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Novel Fuzzy Control Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles Considering State of Health

Author

Listed:
  • Xiao Hu

    (School of Automotive Studies, Tongji University, Shanghai 201804, China)

  • Shikun Liu

    (School of Automotive Studies, Tongji University, Shanghai 201804, China)

  • Ke Song

    (School of Automotive Studies, Tongji University, Shanghai 201804, China
    National Fuel Cell Vehicle and Powertrain System Engineering Research Center, Tongji University, Shanghai 201804, China)

  • Yuan Gao

    (School of Automotive Studies, Tongji University, Shanghai 201804, China)

  • Tong Zhang

    (School of Automotive Studies, Tongji University, Shanghai 201804, China)

Abstract

Due to the low efficiency and high pollution of conventional internal combustion engine vehicles, the fuel cell hybrid electric vehicles are expected to play a key role in the future of clean energy transportation attributed to the long driving range, short hydrogen refueling time and environmental advantages. The development of energy management strategies has an important impact on the economy and durability, but most strategies ignore the aging of fuel cells and the corresponding impact on hydrogen consumption. In this paper, a rule-based fuzzy control strategy is proposed based on the constructed data-driven online estimation model of fuel cell health. Then, a genetic algorithm is used to optimize this fuzzy controller, where the objective function is designed to consider both the economy and durability by combining the hydrogen consumption cost and the degradation cost characterized by the fuel cell health status. Considering that the rule-based strategy is more sensitive to operating conditions, this paper uses an artificial neural network for predictive control. The results are compared with those obtained from the genetic algorithm optimized fuzzy controller and are found to be very similar, where the prediction accuracy is assessed using MAPE, RMSE and 10-fold cross-validation. Experiments show that the developed strategy has a good generalization capability for variable driving cycles.

Suggested Citation

  • Xiao Hu & Shikun Liu & Ke Song & Yuan Gao & Tong Zhang, 2021. "Novel Fuzzy Control Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles Considering State of Health," Energies, MDPI, vol. 14(20), pages 1-20, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6481-:d:653002
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    References listed on IDEAS

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    1. Wu, Yitao & Zhang, Yuanjian & Li, Guang & Shen, Jiangwei & Chen, Zheng & Liu, Yonggang, 2020. "A predictive energy management strategy for multi-mode plug-in hybrid electric vehicles based on multi neural networks," Energy, Elsevier, vol. 208(C).
    2. Li, Junqiu & Wang, Yihe & Chen, Jianwen & Zhang, Xiaopeng, 2017. "Study on energy management strategy and dynamic modeling for auxiliary power units in range-extended electric vehicles," Applied Energy, Elsevier, vol. 194(C), pages 363-375.
    3. Sulaiman, N. & Hannan, M.A. & Mohamed, A. & Ker, P.J. & Majlan, E.H. & Wan Daud, W.R., 2018. "Optimization of energy management system for fuel-cell hybrid electric vehicles: Issues and recommendations," Applied Energy, Elsevier, vol. 228(C), pages 2061-2079.
    4. Hongwen He & Chao Sun & Xiaowei Zhang, 2012. "A Method for Identification of Driving Patterns in Hybrid Electric Vehicles Based on a LVQ Neural Network," Energies, MDPI, vol. 5(9), pages 1-18, September.
    5. Wang, Yun & Chen, Ken S. & Mishler, Jeffrey & Cho, Sung Chan & Adroher, Xavier Cordobes, 2011. "A review of polymer electrolyte membrane fuel cells: Technology, applications, and needs on fundamental research," Applied Energy, Elsevier, vol. 88(4), pages 981-1007, April.
    6. Zhu, Tao & Wills, Richard G.A. & Lot, Roberto & Ruan, Haijun & Jiang, Zhihao, 2021. "Adaptive energy management of a battery-supercapacitor energy storage system for electric vehicles based on flexible perception and neural network fitting," Applied Energy, Elsevier, vol. 292(C).
    7. Song, Ke & Wang, Xiaodi & Li, Feiqiang & Sorrentino, Marco & Zheng, Bailin, 2020. "Pontryagin’s minimum principle-based real-time energy management strategy for fuel cell hybrid electric vehicle considering both fuel economy and power source durability," Energy, Elsevier, vol. 205(C).
    8. Xu, Liangfei & Mueller, Clemens David & Li, Jianqiu & Ouyang, Minggao & Hu, Zunyan, 2015. "Multi-objective component sizing based on optimal energy management strategy of fuel cell electric vehicles," Applied Energy, Elsevier, vol. 157(C), pages 664-674.
    9. Ximing Wang & Hongwen He & Fengchun Sun & Jieli Zhang, 2015. "Application Study on the Dynamic Programming Algorithm for Energy Management of Plug-in Hybrid Electric Vehicles," Energies, MDPI, vol. 8(4), pages 1-20, April.
    10. Tribioli, Laura & Cozzolino, Raffaello & Chiappini, Daniele & Iora, Paolo, 2016. "Energy management of a plug-in fuel cell/battery hybrid vehicle with on-board fuel processing," Applied Energy, Elsevier, vol. 184(C), pages 140-154.
    11. Tie, Siang Fui & Tan, Chee Wei, 2013. "A review of energy sources and energy management system in electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 82-102.
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    Cited by:

    1. Lianghui Huang & Quan Ouyang & Jian Chen & Zhiyang Liu & Xiaohua Wu, 2023. "A Scalable Segmented-Based PEM Fuel Cell Hybrid Power System Model and Its Simulation Applications," Energies, MDPI, vol. 16(17), pages 1-13, August.
    2. 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.

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