising range on the market success of electric vehicles is at the heart of manifold discussions in academia and practice, product portfolio decisions, market introduction strategies, and/or competitive strategies of car manufacturers have been largely neglected so far. In existing market simulation models, all vehicle models are typically assumed to be offered to the market from the beginning on, which does not stand up to reality and ignores the manifold options that manufactures have to shape the market development. In order to allow for an analysis of such kind of measures, the proposed approaches particularly extend existing market simulation models by integrating system dynamics and agent-based modeling, considering manufacturers’ decisions related to the range of products offered explicitly and taking into account aspects of competition. This way, the articles also generally contribute to the field of innovation diffusion, allowing for a better understanding of the manufacturers’ influence on the market success of innovative products in a competitive environment and the underlying mechanisms. On an aggregated level, the results indicate that introducing the “right” electric vehicle with the “right” price at the “right” time and/or eliminating the “right” conventional vehicle at the “right” time may push the diffusion of electric vehicles significantly. Thus, car manufacturers should take assertive actions to shape the market development on their own instead of just waiting for the electric vehicle market to develop. Competition proves helpful in this regard, leading to a broader and more attractive offer of electric vehicles to the customers and thus higher market shares. For electric vehicles to really gain momentum, the best strategy would be to offer a wide range of electric vehicle models and withdraw conventional vehicles from the portfolio. The latter decision must be taken very carefully, depending on the

46

Conclusion

consumers’ attitude towards electric vehicles and the strategies of the competitors. Otherwise, the competiveness of a car manufacturer may be threatened. Future research should especially address the influence of new mobility concepts such as car sharing and autonomous driving and investigate the feasibility of the manufacturers’ decisions in terms of financial and human resources as well as development and production capacities. The scope of the models could be additionally extended by taking into account trucks or other modes of transportation, for instance. In any case, decisions on the adoption of the electric vehicles would need to be adjusted since the purchasing behavior of operators of car sharing, public car, or truck fleets substantially differs from the purchasing behavior of private consumers. Moreover, capturing the complexity of the system structure and the behavior of the actors to an appropriate extent is still an open question. To this end, the accuracy of the models needs to be traded off with computational performance and data requirements. This also concerns validation of the structure and the behavior of market simulation models for which manifold tests exist. Yet, the validation of complex models is very challenging and often not appropriately addressed, impeding credibility of the results and insights as well as the diffusion of market simulation models into industry and politics. Besides producing estimates on the expected market development, the benefit of applying market simulation for industry and politics can be seen in the collection of expert knowledge from different disciplines and departments, contributing to a better understanding of the market behavior and the support of stakeholder discussions as well as decision-making. With respect to the transformation of the supply chain, the developed approaches allow to guide longterm investment decisions in production and recycling networks for electric vehicles lithium-ion batteries. Thereby, distinct characteristics of the problem setting are taken into account, particularly dynamics and uncertainties in the development of the electric vehicle market, product and process technologies, and corresponding cash flows. To this end, the model for the financial evaluation of make-or-buy strategies extends common simulation-based approaches by integrating the impact of volume uncertainty on economies of scale, financial consequences of a technology leap, joint ventures as a form of quasiintegration, and the option to adjust the degree of vertical integration dynamically over time. In order to support investment and capacity decisions under product life cycle uncertainty, the typical assumption of endless cash flow growth made in real options models is overridden by incorporating the characteristics of the product life cycle due to consumers’ adoption decision and new product diffusion over time. The optimization model for the technology and capacity choice in recycling networks features the consideration of multi-level co-production processes and material transformations by means of linear activity analysis at the expense of location decisions. This way, decisions on the decoupling points between the processes and the recycling depth of products become possible, which are usually neglected in existing models for reverse supply chain planning. Due to these characteristics, the models are not only helpful to support investment decisions in a production and recycling network for electric vehicle batteries. In fact, they also contribute to the existing literature on network planning, allowing for new insights on make-or-buy, investment, technology, and capacity decisions that are characterized by uncertainties, dynamics, and/or product and process complexity. The results of the comprehensive computational experiments all point in a similar direction. Investments in production and recycling facilities for lithium-ion electric vehicle batteries may become a via-

Discussion and Recommendations

47

ble options for car manufactures in the long run. The corresponding decisions are particularly influenced by the development of production and recycling volumes, which are directly dependent on the market evolution of electric vehicles. Since electric vehicles are still in the introduction phase and future sales are rather uncertain, hedging against investment risks can be considered to be a very important strategy in the short- to medium-term. This can comprise, amongst others, the deployment of smallsized facilities in the beginning, the engagement in joint ventures or co-operative networks, the formation of cooperation with specialized suppliers, and the use of flexibility options such as waiting. The specific make-or-buy strategy to be chosen, the specific timing of investments, and the specific technologies and capacities to be deployed need to be evaluated individually for every company, taking into account manifold factors such as the market diffusion of electric vehicles, the development of factor prices, the evolution of product and process technologies, and investments in machines and facilities. Due to the strategic character of the planning tasks and their importance for the long-term competitiveness, decisions should not only be based on financial considerations but also rely on further aspects such as technological differentiation, access to technologies, and regulatory requirements. One way to extend the scope of the models would be to consider the further components of the electric powertrain, especially the electric motor and power electronics. These components are closely interlinked with the battery system, which may have an effect on the optimal decisions. Additionally, fuel cell technologies should receive closer attention. In-depth analyses on aspects of competition between technologies as well as between companies can be considered as a further promising avenue for future research. The same holds true for the investigation of mechanisms for an efficient (re-)allocation of profits and risks in a network of individual actors. With regard to a joint planning of a production and recycling network for electric vehicles, the individual decisions considered in the presented models may be integrated in an overarching planning approach. This would allow to take into account existing interdependencies between the decisions. Moreover, financing issues could be integrated in the models, constraining the financial leeway of the companies. The presented models also contribute to the field of impact assessment as a means to analyze the consequences of policy measures for the transformation processes in the automotive industry. By testing the influence of policy measures on the behavior of manufacturers and consumers as well as alternative compliance strategies, new insights into the design of mechanisms to foster the market diffusion of electric vehicles from a policy-maker's perspective and the development of expedient compliance strategies from an industry’s perspective can be gained. The conducted model-based assessments of alternative policy measures illustrate that the mechanisms have to be chosen carefully in order to deploy efficient and effective measures and avoid unintended side-effects. From the results, it can be concluded that policymakers should focus at least as much on steering manufacturers towards improving their offer of electric vehicles as on influencing the consumer behavior. Corresponding measures can range from rather subtle measures such as subsidies for research and development and the deployment of an appropriate charging infrastructure to more drastic interventions such as technology mandates or bans. While the latter can be considered to have the biggest impact on the market behavior, they should be applied very carefully as they may provoke further unintended consequences and threaten competiveness. In order to realize fuel and emission savings with the existing vehicle fleet, policy should addition-

48

Conclusion

ally focus on the rational use of energy in the use phase of a vehicle. Overall, an appropriate mix of policy measures seems to be necessary to achieve long-term goals on the deployment of electric vehicles and emission reduction in the automotive sector. Determining an optimal basket of policy measures can thus still be considered a very important and promising avenue for future research. The same holds true for an in-depth analysis of policy measures striving to facilitate the deployment of production and recycling networks for electric vehicles. The assessment of the mandatory recycling rate for electric vehicle batteries demonstrates how carefully the mechanisms of such a measure must be chosen due to its influence on profits, risks, and materials to be recovered. In summary, this thesis structures the field of strategic planning towards electric mobility in the automotive industry from the angle of production and logistics management. For the first time, a comprehensive framework of the corresponding planning tasks along the product life cycle is provided, outlining the relevant characteristics and accompanying challenges. Connections between the different planning tasks as well as between the different planning approaches are illustrated. Moreover, tailor-made simulation and optimization models are developed and applied based on real-word data, which allow for an expedient decision support. While contributing specifically to the current state of literature in the field of Business Economics, the research integrates knowledge from different disciplines, especially Business Economics, Operations Research, Mechanical and Electrical Engineering, as well as Natural Science, at the interface between management and technology. It is transdisciplinary in nature integrating knowledge and perspectives from industrial and political decision-makers. This way, the thesis additionally contributes to the field of transformation research by addressing one of the major global challenges towards sustainable development, namely the achievement of sustainable mobility. Achieving sustainable mobility will keep science, industry, policy, and society busy for years to come. With regard to the deployment of electric vehicles, the integration of economic, environmental, and social aspects into decision-making should receive particular attention. Otherwise, electric mobility may not unfold its full sustainability potential. For instance, the GHG emissions of electric vehicles are mainly influenced by the current local energy mix. Thus, a significant reduction of emissions requires the use of renewable energies. Moreover, trade-offs between sustainability impacts on a local, regional, and global level must be taken into account. As explained earlier, manifold raw materials are necessary for the production of electric vehicles for which extraction and production take place in politically unstable regions and is associated with severe ecological and social consequences. The deployment of electric vehicles in one region to reduce GHG emissions with a global impact and exhaust emissions with a local impact might therefore come along with negative environmental and social issues in another region. Such aspects need to be addressed in the planning approaches in order to make electric vehicles a real sustainable alternative to other technologies. Moreover, the studies on electric mobility should be complemented by studies on the potential of Power-to-X technologies for the transition towards sustainable mobility. The same holds true for an integrated analysis of the transport and energy sector. From the viewpoint of interdisciplinary research, a further promising avenue for future research can be seen in the integration of models from Business Economics and from Engineering or the creation of appropriate interfaces between the models, respectively. This way, engineering design decisions could be directly evaluated in terms of their contribution to sustainable development from a business or market perspec-

Summary

49

tive. At the same time, information from the business/market level could be fed back into the design process in order to improve technical solutions. Additionally, the integration of perspectives and knowledge on societal transformation processes from Social Science might be worth considering.

7.2

Summary

Being one of the most important economic sectors worldwide, the automotive industry is currently forced to transform their product and technology portfolios as well as their supply chains successfully towards electric mobility. Shaping these transformation processes is a strategic planning issue in particular, which comes along with complex decisions in an uncertain environment and requires expedient decision support. Motivated by the current situation in the automotive industry, the objective of this thesis is to develop an integrated planning framework as well as simulation and optimization models for the strategic planning towards sustainable mobility in the automotive industry. On that basis, the thesis strives to make valuable contributions to the current state of the literature in the field of Business Economics, while at the same time delivering relevant results for industry and society. After an introduction to the fundamentals of electric vehicles and electric vehicle batteries in Chapter 2, a framework structuring and outlining the relevant planning tasks towards electric mobility from the perspective of production and logistics management is developed in Chapter 3. The development of the framework is grounded on planning tasks that are discussed in the scientific literature. These tasks are classified into those that are related to the transformation of the product and technology portfolio and the supply chain in the automotive industry and into those that are related to the use phase of electric vehicles. The latter tasks particularly concern fleet and infrastructure operators and have been in the center of scientific papers so far. Contrary, the strategic planning issues of the automotive industry have received little attention despite of the great challenges especially faced by incumbent car manufacturers. Based on this finding, the focus of the subsequent chapters is set on the development and application of simulation and optimization models to support the transformation of the technology and product portfolio and the transformation of the supply chain. Moreover, it is demonstrated how these models can be used for an impact assessment as a means to analyze the consequences of policy measures for the transformation processes in the automotive industry. Planning issues related to the transformation of the technology and product portfolio are subject of Chapter 4. Here, a hybrid simulation approach of the automotive market that allows for the estimation of the market share evolution of electric vehicles from an industry perspective is developed (Section 4.1). The approach integrates system dynamics and agent-based simulation to consider individual consumer choice and aggregated system behavior simultaneously. The approach is extended by integrating competition and allowing for a change in consumer’s attitude due to communication between the agents and advertising (Section 4.2). Based on this simulation tool, the impact of product and product portfolio decisions on the market diffusion of electric vehicles is analyzed. Moreover, a pure system dynamics model of the automotive market is developed (Section 4.3) with which market introduction strategies for electric vehicles can be analyzed. The application of these models illustrates that manufacturers can have a big

50

Conclusion

influence on the diffusion of electric vehicles by actively shaping their market offer. From an environmental perspective, this allows for substantial fossil fuel and emission savings. Since for a comprehensive sustainability assessment of electric vehicles all relevant ecological, economic, and social sustainability impacts over the entire life cycle must be taken into account, a literature review on the application of methods from Operations Research for sustainability assessment of products is presented in Section 4.4. The analysis reveals that important issues, which are also relevant for the sustainability assessment of electric vehicles, are still unresolved. These comprise particularly the simultaneous consideration of global and local sustainability objectives to allow for a spatial differentiation. Chapter 5 concentrates on planning issues related to the transformation of the supply chain. In this chapter, a simulation-based approach to analyze make-or-buy strategies for electric vehicle batteries (Section 5.1), a real options model to analyze investments in the production of electric vehicle batteries (Section 5.2), and a mixed-integer linear optimization model to plan technologies and capacities for the recycling of electric vehicle batteries (Section 5.3) are presented. All models take into account specific characteristics of the problem setting. With regard to the analysis of make-or-buy strategies, the impact of volume uncertainty on the economies of scale, financial consequences of a technology leap, joint ventures as a form of quasi-integration, and the option to adjust the degree of vertical integration dynamically over time are integrated in the simulation approach. The real options model allows to determine the optimal timing of an investment and the optimal capacity choice simultaneously, considering product life cycle uncertainty by means of a stochastic version of the Bass model. In order to support technology and capacity planning for the recycling of lithium-ion electric vehicle batteries, the modeling of multi-level co-production processes with alternative technologies in different capacities as well as different product variants by means of linear activity analysis is at the heart of the proposed optimization approach. The application of the models showcases how they can guide investment decisions in a production and recycling network despite the prevailing market and technology uncertainties. The focus of Chapter 6 is on the impact assessment of policy measures from both a policy and an industry perspective. First, alternative policy measures for regulating the CO2 emissions from passenger cars in the EU are analyzed, which is related to the discussion on legislative proposals for post-2020 targets (Section 6.1). The study is based on an extensive literature review. It suggests to consider combinations of a regulatory instrument (i.e., emission standard) with a market-based instrument (i.e., emission trading system) in order to maintain the pressure on car manufacturers to utilize technical mitigation potentials while at the same time increase their flexibility to comply with the regulation. Next to this qualitative analysis, model-based impact assessments of alternative policy measures (i.e., purchase premium, ban on diesel cars, and sales quota for electric vehicles) to support the market diffusion of electric vehicles (Section 6.2) and of mandatory rates for the recycling of electric vehicle batteries (Section 6.3) are conducted. To this end, the modeling approaches introduced in the fourth and fifth chapter are applied. The examples illustrate the high potential of using the proposed market simulation and optimization models to analyze the efficiency and effectiveness of alternative policy measures and their consequences for the automotive industry. Overall, the developed models and the conducted analyses allow to support the strategic planning towards electric mobility in the automotive industry at the interface between management and technology.

Summary

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Each approach contributes to a better understanding of the mechanisms for a successful transformation of the automotive industry with regard to the design of future technology and product portfolios as well as the design of future supply chains. Thereby, the integrated planning framework points out the connections and interdependencies between the individual planning tasks and the individual planning approaches. By taking into account these connections and interdependencies, further insights on how to successfully shape the transformation processes in the automotive industry can be revealed. On the one hand, research needs are related to the advancement and extension of the proposed approaches. On the other hand, sustainability aspects need to be further integrated in the modeling approaches in order to guide decision-making towards sustainable mobility. This additionally calls for a far-reaching integration of perspectives and insights from other disciplines, especially Mechanical and Electrical Engineering and Social Sciences.

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Appendix

1.

Kieckhäfer, K.; Volling, T.; Spengler, T. S. (2014): A hybrid simulation approach for estimating the market share evolution of electric vehicles, in: Transportation Science, Vol. 48, pp. 651–670.

2.

Kieckhäfer, K.; Wachter, K.; Spengler, T. S. (2017): Analyzing manufacturers' impact on green products' market diffusion: The case of electric vehicles, in: Journal of Cleaner Production, Vol. 162, pp. S11–S25.

3.

Thies, C.; Kieckhäfer, K.; Spengler, T. S. (2016): Market introduction strategies for alternative powertrains in long-range passenger cars under competition, in: Transportation Research Part D: Transport and Environment, Vol. 45, pp. 4–27.

4.

Thies, C.; Kieckhäfer, K.; Spengler, T. S.; Sodhi, M. S. (2017): Operations Research for sustainability assessment of products: A review, submitted to: European Journal of Operational Research (2nd revision).

5.

Huth, C.; Kieckhäfer, K.; Spengler, T. S. (2015): Make-or-buy strategies for electric vehicle batteries: A simulation-based analysis, in: Technological Forecasting & Social Change, Vol. 99, pp. 22–34.

6.

Lukas, E.; Spengler, T. S.; Kupfer, S.; Kieckhäfer, K. (2017): When and how much to invest? Investment and capacity choice under product life cycle uncertainty, in: European Journal of Operational Research, Vol. 260, pp. 1105–1114.

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Hoyer, C.; Kieckhäfer, K.; Spengler, T. S. (2015): Technology and capacity planning for the recycling of lithium-ion electric vehicle batteries in Germany, in: Journal of Business Economics, Vol. 85, pp. 505–544.

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Kieckhäfer, K.; Feld, V.; Jochem, P.; Wachter, K.; Spengler, T. S.; Walther, G.; Fichtner, W. (2015): Prospects for regulating the CO2 emissions from passenger cars within the European Union after 2023, in: Zeitschrift für Umweltpolitik und Umweltrecht, Vol. 38 (4), pp. 425–450.

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Walther, G.; Wansart, J.; Kieckhäfer, K.; Schnieder, E.; Spengler, T. S. (2010): Impact assessment in the automotive industry: Mandatory market introduction of alternative powertrain technologies, in: System Dynamics Review, Vol. 26, pp. 239–261.

10. Hoyer, C.; Kieckhäfer, K.; Spengler, T. S. (2013): Impact of mandatory rates on the recycling of lithium-ion batteries from electric vehicles in Germany, in: Nee, A. Y. C.; Song, B; Ong, S. (eds.): Reengineering manufacturing for sustainability, Proceedings of the 20th CIRP International Conference on Life Cycle Engineering, Springer, Singapore, pp. 543–548.