1 Copyright 2004 SAE International Energy Storage Requirements for Fuel Cell Vehicles A. Rousseau, P. Sharer, R. Ahluwalia Argonne National Laboratory ABSTRACT Because of their high efficiency and low emissions, fuelcell vehicles are undergoing extensive research and development. As the entire powertrain system needs to be optimized, the requirements of each component to achieve FreedomCAR goals need to be determined. With the collaboration of FreedomCAR fuel cell, energy storage, and vehicle Technical Teams, Argonne National Laboratory (ANL) used several modeling tools to define the energy storage requirements for fuel cell vehicles. For example, the Powertrain System Analysis Toolkit (PSAT), which is a transient vehicle simulation software, was used with a transient fuel cell model derived from the General Computational Toolkit (GCtool). This paper describes the impact of degree of hybridization, control strategy, and energy storage technology on energy storage requirements for a fuel cell SUV vehicle platform. INTRODUCTION Hydrogen fuel cell vehicles are undergoing extensive research and development as a means to address both environmental and oil dependency issues in the United States. Considering the current status of fuel cell technologies, it is very likely that the first fuel cell vehicles will be hybrids. To define the most appropriate energy storage technology for such an application, several FreedomCAR Technical Teams worked to define the future electrochemical energy storage requirements for fuel cell vehicles by using advanced simulation tools. Over several decades, Argonne National Laboratory (ANL) has developed and used a number of computer models in support of the U.S. Department of Energy s (DOE s) advanced automotive R&D program, which has addressed aspects of vehicular life cycles, ranging from design and manufacturing through recycling. Advanced batteries, fuel cells, engines, and vehicle configurations have been developed, tested, and modeled in DOE s facilities at ANL. This combination of analytical, developmental, and testing experience has been applied to several types of advanced vehicle powertrains at the vehicle (PSAT) and fuel cell system (GCtool) levels. In this paper, we describe how GCtool was first used to define fuel cell system characteristics representative of mid-term technologies and the process used to define the impact of hybridization degree, control strategy, and energy storage technology on the requirements by using PSAT. GCTOOL FUEL CELL SYSTEM MODELING GCTool was developed at ANL for steady-state and dynamic analysis of fuel cell systems. It allows users to establish realistic system constraints and conduct constrained optimization studies. The analyses are typically conducted in design or off-design modes, but mixed modes are also permitted. In the design mode, the components are sized to meet specified performance targets. In the off-design mode, GCTool determines the performance of components of a given size and their physical attributes. GCTool has an extensive library of model classes for components and devices that appear in practical energy conversion systems. In particular, the library includes various types of fuel cells (polymer electrolyte, solid oxide, phosphoric acid, and molten carbonate), hydrogen storage devices (compressed gas, liquid hydrogen, metal hydrides, glass microspheres, etc.), catalytic reactors (such as for autothermal reforming, steam reforming, water-gas shift, preferential oxidation, and sulfur removal), and heat exchangers (counterflow, air-cooled condenser, finned radiator, etc.). Several thermodynamic codes are available in GCTool for equations of state of mixtures of gases, liquids, and condensables, which can be used for gaseous (e.g., hydrogen and methane), liquid (methanol, ethanol, octane, etc.), and synthetic fuels (gasoline and diesel). GCTool is focused on design and searches for optimum configurations. The detailed algorithms in GCtool (thermodynamic and chemical transport) are generally inappropriate for use in vehicle studies because of the greatly increased computer run time. For this reason, engineering models of fuel cell systems and components using the GCTool architecture have been developed for vehicle analysis, as has a procedure to automate the linkage to MATLAB-based vehicle codes (i.e., PSAT).
2 PSAT VEHICLE MODELING PSAT is a powerful modeling tool that allows users to evaluate fuel consumption, exhaust emissions, and vehicle driving performance . ANL developed PSAT to study transient effects in future vehicles and the interactions among components with accurate control commands. For this reason, PSAT is a forward-looking model, as it allows users to model with commands. PSAT is also called a command-based model: developed under MATLAB/Simulink, PSAT allows users to realistically estimate the wheel torque needed to achieve a desired speed by sending commands to the different components, such as the throttle for the engine, displacement for the clutch, gear number for the transmission, or mechanical braking for the wheels. In this way, we model a driver who follows a predefined speed cycle. Moreover, as components react to commands as in reality, we can implement advanced component models, take transient effects into account (such as engine starting, clutch engagement/ disengagement or shifting), or develop realistic control strategies. Finally, PSAT has been validated by using several vehicles [2 4]. To automate the GCTool link with PSAT, a translator has been developed to produce a MATLAB/Simulink that is executable from the GCTool model. The GCTool model is written in a C-like language that is interpreted by GCTool. The executable then becomes a member of the drivetrain library in PSAT using an S-function, which can be used for analyzing transient fuel cell system responses during drive-cycle simulations of hybrid vehicles. The executable is specific to the fuel and the system configuration setup in the GCTool model, and a new one must be produced if there is any change in system attributes The methodology has been demonstrated by using direct hydrogen fuel cell systems. VEHICLES DEFINITION (SUV). Only the results with the SUV platform will be presented in this paper. The vehicle s characteristics are provided in Table 1. Table 1. Reference Vehicle Validation Units Test PSAT Vehicle Assumptions Vehicle Mass kg 2104 Glider Mass kg 1290 Engine VL, V6, SOHC, 210hp Frontal Area m Drag Coefficient 0.41 Rolling Resistance Wheel Radius m Model Validation Acceleration (0 60 mph) s Combined Fuel Economy mpg Both fuel economies mentioned in Table 1 are EPAunadjusted values. The fuel economy obtained with PSAT is higher than the reference because the effect of cold start was not taken into account. Several fuel cell vehicles have been defined to provide performance similar to that of the reference vehicle, including 0 60mph acceleration (10.5 s), sustained grade of 6.5% at 55mph, and maximum speed above 100 mph. The goal is to design vehicles with characteristics similar to those of the reference vehicle for customer acceptance. The Federal Urban Driving Schedule (FUDS) and the Federal Highway Driving Schedule (FHDS) have been used in this study. The fuel cell system powertrain, described in Figure 1, includes a fixed ratio in addition to the final drive, as well as DC/DC converters for the high-voltage battery and the 12-V accessories. The fuel cell systems have been designed to provide power for top speed and grade performance and to have 1-s transient response time for a power Three different vehicle platforms have been selected for this study: compact, midsize, and sport utility vehicle
3 DC Link Figure 1. Fuel Cell Powertrain Representation (without vehicle)
4 request change of 10 90% of the maximum power. Moreover, they should reach maximum power in 15 s for cold start from 20 C ambient temperature and in 30 s for cold start from -20 C ambient temperature. As the fuel cell systems defined with GCTool were based upon mid-term technology (2005), the Li-ion technology was selected as the reference energy storage technology. The Saft Li-ion HP6 was used as it was recently tested at ANL, and industry considers it to be state-of-the-art. DEFAULT CONTROL STRATEGY Because of the high efficiency of fuel cell systems, it appears natural not to use the energy storage as the primary power source. Indeed, when comparing the fuel cell system efficiency to the internal combustion engine (ICE), as shown in Figure 2, note that the fuel cell system has high efficiency at low power. For a hybrid ICE, it is interesting to use the battery at low and medium power levels and the ICE at high power levels; that is not the case for fuel cell vehicles. Consequently, the control strategy has been developed so that the main function of the battery is to store the regenerative braking energy from the wheel and return it to the system when the vehicle operates at low power demand (low vehicle speed). The battery also provides power during transient operations when the fuel cell is unable to meet driver demand. Component limits, such as maximum speed or torque, are taken into account to ensure the proper behavior of each component. Battery state-of-charge (SOC) is monitored and regulated so that the battery stays in the defined operating range. The three controller outputs are fuel cell ON/OFF, fuel cell power, and motor torque. A battery state-of-charge equalization algorithm has been used to ensure a fair comparison. To minimize the impact of the variation of SOC, the same values were selected for both the initial conditions and the goal. As shown in Figure 3, the consequence is that the battery will supply the system Efficiency High Efficiency Fuel Cell System Output Power ICE High Efficiency Figure 2. Fuel Cell System Efficiency vs. Internal Combustion Engine Efficiency Figure 3. Default Control Strategy Part of FUDS Cycle 100-kW Fuel Cell Hot Start
5 with the energy that it had just recovered from regenerative braking. For instance, the SOC will go up after regenerative braking, and this recuperated energy will be returned to the vehicle during the next acceleration, thus returning the SOC back to its goal value. In other words, to maintain the SOC goal, the battery does not store any net energy over the cycle. The energy that is recovered during braking is immediately returned to the vehicle during the next acceleration. To implement this aspect of the control strategy, we compare the total power required by the vehicle to a threshold: the minimum power demand needed to use the fuel cell. This control strategy parameter was set by using the PSAT graphic user interface (GUI). More specifically, this control parameter is defined as the sum of the wheel power demand from the driver model (set to zero in the default control strategy) plus an additional power, depending upon the SOC value. If the SOC is above its goal, the additional power will be negative, and, consequently, the fuel cell will be used later. For example, if the SOC is 70%, the value will be zero, but with a higher SOC (71%), the minimum power might be 3 kw, allowing the energy storage to be discharged and return to the SOC goal. IMPACT OF HYBRIDIZATION DEGREES The first step in defining the energy storage requirements consists of selecting the proper hybridization degree. As it has been defined that 160 kw peak electrical power should be provided to the electric motor to obtain performance characteristics similar to those of the reference vehicle, several vehicles were defined for each hybridization degree selected. As shown in Figure 4, four options were selected, from 20 kw energy storage and 140 kw fuel cell (on the left) to 80 kw energy storage and 80 kw fuel cell (on the right). We did not use fuel cell systems with a lower power than 80 kw because it is the minimum power necessary to sustain a 6.5% grade at 55 mph, which was one of the vehicle requirements. For the Li-ion technology and the default control strategy used, the most significant increase in fuel economy is obtained at the lowest hybridization degree (140 kw fuel cell). This large fuel economy increase is mostly due to regenerative braking energy, as shown in Figure 5. A further increase in the degree of hybridization still provides some improvements in fuel economy until we reach the optimum of a 100-kW fuel cell. Fuel economy then starts to decrease. At this point, the decrease in fuel cell system efficiency on the driving cycle is greater than the gain due to regenerative braking. Referring back to figure 2 and the efficiency curve of the fuel cell system, this result is in agreement with the expectations arising from this figure. The fuel cell has a sweet spot at relatively low power. If the average operating point of the cycle falls in this sweet spot, the maximum fuel economy is attained. Downsizing the fuel cell will cause the average operating point to shift to the right. If the initial operating point is before the sweet spot, then downsizing will be advantageous. The operating point will move to the right and enter the sweet spot. (The fuel economy trend in Figure 4 is from 140 kw to 100 kw.) However, additional downsizing will push the operating point farther to the right and out of the optimal efficiency region. (The fuel economy trend in Figure 4 is from 100 kw to 80 kw.) At this point, one observes that the fuel cell has been over-downsized or, to state it another way, the fuel cell vehicle has been over-hybridized Fuel Economy (mpg) FC Only Fuel Cell Pow er (kw) Figure 4. Impact of Degree of Hybridization on Fuel Economy FUDS Cycle
6 R eference C ontrol S trategy (SO C=0.7, P fcdmd = 0) FC system eff Perc enta ge re ge n braking FC HEV 140kW FC HEV 120kW FC HEV 100kW FC HEV 80kW Figure 5. Impact of Degree of Hybridization on Fuel Cell System Efficiency and Regenerative Braking For the component technologies considered, we conclude that a small hybridization degree is the most suitable solution to optimize the regenerative braking gains while maintaining a high fuel-cell-system efficiency. We can conclude that the degree of hybridization has a significant impact on component behavior and, consequently, will be a determining factor of the energy storage requirements IMPACT OF TEMPERATURE As GCtool allows users to evaluate the influence of temperature, we studied the impact of cold (-20 C), ambient (20 C) and hot (80 C) starts. As shown in Figure 6, initial temperature mostly affects the energy storage requirements during the first 200 s of the cycle. Moreover, because of lower efficiencies and, consequently, a higher amount of heat rejected, the cold start temperature of the fuel cell increases faster than that for the ambient condition. IMPACT OF CONTROL STRATEGY As previously mentioned, the battery SOC and the minimum fuel cell power demand threshold are key parameters to the control strategy. To evaluate the impact of control strategies options on the energy storage requirements, we modified both of these parameters. Figure 7 illustrates the impact of the minimum fuel cell power demand threshold. Using 15 kw instead of zero leads to more use of the energy storage, as shown on the right. Since the battery provides more energy to help propel the vehicle and we want to closely monitor the SOC, it is logical that the fuel cell provides more power to recharge the battery. For example, the fuel cell peak power is 25 kw for the default control and 30 kw when using 15 kw for the minimum power demand threshold. Table 2 provides the results of modifying the minimum threshold for fuel cell power demand. As expected on the basis of the efficiency curves of the fuel cell system, increasing the minimum demand threshold (and, consequently, using the energy storage more) leads to a decrease in fuel economy as a result of an increase in powertrain losses even if the amount of regenerative braking increases. As previously discussed, regenerative braking energy and fuel cell system efficiencies are key to the system optimization. However, in this case, an increase in regenerative braking energy does not lead to an increase in fuel economy because a larger increase in fuel cell system energy loss nullifies the benefit associated with regenerative braking. The other parameter of interest is the energy storage SOC target. Figure 8 compares fuel economy results when the SOC is 0.7 and 0.5 (both for initial conditions and goal). Note that an increase in fuel economy of up to 4% can be achieved just by selecting a lower energystorage SOC.
7 The main reason for this improvement in fuel economy is an increase in regenerative braking energy combined with a small increase in fuel cell system efficiency, as shown in Figure 9.
8 Significant Impact Veh spd (m/s) Ess cum Energy Cold(Wh) Ess cum Energy Ambient(Wh Ess cum Energy Hot(Wh) No Impact Figure 6. Impact of Initial Temperature on Energy Storage Requirements Figure7. Impact of the Minimum Power Demand to Use Fuel Cell Table 2. Effect of Increasing Minimum Power Demand on Powertrain Losses Units 0 kw 5 kw 15 kw Mech. Braking Energy Loss W h Fuel Cell Energy Loss W h Difference W h
9 Control Strategy (Different SOC and Pfcdmd - FC 80kW ) FC system eff Percentage regen braking 10 0 SOC = 0.7; Pfc min=0 SOC = 0.5; Pfc min=0 SOC = 0.7; Pfc min=15kw SOC = 0.5Pfc min=15kw Figure 9. Reasons for Increase in Fuel Economy when Using Lower SOC Target We noticed that modifying the parameters of the default control strategy had a significant impact on the behavior of the powertrain and, consequently, on how the components are used and their requirements. Figure 10 illustrates the impact of another control strategy philosophy, where the energy storage will be used as the main energy source rather than the fuel cell ("large ess" case). For both the FUDS and FHDS cycles, the fuel cell system efficiency significantly decreases when the use of the energy storage increases. However, for the US06, a larger SOC window may be desirable because by allowing the battery to be more discharged during acceleration, more regenerative braking energy can be recovered during deceleration. In summary, we conclude that control strategy philosophies and their parameters have a significant impact on energy storage requirements. Several options to increase the energy storage usage were investigated by increasing the minimum wheel power demand to use the fuel cell and by changing the control strategy philosophy by using energy storage as the first choice. The results demonstrated increasing energy storage usage resulted in a decrease infuel economy. A better option to increase regenerative braking would be to decrease the SOC goal. We chose a a value of 50% because there is still enough available energy to start the vehicle at very low temperatures. IMPACT OF ENERGY STORAGE SYSTEM TECHNOLOGIES In the previous example, the Saft Li-ion HP6 battery has been used. To properly define the energy storage requirements for fuel cell vehicles, NiMH and ultracapacitor technologies were investigated. The NiMH battery used had a capacity of 28 Amp-h and was manufactured by Ovonic. The ultra-capacitor had a capacitance of 2,700 F and was manufactured by Maxwell. As shown in Figure 11, the best fuel economy is obtained for different hybridization degrees for each technology. Where, in this example, the Li-ion is optimum with a 100-kW fuel cell and a 60-kW battery, both NiMH and ultracapacitor achieve best performance at low hybridization degrees. These differences are explained both by the difference in power density, as shown in Figure 12, and in physical characteristics. At a low degree of hybridization (i.e., a140-kw fuel cell), the potential regenerative energy capability is the main reason for achieving better fuel economy, whereas at a high degree of hybridization, the mass increase from NiMH and ultracapacitor technologies is significant.
10 65 60 Fuel Economy (mpg) FUDS FHDS US Small ess Small ess Large ess Large ess Figure 10. Effect of Using more Energy Storage on Fuel Cell System Efficiency Fuel Economy (mpg) Fuel Cell P ower (kw ) Li-ion NiMH Ultracap Figure 11. Relationship between Degree of Hybridization Chosen and Energy Storage Technology FUDS Cycle
11 Relative Mass NiMH Ultracap Fu el Cell Pow er (kw) Figure 12. Relative Comparison of Vehicle Test Mass for Each Energy Storage Technology (reference: Li-ion) CONCLUSION By using GCTool and PSAT, specific direct hydrogen fuel cell systems and powertrain were developed to achieve performance characteristics similar to those of conventional vehicles. For a specific vehicle platform, we demonstrated that, to define the energy storage requirements of fuel cell vehicles, a system approach was needed. On the basis of mid-term component technologies, we demonstrated that the degree of hybridization should be chosen to optimize the regenerative braking and yet minimize the fuel cell system s losses. Moreover, selecting a lower battery- SOC target allows an increase in regenerative braking and thus can contribute to further lowering of the degree of hybridization. The control strategy should be oriented toward optimizing regenerative braking energy by using a narrow SOC range for low transient cycles (FUDS and FHDS) and a large one for high transient cycles (US06). This study allowed us to narrow the scope of the study for the other vehicle platforms and component technologies. The results will be used to define the energy storage requirements for each case. REFERENCES 1. A. Rousseau ; Sharer, P. ; Besnier, F "Feasibility of Reusable Vehicle Modeling: Application to Hybrid Vehicles," SAE04-454, SAE World Congress, Detroit. 2. B. Deville, and Rousseau, A "Validation of the Honda Insight Using PSAT", DOE report, September. 3. A. Rousseauand Pasquier, M "Validation Process of a System Analysis Model: PSAT," SAE paper 01P-183, SAE World Congress, Detroit, March P. Sharer and Rousseau, A "Validation of the Japan Toyota Prius Using PSAT," DOE report, March. CONTACT Aymeric Rousseau (630) ACKNOWLEDGMENTS This work was supported by the U.S. Department of Energy, under contract W Eng-38. The authors would like to thank Bob Kost and Lee Slezak from DOE, who sponsored this activity, as well as the FreedomCAR vehicle, battery, and fuel cell technical team members for their support and guidance.