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Vehicle manufacturers claim that fuel cell vehicles are significantly more fuel-efficient and emit fewer emissions than conventional internal combustion engine vehicles /1/. A computer model can help to explore and understand the underlying reasons for this potential improvement. In previous published work, the UC Davis Vehicle Model for the case of a load-following Indirect Methanol Fuel Cell Vehicle (IMFCV) has been introduced and discussed in detail /2/. Because of possible technical barriers with load following vehicles, as well as near term cost issues, hybrid fuel cell vehicle concepts are widely discussed as another fuel cell vehicle option. For load following vehicles, the questions of fast start up and fuel processor dynamics in extreme transient situations, (e.g., during phases of hard acceleration) are not totally resolved at this time. For both of these performance issues, a hybrid design could offer at least an interim solution.

Near term (ca. 2005) Fuel Cell Vehicles (FCVs) will primarily utilize Direct-Hydrogen Fuel Cell (DHFC) systems. The primary goal of this study was to provide an analytical basis for including a realistic Compressed Hydrogen Gas (CHG) fuel supply simulation within an existing dynamic DHFC system and vehicle model. The purpose of this paper is to provide a tutorial describing the process of modeling a hydrogen storage system for a fuel cell vehicle. Three topics were investigated to address the delivery characteristics of H2: temperature change (ΔT), non-ideal gas characteristics at high pressures, and the maximum amount of hydrogen available due to the CHG storage tank effective “state-of-charge” (SOC) -- i.e. how much does the pressure drop between the tank and the fuel cell stack reduce the usable H2 in the tank. The Joule-Thomson coefficient provides an answer to the expected ΔT during expansion of the H2 from 5000 psi to 45 psi.

This work focuses on the algorithms to simulate and analyze the characteristics of an indirect methanol fuel cell vehicle. The individual components of the electric drive train including transmission, the vehicle properties, such as drag, frontal area, wheel inertia etc., and the fuel cell system are modeled in a dynamic manner. Further the interaction between the individual components and a simple driver model is described. The algorithms are coded using the simulation tool Matlab/Simulink. The simulation tool is strictly setup in a modular form allowing modifications of individual component characteristics or control algorithms without the need to change the remainder of the model. For the benefit of a more in depth discussion of the applied algorithms and the setup of the model this paper focuses solely on the case of an Indirect Methanol Fuel Cell Vehicle (IMFCV) with steam reformer and without any additional energy storage.

This paper examines the impacts of fuel cell system parameters and control strategies on the dynamic response of an indirect-methanol fuel cell vehicle (FCV). An indirect-methanol fuel cell system model is introduced and explained, and then the effect of parameter variations on the vehicle acceleration from 0-60 mph (ca. 0-100 kph) is analyzed. Varied parameters are the fuel processor response time, the utilization rate and the fuel cell system size (qualitatively). In addition two different fuel cell system control strategies are introduced and are compared with respect to their impact on vehicle acceleration. If poor fuel processor dynamics are the limiting factor, the vehicle acceleration can be improved adopting an alternative control strategy -- without major changes in hardware but accepting a lower fuel economy.