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An Energy Management Strategy for Fuel-Cell Hybrid Commercial Vehicles Based on Adaptive Model Prediction

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  • Enyong Xu

    (State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004, China
    Commercial Vehicle Technology Center, Dong Feng Liuzhou Automobile Co., Ltd., Liuzhou 545005, China)

  • Mengcheng Ma

    (School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004, China
    Commercial Vehicle Technology Center, Dong Feng Liuzhou Automobile Co., Ltd., Liuzhou 545005, China)

  • Weiguang Zheng

    (School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004, China
    Commercial Vehicle Technology Center, Dong Feng Liuzhou Automobile Co., Ltd., Liuzhou 545005, China
    School of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545616, China)

  • Qibai Huang

    (State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

Fuel-cell hybrid electric vehicles have the advantages of zero pollution and high efficiency and are extensively applied in commerce. An energy management strategy (EMS) directly impacts the fuel consumption and performance. Moreover, model prediction control (MPC) is synchronous and has been a research hotspot of EMS in recent years. The existing MPC’s low-speed prediction accuracy, which results in considerable instability in EMS allocation, is solved by the proposed energy management strategy based on adaptive model prediction. Dynamic programming (DP) is used as the solver, improved condition recognition and a radial basis neural network (RBFNN) are used as the speed predictor, and hydrogen consumption and the state of charge (SOC) are used as the objective function. According to the simulation results, using a 5 s speed prediction improves the forecast accuracy by 9.75%, and compared with employing a rule-based energy management strategy, this strategy reduces hydrogen consumption and the power cell fluctuation frequency by 3.50%.

Suggested Citation

  • Enyong Xu & Mengcheng Ma & Weiguang Zheng & Qibai Huang, 2023. "An Energy Management Strategy for Fuel-Cell Hybrid Commercial Vehicles Based on Adaptive Model Prediction," Sustainability, MDPI, vol. 15(10), pages 1-20, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:7915-:d:1145015
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    References listed on IDEAS

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