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The modeling of the energy storage system (ESS) of a Hybrid Electric Vehicle (HEV) poses a considerable challenge. The problem is not amenable to physical modeling without simplifying assumptions that compromise the accuracy of such models. An alternative is to build conventional empirical models. Such models, however, are time-consuming to build and are data-intensive. In this paper, we demonstrate the application of an artificial neural network (ANN) to modeling the ESS. The model maps the system's state-of-charge (SOC) and the vehicle's power requirement to the bus voltage and current. We show that ANN models can accurately capture the complex, non-linear correlations accurately. Further, we propose and deploy our new technique, Smart Select, for designing ANN training data.

An ADVISOR model of a large sport utility vehicle with a fuel cell / battery hybrid electric drivetrain is developed using validated component models. The vehicle mass, electric traction drive, and total net power available from fuel cells plus batteries are held fixed. Results are presented for a range of fuel cell size from zero (pure battery EV) up to a pure fuel cell vehicle (no battery storage). The fuel economy results show that some degree of hybridization is beneficial, and that there is a complex interaction between the drive cycle dynamics, component efficiencies, and the control strategy.

An ADVISOR model of a PNGV-class (80 mpg) vehicle with a fuel cell / battery hybrid electric drivetrain is developed using validated component models. The vehicle mass, electric traction drive, and total net power available from fuel cells plus batteries are held fixed. Results are presented for a range of fuel cell size from zero (pure battery EV) up to a pure fuel cell vehicle (no battery storage). The fuel economy results show that some degree of hybridization is beneficial, and that there is a complex interaction between the drive cycle dynamics, component efficiencies, and the control strategy.

Hybrid electric vehicles (HEV's) offer additional flexibility to enhance the fuel economy and emissions of vehicles. The Real-Time Control Strategy (RTCS) presented here optimizes efficiency and emissions of a parallel configuration HEV. In order to determine the ideal operating point of the vehicle's engine and motor, the control strategy considers all possible engine-motor torque pairs. For a given operating point, the strategy predicts the possible energy consumption and the emissions emitted by the vehicle. The strategy calculates the “replacement energy” that would restore the battery's state of charge (SOC) to its initial level. This replacement energy accounts for inefficiencies in the energy storage system conversion process. User- and standards-based weightings of time-averaged fuel economy and emissions performance determine an overall impact function. The strategy continuously selects the operating point that is the minimum of this cost function.

This paper describes the design and construction of a fuel cell hybrid electric vehicle based on the conversion of a five passenger production sedan. The vehicle uses a relatively small fuel cell stack to provide average power demands, and a battery pack to provide peak power demands for varied driving conditions. A model of this vehicle was developed using ADVISOR, an Advanced Vehicle Simulator that tracks energy flow and fuel usage within the vehicle drivetrain and energy conversion components. The Virginia Tech Fuel Cell Hybrid Electric Vehicle was tested on the EPA City and Highway driving cycles to provide data for validation of the model. Vehicle data and model results show good correlation at all levels and show that ADVISOR has the capability to model fuel cell hybrid electric vehicles.

Hydrogen fuel cell vehicles (FCVs) appear to be a promising solution for the future of clean and efficient personal transportation. Issues of how to generate the hydrogen and then store it on-board to provide satisfactory driving range must still be resolved before they can compete with conventional vehicles. Alternatively, FCVs could obtain hydrogen from on-board reforming of gasoline or other fuels such as methanol or ethanol. On-board reformers convert fuel into a hydrogen-rich fuel stream through catalytic reactions in several stages. The high temperatures associated with fuel processing present an engineering challenge to warm up the reformer quickly and efficiently in a vehicle environment. Without a special warmup phase or vehicle hybridization, the reformer and fuel cell system must provide all power to move the vehicle, including ¼ power in 30 s, and ½ power in 3 min to satisfy the Federal Test Procedure (FTP) cycle demands.

The impacts of fuel cell system power response capability on optimal hybrid and neat fuel cell vehicle configurations have been explored. Vehicle system optimization was performed with the goal of maximizing fuel economy over a drive cycle. Optimal hybrid vehicle design scenarios were derived for fuel cell systems with 10 to 90% power transient response times of 0, 2, 5, 10, 20, and 40 seconds. Optimal neat fuel cell vehicles where generated for responses times of 0, 2, 5, and 7 seconds. DIRECT, a derivative-free optimization algorithm, was used in conjunction with ADVISOR, a vehicle systems analysis tool, to systematically change both powertrain component sizes and the vehicle energy management strategy parameters to provide optimal vehicle system configurations for the range of response capabilities.