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Real-Time Energy Management Strategy for Fuel Cell Vehicles Based on DP and Rule Extraction

Author

Listed:
  • Yanwei Liu

    (School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China)

  • Mingda Wang

    (School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China)

  • Jialuo Tan

    (School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China)

  • Jie Ye

    (School of Mechatronics Engineering, Foshan University, Foshan 528225, China)

  • Jiansheng Liang

    (Automotive Engineering Research Institute, BYD Co., Ltd., Shenzhen 518118, China)

Abstract

Energy management strategy (EMS), as a core technology in fuel cell vehicles (FCVs), profoundly influences the lifespan of fuel cells and the economy of the vehicle. Aiming at the problem of the EMS of FCVs based on a global optimization algorithm not being applicable in real-time, a rule extraction-based EMS is proposed for fuel cell commercial vehicles. Based on the results of the dynamic programming (DP) algorithm in the CLTC-C cycle, the deep learning approach is employed to extract output power rules for fuel cell, leading to the establishment of a rule library. Using this library, a real-time applicable rule-based EMS is designed. The simulated driving platform is built in a CARLA, SUMO, and MATLAB/Simulink joint simulation environment. Simulation results indicate that the proposed strategy yields savings ranging from 3.64% to 8.96% in total costs when compared to the state machine-based strategy.

Suggested Citation

  • Yanwei Liu & Mingda Wang & Jialuo Tan & Jie Ye & Jiansheng Liang, 2024. "Real-Time Energy Management Strategy for Fuel Cell Vehicles Based on DP and Rule Extraction," Energies, MDPI, vol. 17(14), pages 1-20, July.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:14:p:3465-:d:1434948
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    References listed on IDEAS

    as
    1. Wang, Yujie & Sun, Zhendong & Chen, Zonghai, 2019. "Energy management strategy for battery/supercapacitor/fuel cell hybrid source vehicles based on finite state machine," Applied Energy, Elsevier, vol. 254(C).
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    3. Qiao Zhang & Gang Li, 2019. "A Game Theory Energy Management Strategy for a Fuel Cell/Battery Hybrid Energy Storage System," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-12, January.
    4. Jia, Chunchun & He, Hongwen & Zhou, Jiaming & Li, Jianwei & Wei, Zhongbao & Li, Kunang, 2024. "Learning-based model predictive energy management for fuel cell hybrid electric bus with health-aware control," Applied Energy, Elsevier, vol. 355(C).
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