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Markov velocity predictor and radial basis function neural network-based real-time energy management strategy for plug-in hybrid electric vehicles

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  • Liu, Hui
  • Li, Xunming
  • Wang, Weida
  • Han, Lijin
  • Xiang, Changle

Abstract

Power management strategy of plug-in hybrid electric vehicle for real-time application is a major challenge as the driving pattern is unknown beforehand. In this work, an innovative real-time power management strategy framework is proposed, including short horizon driving pattern prediction, driving pattern recognition, parameter off-line optimisation, parameter on-line prediction modelling, and power management strategy real-time application. A group of characteristic parameters is used to recognise driving patterns and the engine and motor working points are optimised globally by distributed genetic algorithm off-line. The optimised results approximation model is built on the basis of a radial basis function-neural network. Based on a linear programming algorithm, the higher order Markov velocity predictor is designed to obtain the short-horizon driving conditions. Combining the optimisation results approximation model, the real-time power management strategy is proposed. The on-line optimisation power management strategy comparing to the rule-based is analysed and the MATLAB/Simulink/AVL Cruise co-simulation results demonstrate that the fuel economy of real-time power management strategy improved by 16.3%, 12.7%, and 9.1% in HWFET, LA92, and Japanese urban driving patterns, respectively, which is largely higher than with a traditional rule-based strategy and slightly lower than, or approximately equal to, the global optimisation strategy.

Suggested Citation

  • Liu, Hui & Li, Xunming & Wang, Weida & Han, Lijin & Xiang, Changle, 2018. "Markov velocity predictor and radial basis function neural network-based real-time energy management strategy for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 152(C), pages 427-444.
    • Handle: RePEc:eee:energy:v:152:y:2018:i:c:p:427-444
    DOI: 10.1016/j.energy.2018.03.148
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    2. Lu Han & Xiaohong Jiao & Zhao Zhang, 2020. "Recurrent Neural Network-Based Adaptive Energy Management Control Strategy of Plug-In Hybrid Electric Vehicles Considering Battery Aging," Energies, MDPI, vol. 13(1), pages 1-22, January.
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    4. Wang, Yue & Zeng, Xiaohua & Song, Dafeng & Yang, Nannan, 2019. "Optimal rule design methodology for energy management strategy of a power-split hybrid electric bus," Energy, Elsevier, vol. 185(C), pages 1086-1099.
    5. 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).
    6. Wang, Yue & Zeng, Xiaohua & Song, Dafeng, 2020. "Hierarchical optimal intelligent energy management strategy for a power-split hybrid electric bus based on driving information," Energy, Elsevier, vol. 199(C).
    7. Zhang, Zhendong & He, Hongwen & Guo, Jinquan & Han, Ruoyan, 2020. "Velocity prediction and profile optimization based real-time energy management strategy for Plug-in hybrid electric buses," Applied Energy, Elsevier, vol. 280(C).
    8. Zhang, Chi & Zeng, Guohong & Wu, Jian & Wei, Shaoyuan & Zhang, Weige & Sun, Bingxiang, 2023. "Integrated optimization of driving strategy and energy management for hybrid diesel multiple units," Energy, Elsevier, vol. 281(C).
    9. Yang, Ningkang & Han, Lijin & Xiang, Changle & Liu, Hui & Li, Xunmin, 2021. "An indirect reinforcement learning based real-time energy management strategy via high-order Markov Chain model for a hybrid electric vehicle," Energy, Elsevier, vol. 236(C).
    10. Hegde, Bharatkumar & Ahmed, Qadeer & Rizzoni, Giorgio, 2022. "Energy saving analysis in electrified powertrain using look-ahead energy management scheme," Applied Energy, Elsevier, vol. 325(C).
    11. Li, Xunming & Han, Lijin & Liu, Hui & Wang, Weida & Xiang, Changle, 2019. "Real-time optimal energy management strategy for a dual-mode power-split hybrid electric vehicle based on an explicit model predictive control algorithm," Energy, Elsevier, vol. 172(C), pages 1161-1178.
    12. Fan, Likang & Wang, Yufei & Wei, Hongqian & Zhang, Youtong & Zheng, Pengyu & Huang, Tianyi & Li, Wei, 2022. "A GA-based online real-time optimized energy management strategy for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 241(C).
    13. Liu, Yonggang & Liu, Junjun & Zhang, Yuanjian & Wu, Yitao & Chen, Zheng & Ye, Ming, 2020. "Rule learning based energy management strategy of fuel cell hybrid vehicles considering multi-objective optimization," Energy, Elsevier, vol. 207(C).

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