Dean of the College of Engineering. Approved: ... goes to Doug for his valuable help during the entire course of this project. I thank my family ..... Figure 2.9: Comparison of simulation output and vehicle data for Drive Cycle 1.....47. Figure 2.10: ...
Modeling, Simulation and Optimization of Fuel Cell/Battery Hybrid Powertrains
By Piyush Bubna
A thesis submitted to Faculty of the University of Delaware in partial fulfillment of the requirements for the degree of Master of Science in Mechanical Engineering
Summer 2010
Copyright 2010 Piyush Bubna
All Rights Reserved
Modeling, Simulation and Optimization of Fuel Cell/Battery Hybrid Powertrains By Piyush Bubna
Approved: ____________________________________________________ Ajay K. Prasad, Ph.D. Professor in charge of the thesis on behalf of the advisory committee
Approved: ____________________________________________________ Suresh G. Advani, Ph.D. Professor in charge of the thesis on behalf of the advisory committee
Approved: ____________________________________________________ Anette M. Karlsson, Ph.D. Chair of the Department of Mechanical Engineering
Approved: ____________________________________________________ Michael J. Chajes, Ph.D. Dean of the College of Engineering
Approved: ____________________________________________________ Debra Hess Norris, M.S. Vice Provost for Graduate and Professional Education
ACKNOWLEDGEMENTS This thesis has developed with the help and contributions of many individuals and it is a pleasure to extend my appreciation and gratitude to everyone involved. First and foremost, would be my advisors - Dr. Ajay Prasad and Dr. Suresh Advani. Their encouragement, advice and continuous guidance has helped me in completing this thesis as well as the challenging research behind it. I would also like to thank my fellow labmates: Doug, Sudhaker, Srikanth, Darren, Adam, Mike, Glenn, Erik, Manish, Krishnan and Feng Yuan. My sincere appreciation goes to Doug for his valuable help during the entire course of this project. I thank my family (in India) for their unending love and for always placing my dreams and interests before anything else. Finally, I dedicate this work to Aparna who has been a great support and motivator throughout this work.
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TABLE OF CONTENTS LIST OF TABLES.........................................................................................................vi LIST OF FIGURES ..................................................................................................... vii ABSTRACT...................................................................................................................xi
Chapter 1. INTRODUCTION 1.1.
Introduction to Hybrid Vehicles.... .........................................................13
1.2
Fuel Cells ................................................................................................16
1.3
Battery.....................................................................................................18
1.4
Ultracapacitor..........................................................................................19
1.5
The University of Delaware Fuel Cell Bus.............................................20
1.6
Organization of the Thesis ......................................................................24
2. LFM SIMULATOR 2.1
Introduction.............................................................................................26
2.2
LFM ........................................................................................................27
2.3
LFM Subsystems ....................................................................................28 2.3.1
Fuel Cell....................................................................................28
2.3.2
Battery.......................................................................................36
2.3.3
Ultracapacitors ..........................................................................39
2.3.4
Hybrid Controller......................................................................40
2.3.5
Power Combiner .......................................................................42
2.3.6
Accessory Load.........................................................................42
2.3.7
Motor.........................................................................................43
2.3.8
Transmission .............................................................................43
2.3.9
Wheels/Chassis .........................................................................43
2.4
Validation................................................................................................44
2.5
Summary .................................................................................................53
3. PREDICTION-BASED OPTIMAL POWER MANAGEMENT 3.1
Introduction.............................................................................................54
3.2
Power Management Strategy ..................................................................57
3.3
Methodology and Algorithm...................................................................60 iv
3.4
Simulation Results ..................................................................................63
3.5
Validation................................................................................................73
3.6
Summary .................................................................................................76
4. REDUCED BATTERY STRESS THROUGH BLENDED ENERGY STORAGE 4.1
Introduction.............................................................................................78
4.2
Blended ESS topology and energy management ....................................80
4.3
4.4
4.2.1
Battery-only ESS ......................................................................80
4.2.2
Blended ESS .............................................................................82
Simulation Results ..................................................................................85 4.3.1
Simulation Results with 48-cell Ucap.......................................85
4.3.2
Simulation Results with 36-cell Ucap.......................................88
Summary .................................................................................................90
5. BATTERY THERMAL MODEL 5.1
Introduction.............................................................................................91
5.2
Altairnano Lithium-Titanate Cells..........................................................92
5.3
Battery tests.............................................................................................93
5.4
Battery Thermal Model & Simulations.................................................104
5.5
5.4.1
Mathematical Model ...............................................................104
5.4.2
Results.....................................................................................108
5.4.3
Thermal Simulations...............................................................116
Summary ...............................................................................................119
6. SUMMARY AND FUTURE WORK 6.1
Summary ...............................................................................................121
6.2
Future Work ..........................................................................................123 6.2.1
Powertrain model and simulation ...........................................123
6.2.2
Prediction-based power management stratey..........................124
6.2.3
Blended energy storage...........................................................124
6.2.4
Battery thernal modeling and simulation................................125
6.2.5
Intelligent driving....................................................................126
REFERENCES ...........................................................................................................127
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LIST OF TABLES Table 2.1 Quantitative Comparison corresponding to Drive Cycle 1...........................48 Table 2.2 Quantitative Comparison corresponding to Drive Cycle 2...........................52 Table 3.1 Comparison of prediction-based and baseline strategy for SC03.................65 Table 3.2 Comparison of prediction-based and baseline strategy for UDDS...............66 Table 3.3 Comparison of prediction-based and baseline strategy for test drive cycle .76 Table 4.1 Comparison of advanced technology battery and Ucap ...............................79 Table 4.2 Battery Description .......................................................................................81 Table 4.3 Ultracapacitor Description............................................................................83
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LIST OF FIGURES Figure 1.1: Schematic of parallel hybrid [1].................................................................13 Figure 1.2: Schematic of series hybrid [1]....................................................................14 Figure 1.3: Simplified schematic of hybrid powertrain ................................................15 Figure 1.4: A simplified fuel cell and its basic operation [2] .......................................17 Figure 1.5: Ragone plot comparing energy and power density of different power sources [3].........................................................................................................18 Figure 1.6: University of Delaware Fuel Cell Hybrid Bus #1 ......................................21 Figure 2.1: Different subsystems of the LFM Simulink model ....................................26 Figure 2.2: Voltage vs current corresponding to the Ballard Mark 9 SSL 110-cell stack employed in our Phase 1 bus ...................................................................29 Figure 2.3: Variation of gross stack power, net stack power, compressor power, and rest of BOP load with stack current ..................................................................32 Figure 2.4: Variation of fuel cell system efficiency with net fuel cell power as recorded by the Phase 1 bus and as predicted by the model .............................34 Figure 2.5: Variation of gross stack power, net stack power, compressor power, and rest of BOP load with stack current corresponding to our Phase 2 bus employing the dual stack as predicted by LFM ................................................35 Figure 2.6: Variation of fuel cell system efficiency with net fuel cell power corresponding to the Phase 2 dual-stack bus as predicted by LFM .................36 Figure 2.7: Aerial view of UD Express Route............................................................45 Figure 2.8: Drive Cycle profile of UD Express Route................................................46 Figure 2.9: Comparison of simulation output and vehicle data for Drive Cycle 1.....47 Figure 2.10: Aerial view of second test run ................................................................49 Figure 2.11: Drive Cycle profile of second test run....................................................50 Figure 2.12: Comparison of simulation output and vehicle data for Drive Cycle 2...51
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Figure 3.1: Battery SOC drop and fuel cell net power corresponding to the baseline and the predictive control strategy for SC03 (~2 hours, 46 miles)...................59 Figure 3.2: Longer drive cycles formed by repeating standard cycles ......................64 Figure 3.3: Deviation in battery SOC drop and fuel cell net power corresponding to inaccuracy in prediction for the SC03 (~2 hours, 46 miles) ............................67 Figure 3.4: Deviation in battery SOC drop and fuel cell net power corresponding to inaccuracy in prediction for the UDDS (~2 hours, 45 miles) ..........................68 Figure 3.5: Fuel savings and final battery SOC for varying degree of inaccurate predictions and for variable drive lengths for the SC03 driving schedule........69 Figure 3.6: Fuel savings and final battery SOC for varying degree of inaccurate predictions and for variable drive lengths for the UDDS driving schedule......70 Figure 3.7: Possible SOC profiles corresponding to the condition Pavg < Pηmax ..........72 Figure 3.8: Aerial view of the trajectory traced by the fuel cell hybrid bus ...............73 Figure 3.9: Profile of the test drive cycle....................................................................74 Figure 3.10: Battery SOC drop and fuel cell net power corresponding to baseline and predictive control strategy ................................................................................75 Figure 4.1: Topology of a fuel cell/battery hybrid......................................................81 Figure 4.2: Topology of fuel cell/battery/ultracapacitor series hybrid .......................83 Figure 4.3: Simulated battery C-rate frequency distribution for a battery only, as well as blended ESS (48-cell Ucap) at different threshold levels.............................86 Figure 4.4: Simulated energy throughput for a battery only, as well as blended ESS (48-cell Ucap) at different threshold levels ......................................................87 Figure 4.5: SOC swing of 48-cell Ucap module at 0 kW and 30 kW threshold power corresponding to UD Express Route ................................................................88 Figure 4.6: Simulated battery C-rate frequency distribution for a battery only, as well as blended ESS (36-cell Ucap) at different threshold levels.............................89
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Figure 4.7: Simulated energy throughput for a battery only, as well as blended ESS (36-cell Ucap) at different threshold levels ......................................................89 Figure 5.1: An Altairnano Lithium-Titanate cell (2.3 V, 50 Ah, 25x25x1.2 cm).......93 Figure 5.2: Schematic of the 5 cell stack and thermistor locations ............................94 Figure 5.3: Snapshot of the battery pack of five cells used for experiments ..............95 Figure 5.4: Temperature distribution on the surface of the cell recorded by IR Camera at different time instants during charging at 400 A .........................................96 Figure 5.5: Temperature distribution on the surface of the cell recorded by IR Camera at the end of 15 minutes of charging time with 100 A of current.....................97 Figure 5.6: Temperature readings at different locations of the battery pack during 200 A charge/discharge cycles.................................................................................99 Figure 5.7: Temperature readings at different locations of the battery pack during 100 A charge/discharge cycles...............................................................................100 Figure 5.8: Experimentally measured variation of open circuit voltage (Voc) with temperature for LiTi cell.................................................................................101 Figure 5.9: Time trace of water and cell surface temperature during calorimetric test for measuring specific heat capacity of LiTi cell............................................102 Figure 5.10: Rate temperature drop due to heat loss to the environment as a function of water temperature ......................................................................................103 Figure 5.11: Schematic diagram of current flow in parallel electrodes of a cell ......105 Figure 5.12: Distribution of Vp.................................................................................109 Figure 5.13: Distribution of Vn.................................................................................111 Figure 5.14: Distribution of the potential difference (Vp-Vn) .................................113 Figure 5.15: Distribution of current density J...........................................................115 Figure 5.16: Variation of heat generation rate with the height of the cell ................116 Figure 5.17: 3D model of half LiTi cell created in Gambit ......................................117
ix
Figure 5.18: Temperature distribution on the cell surface after 5 minutes of charge at 400A obtained from FLUENT simulation (above) IR imaging (below) .......118
x
ABSTRACT Fuel cells have emerged as one of the most promising candidates for fuel-efficient and emission-free vehicle power generation. Fuel cells are typically paired with reversible energy storage devices such as batteries or ultracapacitors to create hybrid electric powertrains. The electrification of the propulsion system and the presence of multiple onboard power sources require optimization of the hybrid system design in order to achieve good performance, high fuel economy, and enhanced component life at low cost. The overall goal of this research is to develop accurate vehicle models and conduct simulations to explore and demonstrate improvements in a fuel cell/battery hybrid bus. The first part of this thesis presents the features incorporated to improve a hybrid powertrain simulation package called Light, Fast and Modifiable (LFM). The improved LFM simulator was validated against test data acquired from various sensors onboard UD’s Phase 1 fuel cell bus, and shown to be a reliable tool to simulate hybrid powertrain performance which could be used to perform design and optimization studies of future fuel cell hybrid systems. This attribute of LFM was then demonstrated by optimizing the fuel cell/battery hybrid power management by introducing a new prediction-based power management strategy. Simulation results for this strategy showed significant improvements in fuel cell system efficiency and reduction in hydrogen consumption compared to a conventional, baseline strategy of charge sustenance. A stable power request which promotes fuel cell durability was also realized with the help of this novel strategy. Finally, the benefits predicted from simulation studies were confirmed through implementation of the
xi
proposed strategy in the Phase 1 fuel cell/battery hybrid bus. It was concluded that the prediction-based strategy will lead to energy savings for transit applications. The validated LFM tool was next used to evaluate one approach to reducing battery stress by adding an ultracapacitor module, and thereby enhancing battery lifetime. Simulation of the energy storage performance showed a substantial reduction in battery current-load and energy throughput for the blended storage system, which are two of the contributing factors towards battery degradation. These results have opened up new research directions in which powertrain simulations can help in further evaluation of the blended storage concept and assess its feasibility and usefulness in electric-drive vehicles. Finally, the thermal behavior of the Altairnano LiTi battery, the future battery of UD fuel cell buses, was investigated. Preliminary experiments were conducted to understand the thermal behavior of batteries under typical operating conditions. A model was developed to predict the temperature during charging and discharging of the battery. The findings of this work should prove useful in designing effective and efficient battery thermal management systems.
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1
Introduction
1.1 Introduction to Hybrid Vehicles Conventional internal combustion engine (ICE) vehicles rely on a single power source typically fueled with gasoline, to drive a complex transmission mechanism. Although the technology has evolved continuously over the past 100 years, it suffers from a number of disadvantages. These include low energy efficiency, excessive harmful chemical emissions, high noise level, high degree of complexity, and complete dependence on a single fuel source. In contrast, hybrid vehicles use two or more distinctly different power sources to propel the vehicle. The term commonly used to refer to such vehicles is hybrid electric vehicles (HEV), which employ a combination of an internal combustion engine with an electric propulsion system. The electric propulsion system of an HEV mainly comprises an electrical storage (battery or ultracapacitor) and an electric motor. The ICE and the electric propulsion system can be combined in several ways. Two common ways are shown below.
Figure 1.1 Schematic of parallel hybrid [1]
13
The schematic in figure 1.1 shows a parallel HEV where the engine and electric propulsion work in parallel to turn the differential/wheels. The electrical storage provides power which is converted to mechanical power/torque at the electric motor. On the other hand, the engine can be decoupled from the differential/wheels and instead provide electrical power by turning a generator (figure 1.2). This type of configuration is called a series hybrid.
Figure 1.2 Schematic of series hybrid [1] A hybrid propulsion system can overcome many of the problems associated with the conventional ICE vehicle. An ICE is a one-way power source, meaning that it can burn fuel to produce the required drive power, but it cannot run the reverse reaction and convert the vehicle’s kinetic energy back to fuel during deceleration. In city driving conditions, roughly 10 to 20% of the drive system’s energy is lost in braking. The energy loss is expected to be even higher for transit buses due to frequent starts and stops. On the other hand, the electric propulsion system is bidirectional and has the ability to convert the vehicle's kinetic energy into battery-replenishing electric energy, rather than wasting it as heat energy as is the case for conventional friction brakes. Furthermore, an electric motor operates at a higher efficiency than an ICE, and its efficiency is more or less
14
constant over most of its operating range. Also, many HEVs reduce idling emissions by shutting down the ICE when the vehicle is stationary, and restarting it when needed (start-stop system). A hybrid-electric vehicle produces less emissions from its ICE than a comparably-sized ICE vehicle, because the HEV employs a much smaller ICE which is typically operated close to its maximum efficiency point, further improving fuel economy. The advantages of hybridization also apply to fuel cell powered vehicles. A fuel cell engine, which uses hydrogen as the fuel, exhibits highly varying efficiencies depending upon its operating point (current draw) and also does not perform well under rapidly changing power demands. Power assistance from a reversible energy storage system such as a battery or ultracapacitor alleviates the problem and enhances performance. Fuel cells like IC engines are a one-way power source, and therefore, reversible energy storage is required to accomplish regenerative braking.
Figure 1.3 Simplified schematic of the hybrid power train
15
1.2 Fuel Cells Fuel cells are one of the most promising candidates for fuel-efficient and emission-free vehicle propulsive power. The increasing popularity of fuel cells is due to its high efficiency (2-3 times greater than ICE) and no harmful emissions. In particular, proton exchange membrane (PEM) fuel cells have received much attention for automotive applications due to their low operating temperature, rapid start-up, high power density, and high efficiency. Fuel cells are electrochemical cells which convert the source fuel into electrical power along with byproducts of the reaction. They generate electricity through reactions between a fuel and an oxidant within the membrane electrode assembly (MEA), which consists of two electrodes separated by an electrolyte. The fuel cell produces a voltage and a current when it is supplied by reactants that flow into the cell, while removing the reaction products from the cell. Fuel cells can operate virtually continuously as long as the necessary flows are maintained. Figure 1.4 illustrates the process.
16
Figure 1.4 A simplified fuel cell and its basic operation [2]
A single fuel cell can produce a typical voltage of only about 0.6-0.7 V. Therefore, several fuel cells must be joined together in series to create a “stack” in order to produce a useful voltage. Automotive stacks comprise over a hundred cells in series producing up to 100 kW of power. It should be noted that a fuel cell cannot function on
17
its own and requires a significant balance-of-plant (BOP) to handle fuel and oxidant supply, cooling, humidification, power conditioning, and for control and management tasks. In addition to the BOP, fuel cell powered vehicles need a reversible energy storage device such as a battery or ultracapacitor to absorb energy from regenerative braking and meet transient power demands.
1.3 Battery A battery is a combination of one or more electrochemical cells, used to convert stored chemical energy into electrical energy or vice versa. The battery pack in a hybrid vehicle is one of the most important parts of the propulsion system. Batteries, unlike ICEs and fuel cells, can supply as well as absorb energy.
Figure 1.5 Ragone plot comparing energy and power density of different power sources [3].
Batteries have a higher power density than ultracapacitors, but lower energy density than the fuel cell (figure 1.5). Therefore in fuel cell hybrid vehicles, batteries are typically
18
used to meet transient power demands and absorb energy during regenerative braking, while the fuel cell continues to charge the battery and extends the range of the vehicle.
1.4
Ultracapacitors
Ultracapacitors are electric double-layer capacitors that have an unusually high energy density when compared to common capacitors, typically about a thousand times greater than a high-capacity electrolytic capacitor. In a conventional capacitor, energy is stored by the removal of charge carriers, typically electrons, from one metal plate by depositing them on another. The total energy stored is the product of the amount of charge stored and the potential between the plates. The amount of charge stored is a function of the size and the material properties of the plates. The potential between the plates is limited by the dielectric breakdown of the material separating the plates. Ultracapacitors do not have a conventional dielectric sandwiched between the two plates. Instead, these plates are two layers of the same substrate which form an electrical double layer, resulting in effective separation of charge despite a vanishingly thin physical separation of the layers. The elimination of the bulky dielectric layer permits compact packing of layers and with much larger surface area. The resulting devices possess extraordinarily high capacitances resulting in higher storage capacities (Ah) than conventional capacitors. Ultracapacitors possess higher power densities than batteries. Batteries, which are based on the movement of charge carriers in a liquid electrolyte, have relatively slow charge and discharge times. Capacitors, on the other hand, can be charged or discharged at a rate that is typically limited by current heating of the electrodes. However, batteries possess higher energy densities than ultracapacitors. Due
19
to these varying properties, the decision to use batteries or ultracapacitors is dictated by the intended application. Due to their higher energy content, batteries perform better at isolating the fuel cell from transient power demands for the entire duration of the drive cycle. For this reason the University of Delaware fuel cell buses are fuel cell/battery hybrids, and are described next.
1.5
The University of Delaware Fuel Cell Bus
The University of Delaware’s fuel cell hybrid vehicle served as the subject for the research conducted during this thesis. This 22 ft bus was designed and built by EBus, Inc. and can hold 22 seated and 10 standing passengers (Figure 1.5). It is powered by a Ballard Mark 9 SSL 110-cell stack, rated for 19.4 kW gross power. The bus is driven by a single three-phase AC induction motor that is rated for 130 kW peak and 75 kW continuous power and speeds up to 5000 rpm. The motor is coupled to the rear drive wheels through a single-speed chain drive and a differential, with gear ratio selected to allow speeds of up to 45 mph while providing enough torque to climb a 20% grade fully loaded. The bus incorporates a series-hybrid powertrain that employs SAFT NickelCadmium (NiCd) liquid-cooled batteries in two 300 V strings. The strings are connected in parallel because of the traction inverter voltage limitation, and the two together are capable of meeting high power demands (~120 kW). Each string consists of 50 monoblocks, each containing 5 cells. The cells are rated for a nominal charge capacity of 100 Ah and total energy capacity of 60 kWh. This typically gives the vehicle an allelectric range of 40 miles. The bus uses compressed hydrogen stored in twin composite
20
high-pressure tanks mounted on the roof of the bus. The tanks are rated for 350 bar and have a total storage capacity of approximately 12.8 kg. This amount of hydrogen yields an average range of about 140 miles. The plug-in feature of the bus permits the initial portion of each route to be driven solely on battery power with the fuel cell switching on when the battery state-of-charge falls below the chosen threshold value. Hydrogen Tanks
Radiator
Battery Pack Rear panel houses stack and balance of plant
Figure 1.6 University of Delaware Fuel Cell Hybrid Bus #1
The fuel cell stack is fed with air by a scroll-type compressor at pressures of 83 to 124 kPa, depending on load, which is humidified by moisture from the cathode exhaust air using membrane humidifiers. Hydrogen is supplied at a slightly higher pressure, and hydrogen is recirculated from the stack outlet to the inlet using a rotary single-vane pump, to ensure clearance of water from all parts of the anode. The stack is liquid cooled, using a low-conductivity ethylene glycol/water mixture and a fan-cooled radiator which
21
is mounted on the roof of the bus as can be seen from Figure 1.6. After subtracting the power requirements of the balance of plant, the fuel cell stack delivers a maximum net power output of 14 kW. Since the stack's operating voltage is typically between 65 and 75 V, a boost converter is used to deliver power to the main DC bus at a nominal battery voltage of 300 V (which can range from 250 V to 370 V during normal vehicle operation), controlling the amount of power drawn from the fuel cell. The schematic of the hybrid drivetrain for this bus is shown in Figure 1.3. The fuel cell system is controlled by a programmable logic controller (PLC), which coordinates the functions of all parts of the fuel cell system according to the amount of power requested by the vehicle control computer. The bus is equipped with a data acquisition system installed in a laptop computer, currently running custom software within LabVIEW. It monitors the vehicle control computer, the fuel cell system's PLC, and the traction inverter. In addition, it has a GPS receiver. Real-time data are collected from a variety of other on-board sensors monitoring fluid temperatures, flowrates, and humidity levels within the fuel cell system. The overall design of our bus features a battery-heavy hybrid which uses the fuel cell as a range extender. The basic control strategy is to run the bus in battery-only mode until the state-of-charge (SOC) reaches a threshold value. This value can be reprogrammed, but defaults to 65%. Once the SOC reaches 65%, the fuel cell turns on with a power request governed by
FC power request = α ( SOCd − SOCc ) + Pone hr moving avg
22
where SOCd is the threshold value, SOCc is the current calculated SOC, α is the proportionality constant and Pone hr moving avg is the moving average power use of the bus over the last hour. The vehicle runs solely on the battery at the start of the drive cycle resulting in a steady drop in SOC. As soon as the SOC reaches the threshold value of 0.65 the fuel cell stack is turned on and after ramping up at a desired rate, it delivers an average power to sustain the battery SOC at 0.6. The stack is turned off after the completion of the route and the bus returns to its garage on batteries alone to deplete it further. This mode of operation, known as charge depletion, not only reduces hydrogen consumption but also allows the NiCd batteries to be cycled over a large fraction of their capacity, which helps to avoid the effect known as "voltage depression" and thus maintain usable capacity. The electrification of the propulsion system has made the hybrid design process increasingly complex. An optimal hybrid system can be achieved through appropriate sizing of the battery and/or ultracapacitor, and fuel cell or ICE, and intelligent energy management between them. Such decisions depend on many factors such as the vehicle size, performance targets, type of application, fuel economy, component lifetime, and cost. There is no simple or direct way to select and harness the advantages of different components and satisfy the desired targets. The design process is iterative and requires advanced powertrain modeling and simulation efforts in order to facilitate the analysis and optimization of the new generation of vehicle power train. The overall goal of this thesis is to develop and refine a power train modeling and simulation effort and demonstrate its utility on our fuel cell hybrid bus.
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1.6 Organization of the Thesis Chapter 2 presents the development of the fuel cell hybrid powertrain simulator called LFM. The weaknesses of the previous versions are discussed along with the modifications and improvements. The description of the simulator is followed by a validation study with compares the results of the simulation with operational data from the vehicle. Subsequent chapters detail the use of the simulator in a number of studies aimed at improving the design, performance, efficiency, and lifetime of our fuel cell bus. Chapter 3 presents a prediction-based power management strategy for fuel cell/battery plug-in hybrid vehicles with the goal of improving overall system operating efficiency. The results obtained from the implementation of the proposed strategy in LFM are presented along with a sensitivity of parameters important for the study. The effectiveness of the strategy is evaluated by comparing it with a conventional control strategy within the simulation environment. Finally, the validation of the simulated results is demonstrated by implementation of the strategy in the fuel cell bus and by conducting real time testing. Chapter 4 explores the load reduction effects on the battery in blended energy storage (batterty+ultrapacitor) hybrids. This is done by comparing a battery only hybrid and blended energy storage hybrid within the simulation environment. The chapter first introduces the factors leading to battery degradation and briefly describes the role that an ultracapacitor can play. Next, the hybrid topology, energy storage and power management scheme of the two hybrid systems are presented, followed by simulation results.
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Chapter 5 attempts to understand the thermal behavior of Altairnano LiTi cells using two approaches: experiments, and modeling and simulations. First, the features of the new Altairnano LiTi cells are introduced. The experiments performed on the cells and analyses of results are described next. A 2D mathematical model for the evaluation of heat generation is presented, followed by a transient thermal simulation of the cell undergoing charge/discharge cycles. Finally, Chapter 6 summarizes the contributions made in this thesis and presents conclusions. Also, the possible future work for each topic presented here is discussed.
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2
LFM Simulator
2.1
Introduction
The hybrid vehicle simulator used in this thesis to perform hybrid-powertrain performance and optimization studies is called LFM (Light, Fast and Modifiable). LFM was originally developed by the Electric Power Research Institute (EPRI) in Palo Alto, CA, and was subsequently modified at the UD Center for Fuel Cell Research. The accuracy of a simulator is critical for reliable predictions. The LFM simulator has undergone substantial improvement at UD and has proved its capabilities through rigorous validation exercises. This chapter describes the LFM simulator, its development, and the various enhancements that have been incorporated into it. We begin with a general introduction of the simulator and provide a detailed description of its subsystems. Next, we discuss the shortcomings of the earlier versions, followed by the modifications and improvements made during the present work. Finally, a validation exercise is presented to show the agreement between the modified LFM results and test data collected from the Phase 1 UD Fuel Cell Bus. E[E ]
A
[A ]
I[I] F[F]
[B ]
B
Controller
[G ] G
C
[C]
[H] H
D
[D]
[J] J
F
[F]
Battery
G
A
[A ]
[G ]
H
[H]
Accessory
D
[D]
Fuel Cell
Load Combiner
J
C
E
[E ]
B
[B ]
Power Combiner
Motor
[C]
Transmission
[J ]
Figure 2.1 Different subsystems of the LFM Simulink model
26
I
[I]
Chassis
2.2
LFM
LFM is a hybrid vehicle simulator, designed to simulate the performance of a fuel cell and battery powered electric driveline under given driving conditions. The first version of LFM was developed by Marcus Alexander of EPRI. It consisted of a series of subsystems built and connected in Simulink using electrical, mechanical, and control signal links (figure 2.1). The basic structure of the simulator is drive-cycle based, which implies that, the simulation is driven by an input drive cycle. At each time step during the drive cycle, the simulator compares the current speed with the desired speed and calculates the appropriate power command required to propel the vehicle along its virtual trajectory. The command then propagates through various subsystems of the drivetrain to the power sources. The LFM solver is a variable time-step ode45 (Dormand-Prince) solver. It is part of the Runge-Kutta family of ordinary differential equation solvers, and is well suited for a variable time-step simulation. Besides the drive cycle, LFM requires a number of quantitative inputs to accurately describe the drivetrain components such as the vehicle, transmission, drive motor, accessory load, fuel cell system, and battery. In the first version of the simulator, these data were stored in an Excel spreadsheet and read using a special class of objects into Simulink in real time. The Simulink results were written in MATLAB workspace and were available for analysis after the simulation had ended. Darren Brown (M.S. 2008, University of Delaware) modified the input and output system of the simulator to facilitate rapid iterations [4, 5] and modified various subsystems in order to simulate the Phase 1 bus [4]. Despite this first round of modifications, LFM contained additional shortcomings which are addressed in the present work. These include the lack of a reliable and
27
complete fuel cell system (fuel cell + balance-of-plant) model, an over-simplified balance-of-plant (BOP) model incorrectly lumped with the accessory load, an oversimplified battery model, an inaccurate depiction of the hybrid controller, and the need for full-scale validation. Modifications to the LFM simulator were performed to address these drawbacks, and are discussed in the following sections while introducing the LFM subsystems.
2.3
LFM Subsystems
2.3.1 Fuel Cell The fuel cell subsystem receives a power request which is then converted to a DC current request. The current is used to calculate hydrogen consumption and update the voltage output of the stack using a lookup table created in the fuel cell data spreadsheet. Modifications to the fuel cell model from the current work are described next.
Additions The two crucial aspects while setting up a vehicle simulation model for validation are – (i) a reliable physical model, and (ii) the use of technical specifications that accurately reflect the actual performance of the component. Therefore, the fuel cell data and model were revised for better prediction of fuel cell output parameters such as voltage, current, net power, gross power, BOP load, hydrogen consumption and system efficiency. The polarization curve was modified based upon fuel cell data acquired in real time during a test run for our Phase 1 bus (figure 2.2).
28
110 105 100
Voltage (V)
95 90 85 80 75 70 65 60 0
50
100
150
200
250
300
Current (A)
Figure 2.2 Voltage vs current corresponding to the Ballard Mark 9 SSL 110-cell stack employed in our Phase 1 bus The hydrogen flow rate is calculated using standard equations and incorporated a purge rate for better accuracy. •
m H2 =
n fc M H 2 I st 2F
•
+ m purge
(2.1)
where n fc is the number of cells in the stack (provided by the manufacturer- 110 for the single stack and 220 for the dual stack), M H 2 is the molar mass of hydrogen, I st is the •
stack current, F is the Faraday number, and m purge is the purge rate of hydrogen whose average value over 2.5 hours of fuel cell testing was recorded to be close to 0.01 kg/hr for a single stack. A significant component of fuel cell model is the BOP load which is required to operate the stack. The power required to run the BOP is provided by the stack itself. The earlier version of LFM used an over-simplified BOP model. It also lumped the BOP load
29
with the accessory load which introduces inaccuracies, both in fuel cell gross and net power, and therefore affects all the fuel cell output parameters. In order to address this weakness, a more sophisticated and accurate BOP model was created in the present work and the BOP load has been modeled separately from the accessory load. Amongst the fuel cell BOP components, the air compressor consumes the largest portion of power. The power consumption of the BOP has been modeled as the sum of a variable air-compressor load, and a constant load of 1 kW which accounts for the hydrogen pump and the radiator [6]. The compressor power consumption, Pcp , depends on the stack ambient pressure ratio and the air flow rate, and is given by (γ −1) / γ • C pTamb psm m cp Pcp = − 1 η mηcp pamb
(2.2)
where psm is the pressure in the supply manifold, pamb is the ambient pressure, γ is the ratio of C p , specific heat capacity of air at constant pressure, and Cv the specific heat capacity of air at constant volume, Tamb (20 °C) is the ambient temperature, ηm (80%) and ηcp (70%) are the efficiencies of the compressor motor and compressor respectively, •
and m cp is the mass flow rate of air through the compressor. To predict compressor power consumption, Pcp , as a function of the stack current, a knowledge of the quantities in equation 2.2 as a function of stack current is needed. For a given stack current, the stoichiometric inlet oxygen mass flow rate to the cathode is given by •
mO2 =
n fc M O2 I st 4F
30
(2.3)
where M O2 is the molar mass of oxygen. The mass flow rate of air to the cathode is given by •
•
m a ,ca ,in =
λo mo , rct 2
2
yo2
λo n fc M O I st
=
2
(2.4)
2
4 yo2 F
where λO2 is the oxygen excess ratio which is assumed to be maintained at a constant value of 1.6, and the molar fraction of oxygen in air yo2 is 0.21. Thus the total air flow rate through the compressor is given by •
•
•
•
mcp = ma ,cp + mv ,cp = (1 + ψ amb ) ma ,cp
(2.5)
where ψ amb is the humidity ratio of the atmospheric air, and subscripts cp , a , and v denote compressor, air, and water vapor respectively. Also, the mass flow rate of dry air at the cathode inlet and compressor outlet can be assumed to be the same under steady state conditions. Therefore, • Mφ p • Mφ p m cp = 1 + v amb sat , amb m a ,ca ,in = 1 + v amb sat ,amb M a pa ,amb M a pa , amb • Mφ p m cp = 1 + v amb sat ,amb M a pa ,amb
λo2 n fc M O2 I 0 4 yo2 F
λo2 n fc M O2 I st 4 yo2 F
otherwise
if I st > I 0
(2.6)
where M a and M v are the dry air and water vapor molar masses respectively, φamb is the relative humidity of the ambient air (assumed to be 0.7), psat ,amb is the vapor saturation pressure at ambient temperature, and pa ,amb is the pressure of the dry atmospheric air. In the vehicle psm varies from 13.5 psig to 17 psig. The mass flow rate is a constant below a
31
certain threshold current I 0 (150 A). Based on the above calculations the gross stack power, Pfc , gross , and net power, Pfc ,net , can be calculated in the following way: Pfc ,net = Pfc , gross − PBOP
(2.7)
PBOP = Pconst + Pcp
(2.8)
Figure 2.3 depicts the gross power, net power, and BOP load, PBOP , as a function of the stack current for the single stack. Note that compressor power for the single stack can vary from 1 KW at low current draw to 2 KW at high current draw.
18 16 14
Power (kW)
12
Gross Power Net Power Compressor Power Constant BOP Power
10 8 6 4 2 0 -2
50
100 150 Stack Current (A)
200
250
Figure 2.3 Variation of gross stack power, net stack power, compressor power, and rest of BOP load with stack current.
32
The variation of parasitic losses in the fuel cell system with current naturally leads to an evaluation of system efficiency over its operating range. The fuel cell system efficiency is defined as the ratio of the net power delivered by the fuel cell to the lower heating value (LHV) of the fuel, which is hydrogen in our case.
Pfc ,net
η fc , sys =
•
(2.9)
mH 2 LHVH 2 where Pfc ,net is the net power delivered by the fuel cell (i.e. gross fuel cell power minus •
the power consumed by the BOP) and m H 2 is the corresponding fuel consumption rate. The system efficiency is plotted against net power for the single stack in figure 2.4, and offers us guidance into the desired power range of the fuel cell in order to achieve high efficiencies.
33
50 45 40
Efficiency (%)
35 30 25
Test Data
20
Model
15 10 5 0 0
2
4
6
8
9
10
12
14
Fuel Cell Net Power (kW) Figure 2.4 Variation of fuel cell system efficiency with net fuel cell power as recorded by the Phase 1 bus and as predicted by the model.
The close match between the efficiency values derived from vehicle data and the enhanced model as evident in figure 2.4 validates the fuel cell system model used in the present work. It also justifies adopting the same approach for modeling the dual stack employed in our Phase 2 bus. The significance of figure 2.4 has been discussed in Chapter 3 as part of an optimization study. For the dual stack, Pconst is proportionately increased to 2 kW. The compressor power, Pcp , is calculated using equation 2.2. The dualstack compressors are assumed to operate at lower pressures varying from 4 psig to 10 psig, assuming that an improved fuel cell stack will contain larger humidifiers.
34
40 35 Gross Power Net Power Compressor Power Constant BOP Power
30
Power (kW)
25 20 15 10 5 0 -5
0
50
100
150 Current (A)
200
250
300
Figure 2.5 Variation of gross stack power, net stack power, compressor power, and rest of BOP load with stack current corresponding to our Phase 2 bus employing the dual stack as predicted by LFM
35
50
Fuel Cell System Efficiency (%)
45 40 35 30 25 20 15 10 5 0
0
5
10
15
17
20
25
30
35
Net Fuel Cell Power (KW)
Figure 2.6 Variation of fuel cell system efficiency with net fuel cell power corresponding to the Phase 2 dual-stack bus as predicted by LFM
2.3.2 Battery The battery is modeled as a voltage source in series with a resistance, both of which vary with the state-of-charge (SOC).
V = Voc − IRint,dis V = Voc − IRint,ch
if I > 0 if I < 0
(2.10)
where Voc = f ( SOC ) is the open circuit voltage of the battery pack, Rint,dis = f ( SOC ) and
Rint,ch = f ( SOC ) are discharge and charge internal resistances, respectively, for both the battery strings combined.
36
SOC (t ) = SOC0 − ηbatt
∫
t
0
I dt C
(2.11)
where C is the charge capacity (Ah) of both the strings combined, and
ηbatt = 1 ηbatt = 0.85
if I > 0 if I < 0
The battery subsystem takes current request as the input to update the state-of charge (equation 2.11) and calculates the voltage output of the stack (equation 2.10). The charging reaction in NiCd chemistry is accompanied by a side reaction (electrolysis) due to which not all of the charging current goes towards converting active material. The conversion factor ηbatt is higher at low SOCs (less electrolysis), and lower at high SOCs (more electrolysis). Based on data provided by the vehicle manufacturer we have used an average conversion factor of 0.85 over the entire SOC range during charging. No side reactions are present during discharging and so the conversion factor is set to 1.0. Modifications to the battery system from the current work are described next.
Additions An extra subroutine has been added to the battery subsystem which calculates the discharge and charge power limits of the battery pack at every time step. Battery discharge power limit (BDPL) is the maximum power that the battery can supply at a given instant. Similarly, battery charge power limit (BCPL) is the maximum power that the battery can accept at a given instant. The purpose of this calculation is to limit the traction motor power by accounting for the power available from the battery and the fuel cell, and subtracting the power needed by the accessory load at a given time step (equation 2.4). 37
PTraction ,max (t ) = PBattery ,max (t ) + PFC (t ) − PAccessory (t )
(2.12)
The limits are important because they restrict the power request in the simulation to only the range of values that the on-board power sources are capable of providing. For example, at 100% state-of-charge the battery cannot accept power from regenerative braking. The BCPL ensures that the battery does not absorb any power under such a condition. The limits are calculated as follows: If
VOC − Vmin Rint,dis
≥ I Batt ,max ,
BDPL = (VOC − I Batt ,max Rint,dis ) × I Batt ,max
V − Vmin BDPL = OC R int, dis
Else,
(2.13)
× Vmin
Similarly, If
Vmax − VOC Rint,ch
≥ I Batt ,min ,
BCPL = (VOC + I Batt ,min Rint,ch ) × I Batt ,min
V −V BCPL = max OC R int, ch
Else,
(2.15)
× Vmax
The voltage and current limits ( Vmax , Vmin , I max , I min ) are constants and are generally provided by the manufacturer. For our batteries the values used are 370 V, 240 V, 300 A, and -300 A, respectively. It has been observed that the charging reaction in Nickel Cadmium batteries at high current and high SOC is accompanied by a rise in its internal impedance, and the initiation of a side reaction resulting in the evolution of oxygen. This occurs due to mass transportation limitations inside the cell that limits the charge acceptance rate of the battery. In such a situation, the energy recovered from regenerative braking is also
38
limited. The phenomenon is generally observed at SOCs higher than 0.75. Since the model does not capture this phenomenon, all the simulations have been carried out with an initial SOC of 0.75. However, the complete SOC range can be employed while simulating other battery chemistries such as lithium–ion due to the absence of the side reaction phenomenon. The model used for simulating the other battery chemistries remains the same, except that the battery specifications would differ (OCV, resistance, voltage, and current limitations).
2.3.3 Ultracapacitor An ultracapacitor subsystem has been added to the Simulink model in the current work in order to expand the simulation capability, and simulate hybrid systems consisting of a battery, ultracapacitor, or a combination of both. Chapter 4 presents a study of battery stress reduction in a fuel cell/battery/ultracapacitor series hybrid vehicle. The ultracapacitor is also modeled as a voltage source in series with a resistance. V = Voc − IR
Voc =
(2.12)
Q C
SOC (t ) = SOC0
(2.13)
∫ −
t
0
I dt Q
(2.14)
where Voc is the open circuit voltage, R is a constant internal resistance, Q is the charge and C is the capacitance of the ultracapacitor pack. Similar to the battery subsystem, the ultracapacitor subsystem incorporates a subroutine to calculate the charge and discharge power limits. This is particularly useful for the study in Chapter 4 where small ultracapacitors are used which frequently reach their power limits during a typical drive
39
cycle. The ultracapacitor data are stored in Excel spreadsheets and loaded with the rest of the data before running the simulation.
2.3.4 Hybrid Controller The most important role of the hybrid controller is to decide the traction power request. The hybrid controller houses a Driver or Cockpit subsystem where the force required to propel the vehicle at the target drive-cycle speed is calculated.
(
1 FTotal = ( ma ) + CD ρ AV 2 + ( mgCrrV ) + mg sin tan −1 grade 2
(
))
(2.16)
where m is the vehicle mass, a is the acceleration, CD is the drag coefficient, A is the vehicle frontal area, V is the velocity, and Crr is the rolling resistance coefficient. The terms constituting the required force are acceleration, air drag, rolling resistance, and inclination, respectively. The calculated force is propagated through different subsystems to the power sources as the electrical power requirement. The hybrid controller also determines the amount of mechanical braking required if the available regeneration power exceeds the acceptance limit of the energy storage (battery, ultracapacitor, or both). Another important function of the hybrid controller is to calculate the fuel cell power request according to the power management strategy used in the simulation. For simulating our Phase 1 bus, for example, the controller takes SOC and vehicle power requirement inputs to compute the one-hour average load and the SOC correction term. Modifications to the hybrid controller from the current work are described next.
40
Additions The force calculation at the cockpit is the starting point of the simulation. Correct calculation of the force and its propagation through the hybrid power train is crucial for accuracy in power requests at the power sources. Accordingly, the mass of the vehicle was measured at a weighing station and corrected in the simulation. For simulating a route traced by the bus, the drive cycle (velocity) data are obtained from an onboard GPS device. However, the GPS data do not include elevation, and hence cannot be used to calculate the inclination of the route. Therefore, an accelerometer was installed within the vehicle, and the accelerometer data were used to calculate the inclination of the route as follows:
1 dV
θ = sin −1 − aaccel g dt
(2.17)
The inclination data derived from the accelerometer data were used during validation on a more recent vehicle test drive and is presented in section 2.4. Also, the inclusion of an energy storage charge limit is expected to improve the calculation of the amount of braking energy available for regeneration because regenerative energy is occasionally limited by the charge acceptance capacity of the battery. In order to validate the simulator performance with the vehicle test data, the fuel cell power request had to undergo a few modifications in order to replicate the actual vehicle. Specifically, the time period for the average power calculation and the proportionality constant in the SOC correction term were changed to the values used by the bus #1. The earlier version used a 10-minute average power instead of one-hour average, and a value of 80000 for the proportionality constant which was corrected to 30000. The difference between the two values is significant enough to cause major errors
41
in the predicted energy contributions of the fuel cell and battery. Consequently, the fuel cell parameters have shown close agreement with the vehicle test data and will be discussed in section 2.4 Also, new power management strategies have been added to the embedded MATLAB functions within this subsystem which have been used for simulation studies detailed in Chapters 3 and 4.
2.3.5 Power Combiner This subsystem is responsible for distributing the vehicle (traction + accessory) power demand to the fuel cell stack and the battery. In the presence of multiple energy storage devices such as a battery and ultracapacitor, the subsystem is modified to distribute the power request according to the governing strategy to all the respective power sources.
2.3.6 Accessory Load In the previous version of LFM, an oversimplified model of the BOP was lumped with the accessory load. The BOP component has been deleted in the new version and the rest of accessory load has been retained as before. The accessories in the original model comprised of the vehicle air compressor, hydraulic pump, battery chiller pump, battery chiller compressor, 12V accessories, and air conditioning, and have been modeled as a constant average load. Air conditioning and the battery chiller compressor constitute the bulk of the accessory load. Air conditioning is optional and can be turned on or off before running the simulation. The battery chiller compressor is operated intermittently to cool the NiCd battery pack. The compressor is turned on and off when corresponding predefined threshold temperatures are reached.
42
The chiller compressor power requirement is assumed as a constant value and is based on its duty cycle.
2.3.7 Motor The traction motor subsystem receives a positive or negative torque request based on the drive cycle requirement at a particular instant. The torque request is bounded within the specified limits and then used to calculate the power requirement (torque multiplied by angular speed) and the expected losses based on the supplied value of motor efficiency.
2.3.8 Transmission The transmission in the vehicle employs a single gear ratio, and therefore this subsystem is responsible for performing two very simple calculations. It scales the torque request and angular velocity at the motor and wheel end based on the gear ratio. It also calculates the torque loss due to transmission losses using the supplied efficiency value.
2.3.9 Wheels/Chassis This is the final subsystem in the model which receives the torque input at the wheels from the traction motor and transmission, and calculates the acceleration of the vehicle as well as updates the vehicle velocity and wheel angular speed. The vehicle acceleration is given as:
(
Torqueinput 1 2 −1 ma = − CD ρ AV − ( mgCrrV ) − mg sin tan grade rwheel 2
43
(
))
(2.18)
2.4 Validation As mentioned earlier, the simulator can be used as a useful tool for various design and optimization studies. However, it is crucial to ensure the validity of the simulator before making decisions based on its predictions. Therefore, the simulation development is followed by a validation exercise using the Phase 1 fuel cell hybrid bus described in section 1.2 as the test vehicle. The validation procedure begins by driving the test vehicle on a predefined route to gather drive cycle information and powertrain data using a variety of onboard sensors. The same drive cycle and initial conditions are then provided as input to the LFM simulator and the simulation is executed. Key outputs from the simulator are analyzed and compared with the vehicle data. The validation exercise has been conducted by comparing LFM results with vehicle data on two distinct drive cycles.
Drive Cycle 1 During the first test run the vehicle was driven on the UD Express Route for a total of 16 miles and 1.5 hours (figures 2.7 and 2.8).
44
Figure 2.7 Aerial view of UD Express Route
45
18 16 14
Speed (m/sec)
12 10 8 6 4 2 0 0
1000
2000
3000 Time (s)
4000
5000
Figure 2.8 Drive Cycle profile of UD Express Route
Output parameters obtained from the simulation are plotted with the corresponding real time test data from the vehicle. Figure 2.9 indicates that the battery SOC starts at the same initial point in the actual vehicle and in the simulation. Since the vehicle runs solely on the battery at the start of the drive cycle a steady drop in SOC is observed. As soon as the SOC reaches the threshold value of 0.65 the fuel cell stack is turned on both in the actual vehicle and in the simulation. From then on the controller calculates the fuel cell power request and sustains the battery SOC.
46
Battery SOC
0.8 Simulation Output Vehicle Test Data
0.75 0.7 0.65
0
1000
2000
3000
4000
5000
4000
5000
4000
5000
4000
5000
Fuel Cell Gross Power (kW)
15 10 5 0 0
1000
2000
3000
Fuel Cell Net Power (kW)
10
5
0 0
1000
2000
3000
Hydrogen Consumption (kg)
0.8 0.6 0.4 0.2 0 0
1000
2000
3000
Figure 2.9 Comparison of simulation output and vehicle data for Drive Cycle 1
47
Good agreement between the simulated and real SOC throughout the drive cycle reflects the reasonably accurate simulation of vehicle powertrain and ensures that the fuel cell stack turned on at about the same time. This in turn ensures correct prediction of contributions of the stack and battery towards meeting vehicle’s energy requirement at the end of the drive cycle. Also, a close match between reality and simulation with regard to fuel cell net power and hydrogen consumption validates the modifications in fuel cell system model and specifications used. The corrected fuel cell power request as discussed in section 2.2.4 also contributes to the reasonably accurate results. The comparison can also be observed in Table 2.1 which quantifies the errors with respect to important vehicle output parameters. A hybrid vehicle is a fairly complicated system and there are many sources of errors involved within a vehicle simulator. Therefore error values within 10 % are quite acceptable. The good agreement between traction and regenerative energy is due to the changes made within the hybrid controller.
Table 2.1 Quantitative Comparison corresponding to Drive Cycle 1 Vehicle
Simulation
Data
Output
Battery Energy (Wh)
5885
5728
2.7
Fuel Cell Net Energy (Wh)
10050
9191
8.5
Fuel Cell Gross Energy (Wh)
12250
11713
4.4
Traction Energy Input (Wh)
18423
17267
6.3
Energy Recovered (Regenerative Braking) (Wh)
6207
6857
10.5
Battery State of Charge Drop
0.126
0.13
2.9
Hydrogen Consumed (Kg)
0.7084
0.6786
4.2
Output Parameters
48
Error (%)
Drive Cycle 2 During the second test run the vehicle made six trips of a selected route and drove a total of 24 miles for 100 minutes (Figure 2.10 & 2.11).
Figure 2.10 Aerial view of second test run
49
Test Drive Cycle (100 minutes, 24 miles) 20 18 16
Speed (m/sec)
14 12 10 8 6 4 2 0
0
1000
2000
3000
4000
5000
6000
Time (secs) Figure 2.11 Drive Cycle profile of second test run The second drive cycle involves a different power management strategy. Both the simulation and real data in figure 2.12 show a decline of battery SOC from an initial state of 0.6. At a threshold SOC value the fuel cell is turned on and a constant power request of is sent to the stack. Similarly, the variation of battery SOC and fuel cell parameters in figure 2.12, and quantitative comparison of key vehicle parameters in table 2.2 validate the simulator output.
50
Battery State of Charge
Simulation Result Vehicle Test Data
0.55 0.5 0.45 0.4 0.35 0
1000
2000
3000
4000
5000
6000
5000
6000
5000
6000
5000
6000
Time (s)
Fuel Cell Gross Power (kW)
12 10 8 6 4 2 0 0
1000
2000
3000
4000
Time (s)
Fuel Cell Net Power (kW)
10 8 6 4 2 0 -2 0
1000
2000
3000
4000
Time (s)
Hydrogen Consumption (kg)
1 0.8 0.6 0.4 0.2 0 0
1000
2000
3000
4000
Time (s)
Figure 2.12 Comparison of simulation output and vehicle data for Drive Cycle 2 51
Table 2.2 Quantitative comparison corresponding to Drive Cycle 2 Vehicle
Simulation
Data
Output
Battery Energy (Wh)
9493
10116
6.6
Fuel Cell Net Energy (Wh)
13503
13584
0.6
Fuel Cell Gross Energy (Wh)
17186
16952
1.4
Traction Energy Input (Wh)
26356
26264
0.3
Energy Recovered (Regenerative Braking) (Wh)
7877
7408
6
Battery State of Charge Drop
0.198
0.1885
4.8
Hydrogen Consumed (Kg)
0.9063
0.2
5.8
Output Parameters
Error (%)
One of the major sources of error in the hybrid simulator is the force calculation at the cockpit in the hybrid controller subsystem which uses a basic and commonly used formula (equation 2.16). In order to enhance accuracy, an advanced model for drag and frictional force calculation is needed along with precise drag and friction coefficient values. Slight inaccuracies in drive cycle information obtained from the GPS are another source of error. In the earlier part of the chapter the importance of component specifications and physics-based modeling was emphasized. Some of the deviations between simulation results and vehicle data could be reduced with detailed component maps and advanced models. For example, the simulator uses a constant value of traction motor efficiency which in reality depends upon the motor torque and speed. A detailed torque, speed, efficiency map can improve the prediction of traction motor electrical power demand.
52
There is definitely scope to achieve higher accuracy with the power train simulator, but the level of accuracy achieved during this exercise for system level simulations is very reasonable, and the simulator can be used for further optimization studies in this modified form (Chapters 3 and 4).
2.5 Summary This chapter presented the LFM simulator and identified avenues of improvement. The individual subsystem models were discussed and major additions and improvements were highlighted. In particular, the fuel cell, battery, and hybrid controller were revisited. The data pertaining to different subsystems were updated according to the observations from real time data. The simulator was then validated against two test drive cycles. The comparison between simulator output and vehicle data demonstrated good agreement between the two. The vehicle energy requirements (traction + accessory) were predicted with reasonable accuracy. In particular, by virtue of the extensive fuel cell system model, the simulator showed good results with respect to fuel cell parameters (net power, gross power, voltage, current, and fuel consumption). The possible sources of errors were discussed and it was concluded that LFM can be used as a reliable tool for design and optimization studies.
53
3
Prediction-based optimal power management in a fuel cell/battery plug-in hybrid vehicle
3.1
Introduction
This chapter presents a prediction-based power management strategy for fuel cell/battery plug-in hybrid vehicles with the goal of improving overall system operating efficiency. The main feature of the proposed strategy is that, if the total amount of energy required to complete a particular drive cycle can be reliably predicted, then the energy stored in the onboard electrical storage system can be depleted in an optimal manner that permits the fuel cell to operate in its most efficient regime. The strategy has been implemented in LFM and its effectiveness was evaluated by comparing it with a conventional control strategy. A sensitivity analysis has also been conducted to study the effects of inaccurate predictions of the remaining portion of the drive cycle on hydrogen consumption and the final battery state-of-charge. Finally, the advantages of the proposed control strategy over the conventional strategy have been validated through implementation in the University of Delaware’s fuel cell hybrid bus with operational data acquired from on-board sensors. Power management strategies have been a subject of study in both fuel cell and IC engine hybrids. Rodatz et al. [7] proposed a control strategy (equivalent consumption minimization strategy) to determine the real-time optimal power distribution. Peng et al. [8] formulated a combined power management/design optimization approach and proposed a parameterizable and near-optimal controller for power management optimization using a stochastic dynamic programming algorithm. Paladini et al. [6] performed an optimization of vehicle configuration and control strategy to minimize
54
hydrogen consumption while sustaining battery state-of-charge. Paladini et al. [9] have performed control strategy optimization for charge-sustaining operation of batteries and have reported good fuel economy and final battery state of charge (SOC) for a fuel cell/battery hybrid system. These proposed control strategies are aimed towards fuel savings for a charge-sustaining operation in Hybrid Electric Vehicles (HEVs). This chapter is aimed at optimizing the control strategy for a charge-depletion operation while maintaining safe power requests to the fuel cell. As stated earlier, the objective of the charge-depletion operation is to exploit the energy stored in the onboard electrical storage system in an optimal manner such that the fuel cell is able to operate in its most efficient regime. In addition to efficiency, any power management strategy must also maintain operating conditions that prolong the life of the fuel cell system. Specifically, it is well known that the transient nature of the power load can influence fuel cell durability and its long-term performance. For example, Kusoglu et al. [10] have shown that the proton exchange membrane can undergo compressive, plastic deformation due to hygrothermal loading, resulting in residual tensile stresses after unloading. These residual in-plane stresses in the membrane may explain the occurrence of cracks and pinholes in the membrane under cyclic loading. Pei et al. [11] have studied the effects of four different kinds of operating conditions on the fuel cell and have concluded that 56% of deterioration is due to load-change cycling and 33% due to start-stop cycling. Furthermore, frequent exposure of the cells to high voltages typical of open circuit conditions can accelerate membrane and catalyst degradation [12]. It is therefore desirable that the hybrid controller sends a stable power request to the fuel cell stack and avoids frequent load changes and multiple starts and stops of the stack.
55
The primary factors that affect the life of a battery pack are storage conditions, charge and discharge control, and depth-of-discharge. Fast charge and discharge are inevitable when the batteries operate within an automotive drivetrain. The permissible depth-of-discharge and hence the available energy density is an important factor that decides the suitability of batteries in HEVs and Plug-in Hybrid Electric Vehicles (PHEVs). Some batteries, such as NiMH, are suitable for powering HEVs in which the energy from the fuel is used to keep the batteries charged up. In such applications the battery cycle life is conserved by cycling to shallow depths-of-discharge. This mode of operation is termed as charge sustaining. For application in plug-in hybrid vehicles, batteries must be deep-discharge, long cycle-life batteries [13]. Recent advancements in Li-ion technology have led to the development of Lithium-titanate batteries which have higher energy density, more than 12000 cycles (at 100% depth-of-discharge) and life expectancy of 20 calendar years [14] and thus are quite suitable for use in plug-in hybrids. The Nickel Cadmium (NiCad) battery, if cycled to a certain shallow depth-ofdischarge for a large number of cycles may not yield a storage capacity as large as that corresponding to normal discharge-charge cycles [15,16]. A phenomenon known as “memory effect” occurs due to a sudden depression of voltage as a result of highly repetitive patterns of use [16]. While the effect is completely reversible, it requires a dedicated and lengthy maintenance schedule [17]. It has therefore been found that it is best to discharge the NiCads as deeply as possible at the end of the drive cycle, followed by slow recharge to 100% state-of-charge thus reducing the need for maintenance cycles. Therefore, despite a limited cycle life (1200 cycles) this renders the NiCads suitable for use in PHEVs.
56
The following sections describes the analysis, implementation and validation of a prediction-based power management strategy that reduces fuel consumption while managing power flow in a manner that promotes fuel cell stack life and performance, while depleting the battery to a desired state-of-charge at the end of the drive cycle. The main feature of the proposed strategy is that, if the total amount of energy required to complete a particular drive cycle can be reliably predicted, then the energy stored in the battery pack can be depleted in an optimal manner that permits the fuel cell to operate in its most efficient regime. The following sections describe the methodology and algorithm of the proposed strategy, LFM simulation results including a sensitivity analysis, and validation of the simulation results by an actual implementation of the proposed strategy in our first fuel cell bus.
3.2
Power Management Strategy
Power flow from onboard energy sources has to be managed in order to maintain the battery SOC at a desired level. It is assumed that the battery is charged to a state of 0.75 at the start of a drive cycle in our LFM simulations. It has been observed that at SOCs higher than 0.75, the charging reaction in the NiCad battery is accompanied by the initiation of a side reaction and a limited ability to recover energy due to regenerative braking. The LFM simulator does not model this phenomenon and hence, the initial SOC is set to 0.75. Ordinarily, a charge depletion operating mode can be achieved by driving all electric until the battery is depleted to the desired SOC, followed by turning on the fuel cell system to sustain the battery at the desired SOC. This power management
57
strategy, denoted as the baseline strategy, is depicted in Figure 3.1. The following relations hold for this mode, Fuel cell turn-on condition: SOC (t ) ≤ SOCd Fuel cell power request:
P (t ) = Pavg + α ( SOCd − SOC (t ))
(3.1)
where Pavg is the power consumption of the traction motor and accessory load combined, averaged over a moving time frame (one hour in this case), SOCd is the SOC to which the battery is desired to be depleted, and α is a constant in the correction term which alters the power request based on the deviation of the real time SOC from the desired value. The value of α used in the current simulations is 600,000 W. Hence, if the SOC differential is 1% for example, then the fuel cell power request is incremented by 6 kW over Pavg . The overall performance of this strategy is relatively insensitive to the value of
α. For instance, the only effect resulting from a smaller α would be somewhat larger fluctuations in the subsequent time trace of SOC because the fuel cell would take longer to restore the SOC to the desired value.
58
Battery State of Charge
0.7
Baseline strategy Prediciton based strategy
0.6 0.5 0.4 0.3 0.2
0
1000
2000
3000
4000
5000
6000
7000
8000
5000
6000
7000
8000
Fuel Cell Net Power (kW)
Time (secs)
30 20 10 0 0
1000
2000
3000
4000
Time (secs)
Figure 3.1 Battery SOC drop and fuel cell net power corresponding to the baseline and the predictive control strategy for SC03 (~2 hours, 46 miles)
Such a power management strategy suffers from a lack of control over the operating point of the fuel cell stack. For example, referring to Figure 3.1, it is possible that when the fuel cell needs to be turned on, the fuel cell power request is higher than Pηmax , the value at which the fuel cell efficiency is maximized. This is because the power request to the stack is essentially governed by the average power demand of the drive cycle and the deviation of the battery SOC from the desired level. Consequently, this baseline power management strategy does not yield the highest possible fuel efficiency as the fuel cell will be operating at lower efficiency. We will use this baseline
59
strategy as a benchmark to compare the results from the prediction-based strategy which can deliver higher efficiencies as proposed below. Transit buses have been the most widely chosen platforms for fuel cell technology demonstration for a number of reasons as outlined in [18]. The proposed prediction-based power management strategy uses a priori knowledge of the driving route that would be typically available in transit applications and hence is particularly well suited for transit buses. This information can be exploited to manage power flow from onboard energy sources and achieve the following objectives: •
Operate the fuel cell stack in an efficient zone.
•
Reduce fuel consumption.
•
Send a smooth power request to the stacks and operate them without multiple starts and stops or frequent load changes.
•
Discharge the battery to a desired state-of-charge at the end of the drive cycle.
3.3 Methodology and Algorithm The key to meeting the objectives stated above, is the knowledge of the expected net energy, E fc ,net , required from the fuel cell stack, which will also be referred to as the predictive parameter in this chapter. This can be achieved either with the help of simulation software and a priori knowledge of the drive cycle or from data acquired in real time during an excursion of the drive cycle. Now, the ideal way to meet this energy demand is to draw net power from the fuel cell system such that the stack functions at peak efficiency. This logic is implemented in the prediction-based strategy by determining the stack turn-on time and net power request as outlined in the following
60
algorithm. It should be noted that battery also contributes to the energy requirement of the vehicle. However, only the fuel cell energy is considered in the equations because the goal is to maximize operating efficiency of the fuel cell system.
The fuel cell stack is turned on and continues to operate the moment the following condition is met:
E fc ,net t ≥ Tcycle − + δ corr Pη max
(3.2)
The power request is given by
Power Request =
E fc ,net Tcycle − tturn on − δ corr
(3.3)
If the battery SOC reaches SOCd at any point during the drive cycle, the battery is operated in charge-sustaining mode for the rest of the drive cycle as has been discussed while introducing the baseline approach. The net power request and implementation condition is given by
Power Request = Pavg + α ( SOCd − SOC (t )) where
If SOC (t ) ≤ SOCd
(3.4)
t is the current time tturn on denotes the time when the stack is turned on Pηmax is the net fuel cell power corresponding to maximum system efficiency E fc ,net is the energy requirement from the fuel cell for the duration of the drive
cycle
δ corr is a correction time to start the stack earlier so as to account for the deficit in power supply during ramp up and is equal to half of the ramp up time
61
Tcycle is the total duration of the drive cycle
E fc ,net The term + δ corr denotes the time for which the stack should be operated with a Pη max net power supply of Pηmax to meet the energy requirement E fc ,net . The conditions stated in Equations (3.2) and (3.3) can be understood by considering three cases that arise. They are
E fc ,net Case 1: Tcycle > + δ corr implies that the duration for which the stack needs to Pη max operate is less than the total duration of the drive cycle. As the drive cycle progresses, time t increases from 0 (at the start) until it reaches the value
E fc ,net tturn on = Tcycle − + δ corr which is when the stack turns on and continues to operate Pη max till the end of the drive cycle. Substituting for tturn on in Equation (3.3) we obtain Power Request = Pηmax . This is exactly the desired objective.
E fc ,net Case 2: Tcycle = + δ corr implies that the duration for which the stack needs to Pη max operate is equal to the duration of the drive cycle. Therefore,
E fc ,net tturn on = Tcycle − + δ corr = 0 and Power Request = Pηmax . Pη max
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E fc , net Case 3: Tcycle < + δ corr implies that the duration for which the stack needs to Pη max operate is greater than the duration of the drive cycle. The earliest the stack can start is at the beginning of the drive cycle, t = 0 . This condition is enforced by the inequality of Equation (3.2). An obvious deduction is that the energy requirement E fc ,net is met by drawing net fuel cell power which is higher than Pηmax and is given by Equation (3.3) with
tturn on = 0. It should be noted that the implementation of charge-sustaining operation (equation 18) ensures that the stack is operating at required power the moment the battery state of charge drops down to SOCd thus safeguarding against the danger of draining the battery completely due to a delayed turn on time, obtained from the condition specified in Equation (3.2). Such a miscalculation in stack turn on time can result from inaccurate prediction of E fc ,net and will be discussed in the following sections.
3.4 Simulation Results The proposed power management strategy has been implemented in the LFM simulation software and compared with the baseline approach for drive cycles of different lengths which have been created by simply repeating the standard cycle multiple times as shown in Figure 3.2. Figure 3.1 demonstrates the difference between the predictive strategy and the baseline approach for the dual stack bus. Based on the prior information of net energy requirement from the fuel cell, it can be seen that the fuel cell stack was turned on at an earlier time within the drive cycle such that the power requirement corresponds to the
63
maximum efficiency point of the fuel cell system. The earlier start time of the stack results in a slower rate of SOC drop from the moment the stack begins to operate. SC03 (~2hours, 46 miles) Speed (m/sec)
25 20 15 10 5 0
0
1000
2000
3000
4000 5000 Time (secs)
6000
7000
8000
UDDS (~2hours, 45 miles) Speed (m/sec)
30 25 20 15 10 5 0
0
1000
2000
3000
4000 5000 Time (secs)
6000
7000
8000
Figure 3.2 Longer drive cycles formed by repeating standard cycles
Fuel consumption, average fuel cell operating efficiency, and final battery stateof-charge are reported in Tables 1 and 2. A comparison of average fuel cell operating efficiency between the two control strategies indicates that prediction-based power management allows the stack to operate in a more efficient regime thereby reducing fuel consumption. The final battery SOC is within 3% of the desired value (0.3). The extent of fuel savings is evidently dependent upon the average operating efficiency of the fuel cell system with the baseline control strategy. For example, the average efficiency corresponding to the SC03 (supplemental drive cycle for number 3 for federal test 64
procedure) driving schedule is 41.5% as opposed to 44.5% for UDDS (Urban Dynamometer Driving Schedule). This explains the relatively higher fuel savings when the new power management strategy is applied to SC03.
Table 3.1 Comparison of prediction-based and baseline strategy for SC03 as shown in Figure 3.2 Drive Cycle
Prediction Based
Output Parameters
Length ~2 Hours 46 miles
~3 Hours 68 miles
~5 Hours 111 miles
~7 Hours 154 miles
Strategy
Baseline
Fuel
Strategy
Savings (%) 13.65
Hydrogen Consumption (kg)
1.5855
1.8362
Average FC System Efficiency (%)
47.62
40.23
Final Battery SOC
0.3003
0.2931
Hydrogen Consumption (kg)
3.1866
3.6466
Average FC System Efficiency (%)
47.71
41.29
Final Battery SOC
0.298
0.2935
Hydrogen Consumption (kg)
6.4971
7.2621
Average FC System Efficiency (%)
47.03
41.85
Final Battery SOC
0.295
0.294
Hydrogen Consumption (kg)
9.9537
10.8776
Average FC System Efficiency (%)
46.13
42.03
Final Battery SOC
0.2938
0.2935
12.32
10.53
8.49
It should be noted that the preceding results have been generated using the same drive cycle which was also used for obtaining the parameter E fc ,net . Therefore, the predicted value of net fuel cell energy is identical to the actual value. However, in reality two realizations of the same route could lead to different drive cycles (velocity vs. time profile) due to factors that cannot be completely predicted such as instantaneous traffic
65
conditions and ridership. Consequently, the net energy delivered by the fuel cell stack during one excursion on a chosen route may differ from the value obtained during a different excursion on the same route. These variations can lead to an inaccuracy in the predicted parameter and its effect has been studied by means of a sensitivity analysis in the following section.
Table 3.2 Comparison of prediction-based and baseline strategy for UDDS as shown in Figure 3.2 Drive Cycle
Prediction Output Parameters
Based
Length ~2 Hours 45 miles
~3 Hours 60 miles
~5 Hours 104 miles
~7 Hours 142 miles
Strategy
Baseline Strategy
Hydrogen Consumption (kg)
1.3383
1.4309
Average FC System Efficiency (%)
47.69
44.49
Final Battery SOC
0.3033
0.3007
Hydrogen Consumption (kg)
2.3946
2.5724
Average FC System Efficiency (%)
47.8
44.48
Final Battery SOC
0.3037
0.3009
Hydrogen Consumption (kg)
5.5909
5.9937
Average FC System Efficiency (%)
47.86
44.49
Final Battery SOC
0.3099
0.3009
Hydrogen Consumption (kg)
8.292
8.8448
Average FC System Efficiency (%)
47.62
44.49
Final Battery SOC
0.3097
0.3009
Fuel Savings (%) 6.47
6.91
6.72
6.25
Sensitivity Analysis: The inconsistency in E fc ,net can be modeled by varying the prediction parameter corresponding to a given drive cycle and then using the modified value in the predictive control strategy for the same drive cycle. The effect of such an inaccuracy has been
66
studied by varying the parameter by the following percentages (-15%, -10%, -5%, 5%, 10%, 15%) to reflect different degrees of inaccuracy. The modified value is then inserted into by the prediction-based strategy in order to calculate fuel cell turn-on time and determine the power request. Modifying E fc ,net by -x% implies that we are intentionally under predicting the parameter value such that it is smaller than the correct value by x%. Modifying E fc ,net by -15% results in under prediction of fuel cell net energy required to execute the chosen drive cycle. Consequently, the fuel cell turns on later than it should and the battery depletes to the desired SOC before reaching the destination as
Battery State of Charge
shown in Figures 3.3 and 3.4.
0.7
Under Prediction -15% Over Prediction 15% Accurate Prediction Baseline Control
0.6 0.5 0.4 0.3 0.2
0
1000
2000
3000
4000
5000
6000
7000
8000
5000
6000
7000
8000
Fuel Cell Net Power (W)
Time (secs) x 10
4
3 2 1 0 0
1000
2000
3000
4000
Time (secs) Figure 3.3 Deviation in battery SOC drop and fuel cell net power corresponding to
inaccuracy in prediction for the SC03 (~2 hours, 46 miles) 67
On reaching the desired SOC the strategy switches to charge-sustaining mode in accordance with the control algorithm such that the net energy supplied by the fuel cell is still equal to the original, unscaled, E fc ,net value. However, the average operating efficiency decreases because, late in the cycle, the fuel cell is required to produce power at a higher rate at which its efficiency is lower than the maximum possible efficiency. Similarly, scaling E fc ,net by 15% results in an over prediction of fuel cell net energy. But, unlike under prediction, in case of an over prediction, the net energy supplied by the
Battery State of Charge
stack is greater than required. Consequently the terminal battery SOC stays higher than
Under Prediction -15% Over Prediction 15% Accurate Prediction Baseline Control
0.7 0.6 0.5 0.4 0.3 0.2
0
1000
2000
3000
4000
5000
6000
7000
8000
6000
7000
8000
Fuel Cell Net Power (W)
Time (secs) x 10
4
3 2 1 0 0
1000
2000
3000
4000
5000
Time (secs)
Figure 3.4 Deviation in battery SOC drop and fuel cell net power corresponding to
inaccuracy in prediction for the UDDS (~2 hours, 45 miles)
68
the desired SOC and fuel savings decline (Figures 3.3 and 3.4). In both cases of inaccurate drive cycle predictions, it is of interest to analyze fuel savings with respect to the baseline control strategy which is shown in Figures 3.5 and 3.6. A decrease in the magnitude of fuel savings is observed for increasing degree of under prediction. For a 74 km (46-mile) SC03 drive cycle, for example, the savings are reduced to 11.39 % for an under prediction of -15 % as opposed to 13.65 % for accurate prediction (Figure 3.5). The reason, as has been stated earlier, is attributed to a decrease in average operating efficiency of the fuel cell system. A similar trend is observed for
Fuel Savings (%)
15 10 5 0 -5
Final Battery SOC
-10
~2hours, 46 miles
~3hours, 68 miles
~5hours, 111 miles
~7hours, 154 miles
~2hours, 46 miles
~3hours, 68 miles
~5hours, 111 miles
~7hours, 154 miles
0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
-15% UP
-10% UP
-5% UP
AP
5% OP
10% OP
15% OP
Figure 3.5 Fuel savings and final battery SOC for varying degree of inaccurate predictions and for variable drive lengths for the SC03 driving schedule UP - Under Prediction, AP – Accurate Prediction, OP – Over Prediction
69
drive cycles of increasing lengths. However, the key inference from this part of study is that, the fuel savings are still positive; i.e. there is still an overall reduction in hydrogen consumption as compared to the baseline strategy while maintaining the battery SOC close to the desired level (within 3%). As expected, the magnitude of improvement diminishes with increasing amounts of under predictions. Increasing the degree of over prediction also results in a decline in fuel savings. However, in this case, the decline occurs because the fuel cell provides more energy than what is required with the result that the battery is not discharged to the desired level. For
Fuel Savings (%)
10 5 0 -5 -10
~2hours, 45 miles
~3hours, 60 miles
~2hours, 45 miles
~3hours, 60 miles
~5hours, 104 miles
~7hours, 142 miles
~5hours, 104 miles
~7hours, 142 miles
Battery Final SOC
0.8 0.6 0.4 0.2 0
-15% UP
-10% UP
-5% UP
AP
5% OP
10% OP
15% OP
Figure 3.6 Fuel savings and final battery SOC for varying degree of inaccurate predictions and for variable drive lengths for the UDDS driving schedule UP - Under Prediction, AP – Accurate Prediction, OP – Over Prediction
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a 74 km (46-mile) drive cycle of SC03, the terminal battery SOC is 0.35 for a 15% over prediction compared to an SOC of 0.3 for an accurate prediction (Figure 3.5). For drive cycles of greater lengths the terminal battery SOC increases. This not only leads to a decrease in fuel savings, but may also result in higher fuel consumption compared to the baseline approach. Hydrogen consumption can be expected to be higher in comparison to the baseline strategy in the case of over prediction and the probability increases with the degree of over prediction and the drive cycle length. It should be noted that for each drive cycle considered in the present work, the average power required to sustain battery SOC, Pavg is greater than Pηmax . This is expected for cost-effective power source configurations where the fuel cell is down-sized compared to the battery pack and is just enough to meet the average power requirement of urban transit drive cycles [18]. If however, Pavg is less than Pηmax , the situation always degenerates to the baseline control strategy as depicted in Figure 3.7.
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SOCi
ABC – Baseline Strategy
A
E
SOCd
C
B D
Figure 3.7 Possible SOC profiles corresponding to the condition Pavg < Pηmax
Trajectory ADC shows the variation of SOC with time for Pavg < Pηmax if a predictionbased strategy is followed without enforcing the charge-sustaining mode at SOCd . Evidently, the SOC reaches the desired level at B before the turn-on time at D as calculated by Equation 16. Since it is not desirable to let the SOC fall below SOCd , the charge-sustaining mode comes into effect at B which implies no fuel savings as the fuel cell power is below the level at which efficiency is maximized. An alternative approach, depicted by trajectory ABEC is to turn on the stack when the desired SOC is reached (at B) and draw E fc ,net amount of energy at Pηmax before shutting it down (at E). In this manner, the stack can be operated at peak efficiency with additional savings in fuel.
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3.5 Validation Both the prediction-based and baseline power management strategies were evaluated by implementing them on the University of Delaware’s fuel cell/battery hybrid bus. The vehicle selected for this test was UD’s first fuel cell bus; as described earlier it is equipped with a single stack rated at 19.4 kW and 60 kWh of NiCad batteries. The test was conducted by driving the bus on a defined route (Figure 3.8) on two separate days, first with the baseline control strategy, and next with the prediction-based strategy.
Figure 3.8 Aerial view of the trajectory traced by the fuel cell hybrid bus
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During each test run the vehicle made six trips on the route and drove a total of 38.6 km (24 miles) for 100 minutes. The route includes two bus stops and the duration of each round trip is matched to a typical time-bound transit operation. The drive cycle (Figure 3.9) includes high and low speed segments with an average of 23.3 km/h (14.5 mph).
Test Drive Cycle (100 minutes, 24 miles) 20 18 16
Speed (m/sec)
14 12 10 8 6 4 2 0
0
1000
2000
3000
4000
5000
6000
Time (secs) Figure 3.9 Profile of the test drive cycle
The initial and desired SOC were chosen to be 0.6 and 0.4 respectively, which allowed the control strategies to be tested on a drive cycle of smaller distance and duration. While operating with the baseline control strategy, the fuel cell was turned on when the SOC reached 0.41 (Figure 3.10). This allowed for some warm up time so that the stack could ramp up and provide 13.5 kW of net power in order to sustain the battery
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at 0.4 SOC. In Figure 3.10 the periodic sharp declines in SOC correspond to high power demands when the vehicle executes the high speed segment of the drive cycle. On the other hand, frequent occurrences of SOC rise are attributed to cell charging while the vehicle is idling at a bus stop or a traffic intersection. The optimal net fuel cell power of the test vehicle was obtained experimentally as 9 kW with a corresponding fuel cell system efficiency of 45.9 %.
Battery State of Charge
Test Drive Cycle 0.6
Baseline Strategy Prediction Based Strategy
0.55 0.5 0.45 0.4 0.35 0
1000
2000
3000
4000
5000
6000
4000
5000
6000
Fuel Cell Net Power (W)
Time (secs) 15000 10000 5000 0 -5000
0
1000
2000
3000
Time (secs)
Figure 3.10 Battery SOC drop and fuel cell net power corresponding to baseline and predictive control strategy
The optimal power along with the net energy spent by the fuel cell during the first run (baseline strategy) was used as an input to determine the stack turn on time for the second run that employed the prediction-based strategy. Figure 12 shows that for the
75
prediction-based strategy the stack turned on earlier and operated at stable optimal power for the rest of the drive cycle. A quantitative comparison of the key output parameters confirms the benefits of using the proposed power management (Table 3). Through intelligent management of energy flow and with no additional costs, the stack was operated at higher efficiency resulting in 11.7 % savings in fuel consumption. Moreover, the stable operation of the fuel cell system also extends the life of the stack. The battery SOC at the end of the drive cycle was close to the desired lower limit, which is one of the considerations for plug-in hybrid operation.
Table 3.3 Comparison of prediction-based and baseline strategy for test drive cycle Prediction Output Parameters
Based Strategy
Baseline
Fuel Savings
Strategy
(%) 11.7
Hydrogen Consumption (Kg)
0.9063
1.0124
Average FC System Efficiency (%)
44.7
39.5
Final Battery SOC
0.4115
0.402
3.6 Summary and Conclusions A new prediction-based power management strategy for fuel cell/battery plug-in hybrids has been proposed and implemented in the LFM simulation software. Simulation results for the prediction-based strategy showed significant improvements in fuel cell system efficiency and reduction in hydrogen consumption compared to a conventional, baseline strategy of charge sustenance. The importance of a stable power request to the fuel cell has been stated and realized. A sensitivity analysis was conducted to study the effects of
76
inaccurate predictions. Results indicate that under prediction reduces the magnitude of fuel savings, and in the borderline case, may show results identical to the baseline strategy. A large degree of over prediction, on the other hand, may even lead to higher fuel consumption than the baseline strategy while resulting in a higher terminal battery SOC than desired. A conservative approach may therefore be adopted by using a downscaled predicted parameter value, which results in fuel savings that may be less than the maximum possible but will safeguard against entering into the over predicted zone and the associated risk of increased fuel consumption. The implementation of the proposed strategy and its comparison with the baseline control strategy in a fuel cell and battery powered hybrid bus has confirmed the benefits predicted from simulation studies. Finally, good agreement between the simulator outputs and data acquired in real time confirms the validity of the power-train simulator.
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4
Reduced battery stress through blended energy storage
4.1
Introduction
The estimated lifetime of the battery is an important consideration while designing a hybrid power train for automotive applications. The objective of the present work is to use our validated LFM simulation tool to evaluate one approach to reduce battery loads by adding an ultracapacitor module, and thereby enhance battery lifetime. Batteries and ultracapacitors are the most commonly used energy storage systems (ESS) in hybrid electric vehicles. Batteries usually have high energy density but limited power density, while ultracapacitors (Ucaps) have high power density but low energy density. Due to these complementary properties, batteries can be combined with Ucaps to create a lightweight, compact ESS that exhibits a good compromise between energy and power densities. Another significant difference between the two systems is their cycle life. Batteries typically lose their effectiveness after a few thousand charge-discharge cycles. The best cycle life for commercial battery systems is that of Altairnano’s LithiumTitanate cells which have shown up to 12000 cycles (100% depth of discharge) at 2C charge and discharge, and 25 °C (Table 4.1). Charge and discharge current of a battery is typically measured in C rates. A current rate of 1C is equal to the current required to fully charge the battery to its rated capacity in one hour; a current rate of nC is n times the current at 1C. In contrast, Ucaps are able to maintain performance for about one million cycles. Table 4.1 compares an advanced technology battery with an Ucap. Storage system lifetime is therefore another metric which can be enhanced by employing a suitable combination of the two energy storage systems.
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Table 4.1 Comparison of advanced technology battery and Ucap Altairnano (LiTi cell)
Maxwell Ultracapacitor
Peak W/kg*
760
5900
Wh/kg
72
5.96
Cycle Life
>12000 cycles at 100 % DoD** (2C rate and 25 °C) >4000 cycles at 100% DoD (1C rate and 55 °C)
1 million cycles at 50 % DoD
* The peak powers are calculated based on peak pulse currents of the ESS which may not be allowed by the traction inverter ** DoD is Depth of Discharge Yang et al. have mentioned that stress factors such as temperature, SOC swing, current load (C rate), energy throughput, and also calendar time affect the cell degradation rate [9]. Amongst these factors, the adverse effects of current load (C rate) and energy throughput can be mitigated by using an Ucap to share the load with batteries. In the literature, batteries and Ucaps have been considered separately on most occasions while studying the hybrid powertrain. Gao [20] developed and implemented a fuzzy logic based energy management on a fuel cell/battery/Ucap hybrid bus. Bauman and Kazerani [21] performed optimization studies on fuel cell/battery/Ucap vehicle to find the optimal configuration with respect to acceleration performance, fuel economy, and cost. Blended energy storage has rarely been studied with the objective of reducing stress on the battery and improving its lifetime. The goal of this chapter is to investigate and compare the battery stress for a battery-only ESS with a blended ESS (battery+Ucap) using our previously validated LFM simulation tool. The specific objective here is to conduct an analysis to quantify how a blended ESS relieves the load on the battery and thereby extends its life. We begin
79
by describing the blended ESS topology, energy management scheme, and energy storage details, followed by the simulation results.
4.2
Blended ESS topology and energy management
The vehicle platform used for this analysis corresponds to the 22-ft UD fuel cell bus described in chapter 1. The analysis is conducted for a battery-only ESS, followed by a blended ESS. The hardware and energy management schemes for each are described next.
4.2.1 Battery-only ESS The drivetrain topology for a fuel cell/battery series hybrid vehicle is shown in figure 4.1. As described earlier, this drivetrain corresponds to the UD fuel cell bus. While the ESS on the bus currently consists of NiCd batteries, the analysis presented in this chapter employs LiTi batteries. Power from the battery and fuel cell feeds the traction motor and the accessory load. Note that power flow is bidirectional in the traction motor and battery. The battery can accept power from either the fuel cell, or the traction motor during regenerative braking. The fuel cell is rated at 20 kW and the battery pack comprises 144 Altairnano (50Ah Li-Ti) cells (Table 4.2).
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Unidirectional flow
Fuel Cell
Bidirectional flow
DC/DC Converter
Traction Motor
Battery
Accessory Load
Figure 4.1 Topology of a fuel cell/battery hybrid
Table 4.2 Battery Description Altairnano (50 Ah cells) Number of cells
144
Max/Min Voltage
400/240 V
Max. Current
300 A
Max. Power
120 kW
Available Energy
16.5 kWh
Hybrid Energy Management: The fuel cell net power is given by
PFC ,net = Pavg + α ( SOCd − SOCc )
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(4.1)
where Pavg is the combined power consumption of the traction motor and accessory load averaged over a moving time frame (one hour in this case), SOCd is the SOC to which the battery is desired to be depleted, and α is a constant in the correction term which alters the power request based on the deviation of the real time SOC ( SOCc ) from the desired value. The battery power is given by
PBattery = ( Ptract + Pacc − PFC ,net )
(4.2)
where Ptract and Pacc are the power consumption of the traction motor and accessory load, respectively. Note that Ptract is negative during regenerative breaking.
4.2.2 Blended ESS The topology of a series hybrid with blended ESS is shown in Figure 4.2. This hybrid system includes an Ucap module in addition to the battery and the fuel cell. Since the Ucap operating voltage (50V to 120V) is smaller than the bus voltage (240V to 400V), a DC/DC converter is added to boost the voltage of the Ucap. For the present analysis, the system uses the same fuel cell and battery as in the case of the fuel cell/battery hybrid described in Section 4.2.1. However, an additional component consisting of a Ucap module is considered here to create a blended ESS. Two Maxwell Ucap modules consisting of 48 and 36 cells are considered as described in Table 4.3. For a given drive cycle, the size of the Ucap module determines the extent of battery load reduction. The battery load is expected to reduce with increasing Ucap module size. The above modules sizes were selected to obtain 25 to 35 kW of average Ucap power which is expected to demonstrate an appreciable degree of battery load sharing.
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Unidirectional flow
Fuel Cell
Bidirectional flow
DC/DC Converter
Traction Motor
Battery
DC/DC Converter
Accessory Load Ultra Capacitor Figure 4.2 Topology of fuel cell/battery/ultracapacitor series hybrid
Table 4.3 Ultracapacitor Description Maxwell (BCAP 3000) Ultracapacitor Number of cells
48
36
Max/Min Voltage
120/60 V
90/45 V
Max. Current
400 A
400 A
Max. Power
48 kW
36 kW
Available Energy
94 Wh
70 Wh
Hybrid Energy Management: The fuel cell net power remains unchanged in the present energy management scheme and is given by Equation 4.1; it should be noted that the SOC here still refers to the
battery state-of-charge. 83
The ultracapacitor power request, PUcap , req is given by
PUcap , req = PESS − Pcutoff
if PESS ≥ Pcutoff
PUcap , req = PESS + Pcutoff
if PESS ≤ − Pcutoff
PUcap , req = 0
otherwise
PESS = ( Ptract + Pacc − PFC ,net )
(4.3) (4.4)
The condition PESS ≤ -Pcutoff arises mostly during regenerative braking when Ptract is negative. It can also occur for small positive values of Ptract. Based on the traction power, accessory power, and fuel cell net power, PESS is the resulting power requirement from the ESS, which in this case is shared by ultracapacitor and battery. Pcutoff is a threshold value beyond which the Ucap starts contributing. Therefore, if PESS is greater than Pcutoff , the extra power request is sent to the ultracapacitor. If this extra power request can be met by the Ucap, then the battery only needs to provide power up to Pcutoff . If, however, the Ucap power supply is limited by its size, the remaining power request is again met by the battery. The actual power supplied or accepted by the Ucap is given by PUcap . The rationale for using a threshold power parameter is to allow the ultracapacitor to contribute only at high power demands and reduce the peak power demand on the battery. The battery power request is given by
PBattery = PESS − PUcap Thus, the remaining energy storage power requirement is met by the battery.
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(4.5)
4.3
Simulation Results
The energy storage performance was simulated on the UD Express Route for battery-only as well as blended ESS topologies using LFM. As stated earlier, the blended ESS analysis was conducted for two Ucap module sizes (36 and 48 cells), and the effect of the parameter Pcutoff was also analyzed.
4.3.1 Simulation results with 48-cell Ucap Figure 4.3 demonstrates the reduction in the frequency of high C-rate current draws from the battery for a 48-cell, Ucap-assisted energy storage system. Our expectation is that the battery in a blended ESS would experience fewer occurrences of current draws within any given range. For example, figure 4.3 shows that a battery-only ESS experiences current draws in the 2C to 4C range during 8.99% of the drive cycle. In contrast, the battery in a blended ESS experiences 2C to 4C currents during only 1.11 to 3.18% of the drive cycle. The frequency of high battery-current draws decreases because of load sharing by the Ucap. Ucaps have very low charge storage capacity (Ah) as compared to the batteries. As the cutoff power is raised from 0 kW to 30 kW, there is further reduction in C rate frequency. Low values of cutoff power can result in situations when the Ucap runs out of available energy while providing nominal power (low drivemotor power demands and accessory load) and has nothing left to contribute if a peak power request is sent by the traction motor. Raising the cutoff power ensures that the ultracapacitor energy is reserved for situations when the power requirement is high, thereby reducing possibilities of premature energy drain out. Therefore, as evident in figure 4.3, higher cutoff powers increase the Ucap’s capability to share peaks loads and
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thus reduce the occurrence of high battery current. It should be noted that in an extreme event when the cutoff power is higher than the peak power requirement, the Ucap will be rendered useless for the entire drive cycle.
Frequency of occurence (%)
Battery current distribution corresponding tp UD Express Route with 48 cell Ultracapacitor 10 9 8 7 6 5 4 3 2 1 0
Battery only
8.99
Battery+Ucap(Pcutoff=0 kW) Battery+Ucap(Pcutoff=10kW)" Battery+Ucap(Pcutoff=20kW) Battery+Ucap(Pcutoff=30kW) 3.18
2.75
2.60 1.11
0.56
0.13 0.07
2C
