Evaluating the life cycle CO2 emissions and costs of ...

Journal of Cleaner Production 126 (2016) 607e619

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Evaluating the life cycle CO2 emissions and costs of thermoelectric generators for passenger automobiles: a scenario analysis Yusuke Kishita a, b, *, Yuji Ohishi c, Michinori Uwasu a, Masashi Kuroda a, c, Hiroyuki Takeda a, d, Keishiro Hara a a

Center for Environmental Innovation Design for Sustainability, Osaka University, 2-1 Yamada-oka, Suita, Osaka 565-0871, Japan Advanced Manufacturing Research Institute, National Institute of Advanced Industrial Science and Technology, 1-2-1, Namiki, Tsukuba, Ibaraki 305-8564, Japan c Department of Sustainable Energy and Environmental Engineering, Graduate School of Engineering, Osaka University, 2-1 Yamada-oka, Suita, Osaka 5650871, Japan d Management of Industry and Technology, Graduate School of Engineering, Osaka University, 2-1 Yamada-oka, Suita, Osaka 565-0871, Japan b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 17 November 2015 Received in revised form 25 February 2016 Accepted 25 February 2016 Available online 8 March 2016

A thermoelectric generator (TEG) is a device used for energy harvesting that enables electricity generation from waste heat. Among the various types of energy harvesting technologies developed for achieving a low-carbon society, the TEG is characterized by its ability to recover energy from heat sources with temperatures as low as 200e300 C. However, for economic and technological reasons, the TEG market has not yet been developed. With the goal of clarifying the performance required in order for TEGs to be practical and widely available in society, this paper analyzes several life cycle scenarios from both environmental and economic viewpoints. We herein focus on passenger automobiles, because the temperature of their exhaust gas is suitable for TEGs. A case study is carried out in which TEGs are installed in passenger automobiles in Suita City, Osaka, Japan. By applying a scenario planning method, we describe four scenarios that differ according to the technological performance of the TEGs and the driving pattern, under which the life cycle CO2 emissions (LCCO2) and costs of each scenario are evaluated. Comparison of the four scenarios reveals that improving the thermoelectric figure-of-merit by a factor of 1.9 is necessary in order to reduce the LCCO2 to zero while assuming the average driving pattern in Suita City. In addition, in order to make TEGs profitable over their life cycle, the price of TEGs must be reduced to approximately 10e40% of their current price. © 2016 Elsevier Ltd. All rights reserved.

Keywords: Thermoelectric generator Energy harvesting Scenario analysis Life cycle scenario Automobile CO2 emission

1. Introduction As reported by the Intergovernmental Panel on Climate Change (IPCC, 2013), it is important that we take available actions to avoid catastrophic and irreversible climate changes on a global scale. In the transportation sector, the CO2 emissions in 2009 accounted for

Abbreviations: CHP, Combined heat and power; DOE, Department of energy; IEA, International energy agency; IPCC, Intergovernmental panel on climate change; JEMAI, Japan environmental management association for industry; LCA, Life cycle assessment; LCC, Life cycle cost; LCCO2, Life cycle CO2 emissions; NEDO, New energy and industrial technology development organization; TEG, Thermoelectric generator. * Corresponding author. Advanced Manufacturing Research Institute, National Institute of Advanced Industrial Science and Technology, 1-2-1, Namiki, Tsukuba, Ibaraki 305-8564, Japan. E-mail address: [email protected] (Y. Kishita). http://dx.doi.org/10.1016/j.jclepro.2016.02.121 0959-6526/© 2016 Elsevier Ltd. All rights reserved.

21% (6.6 GtCO2) of the total global emissions (32.0 GtCO2) and will account for 22e29% (4.9e12.6 GtCO2) of the estimated total global emissions in 2050 (16.7e58.5 GtCO2; International Energy Agency (IEA), 2012). Due to the expansion of the automobile market in emerging countries, such as China and India, the transportation sector is expected to continue to produce high levels of CO2 emissions until 2050 (IEA, 2012). One way to foster energy savings in automobiles is to use a thermoelectric generator (TEG), which is a promising energy harvesting technology that can generate electricity from the exhaust gases of automobiles with internal combustion engines, i.e., gasoline and diesel vehicles (Crane, 2012; Fairbanks, 2012). The primary advantage of TEGs is that they can recover unused energy from waste heat even when the temperature is as low as 200e300 C. However, the importance of TEGs relative to other competing technologies (e.g., steam turbines) diminishes as the temperature increases (He et al., 2015).

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As of 2015, TEGs are used only in a limited number of applications because the conversion efficiency of currently available TEGs remains low, at a level of 5e10% (He et al., 2015; Kaibe et al., 2011). Many researchers have been attempting to create new thermoelectric materials that will be able to achieve a higher conversion efficiency (e.g., Biswas et al., 2012; Kim et al., 2015; Snyder and Toberer, 2008; Zhao et al., 2014). Moreover, a number of studies have been exploring the feasibility of TEG applications, primarily from a technological viewpoint (e.g., Bell, 2008; Kajikawa, 2006; Matsubara and Matsuura, 2006). However, less research has been directed toward answering the following questions, taking into account environmental and economic sustainability factors: (I) What is necessary for the widespread use of TEGs to become environmentally and economically sustainable? and (II) From a life cycle perspective, what is the potential for TEGs to contribute to a low-carbon society? The key obstacle is that TEGs are not currently cost-effective (Snyder and Toberer, 2008; Yazawa and Shakouri, 2011), due to their low efficiency and high energy consumption required in the manufacture of thermoelectric materials. For this reason, these questions must be answered. In order to tackle these questions, this paper undertakes an analysis in which we describe and compare several life cycle scenarios for TEGs. Here, a life cycle scenario is defined as a sequence of processes consisting of material production, product/part assembly, distribution, use, and end-of-life. We assess the environmental impact and life cycle cost (LCC) of each life cycle scenario, in order to clarify the required performance and cost of TEGs. In particular, we focus on TEGs for passenger automobiles because the temperature of their exhaust gases (approximately 300 C) is suitable for TEGs. In a previous paper (Kishita et al., 2014), we defined a formula for evaluating the cost and CO2 emissions based on the energy flow of TEGs installed in automobiles. In this paper, we use the outcome reported by Kishita et al. (2014), based upon which we formalize a procedure for performing a scenario analysis of TEGs for automobiles. We then use this procedure to present a case study of TEGs for passenger automobiles in Suita City, a suburb of Osaka Prefecture, Japan. In the case study, we consider bismuth telluride-based (Bi-Te) TEGs, because they are already available for commercial use. The remainder of the paper is organized as follows. Section 2 gives a brief review of recent developments and problems with TEGs. Section 3 proposes a method for conducting a scenario analysis for TEGs in automobile applications. Section 4 presents a case study of a Japanese community, in which several life cycle scenarios of TEGs for passenger automobiles are analyzed. Section 5 discusses the effectiveness of the proposed method based on the case study results, and Section 6 concludes the paper.

The parameters S, s, and k are properties of the given material. There are a variety of thermoelectric materials, each of which has a specific suitable range of temperature difference between heat sources and sinks. Fig. 1 shows the traits of seven types of thermoelectric materials and the relationship between ZT and T. Theoretically, the relationship between ZT and the maximum conversion efficiency h (the maximum rate of electric power generated from the input thermal power) is a function of ZT, given by

hðZTÞ ¼

pffiffiffiffiffiffiffiffiffiffiffiffiffiffi TH TL 1 þ ZT 1 pffiffiffiffiffiffiffiffiffiffiffiffiffiffi T TH 1 þ ZT þ THL

where TH and TL are the absolute temperatures of the heat source and the heat sink, respectively (Funahashi, 2011). Some examples derived from Eq. (2) are shown in Fig. 2. While various thermoelectric materials have been developed, as shown in Fig. 1, Bi-Te TEGs are already commercially available. Table 1 lists the specifications of the Bi-Te TEG (Kaibe et al., 2011) that is used in the case study (see Section 4). The maximum conversion efficiency is 7.2% when TH and TL are 280 C (553 K) and 30 C (303 K), respectively. According to the characteristic curve in Fig. 2 (dashed line), the performance of the thermoelectric material in Kaibe et al. (2011) is ZT ¼ 0.7. Since a technology roadmap (Funahashi, 2011) projects that the value of ZT will increase to 3.0 by 2030, the conversion efficiency h in 2030 may reach 17.7% (see Fig. 2). 2.2. Related studies and problems A number of studies related to life cycle analysis and life cycle assessment of energy-related technologies, some of which focus on energy recovery from waste heat and solid waste, have been conducted. Several articles from the Journal of Cleaner Production are briefly introduced here. Karvonen et al. (2016) provided a literature review and analysis of patents for waste heat recovery technologies used in the automotive industry, including thermoelectric generation. Evangelisti et al. (2015) presented a life cycle assessment of energy-generating technologies from municipal solid waste. Murphy et al. (2015) conducted a life cycle analysis of wood supply chains involving pellet production and energy generation with combined heat and power (CHP). Domingues et al. (2015) performed an integrated life cycle assessment with multi-criteria

2. Recent developments in TEGs 2.1. Fundamental theory of TEGs The thermoelectric effect, as a physical phenomenon, refers to the direct conversion of temperature differences to electric voltages (Goldsmid, 2009; Snyder and Toberer, 2008). The maximum efficiency of thermoelectric generation by a particular material is determined by a non-dimensional parameter called the thermoelectric figure-of-merit (ZT), which is defined as follows:

ZT ¼

S2 s T k

(1)

where S is the thermopower (V K1, also called the Seebeck coefficient), s is the electrical conductivity (U1 m1), k is the thermal conductivity (Wm1 K1), and T is the absolute temperature (K).

(2)

Fig. 1. Characteristics of thermoelectric materials (adapted from Uher, 2006).


35%

Conversion Efficiency η

30% 25% 20% 17.7% 15%

700K/300 K/300KK 700 600K/300 K/300KK 600 553K/303 K/303KK 553 500 K/300 K 500 K/300 K 400 K/300 K

10% 7.2% 5% 0% 0.0

0.7 1.0

2.0 3.0 Figure-of-merit ZT

4.0

5.0

Fig. 2. Relationship between ZT and h (adapted from Kishita et al., 2014).

decision analysis to assess the environmental impacts of various types of vehicles, including gasoline-, hybrid electric-, and battery electric powered vehicles. Considering thermoelectric generation as a promising energy harvesting technology, a number of researchers have been developing new thermoelectric materials with higher conversion efficiencies, and industrial case studies for future applications of TEGs have been conducted (LeBlanc, 2014). Expected applications of TEGs include recovering plant waste heat (Kaibe et al., 2011) and heat from the exhaust gases of passenger automobiles and buses (Crane, 2012; Fairbanks, 2012; Hendricks, 2007; New Energy and Industrial Technology Development Organization (NEDO), 2004). Focusing on automobile applications, some companies have built prototype vehicles to test the effect of using TEGs (Mazar, 2012). In the United States, the automobile industry and the Department of Energy (DOE) have been undertaking feasibility studies on TEGs for passenger automobiles in order to increase energy efficiency (Fairbanks, 2012). From a methodological viewpoint, Massaguer et al. (2015) developed a mathematical model to simulate the thermal and electrical behaviors of a longitudinal TEG. Fernandes et al. (2014) examined the output voltage in response to the temperature gradient using Peltier cells to harvest waste heat from spas. As described above, the development of applications for TEGs is progressing, among which automobile applications are considered to be particularly promising and are expected to be put into practical use in the near future. However, less effort has been devoted to clarifying (I) the conditions under which the use of TEGs will be environmentally and economically sustainable and (II) the extent to which TEGs can contribute to achieving a low-carbon society.

609

Understanding issue (I) involves determining how profitable TEGs will be throughout their product life cycle. In order to clarify both issues, some researchers have started to pay more attention to life cycle analysis of TEGs (e.g., Ghojel, 2005; Kikuchi et al., 2013; Patyk, 2013; Sergienko et al., 2010). Patyk (2013) assessed the environmental impacts and costs through the life cycle of TEGs, with a particular focus on an application to natural-gas-engine-driven power units. Patyk (2013) compared TEGs with the competing steam expander technology and determined that TEGs with engine-driven generators have a lower environmental impact. Kikuchi et al. (2013) estimated the environmental impact of TEGs throughout their life cycle in terms of materials and energy balance. Sergienko et al. (2010) assessed the environmental impact related to the production of thermoelectric cooling modules. Ghojel (2005) conducted a life cycle assessment (LCA) of TEGs for an automotive application, focusing on fuel consumption savings in the use phase while ignoring the environmental impact during the manufacturing process. Regardless of the studies mentioned above, little is known about issues (I) and (II) because methodologies for analyzing the influence of TEG usage in society have not been sufficiently developed. Moreover, the lack of life cycle inventory data on TEGs remains a critical issue. 3. Methodology for a scenario analysis of TEGs for automobiles 3.1. Approach In order to solve the problem discussed in Section 2.2, we describe different life cycle scenarios and assess them from the viewpoints of environmental impact and LCC. In this paper, we choose life cycle CO2 emissions (LCCO2) as a representative environmental indicator toward achieving a low-carbon society. Note that other greenhouse gases (e.g., methane) are not considered herein because their CO2 equivalent emissions appear to be lower. As such, we formalize the procedure for undertaking a scenario analysis of TEGs within the context of automobile applications. The reason for choosing automobile applications is their large potential to reduce worldwide CO2 emissions and the technological feasibility of installing TEGs in automobiles in the near future (e.g., within 10 years). In addition, we collect as much inventory data as possible in order to enable this analysis. In the formalization of the scenario analysis procedure, we apply a scenario planning method (Foresight Horizon Scanning Centre, 2009; Kishita et al., 2016) to describe the scenarios, where we choose the key drivers as the most influential factors for the sustainability of TEG usage. The idea here is to generate multiple distinct futures with the goal of examining the possible range of

Table 1 Specifications of the Bi-Te TEG used in the case study. Item

Value

Reference

Size

See Kaibe et al. (2011).

Conversion efficiency h Allowable maximum temperature TH Maximum power output

50 mm (L) 50 mm (W) 4.2 mm (H) 7.2% 280 C (553 K) 24 W module1

Total weight of the TEG Weight of Bi-Te thermoelectric materials in the TEG CO2 emissions from producing Bi-Te thermoelectric materials

47 g module1 27 g module1 7.39 kgCO2 module1

See Kaibe et al. (2011) where ZT ¼ 0.7 (see Fig. 2). See Kaibe et al. (2011). See Kaibe et al. (2011) where TH and TL are 280 C (553 K) and 30 C (303 K), respectively. See Kaibe et al. (2011). Estimate based on authors' measurement (Kishita et al., 2013). Estimate based on the authors' experiment (Kishita et al., 2013) and data from Refs. Poudel et al., 2008; Yan et al., 2010; Japan Environmental Management Association for Industry (JEMAI), 2012; Kansai Electric Power Co., Inc., 2013. See Table A1 for CO2 emission factors used for this estimation.

610


environmental and economic effects of TEG usage. When assessing the effects of using TEGs, we use the mathematical formula developed in our previous study (Kishita et al., 2014), which enables the estimation of gasoline savings as a result of TEG usage (see Section 3.2 for details). We then delineate several variant scenarios by changing the status of the key drivers based on the sensitivity analysis of a baseline scenario (or status quo scenario). We assume that the system boundary of interest covers an entire life cycle, from material extraction to end-of-life, and we model a life cycle scenario of TEGs as the combination of five processes: (i) material production, (ii) product/part assembly, (iii) distribution, (iv) use, and (v) end-of-life (see Fig. 3). We calculate the CO2 emissions and the cost of each process i in Fig. 3 using the following basic formulas:

CO2 emissionsðiÞ ¼

X funit CO2 emissionsði; jÞ

costðiÞ ¼

(3)

X funit variable costði; jÞ processed amountði; jÞ j

þ fixed costði; jÞg (4) where the unit CO2 emissions (i, j) and the unit variable cost (i, j) are the CO2 emissions and the cost when a unit amount of object j (e.g., material, part, product, and electricity) is used in process i, while processed amount (i, j) is the amount of object j used in process i. In Eq. (4), fixed cost (i, j) is the cost that does not change in response to the amount of object j (e.g., installation cost of an object) in process i. Based on Eqs. (3) and (4), we define the formula by which to calculate the CO2 emissions and costs in each process as follows. (i) Material production process: calculates the CO2 emissions and cost of producing thermoelectric and other materials for the production of TEGs, including housings and cables. This process covers both raw material extraction and the manufacturing of thermoelectric materials from raw

Raw Materials (i) Material Production (ii) Product/Part Assembly

System Boundary

Thermoelectric Materials Thermoelectric Generators

(iii) Distribution Thermoelectric Generators (iv) Use (v) End-of-life (EOL) Process

(iii)

(iv)

(v)

j

processed amountði; jÞg

(ii)

materials. The CO2 emissions and costs of this process come primarily from the electricity required to manufacture the thermoelectric materials and the procurement of the materials, respectively. Product/part assembly process: calculates the CO2 emissions and the cost of assembling the parts necessary to build the TEGs, including the thermoelectric materials and housings. Distribution process: calculates the CO2 emissions and the cost of transporting the TEGs from the production site to the end user. Use process: calculates the reductions in CO2 emissions and costs due to the gasoline savings when using TEGs. This is done by comparing the energy consumption to appropriate reference values without the use of TEGs. Note that TEGs are maintenance-free and do not emit any CO2 during use. The use process is explained in detail in Section 3.2. End-of-life process: calculates the CO2 emissions and cost of returning, dismantling, and disposing of TEGs (e.g., landfill, material recycling, and thermal recovery).

When we describe various life cycle scenarios for the use of TEGs with automobiles based on the concept depicted in Fig. 3, we assume certain conditions for each process. These conditions include the performance of the TEG and how automobiles equipped with the TEG are used. In particular, the evaluation of the use process requires information about how fast and how long the automobile is driven. We focus on (i) material production, (iii) distribution, (iv) use, and (v) end-of-life. Note that process (ii) is beyond the scope of this paper because the CO2 emissions from this process appear to be relatively small, and, to the best of our knowledge, only a small amount of relevant data are available.

3.2. Mathematical formulation to evaluate gasoline savings of TEGs in the use process (adapted from Kishita et al., 2014) Assuming that a TEG is used to reduce the gasoline consumption of an automobile with an internal combustion engine, we define a formula by which to calculate the electricity recovered from the exhaust gas during the use process. Fig. 4 illustrates the typical energy flow from the exhaust heat to the TEG. The heat collection rate r is defined as

r ¼ QH =Qin

(5)

where QH(t) and Qin(t) express the heat flowing into the TEG and the heat of the exhaust gas, respectively. Eq. (2) is used to derive QH from Qin (see Eq. (13) in Section 4.2). Taking into account the deterioration of the conversion efficiency, which is associated with heat fatigue, we obtain the electric power generated by the TEG at year t from QH (t) as

Inflow of exhaust gas: Qin

Thermoelectric Generators

Heat exchanger

Outflow of exhaust gas: Qout

TH Heat flow to TEG: QH

TEG (Conversion efficiency: η)

Goods

Fig. 3. Modeling life cycle scenarios of TEGs (adapted from Kishita et al., 2014).

TL Heat pipe radiator Fig. 4. Diagram of exhaust gas flow and TEG (adapted from Kishita et al., 2014).


PðtÞ ¼ QH hðZTðtÞÞ

(6)

where h(ZT(t)) is the conversion efficiency at year t and is calculated by Eq. (2), in which we estimate the figure-of-merit at year t (ZT(t)) as

ZTðtÞ ¼ ZTðt0 Þ DZT Nðt0 ; tÞ

(7)

in which t ¼ t0 is the starting year, DZT is a degradation coefficient (indicating the decrease in ZT in response to temperature cycles), and N (t0, t) expresses the cumulative temperature cycles from the starting year (t ¼ t0) to year t. In other words, ZT (t) decreases with the number of temperature cycles N (t0, t). The term temperature cycles refers to the number of repetitions between DT ¼ 0 (when the engine is stopped) and DT > 0 (when the engine is running), where DT is defined as

DT ¼ TH TL

(3) Scenario description: describe life cycle scenarios as life cycle flows, based on the concept shown in Fig. 3. Assumptions are made for processes (i) through (v) so that the CO2 emissions and costs of each process can be evaluated. The scenario description process consists of two steps. The first step is to extract a few key drivers based on a sensitivity analysis of a baseline scenario (or status quo scenario), which assumes the current TEG usage in the targeted region. The second step is to determine several contrasting variant scenarios by assuming different status values for the key drivers. (4) Scenario evaluation: evaluate and compare the scenarios described in Step (3) in terms of CO2 emissions and cost. Conduct a what-if analysis by modifying the assumptions of the life cycle scenarios until the requirements for achieving the goals specified in Step (1) are clarified. During this analysis, Steps (2) through (4) are iterated as necessary (as indicated by the feedback loop in Fig. 5).

(8)

The power output P (t) improves the mileage by decreasing the load on the alternator of the automobile. The resulting annual gasoline savings GR (t) is calculated as

GR ðtÞ ¼ ðPðtÞ MIÞ GC

(9)

where MI is the mileage improvement (gasoline savings) per unit power output (%W1), and GC is the annual gasoline consumption of an automobile without a TEG (L year1). For simplicity, we assume that the mileage will be improved by using a TEG whenever the engine is running. However, realistically, no electricity will be generated until the engine has run long enough to generate sufficient heat. Finally, we calculate the cumulative CO2 and cost reductions over the entire life cycle of TEGs based on the gasoline savings for the automobile, as follows:

0 CO2reduction

611

tZ 0 þLT

B ¼@

1 C GR ðtÞdt A uCO2gasoline

(10)

t¼t0

0 B Costreduction ¼ @

tZ 0 þLT

1 C GR ðtÞdt A uCostgasoline

(11)

t¼t0

where LT indicates the lifetime of the TEG, uCO2gasoline is the CO2 emission factor of gasoline (2.322 kgCO2 L1), and uCostgasoline is the unit cost of gasoline (price per liter).

3.3. Formalizing a procedure for scenario analysis of TEGs for automobiles We formalize the procedure for a scenario analysis of TEGs for automobiles by integrating the models presented in Sections 3.1 and 3.2. The procedure is defined in four steps (see Fig. 5), and the details of each step are described below. (1) Problem settings: define the problem to be addressed in the analysis, identify the specifications of the TEG, and determine the time horizon, region of interest, and the goals to be achieved. (2) Data acquisition: use various means (e.g., literature reviews, questionnaires, and interviews) to collect sufficient inventory data so that the TEG scenario analysis from both environmental and economic viewpoints can be undertaken.

4. Case study: scenario analysis of TEGs installed in passenger automobiles in a Japanese community This section illustrates a case study corresponding to Steps (1) through (4) in Fig. 5 that was conducted in order to verify the effectiveness of the proposed method. 4.1. Problem settings In the case study, we analyzed the LCCO2 and LCC of Bi-Te TEGs for passenger automobiles in Suita City, Osaka, Japan in 2030. It is assumed that TEGs will be technologically available for passenger automobiles by 2030, as demonstrated by feasibility tests (e.g., Crane, 2012; Fairbanks, 2012). The specifications of the TEGs are shown in Table 1. For the analysis, we defined the functional unit as the typical 12-year life cycle of a passenger automobile (Japan Automobile Manufacturers Association, 2012). This study was conducted in collaboration with Suita City, beginning in 2012, with the primary goal of estimating the potential environmental sustainability of the widespread use of TEGs. The population of Suita City in 2012 was 360,718 (158,925 households), and the area is 36.1 km2 (Suita City, 2013). Suita City will be faced with an expected population decrease to 144,494 households by 2030, whereupon the number of gasoline passenger automobiles is estimated to decrease from 79,039 in 2012 to 75,780 in 2030 (National Institute of Population and Social Security Research, 2009; Suita City, 2012, 2013). Suita City is keen to achieve a lowcarbon society (Suita City, 2012). Specifically, the city has a longterm environmental goal of reducing CO2 emissions to 1315 thousand tCO2 in 2020 from 1499 thousand tCO2 in 2010, resulting in a 12% decrease over 10 years (Suita City, 2012). The final goals of the analysis were to clarify (I) the conditions for the sustainability of TEG usage in terms of the LCCO2 and the LCC, and (II) the potential CO2 reduction for the entire city due to widespread use of TEGs. With regard to (I), we explored the conditions that reduce the LCCO2 and LCC to zero, thereby making TEG usage carbon neutral and covering the cost of the TEGs with the fuel savings. 4.2. Data acquisition We collected data associated with the current performance of TEGs and the current situation in Suita City from journal articles, technical reports, interviews, and questionnaires. There were three types of data: product data, regional data, and process data. The data types are described below.

612


(1) Problem settings: Define a problem to be addressed in the analysis - Select a TEG to analyze and set goals to achieve - Set a time horizon and region of concern

(2) Data acquisition: Collect relevant inventory data to undertake the life cycle analysis - Collect inventory data of the TEG through literature reviews, interviews, etc.

(3) Scenario description: Describe life cycle scenarios of the TEG for automobiles - Extract key drivers from sensitivity analysis of a baseline scenario, assuming the current situation of TEG usage in the targeted region - Describe several scenarios by changing the status of the key drivers

(4) Scenario evaluation: Evaluate the described life cycle scenarios - Evaluate each scenario to clarify requirements for achieving the predetermined goals in Step (1) - Modify the specifications of the TEG and other assumptions in each scenario for detailed analysis Fig. 5. Flowchart for scenario analysis of TEGs.

Product data Table 2 summarizes the performance, CO2 emissions, and cost of the TEG unit for passenger automobiles. The current conversion efficiency of TEGs is 7.2%. We assumed that a TEG unit for a passenger automobile consists of 20 modules, resulting in a maximum generation of 480 W of electricity from the exhaust gas. Although each TEG unit has several components, including the TEG itself, a heat exchanger, an electric pump, and a heat pipe radiator (Crane, 2012), due to limited available data, we focused on only the TEG

itself and the heat exchanger. When calculating the CO2 emissions to produce the TEG units, we used the CO2 emission factors listed in Table A1. Process data Table 3 shows the process data. These values are not dependent on the region of interest and are used for calculating the CO2 emissions and costs in the (iii) distribution and (iv) use processes.

Table 2 Performance, CO2 emissions, and cost of the TEG unit. Item

Value

Reference

Conversion efficiency h at the starting year (t ¼ t0) Figure-of-merit ZT

7.2%

The current conversion efficiency is 7.2%, according to Kaibe et al. (2011).

0.7

Maximum power output

480 W

Total weight Weight of the TEG Weight of the housing of heat exchanger

10.20 kg 0.94 kg 9.26 kg

TEG price CO2 emissions from producing thermoelectric materials CO2 emissions from producing the heat exchanger housings

2000 USD 148 kgCO2

See Fig. 2 for the relationship between h and ZT, where TH and TL are 280 C (553 K) and 30 C (303 K), respectively Assuming that a TEG unit for automobiles consists of 20 modules, where the maximum output of each module is 24 W (see Table 1). See Fairbanks (2012). 47 g module1 20 modules (see Table 1). Assuming that a TEG unit consists of the TEG and the housing of heat exchanger (made of stainless steel). Cost target set by the US Department of Energy (NEDO, 2008). Assuming that a TEG unit has 20 modules, where the CO2 emission from producing Bi-Te thermoelectric materials is 7.39 kgCO2 module1 (see Table 1). Authors' estimate based on the assumption that stainless steel is used as the material. The data were obtained from Fairbanks (2012) and JEMAI (2012). See Table A1 for the CO2 emission factor data (stainless steel) used for this estimation.

35.2 kgCO2


613

Table 3 Process data. Item

Value

Reference

Mileage of truck Truck payload Distance in land transportation Load factor in land transportation

11.4 km L1 2000 kg 300 km 44%

Heat collection rate from exhaust gas r Degradation coefficient DZT

50% 0.018 103 cycle1

Inlet heat of exhaust gas Qin

Qin ¼ 1.64 e0.0173v (Qin: kW, v: km h1)

Temperature of the heat source TH

TH ¼ 273 þ 207.3e0.011v (TH: K, v: km h1)

Mileage improvement by thermoelectric power generation MI Diesel oil Gasoline

0.009% W1

See Isuzu Motors Limited (2015); the maximum payload of the truck used for land transportation was assumed to be 2000 kg. Authors' assumption. See Ministry of Land, Infrastructure, Transport and Tourism, Japan (2007); the load factor here is defined as the ratio of the weight of carried goods (i.e., TEG units) to the payload. See NEDO (2004); the definition of r is given in Eq. (5). See Barako et al. (2013); assuming a linear relationship between ZT degradation and temperature cycles. Assuming the correlation formula between Qin and v, where the data were obtained from Matsubara and Matsuura (2006) and NEDO (2001). Derived by regression analysis using the data provided in Matsubara and Matsuura (2006). Assuming that the gas consumption is reduced 3% when the power generation from the TEG reaches 350 W (NEDO, 2008). See Ministry of the Environment, Japan (2014).

2.585 kgCO2 L1 2.32 kgCO2 L1

Regional data Table 4 shows the regional data, which reflect the characteristics of the city. For process (iv), we took into account the number of passenger automobiles running in Suita City, the driving patterns (i.e., average speed, person trips, and annual driving distance), gasoline price, and landfill cost. A person trip is defined as a trip by one person in any mode of transportation (United States Department of Transportation Federal Highway Administration (2015)). The average annual person trips were used to estimate the degradation of ZT with the temperature cycles (see Eq. (7)). Using the degradation coefficient DZT (0.018 103 cycle1; see Table 2) and the average number of annual person trips (700 trips year1; see Table 4), we estimated the figure-of-merit at year t (ZT(t)) by assuming a linear relationship between the degradation of ZT and the number of temperature cycles as

ZTðtÞ ¼ ZTðt0 Þ 0:018 ð700 t=1; 000Þ

(12)

where the value of ZT (t0) is 0.7. We then calculated the conversion efficiency h at year t from Eqs. (2) and (12), thereby enabling the calculation of P (t) using Eq. (6). In collaboration with Suita City, we conducted a questionnaire to obtain data on annual driving distances and automobile mileages (Suita City and Center for Environmental Innovation Design for

Sustainability, Osaka University, 2013). These data were used to estimate the gasoline savings due to TEG usage. The respondents were citizens living in Suita City who owned automobiles (502 samples). In addition, we used the average speed of automobiles (v ¼ 18 km h1; see Table 4) to calculate the inlet exhaust heat Qin ¼ 1.64 e0.0173v (see Table 3). Using Eq. (5), the inlet heat to the TEG QH was obtained as follows:

QH ¼ Qin r ¼ 1:64 e0:017318 50% ¼ 1:12 kW

(13)

Fig. 6 depicts the relative frequency of the annual driving distances of automobiles. The average driving distance in Suita City was 5460 km year1, which is approximately one-quarter that in the United States (20,000 km year1; United States Department of Transportation Bureau of Transportation Statistics (2015)). Given the results shown in Fig. 6 and the distribution of automobile mileage, where the average mileage was 10.4 km L1, according to the questionnaire by Suita City and the Center for Environmental Innovation Design for Sustainability, Osaka University (2013), we estimated the annual gasoline consumption without TEGs, as described in Fig. 7. Hence, the average annual gasoline consumption was calculated as

GC ¼ 589 L year1 automobile1

(14)

We used the above data to estimate the average gasoline savings

Table 4 Regional data for Suita City. Item

Value

Reference

Number of gasoline passenger vehicles within the city in 2030

75,780

Temperature of the heat sink TL Average lifetime of automobile LT Average mileage

303 K (30 C) 12 years 10.4 km L1

Average speed of automobiles in Suita City v

18 km h1

Average annual person trips by automobile Temperature cycles per life cycle of an automobile N

700 trips year1 8400 cycles

Gasoline price Landfill cost

1.33 USD L1 0.28 USD kg1

Authors' estimation based on the data from National Institute of Population and Social Security Research (2009) and Suita City (2012, 2013). Authors' assumption. See Japan Automobile Manufacturers Association (2012). See Suita City and Center for Environmental Innovation Design for Sustainability, Osaka University (2013). See Council for Transportation Planning in the Keihanshin Urban Area (2012). Ibid. Assuming that one temperature cycle occurs per person trip, i.e., 700 trips year1 12 years ¼ 8400 cycles. See Agency for Natural Resources and Energy, Japan (2014). See Eco-Park Izumozaki (2015).

614


40%

Average annual driving distance in Suita: 5,460 km/year/automobile

Relative Frequency

35% 30% 25% 20% 15% 10% 5% 0%

< 3,000

5,000 7,000 10,000 < Annual Driving Distance per Automobile (km/year/automobile)

Fig. 6. Distribution of annual driving distance in Suita City (502 samples; adapted from Suita City and Center for Environmental Innovation Design for Sustainability, Osaka University, 2013).

per TEG-equipped GR (t). By applying Eq. (9), we calculated GR (t) using MI (0.009% W1; see Table 3) and Eq. (14) as follows:

GR ðt Þ ¼ P ðt Þ 0:009% W1 589 L year1 automobile1 (15) 4.3. Scenario description In order to choose the key drivers, we used a sensitivity analysis of the baseline scenario, which assumes the current situation in Suita City, followed by the description of several scenarios. 4.3.1. Extracting key drivers from sensitivity analysis The baseline scenario assumed that a TEG unit with the current level of performance (i.e., h ¼ 7.2%) was installed in a gasolinepowered passenger automobile that was driven according to the average driving pattern in Suita City. The corresponding data are mentioned in Section 4.2. We conducted a sensitivity analysis of the baseline scenario to identify the key drivers that would critically affect the environmental and economic assessment of TEG usage. Table 5 shows the sensitivity of nine selected parameters, all of which appeared to have a relatively large impact on either LCCO2 or LCC. Here, sensitivity refers to the difference in LCCO2 (LCC) due to a

Fig. 7. Distribution of annual gasoline consumption in Suita City (502 samples).

10% increase in the parameter divided by the original LCCO2 (LCC) in the baseline scenario. In terms of LCCO2, the most influential parameters were the temperature of the heat sink TL, mileage improvement MI, annual gasoline consumption GC, and figure-ofmerit ZT. The average speed v also played an important role in reducing CO2 emissions, as faster automobiles generate more waste heat. In contrast, the parameters of DZT and N, which are related to the degradation of TEGs due to heat cycles, were less dominant. For the LCC, the price of the TEG was the most influential, whereas the price of gasoline had little influence. Based on the sensitivity analysis results, we identified the key drivers as the “technological level of the TEG” (corresponding to the figure-of-merit ZT) and the “driving pattern” (corresponding to the annual gasoline consumption GC and the average speed v). This was because they were of higher sensitivity to both the LCCO2 and the LCC, and we assumed that these key drivers were more uncertain because of the technological development of the TEG between now and 2030 and the diversity of driving patterns in Suita City. Note that other parameters (e.g., temperature of the heat sink TL) may be chosen as key drivers, which would result in the generation of different scenarios. Note also that evaluating other scenarios by choosing different key drivers might be an area of future study.

4.3.2. Describing variant scenarios Fig. 8 depicts four variant scenarios (Scenarios (A-1), (A-2), (B1), and (B-2)) that were obtained from the two key drivers. Each quadrant expresses one of the scenarios. Our intention was to examine the four extremes in terms of the technological level of the TEG and the driving pattern, with the intent to ascertain the full range of what might happen by 2030. Regarding the driving pattern, we assumed two contrasting cases by referring to the averaged data for Suita City and for the United States (see Table 6), because the data for the United States were quite different from those for Suita City. The storylines of the scenarios are as follows. (A-1) Red Scenario (Baseline Scenario): The conversion efficiency of the TEGs h in 2030 is 7.2% (ZT ¼ 0.7), assuming that h remains at the same level from 2015 to 2030. The driving pattern is the average of all the drivers in Suita City, i.e., the annual gasoline consumption is GC ¼ 589 L year1 automobile1 and the average speed is v ¼ 18 km h1. (A-2) Purple Scenario: The conversion efficiency h in 2030 has increased by 17.7% (ZT ¼ 3), based on the technology roadmap (Funahashi, 2011). The driving pattern is the same as in Scenario (A-1). (B-1) Green Scenario: The conversion efficiency h in 2030 is 7.2% (ZT ¼ 0.7), while the driving pattern is that of the averaged data in the United States (GC ¼ 1960 L year1 automobile1, v ¼ 44 km h1). Note that the driving distance is longer and the speed is faster than the corresponding averages for Suita City. (B-2) Blue Scenario: The conversion efficiency h in 2030 has increased by 17.7% (ZT ¼ 3), and the driving pattern is the same as in Scenario (B-1). For simplicity, except for the conversion efficiency and the driving pattern, the other factors were the same across all four scenarios. Although the thermoelectric materials used to produce TEGs in 2030 are likely to be different from those used at present, due to data limitations, we assumed the same material composition for all of the scenarios. We also assumed that the (i) material production, (iii) distribution, and (v) end-of-life processes would be the same across the four scenarios. In process (v), all of the components of the TEG unit (10.20 kg; see Table 2) were assumed to be landfilled and thus unrecovered as either materials or energy.


615

Table 5 Sensitivity analysis results of the baseline scenario. Parameter

Value

1 2 3 4 5 6 7 8 9

Figure-of-merit ZT Mileage improvement by thermoelectric power generation MI Annual gasoline consumption per automobile GC Average speed v Temperature of the heat sink TL Degradation coefficient DZT Temperature cycles per life cycle of an automobile N Gasoline price TEG unit price

(B-2) Blue Scenario

(A-1) Red Scenario (Baseline Scenario)

(A-2) Purple Scenario

Suita Pattern

Short & Slow

(B-1) Green Scenario

Low (η=7.2%)

0.7 0.009% W1 589 L year1 automobile1 18 km h1 303 K 0.0183 103 cycle1 8400 cycles 1.33 USD L1 2000 USD

LCCO2

LCC

14.2 16.9 16.9 8.1 27.3 1.8 1.8 0.0 0.0

0.3 0.3 0.3 0.1 0.5 0.0 0.1 0.3 10.3

Technology Innovation

US Pattern

Long Driving Pattern & Fast

Current Performance

Sensitivity to a 10% increase in the parameter (%)

Technological Level of TEGs

High (η=17.7%)

Fig. 8. Four quadrants representing contrasting scenarios.

4.4. Scenario evaluation We assessed the LCCO2 and LCC of the four scenarios. The LCCO2 was calculated by summing the CO2 emissions from processes (i) to (v), and the LCC was calculated by subtracting the cost reduction in process (iv) from the costs in processes (iii) and (v). The reductions in CO2 emissions and costs in process (iv) were evaluated by applying Eqs. (10), (11) and (15). In process (iii), we assessed the cost of the TEGs to the end users (2000 USDunit1; see Table 2). Fig. 9 summarizes the evaluations of the LCCO2 and LCC for each of the four scenarios. While Scenarios (A-2), (B-1), and (B-2) successfully reduced LCCO2 to below zero, none of the scenarios reduced the LCC to zero, meaning that none of the four scenarios were cost-effective from the viewpoint of individual car owners. Fig. 10 shows the results for each scenario in terms of the average LCCO2 per automobile. The average LCCO2 in Scenarios (A1), (A-2), (B-1), and (B-2) are 61.1 kgCO2, 104.5 kgCO2,

Fig. 9. LCCO2 versus LCC for each of the four scenarios.

406.6 kgCO2, and 1317.5 kgCO2, respectively. The power output P (t) per automobile in Scenarios (A-1), (A-2), (B-1), and (B-2) are 63.5e76.7 W, 179.7e184.2 W, 107.9e130.3 W, and 304.2e311.7 W, respectively. This results in gasoline savings in Scenarios (A-1), (A2), (B-1), and (B-2) of 0.57e0.60% year1 (i.e., 3.4e4.1 L year1 automobile1), 1.62e1.66% year1 (i.e., 9.5e9.8 L year1 automobile1) 0.97e1.17% year1 (i.e., 18.6e22.5 L year1 automobile1), and 2.74e2.81% year1 (i.e., 52.6e53.9 L year1 automobile1), respectively. The range of these results is caused by the degradation of ZT due to temperature cycles over the 12 years of the lifetime of the TEGs. Therefore, the CO2 reduction in the use process throughout the life cycle of a TEG unit reaches 103.0 kgCO2 automobile1 (A-1) to 1481.5 kgCO2 automobile1 (B-2), while the material production process emits 163.3 kgCO2 automobile1 in all four scenarios (see Fig. 10). Note that the CO2 reduction in Scenarios (B-1) and (B-2) was larger than that in Scenarios (A-1) and (A-2), because the driving distance in Scenarios (B-1) and (B-2) (20,000 km year1 automobile1) was 3.7 times longer than that in Scenarios (A-1) and (A-2) (5460 km year1 automobile1).

Table 6 Driving patterns for the scenarios. Item

Value

Reference

A-1, A-2

B-1, B-2

Average speed of automobiles v (km h1)

18

44

Average annual driving distance (km year1 automobile1)

5460

20,000

Annual gasoline consumption GC (L year1 automobile1)

589

1920

Based on the Council for Transportation Planning in the Keihanshin Urban Area (2012) and the assumption provided by the United States Environmental Protection Agency (2008). See United States Department of Transportation Bureau of Transportation Statistics (2015). Calculated using the above data: 20,000 (km year1 automobile1)/10.4 (km L1) z 1920 L year1 automobile1.

616


Material production

(A-2) Purple Scenario (η=17.7%)

Use

Gas savings: 0.57-0.69%/year

Distribution, 0.8

Gas savings: 1.62-1.66%/year

Distribution, 0.8

Gas savings: 0.97-1.17%/year (B-1) Green Scenario Gas savings: (η=7.2%) 2.74-2.81%/year (B-2) Blue Scenario (η=17.7%)

Distribution, 0.8 Distribution, 0.8

Suita Pattern US Pattern

(A-1) Red Scenario (η=7.2%)

Distribution

-1600 -1200 -800 -400 0 400 800 Average LCCO2 per Automobile (kgCO2/automobile) Fig. 10. LCCO2 of the TEG unit per automobile.

If all the gasoline-powered passenger automobiles in Suita City in 2030 are equipped with TEG units, the annual CO2 emissions from the TEG units in Suita City will reach 385 tCO2 year1 (¼ 0.0611 tCO2 75,780/12 years) in Scenario (A-1), 660 tCO2 year1 (¼ 0.1045 tCO2 75,780/12 years) in Scenario (A-2), 2568 tCO2 year1 (¼ 0.4066 tCO2 75,780/12 years) in Scenario (B-1), and 8320 tCO2 year1 (¼ 1.3175 tCO2 75,780/12 years) in Scenario (B-2), where the number of gasoline-powered passenger automobiles in 2030 is projected to be 75,780 (see Table 4). Since the total CO2 emissions of the passenger automobiles in Suita City in 2030 are estimated to be 103.6 ktCO2 (¼ 589 L automobile1 2.322 kgCO2 L1 75,780 automobiles) in Scenarios (A-1) and (A-2) and 337.8 ktCO2 (¼ 1920 L automobile1 2.322 kgCO2 L1 75,780 automobiles) in Scenarios (B-1) and (B-2), the CO2 emissions due to TEGs in 2030 amounts to 0.37% (¼ 0.385/103.6 100) of the CO2 emissions from passenger automobiles in Scenario (A-1), 0.64% (¼ 0.660/103.6 100) in Scenario (A-2), 0.76% (¼ 2.568/337.8 100) in Scenario (B-1), and 2.46% (¼ 8.320/337.8 100) in Scenario (B-2). Fig. 11 shows the LCC in Scenarios (A-1), (A-2), (B-1), and (B-2) as being 1944 USD automobile1, 1849 USD automobile1, 1677 USD automobile1, and 1156 USD automobile1, respectively. The difference between the scenarios lies in the use process, in which the cost reduction ranged from 59 USD automobile1 (Scenario (A-1)) to 846 USD automobile1 (Scenario (B-2)). In all of the scenarios, the price of a TEG unit (2000 USD) in the distribution process dominates the cost structure. 4.5. What-if analysis

in Fig. 9, we focused on the figure-of-merit ZT and the TEG price as examples of the critical parameters shown in Table 5. Fig. 12 describes the trajectory of the LCCO2 in Scenario (A-1) as ZT increases from 0.7 to 3. The LCCO2 became zero in Scenario (A-1) when ZT was equal to 1.3, which is 1.9 (¼ 1.3/0.7) times larger than the current level (ZT ¼ 0.7 in Scenario (A-1)). While Fig. 9 revealed that reducing the LCC was critical in realizing a sustainable condition in all of the scenarios, Fig. 13 shows the reduction in the LCC when the TEG price decreased from the current TEG unit price (2000 USD) to zero. In Scenario (A2), when we attempted to set the LCC to zero, the TEG unit price had to be reduced to 151 USD, which is approximately 7.6% (¼ 151/ 2000 100) of the current price. Moreover, the LCC in Scenario (B2) became zero if the TEG unit price decreased to 42.2% (843 USD) of the current price.

5. Discussion 5.1. Effectiveness of the proposed method As revealed by the case study for the Japanese community, we confirmed that the proposed method successfully enables a scenario analysis of TEGs for automobile applications and allows us to clarify issues (I) and (II), as defined in Section 2.2. One key contribution of this paper is to provide a formal procedure for conducting a scenario analysis of TEGs for automobile applications. In order to execute the procedure, we modeled and evaluated life cycle scenarios of TEGs for automobile applications in terms of CO2 emissions and economic costs. The proposed method is applicable to any region, providing that the necessary data are available. In addition, the proposed method would be workable in a very early phase of the design of a TEG, because the method was useful for clarifying the required performance (e.g., conversion efficiency) and appropriate situations for use (e.g., driving speed and annual driving distance). In an attempt to verify the effectiveness of the proposed model in Section 3.2, we examined a test case provided by NEDO (2008), in which a 7% improvement in mileage was reported when a passenger automobile was equipped with TEGs. In this text case, the key conditions were the conversion efficiency of the TEGs (h ¼ 12%) and the average speed (v ¼ 60 km h1). We compared the reported mileage improvement (i.e., 7%) and the mileage improvement estimated using our model for the same conditions.

In order to further examine what is required to reduce the LCCO2 and LCC to zero or below, which corresponds to the sustainable area

Distribution

(A-2) Purple Scenario (η=17.7%) (B-1) Green Scenario (η=7.2%) (B-2) Blue Scenario (η=17.7%)

End-of-life End-of-life, 2.8 End-of-life, 2.8

End-of-life, 2.8

End-of-life, 2.8

Suita Pattern US Pattern

(A-1) Red Scenario (η=7.2%)

Use

-1000 -500 0 500 1000 1500 2000 2500 Average LCC per Automobile (USD/automobile) Fig. 11. LCC of the TEG unit per automobile.

Fig. 12. Reduction of the LCCO2 in Scenario (A-1) due to improving ZT.


617

the price of TEGs. Reducing the LCC to zero requires a reduction of the TEG price to 7.5% of the current price (2000 USD, see Fig. 13) in Scenario (A-2) and to 42.1% of the current price in Scenario (B-2). Consequently, a reduction in the TEG price of approximately 60e90% is vital for the widespread use of TEGs. Possible measures that could be taken to reach this target include: Developing much cheaper thermoelectric materials, Developing more cost-efficient processes for producing TEGs, and Introducing governmental/municipal subsidies for the initial purchase of TEGs.

Fig. 13. Reduction of the LCC due to a decrease in the TEG price.

Table 7 summarizes the process of estimating the mileage improvement using the proposed model. The comparison between the mileage improvement estimated using our model (6.95%) and the result reported by NEDO (2008; 7%) showed that the error was only 0.7%. Hence, this comparison supports the validity of our model to an extent, although it would be desirable to examine more test cases with different conditions. 5.2. Findings and implications of the case study Regarding issues (I) and (II), new findings based on the results of the case study are summarized in the following three points. First, a comparison of Scenarios (A-1) and (A-2) revealed the necessity of improving the conversion efficiency from the current level (h ¼ 7.2% in Scenario (A-1)) in order to reduce the LCCO2 to less than or equal to zero, under the average driving pattern in Suita City. In particular, according to Fig. 12, the marginal value of ZT is 1.3. Therefore, the performance of Bi-Te TEGs needs to be improved by developing new thermoelectric materials to ensure their carbon neutrality. If the high conversion efficiency assumed in Scenarios (A-2) and (B-2) (h ¼ 17.7%) can be achieved, a CO2 emissions reduction of 0.64% and 2.46% could be realized by installing TEGs in all passenger automobiles (see Section 4.4). In contrast, if the driving pattern is that of the United States, the LCCO2 will be reduced even if the conversion efficiency remains at the current level (h ¼ 7.2%), as described in Scenario (B-1). Second, from an economic viewpoint, the results revealed that the use of TEGs is not profitable in all four scenarios considered herein (see Fig. 11). Therefore, it is necessary to drastically reduce

The proposed scenario analysis method will be helpful for evaluating different scenarios under various combinations of various measures, as shown above. Furthermore, such scenarios will be useful to various stakeholders (e.g., manufacturers and policy makers) when discussing what actions to be taken in order to promote TEG deployment. Third, the results of the sensitivity analysis in Table 5 indicated that the effect of using TEGs for automobiles would vary greatly depending on the region of interest. The average vehicle speed in Suita City is 18 km h1, which is relatively slow. However, if we consider buses and trucks traveling long distances in the United States, for example, then the effect of gasoline savings would become much larger. Moreover, based on the formula relating Qin and v in Table 3 and Eqs. (5) and (6), the following relation holds: the larger the average speed v, the greater the power output P (t) obtained. For this reason, the LCCO2 in Scenario (B-2) (1317.5 kgCO2) was 13 times larger than that in Scenario (A-2) (104.5 kgCO2), because Scenario (B-2) assumed the driving pattern in the United States, where the speed is faster and the distances are longer. Furthermore, using TEGs in a cold region (e.g., the northern part of the United States or Russia) would be more effective because the LCCO2 is reduced when TL becomes smaller (see Table 5). Further comparative analyses of a variety of different regions would yield more insight into future applications and the dissemination of TEGs. 5.3. Uncertainties in the data Although we tried to acquire sufficient and accurate inventory data for the scenario analysis, there was lack of data and a large degree of uncertainty. For example, the CO2 emissions due to producing the thermoelectric materials (7.39 kgCO2 module1; see Table 1) might be overestimated because the data were estimated from laboratory experiments (Kishita et al., 2013). However, the actual values might be drastically lower in a future mass production

Table 7 Verification of the proposed model. Item Parameter Conversion efficiency h Figure-of-merit ZT Average speed v Inlet heat of exhaust gas Qin Temperature of the heat source TH Inlet heat to the TEG QH Temperature of the heat sink TL Mileage improvement by thermoelectric power generation MI Electric power generated by the TEG P Assessment results Mileage improvement estimated by the proposed model MI Error of the estimated result compared to the result by NEDO

Value

Reference

12% 1.43 60 km h1 4.63 kW 553 K 2.32 kW 303 K 0.025% W1 278 W

See NEDO (2008). Calculated by Eq. (2) under the condition that h ¼ 12%. See NEDO (2008). Calculated by average speed v using the formula in Table 3. Ibid. Calculated by Eq. (13). Assuming the same condition in our case study (see Table 4). See NEDO (2008). Calculated by Eq. (6).

6.95% 0.7%

MI ¼ 278 W 0.025% W1 ¼ 6.95%. Since the mileage improvement reported by NEDO (2008) was 7%, the error was calculated as (7e6.95)/7 100 ¼ 0.7%

618


phase. It is difficult to obtain sufficient and accurate data on TEGs because they have not yet been widely marketed. Nevertheless, the collected data and the results presented in this paper can serve as a benchmark, allowing the promotion of future research on TEGs both in academia and industry. In order to improve the accuracy of the case study results, the collection of more relevant data must be continued through literature reviews, interviews, and experiments. Examples of needed data include the following:

carbon neutrality. If the figure-of-merit ZT becomes 4.3 times larger (ZT ¼ 3) than the current level (ZT ¼ 0.7), the CO2 emissions from passenger automobiles in the city could be reduced by 0.64e2.46%. The results also indicated that it is necessary to reduce the current TEG price by 60e90% in order to reduce the LCC to zero. Future issues include collecting more data related to TEGs for a more accurate assessment. Acknowledgments

Data on all new TEGs that are developed in the future. There may be a large difference between the new thermoelectric materials in Scenarios (A-2) and (B-2) and the currently available materials in Scenarios (A-1) and (B-1), both in terms of material composition and manufacturing process. New thermoelectric materials with higher conversion efficiency might have higher CO2 emissions during the manufacturing process, thereby bringing about a larger LCCO2 than that observed in Scenarios (A-1) and (B-1). It is thus necessary to collect up-todate data, which allow a more accurate assessment of the LCCO2 in Scenarios (A-2) and (B-2). Data on the extra automobile fuel consumption due to the added mass of a TEG unit (e.g., Stabler, 2011b). In the case study, we assumed that this consumption would not be important because one automobile TEG unit weighs approximately 10e11 kg according to Fairbanks (2012). However, the added mass impact on fuel consumption should be investigated by taking into account all auxiliary components for a TEG unit (including radiators and heat exchangers). Another remaining issue is the multifaceted assessment of environmental impacts using several environmental indicators, including CO2 emissions. In particular, investigating the environmental impact in the end-of-life process is important because bismuth telluride (Bi2Te3) is a toxic substance (United Nations Economic Commission for Europe, 2015). Moreover, from the viewpoint of resource scarcity, tellurium (Te) is categorized as a near-critical element in the medium term (2015e2025) according to the United States DOE (2011). Therefore, analysis of several endof-life scenarios, which may include not only landfill but material recycling and reuse, should be addressed in future research. It should be noted that TEG units are potentially reusable as it is reported that there would be no significant degradation over the life of TEGs (i.e., 10e20 years) (Stabler, 2011a). 6. Conclusions In order to clarify the conditions for making TEG usage environmentally and economically sustainable, we conducted a scenario analysis of TEGs, where we analyzed several life cycle scenarios in terms of life cycle CO2 emissions (LCCO2) and life cycle cost (LCC). The focus was on automobile applications because the temperature of the exhaust gases is suitable for operating TEGs. For this purpose, we formalized the procedure for the scenario analysis by applying the scenario planning method (Foresight Horizon Scanning Centre, 2009; Kishita et al., 2016) and the mathematical formulation to estimate the gasoline savings of TEG usage (Kishita et al., 2014). In addition, we collected data related to TEGs from literature reviews, interviews, and questionnaires. In the case study for Suita City, Osaka, Japan in 2030, we described four variant scenarios by choosing two key drivers (i.e., “technological level of the TEG” and “driving pattern”) based on the sensitivity analysis of the baseline scenario (or status quo scenario). The comparison of the four scenarios revealed that, when we assume the current average driving pattern in Suita City, the conversion efficiency h of TEGs must be improved in order to achieve

We are grateful to Suita City in Osaka Prefecture, Japan, for providing the necessary data for performing this research. This research was supported by a Grant-in-Aid for Young Scientists (A) (No. 26701015) from the Japan Society for the Promotion of Science. Appendix Table A1 shows the CO2 emission factor data used in the case study. The data were obtained from an LCA database and a utility company website.

Table A1 CO2 emission factors for TEG production. Category

Item

Material Bi production Te Se Sb Stainless steel cold-rolled 2 B Utility Electricity generation

CO2 emission factor 23.2 28.6 26.2 21.1 3.45

kgCO2 kgCO2 kgCO2 kgCO2 kgCO2

kg1 kg1 kg1 kg1 kg1

Reference See JEMAI (2012); the amount of CO2 emitted when 1 kg of materials is produced

0.522 kgCO2 kWh1 Kansai Electric Power Co., Inc. (2013); the data are used for calculating the CO2 emission to produce thermoelectric materials

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