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Energy Control Strategy of Fuel Cell Hybrid Electric Vehicle Based on Working Conditions Identification by Least Square Support Vector Machine

Author

Listed:
  • Yongliang Zheng

    (Department of Automotive Engineering, School of Mechanical Engineering, Guizhou University, Guiyang 550025, China)

  • Feng He

    (Department of Automotive Engineering, School of Mechanical Engineering, Guizhou University, Guiyang 550025, China)

  • Xinze Shen

    (Department of Automotive Engineering, School of Mechanical Engineering, Guizhou University, Guiyang 550025, China)

  • Xuesheng Jiang

    (Guizhou Changjiang Automobile Co., Ltd., Guiyang 550025, China)

Abstract

Aimed at the limitation of traditional fuzzy control strategy in distributing power and improving the economy of a fuel cell hybrid electric vehicle (FCHEV), an energy management strategy combined with working conditions identification is proposed. Feature parameters extraction and sample divisions were carried out for typical working conditions, and working conditions were identified by the least square support vector machine (LSSVM) optimized by grid search and cross validation (CV). The corresponding fuzzy control strategies were formulated under different typical working conditions, in addition, the fuzzy control strategy was optimized with total equivalent energy consumption as the goal by particle swarm optimization (PSO). The adaptive switching of fuzzy control strategies under different working conditions were realized through the identification of driving conditions. Results showed that the fuzzy control strategy with the function of driving conditions identification had a more efficient power distribution and better economy.

Suggested Citation

  • Yongliang Zheng & Feng He & Xinze Shen & Xuesheng Jiang, 2020. "Energy Control Strategy of Fuel Cell Hybrid Electric Vehicle Based on Working Conditions Identification by Least Square Support Vector Machine," Energies, MDPI, vol. 13(2), pages 1-18, January.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:2:p:426-:d:309197
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    References listed on IDEAS

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    Cited by:

    1. Tang, Xiaolin & Zhou, Haitao & Wang, Feng & Wang, Weida & Lin, Xianke, 2022. "Longevity-conscious energy management strategy of fuel cell hybrid electric Vehicle Based on deep reinforcement learning," Energy, Elsevier, vol. 238(PA).
    2. Mehroze Iqbal & Amel Benmouna & Frederic Claude & Mohamed Becherif, 2023. "Efficient and Reliable Power-Conditioning Stage for Fuel Cell-Based High-Power Applications," Energies, MDPI, vol. 16(13), pages 1-15, June.
    3. Mohsen Kandidayeni & Alvaro Macias & Loïc Boulon & João Pedro F. Trovão, 2020. "Online Modeling of a Fuel Cell System for an Energy Management Strategy Design," Energies, MDPI, vol. 13(14), pages 1-17, July.
    4. Liu, Huimin & Lin, Cheng & Yu, Xiao & Tao, Zhenyi & Xu, Jiaqi, 2024. "Variable horizon multivariate driving pattern recognition framework based on vehicle-road two-dimensional information for electric vehicle," Applied Energy, Elsevier, vol. 365(C).
    5. Marouane Adnane & Ahmed Khoumsi & João Pedro F. Trovão, 2023. "Efficient Management of Energy Consumption of Electric Vehicles Using Machine Learning—A Systematic and Comprehensive Survey," Energies, MDPI, vol. 16(13), pages 1-39, June.
    6. Babar, Abdul Haseeb Khan & Ali, Yousaf, 2021. "Enhancement of electric vehicles’ market competitiveness using fuzzy quality function deployment," Technological Forecasting and Social Change, Elsevier, vol. 167(C).

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