Fuel cell hybrid electric vehicles (FCHEVs) composed of fuel cells and batteries can improve the dynamic response and durability of vehicle propulsion. In addition, braking energy can be recovered by batteries. The energy management strategy (EMS) for distributing the requested power through different types of energy sources plays an important role in FCHEVs. Reasonable power split not only improves vehicle performance but also enhances fuel economy. In this paper, considering the power tracking control strategy which is widely adopted in Advanced Vehicle Simulator (ADVISOR), a constrained nonlinear programming parameter optimization model is established for minimizing fuel consumption. The principal parameters of power tracking control strategy are set as the optimized variables, with the dynamic performance index of FCHEVs being defined as the constraint condition. Then, the genetic algorithm (GA) is applied in the control strategy design for solving the optimization problem. The GA is combined with the vehicle model in ADVISOR to optimize parameters of control strategy respectively for two standard driving cycles, i.e. the Urban Dynamometer Driving Schedule (UDDS) and the Highway Fuel Economy Test (HWFET). Finally, the control strategies before and after optimization are simulated, then the related performances compared, and the optimal control parameters under different driving cycles analyzed. The simulation results demonstrate that by using the optimized power tracking control strategy, total fuel consumption of FCHEVs can be reduced by 17.6% and 9.7%, respectively, under UDDS and HWFET without compromising dynamic performance. Therefore, the GA optimization approach has the potential to reasonably adjust the parameters of EMS. In addition, even with the same control strategy, there should be different optimal control parameters value for different driving cycles.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Genetic Algorithm-Based Parameter Optimization of Energy Management Strategy and Its Analysis for Fuel Cell Hybrid Electric Vehicles


    Additional title:

    Sae Technical Papers


    Contributors:
    Jin, Jie (author) / Zhou, Shangwei (author) / Zhou, Su (author) / Zhi, Xuelei (author) / Wen, Zejun (author)

    Conference:

    WCX SAE World Congress Experience ; 2019



    Publication date :

    2019-04-02




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




    Genetic Algorithm-Based Parameter Optimization of Energy Management Strategy and Its Analysis for Fuel Cell Hybrid Electric Vehicles

    Zhou, Su / Wen, Zejun / Zhi, Xuelei et al. | British Library Conference Proceedings | 2019


    Fuel cell hybrid electric vehicle energy management online optimization method based on improved genetic algorithm

    PENG CHAO / ZOU JIANXIAO / XIAO DONG et al. | European Patent Office | 2020

    Free access

    Adaptive Energy Management Strategy for Fuel Cell Hybrid Vehicles

    Jeanneret, B. / Markel, T. / Society of Automotive Engineers | British Library Conference Proceedings | 2004


    Adaptive Energy Management Strategy for Fuel Cell Hybrid Vehicles

    Jeanneret, B. / Markel, T. | British Library Conference Proceedings | 2004


    Adaptive energy management strategy for fuel cell hybrid vehicles

    Jeanneret,B. / Markel,T. / French Nat.Inst.f.Transport a.Safety Res.,FR et al. | Automotive engineering | 2004