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Adaptive Equivalent Consumption Minimization Strategy for Fuel Cell Buses Based on Driving Style Recognition

Author

Listed:
  • Kun He

    (School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China)

  • Dongchen Qin

    (School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China)

  • Jiangyi Chen

    (School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China)

  • Tingting Wang

    (School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China)

  • Hongxia Wu

    (School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China)

  • Peizhuo Wang

    (School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China)

Abstract

Driving style has a significant effect on the operating economy of fuel cell buses (FCBs). To reduce hydrogen consumption and prolong the fuel cell life of FCBs, this paper proposes an online adaptive equivalent consumption minimum strategy (A-ECMS) based on driving style recognition. Firstly, driving data from various drivers is collected, and a standard driving cycle is created. Neural networks are then used to identify driving conditions, and three fuzzy logic recognizers are developed to identify driving styles for different driving conditions. The driving style factor is associated with the equivalent factor using an optimization algorithm that incorporates hydrogen consumption cost and fuel cell degradation cost into the objective function. Simulation results demonstrate that the proposed A-ECMS can reduce equivalent hydrogen consumption, prolong fuel cell life, and result in a 6.2% reduction in total operating cost compared to the traditional method.

Suggested Citation

  • Kun He & Dongchen Qin & Jiangyi Chen & Tingting Wang & Hongxia Wu & Peizhuo Wang, 2023. "Adaptive Equivalent Consumption Minimization Strategy for Fuel Cell Buses Based on Driving Style Recognition," Sustainability, MDPI, vol. 15(10), pages 1-17, May.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:10:p:7781-:d:1142973
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    References listed on IDEAS

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    1. Seydali Ferahtia & Hegazy Rezk & Rania M. Ghoniem & Ahmed Fathy & Reem Alkanhel & Mohamed M. Ghonem, 2023. "Optimal Energy Management for Hydrogen Economy in a Hybrid Electric Vehicle," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
    2. Guo, Qiuyi & Zhao, Zhiguo & Shen, Peihong & Zhan, Xiaowen & Li, Jingwei, 2019. "Adaptive optimal control based on driving style recognition for plug-in hybrid electric vehicle," Energy, Elsevier, vol. 186(C).
    3. Wu, Peng & Partridge, Julius & Bucknall, Richard, 2020. "Cost-effective reinforcement learning energy management for plug-in hybrid fuel cell and battery ships," Applied Energy, Elsevier, vol. 275(C).
    4. Kim, Jintae & Kim, Minjin & Kang, Taegon & Sohn, Young-Jun & Song, Taewon & Choi, Kyoung Hwan, 2014. "Degradation modeling and operational optimization for improving the lifetime of high-temperature PEM (proton exchange membrane) fuel cells," Energy, Elsevier, vol. 66(C), pages 41-49.
    5. Alberto Broatch & Pablo Olmeda & Benjamín Plá & Amin Dreif, 2022. "Novel Energy Management Control Strategy for Improving Efficiency in Hybrid Powertrains," Energies, MDPI, vol. 16(1), pages 1-21, December.
    6. Yaqian Wang & Xiaohong Jiao, 2022. "Dual Heuristic Dynamic Programming Based Energy Management Control for Hybrid Electric Vehicles," Energies, MDPI, vol. 15(9), pages 1-19, April.
    7. Dongwei Yao & Xinwei Lu & Xiangyun Chao & Yongguang Zhang & Junhao Shen & Fanlong Zeng & Ziyan Zhang & Feng Wu, 2023. "Adaptive Equivalent Fuel Consumption Minimization Based Energy Management Strategy for Extended-Range Electric Vehicle," Sustainability, MDPI, vol. 15(5), pages 1-18, March.
    8. 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).
    9. Jiaming Zhou & Chunxiao Feng & Qingqing Su & Shangfeng Jiang & Zhixian Fan & Jiageng Ruan & Shikai Sun & Leli Hu, 2022. "The Multi-Objective Optimization of Powertrain Design and Energy Management Strategy for Fuel Cell–Battery Electric Vehicle," Sustainability, MDPI, vol. 14(10), pages 1-19, May.
    10. Fengyan Yi & Dagang Lu & Xingmao Wang & Chaofeng Pan & Yuanxue Tao & Jiaming Zhou & Changli Zhao, 2022. "Energy Management Strategy for Hybrid Energy Storage Electric Vehicles Based on Pontryagin’s Minimum Principle Considering Battery Degradation," Sustainability, MDPI, vol. 14(3), pages 1-17, January.
    11. Zeng, Tao & Zhang, Caizhi & Zhang, Yanyi & Deng, Chenghao & Hao, Dong & Zhu, Zhongwen & Ran, Hongxu & Cao, Dongpu, 2021. "Optimization-oriented adaptive equivalent consumption minimization strategy based on short-term demand power prediction for fuel cell hybrid vehicle," Energy, Elsevier, vol. 227(C).
    12. Tian, Xiang & Cai, Yingfeng & Sun, Xiaodong & Zhu, Zhen & Xu, Yiqiang, 2019. "An adaptive ECMS with driving style recognition for energy optimization of parallel hybrid electric buses," Energy, Elsevier, vol. 189(C).
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    Cited by:

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