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An optimal design methodology is developed in this paper for fuel cell hybrid electric vehicles (FCHEV) based on ordinal optimization (OO) and dynamic programming (DP); the optimal design aims to determine the appropriate sizes of the hydrogen tank, fuel cell, battery, and motor for the purpose of minimizing investment and operational cost given some specification of the car range, the road type and its gradeability. The DP simulates the operation of the vehicle for a set of specified components' sizes for given driving cycles and provides the total vehicle cost per year. The OO method offers an efficient approach for optimization by focusing on ranking and selecting a finite set of “good enough” alternatives through two models: a simple model and an accurate model. The OO program uses the specified sizes of the components that uniformly sample the search space and evaluates these designs using a simple but fast model.

As part of a system model, a PEM fuel cell stack model is presented for functional tests and pre-calibration of control units on hardware-in-the-loop (HiL) test benches. From the basic idea to couple a 1-D membrane model with a spatially distributed abstraction of the gas channel, a real-time capable 1-D+1-D PEM FC stack model is constructed. Fundament for the HiL usage is an explicit formulation of the commonly implicit model equations. With that, not only calculation time can be reduced, but also model accuracy is preserved. A validation using test bench data emphasizes the accuracy of the model. Finally, a runtime and eigenvalue analysis of the stack model proves the real-time capability.

Starting with NECAR 1 in 1994 DaimlerChrysler has developed a series of fuel cell (FC) concept vehicles to prove the practicability of FC technology in mobile applications. Within the next two years the first buses (2003) and passenger cars (2004) will be given into customer hands indicating the start of a new phase within FC technology development. Among DaimlerChrysler's concept vehicles, Necar 3 and Necar 5 are using methanol as fuel. Methanol is an interesting option, because its storing is much easier than the storage of pure hydrogen. In a rather simple process and at low temperature it can be reformed into a hydrogen rich gas. This paper is dealing with the simulation and the design of methanol FC powertrains with Matlab/Simulink®. The results are fuel consumption with special regard to the operating strategy and the dimensioning of the FC powertrain's components.

The development of an energy management system for a fuel cell hybrid electric vehicle (FCHEV) based on single step dynamic programming (SSDP) is described in this paper. The SSDP method is used to minimize a weighted cost of hydrogen and battery degradation with the latter being controlled to carry out charge-depleting (CD) as well as charge-sustaining (CS) strategies with simple lower bound enforcement or relaxation. The problem formulation accounts for the power balance at each stage, the fuel cell and battery power limits, the battery state-of-charge limits, and the ramp-rates constraints of the fuel cell and battery. Its chief advantage over forward dynamic programming (DP) or other formal optimization methods is that it does not require the speed forecast of the whole drive cycle but requires only a one-step-ahead speed forecast.

A hybrid electric vehicle simulation tool (IBZ-Simulator) has been developed at the Fuel Cell Institute of the University of Applied Sciences Esslingen to study the fuel economy potential of a Fuel Cell hybrid urban bus. In this paper, the fundamental architecture of the FC urban buses was described, as well as the control strategy to manage the power flow between the different elements of the drive train. A comparison of the hybrid with the conventional type and ICE-hybrid type is performed, and important factors relating to the vehicle efficiency (accessory loads, vehicle mass, Fuel Cell system ramping rate and battery capacity) were assessed. The using of supercapacitor (or ultracapacitors) as peak power buffer has been investigated.

An optimal energy management system is presented to minimize hydrogen utilization over driving cycles using forward dynamic programming (FDP). The objective is to minimize the cost of hydrogen with the battery cost being used as a parameter to carry out charge-depleting as well as charge-sustaining strategies along with bound enforcement or relaxation. The problem formulation accounts for the power balance at each stage, the power limits, the state-of-charge limits, and the ramp rates constraints of the fuel cell and battery. FDP is selected because it can easily cater for non-linearity in system cost and constraints. It employs heuristic rules to limit the number of states at each stage and is shown to be a very fast algorithm using simple computations and thus may easily lend itself for real-time implementation.