Multi-Objective Optimization of Fuel Cell Hybrid Vehicle Powertrain Design - Cost and Energy

The scope of this study is to optimize the powertrain of a fuel cell powered hybrid electric vehicle and plug-in hybrid electric vehicle, aiming to minimize the cost, fuel consumption minimization, and all-electric range (AER) maximization. A genetic algorithm (GA) was used to perform single objective optimization, and a non-dominated sorting genetic algorithm (NSGA-II) to perform multi-objective optimization. Both algorithms were programed in MATLAB. The cost, fuel consumption and AER were optimized by the GA individually, and the couples cost and fuel consumption, and cost and AER, were evaluated by the NSGA-II. In order to optimize the vehicle powertrain, not only the fuel cell, electric motor, and battery, are sized but different component models are also considered, including different battery chemistries (Lithium and Nickel-metal hydride). The battery charge sustaining level is also an optimization variable. The vehicle design is evaluated by a vehicle simulation software, ADVISOR which is connected to the optimization algorithms. The designed vehicles are simulated in a real measured driving cycle and official European driving cycle, and must comply to several performance constraints, such as maximum speed, acceleration, and maximum electric range (only for plug-in vehicle). The developed methodology main objective is to present a possible best vehicle option regarding a specified objective and conditions.

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