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Genetic algorithm-based fuzzy optimization of energy management strategy for fuel cell vehicles considering driving cycles recognition

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  • Wang, Yichun
  • Zhang, Yuanzhi
  • Zhang, Caizhi
  • Zhou, Jiaming
  • Hu, Donghai
  • Yi, Fengyan
  • Fan, Zhixian
  • Zeng, Tao

Abstract

The energy management in fuel cell vehicles (FCVs) is crucial to maintain the economical operation of FCVs and the fuzzy logic control (FLC) is mainly used to manage the energy split between the fuel cell and other energy sources. To overcome the limitation of traditional FLC, the dependence on expert knowledge leading to the insufficient energy split, this paper proposes strategy optimization based on FLC with driving cycles recognition achieve near-optimal fuel economy and stable battery charge sustenance. Initially, the whole FCV model is established, which includes electrical system, vehicle dynamic system, energy management system. Additionally, with the objective function which is the minimum equivalent hydrogen consumption of four typical driving cycles, the centers and widths of FLC membership function are optimized by genetic algorithm (GA), respectively. Finally, the driving cycles recognition is achieved based on K-means clustering method, and characteristic parameters are extracted and classified. Compared with GA-optimized fuzzy EMS and the traditional fuzzy EMS, the simulation results demonstrate that the equivalent hydrogen consumption based on proposed strategy is reduced by 16.55% and 40.50%, respectively. Therefore, the proposed strategy can effectively smooth the output of proton exchange membrane fuel cell (PEMFC) and enhance the total fuel economy.

Suggested Citation

  • Wang, Yichun & Zhang, Yuanzhi & Zhang, Caizhi & Zhou, Jiaming & Hu, Donghai & Yi, Fengyan & Fan, Zhixian & Zeng, Tao, 2023. "Genetic algorithm-based fuzzy optimization of energy management strategy for fuel cell vehicles considering driving cycles recognition," Energy, Elsevier, vol. 263(PF).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pf:s036054422202998x
    DOI: 10.1016/j.energy.2022.126112
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    References listed on IDEAS

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    Cited by:

    1. Zhou, Hongxu & Yu, Zhongwei & Wu, Xiaohua & Fan, Zhanfeng & Yin, Xiaofeng & Zhou, Lingxue, 2023. "Dynamic programming improved online fuzzy power distribution in a demonstration fuel cell hybrid bus," Energy, Elsevier, vol. 284(C).
    2. Antoine Bäumler & Jianwen Meng & Abdelmoudjib Benterki & Toufik Azib & Moussa Boukhnifer, 2023. "A System-Level Modeling of PEMFC Considering Degradation Aspect towards a Diagnosis Process," Energies, MDPI, vol. 16(14), pages 1-19, July.
    3. Jia, Chunchun & Zhou, Jiaming & He, Hongwen & Li, Jianwei & Wei, Zhongbao & Li, Kunang & Shi, Man, 2023. "A novel energy management strategy for hybrid electric bus with fuel cell health and battery thermal- and health-constrained awareness," Energy, Elsevier, vol. 271(C).
    4. 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.
    5. Mubashir Rasool & Muhammad Adil Khan & Runmin Zou, 2023. "A Comprehensive Analysis of Online and Offline Energy Management Approaches for Optimal Performance of Fuel Cell Hybrid Electric Vehicles," Energies, MDPI, vol. 16(8), pages 1-33, April.
    6. Yang Shen & Jiaming Zhou & Jinming Zhang & Fengyan Yi & Guofeng Wang & Chaofeng Pan & Wei Guo & Xing Shu, 2023. "Research on Energy Management of Hydrogen Fuel Cell Bus Based on Deep Reinforcement Learning Considering Velocity Control," Sustainability, MDPI, vol. 15(16), pages 1-19, August.

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