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Multi-step reinforcement learning for model-free predictive energy management of an electrified off-highway vehicle

Author

Listed:
  • Zhou, Quan
  • Li, Ji
  • Shuai, Bin
  • Williams, Huw
  • He, Yinglong
  • Li, Ziyang
  • Xu, Hongming
  • Yan, Fuwu

Abstract

The energy management system of an electrified vehicle is one of the most important supervisory control systems which manages the use of on-board energy resources. This paper researches a ‘model-free’ predictive energy management system for a connected electrified off-highway vehicle. A new reinforcement learning algorithm with the capability of ‘multi-step’ learning is proposed to enable the all-life-long online optimisation of the energy management control policy. Three multi-step learning strategies (Sum-to-Terminal, Average-to-Neighbour Recurrent-to-Terminal) are researched for the first time. Hardware-in-the-loop tests are carried out to examine the control functionality for real application of the proposed ‘model-free’ method. The results show that the proposed method can continuously improve the vehicle’s energy efficiency during the real-time hardware-in-the-loop test, which increased from the initial level of 34% to 44% after 5 h’ 35-step learning. Compared with a well-designed model-based predictive energy management control policy, the model-free predictive energy management method can increase the prediction horizon length by 71% (from 35 to 65 steps with 1 s interval in real-time computation) and can save energy by at least 7.8% for the same driving conditions.

Suggested Citation

  • Zhou, Quan & Li, Ji & Shuai, Bin & Williams, Huw & He, Yinglong & Li, Ziyang & Xu, Hongming & Yan, Fuwu, 2019. "Multi-step reinforcement learning for model-free predictive energy management of an electrified off-highway vehicle," Applied Energy, Elsevier, vol. 255(C).
    • Handle: RePEc:eee:appene:v:255:y:2019:i:c:s0306261919314424
    DOI: 10.1016/j.apenergy.2019.113755
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    Cited by:

    1. Zhou, Quan & Li, Yanfei & Zhao, Dezong & Li, Ji & Williams, Huw & Xu, Hongming & Yan, Fuwu, 2022. "Transferable representation modelling for real-time energy management of the plug-in hybrid vehicle based on k-fold fuzzy learning and Gaussian process regression," Applied Energy, Elsevier, vol. 305(C).
    2. Shafqat Jawad & Junyong Liu, 2020. "Electrical Vehicle Charging Services Planning and Operation with Interdependent Power Networks and Transportation Networks: A Review of the Current Scenario and Future Trends," Energies, MDPI, vol. 13(13), pages 1-24, July.
    3. Hua, Min & Zhang, Cetengfei & Zhang, Fanggang & Li, Zhi & Yu, Xiaoli & Xu, Hongming & Zhou, Quan, 2023. "Energy management of multi-mode plug-in hybrid electric vehicle using multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 348(C).
    4. Yang, Ningkang & Ruan, Shumin & Han, Lijin & Liu, Hui & Guo, Lingxiong & Xiang, Changle, 2023. "Reinforcement learning-based real-time intelligent energy management for hybrid electric vehicles in a model predictive control framework," Energy, Elsevier, vol. 270(C).
    5. 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).
    6. Mojgan Fayyazi & Paramjotsingh Sardar & Sumit Infent Thomas & Roonak Daghigh & Ali Jamali & Thomas Esch & Hans Kemper & Reza Langari & Hamid Khayyam, 2023. "Artificial Intelligence/Machine Learning in Energy Management Systems, Control, and Optimization of Hydrogen Fuel Cell Vehicles," Sustainability, MDPI, vol. 15(6), pages 1-38, March.
    7. Mi, Jia & Li, Qiaofeng & Liu, Mingyi & Li, Xiaofan & Zuo, Lei, 2020. "Design, modelling, and testing of a vibration energy harvester using a novel half-wave mechanical rectification," Applied Energy, Elsevier, vol. 279(C).
    8. Zhang, Hao & Fan, Qinhao & Liu, Shang & Li, Shengbo Eben & Huang, Jin & Wang, Zhi, 2021. "Hierarchical energy management strategy for plug-in hybrid electric powertrain integrated with dual-mode combustion engine," Applied Energy, Elsevier, vol. 304(C).
    9. Dong, Peng & Zhao, Junwei & Liu, Xuewu & Wu, Jian & Xu, Xiangyang & Liu, Yanfang & Wang, Shuhan & Guo, Wei, 2022. "Practical application of energy management strategy for hybrid electric vehicles based on intelligent and connected technologies: Development stages, challenges, and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    10. Shuai, Bin & Zhou, Quan & Li, Ji & He, Yinglong & Li, Ziyang & Williams, Huw & Xu, Hongming & Shuai, Shijin, 2020. "Heuristic action execution for energy efficient charge-sustaining control of connected hybrid vehicles with model-free double Q-learning," Applied Energy, Elsevier, vol. 267(C).
    11. Qiu, Dawei & Wang, Yi & Sun, Mingyang & Strbac, Goran, 2022. "Multi-service provision for electric vehicles in power-transportation networks towards a low-carbon transition: A hierarchical and hybrid multi-agent reinforcement learning approach," Applied Energy, Elsevier, vol. 313(C).
    12. 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).
    13. Liu, Huanlong & Chen, Guanpeng & Li, Dafa & Wang, Jiawei & Zhou, Jianyi, 2021. "Energy active adjustment and bidirectional transfer management strategy of the electro-hydrostatic hydraulic hybrid powertrain for battery bus," Energy, Elsevier, vol. 230(C).
    14. Chen, Zheng & Hu, Hengjie & Wu, Yitao & Zhang, Yuanjian & Li, Guang & Liu, Yonggang, 2020. "Stochastic model predictive control for energy management of power-split plug-in hybrid electric vehicles based on reinforcement learning," Energy, Elsevier, vol. 211(C).
    15. Hu, Dong & Xie, Hui & Song, Kang & Zhang, Yuanyuan & Yan, Long, 2023. "An apprenticeship-reinforcement learning scheme based on expert demonstrations for energy management strategy of hybrid electric vehicles," Applied Energy, Elsevier, vol. 342(C).
    16. Daniel Egan & Qilun Zhu & Robert Prucka, 2023. "A Review of Reinforcement Learning-Based Powertrain Controllers: Effects of Agent Selection for Mixed-Continuity Control and Reward Formulation," Energies, MDPI, vol. 16(8), pages 1-31, April.
    17. Liu, Huanlong & Chen, Guanpeng & Xie, Chixin & Li, Dafa & Wang, Jiawei & Li, Shun, 2020. "Research on energy-saving characteristics of battery-powered electric-hydrostatic hydraulic hybrid rail vehicles," Energy, Elsevier, vol. 205(C).
    18. Wei, Hongqian & Zhang, Nan & Liang, Jun & Ai, Qiang & Zhao, Wenqiang & Huang, Tianyi & Zhang, Youtong, 2022. "Deep reinforcement learning based direct torque control strategy for distributed drive electric vehicles considering active safety and energy saving performance," Energy, Elsevier, vol. 238(PB).
    19. Xing, Yang & Lv, Chen & Cao, Dongpu & Lu, Chao, 2020. "Energy oriented driving behavior analysis and personalized prediction of vehicle states with joint time series modeling," Applied Energy, Elsevier, vol. 261(C).
    20. Fuwu Yan & Jinhai Wang & Changqing Du & Min Hua, 2022. "Multi-Objective Energy Management Strategy for Hybrid Electric Vehicles Based on TD3 with Non-Parametric Reward Function," Energies, MDPI, vol. 16(1), pages 1-17, December.
    21. Yang, Ningkang & Han, Lijin & Bo, Lin & Liu, Baoshuai & Chen, Xiuqi & Liu, Hui & Xiang, Changle, 2023. "Real-time adaptive energy management for off-road hybrid electric vehicles based on decision-time planning," Energy, Elsevier, vol. 282(C).
    22. Perera, A.T.D. & Kamalaruban, Parameswaran, 2021. "Applications of reinforcement learning in energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    23. Mudhafar Al-Saadi & Maher Al-Greer & Michael Short, 2023. "Reinforcement Learning-Based Intelligent Control Strategies for Optimal Power Management in Advanced Power Distribution Systems: A Survey," Energies, MDPI, vol. 16(4), pages 1-38, February.
    24. Wang, Bingzheng & Yu, Xiaoli & Xu, Hongming & Wu, Qian & Wang, Lei & Huang, Rui & Li, Zhi & Zhou, Quan, 2022. "Scenario analysis, management, and optimization of a new Vehicle-to-Micro-Grid (V2μG) network based on off-grid renewable building energy systems," Applied Energy, Elsevier, vol. 325(C).
    25. Hou, Shengyan & Yin, Hai & Xu, Fuguo & Benjamín, Pla & Gao, Jinwu & Chen, Hong, 2023. "Multihorizon predictive energy optimization and lifetime management for connected fuel cell electric vehicles," Energy, Elsevier, vol. 266(C).

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