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Stochastic velocity-prediction conscious energy management strategy based self-learning Markov algorithm for a fuel cell hybrid electric vehicle

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  • Lin, Xinyou
  • Ren, Yukun
  • Xu, Xinhao

Abstract

The stochasticity of vehicle velocity poses a significant challenge to enhancing fuel cell energy management strategy (EMS). Under these circumstances, a self-learning Markov algorithm-based EMS with stochastic velocity prediction capability is proposed. First, building upon the traditional offline-trained Markov model, a real-time self-learning Markov predictor (SLMP) is proposed, which collects historical data during the vehicle's driving process and continuously updates the state transition matrix on a rolling basis. It provides excellent prediction performance under stochastic driving cycles. and the impact of different prediction time-steps is analyzed. Subsequently, by employing sequential quadratic programming for optimal power allocation, the Stochastic Velocity-Prediction Conscious EMS for fuel cell hybrid electrical vehicle based on SLMP is constructed. Finally, the predictors and EMSs based on back-propagation neural network and offline-trained Markov are selected for performance comparison. The validation results indicate that the performance of SLMP improves as driving mileage accumulates. Meanwhile, the proposed Stochastic Velocity-Prediction Conscious EMS significantly improves economic performance in different driving cycles. Hardware-in-the-Loop experiments further validate the superior fuel cell efficiency and robustness of the proposed EMS. The key contribution lies in the real-time adaptability of the SLMP, which ensures improved prediction accuracy and economic performance as driving mileage accumulates.

Suggested Citation

  • Lin, Xinyou & Ren, Yukun & Xu, Xinhao, 2025. "Stochastic velocity-prediction conscious energy management strategy based self-learning Markov algorithm for a fuel cell hybrid electric vehicle," Energy, Elsevier, vol. 320(C).
  • Handle: RePEc:eee:energy:v:320:y:2025:i:c:s0360544225008096
    DOI: 10.1016/j.energy.2025.135167
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    References listed on IDEAS

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    1. Lin, Xinyou & Wu, Jiayun & Wei, Yimin, 2021. "An ensemble learning velocity prediction-based energy management strategy for a plug-in hybrid electric vehicle considering driving pattern adaptive reference SOC," Energy, Elsevier, vol. 234(C).
    2. Lin, Xinyou & Zeng, Songrong & Li, Xuefan, 2021. "Online correction predictive energy management strategy using the Q-learning based swarm optimization with fuzzy neural network," Energy, Elsevier, vol. 223(C).
    3. 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).
    4. Chen, Ruihu & Yang, Chao & Ma, Yue & Wang, Weida & Wang, Muyao & Du, Xuelong, 2022. "Online learning predictive power coordinated control strategy for off-road hybrid electric vehicles considering the dynamic response of engine generator set," Applied Energy, Elsevier, vol. 323(C).
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