IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v242y2022ics0360544221027973.html
   My bibliography  Save this article

Self-learning energy management strategy for hybrid electric vehicle via curiosity-inspired asynchronous deep reinforcement learning

Author

Listed:
  • Zhou, Jianhao
  • Xue, Yuan
  • Xu, Da
  • Li, Chaoxiong
  • Zhao, Wanzhong

Abstract

In this study, the formulation of a curiosity-inspired asynchronous advantage actor-critic (A3C+) based model-free, self-learning energy management strategy (EMS) is elaborated for hybrid powertrain. The fuel optimality of standard A3C based EMS is strongly influenced by the target strategy including charge sustenance (CS) and charge depletion (CD), being around 92% and 83% in comparison to dynamic programming (DP) based EMS under training cycle. The corresponding performance of A3C derived CD policy further declined to 75% under testing cycles. Similar tendencies are verified by model predictive control (MPC) and deep deterministic policy gradient (DDPG) based EMSs. To this end, random network distillation (RND) and inverse dynamics model (IDM) techniques are incorporated to form novelty-seeking module (NSM) which is leveraged as intrinsic rewards to facilitate the exploration performance. As a result, the proposed A3C + based EMS obtains significant improvement of global optimality, generalization ability and adaptivity. The fuel optimality of A3C + derived CS and CD policies can be guaranteed by at least 92% and 88% under training and testing cycles, comparing with DP based EMS. Besides, the training and running efficacy of A3C + based EMS are apparently superior to MPC based EMS, demonstrating its realtime implementation potential.

Suggested Citation

  • Zhou, Jianhao & Xue, Yuan & Xu, Da & Li, Chaoxiong & Zhao, Wanzhong, 2022. "Self-learning energy management strategy for hybrid electric vehicle via curiosity-inspired asynchronous deep reinforcement learning," Energy, Elsevier, vol. 242(C).
    • Handle: RePEc:eee:energy:v:242:y:2022:i:c:s0360544221027973
    DOI: 10.1016/j.energy.2021.122548
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221027973
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2021.122548?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Xie, Shaobo & Hu, Xiaosong & Xin, Zongke & Brighton, James, 2019. "Pontryagin’s Minimum Principle based model predictive control of energy management for a plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 236(C), pages 893-905.
    2. Lian, Renzong & Peng, Jiankun & Wu, Yuankai & Tan, Huachun & Zhang, Hailong, 2020. "Rule-interposing deep reinforcement learning based energy management strategy for power-split hybrid electric vehicle," Energy, Elsevier, vol. 197(C).
    3. Hannan, M.A. & Azidin, F.A. & Mohamed, A., 2014. "Hybrid electric vehicles and their challenges: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 135-150.
    4. Zou Yuan & Liu Teng & Sun Fengchun & Huei Peng, 2013. "Comparative Study of Dynamic Programming and Pontryagin’s Minimum Principle on Energy Management for a Parallel Hybrid Electric Vehicle," Energies, MDPI, vol. 6(4), pages 1-14, April.
    5. Zhang, Fengqi & Hu, Xiaosong & Langari, Reza & Wang, Lihua & Cui, Yahui & Pang, Hui, 2021. "Adaptive energy management in automated hybrid electric vehicles with flexible torque request," Energy, Elsevier, vol. 214(C).
    6. Zou, Runnan & Fan, Likang & Dong, Yanrui & Zheng, Siyu & Hu, Chenxing, 2021. "DQL energy management: An online-updated algorithm and its application in fix-line hybrid electric vehicle," Energy, Elsevier, vol. 225(C).
    7. Qi, Chunyang & Song, Chuanxue & Xiao, Feng & Song, Shixin, 2022. "Generalization ability of hybrid electric vehicle energy management strategy based on reinforcement learning method," Energy, Elsevier, vol. 250(C).
    8. M. Sabri, M.F. & Danapalasingam, K.A. & Rahmat, M.F., 2016. "A review on hybrid electric vehicles architecture and energy management strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1433-1442.
    9. Zou, Yuan & Liu, Teng & Liu, Dexing & Sun, Fengchun, 2016. "Reinforcement learning-based real-time energy management for a hybrid tracked vehicle," Applied Energy, Elsevier, vol. 171(C), pages 372-382.
    10. 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).
    11. Wu, Jingda & He, Hongwen & Peng, Jiankun & Li, Yuecheng & Li, Zhanjiang, 2018. "Continuous reinforcement learning of energy management with deep Q network for a power split hybrid electric bus," Applied Energy, Elsevier, vol. 222(C), pages 799-811.
    12. Hu, Jiayi & Li, Jianqiu & Hu, Zunyan & Xu, Liangfei & Ouyang, Minggao, 2021. "Power distribution strategy of a dual-engine system for heavy-duty hybrid electric vehicles using dynamic programming," Energy, Elsevier, vol. 215(PA).
    13. Amjad, Shaik & Neelakrishnan, S. & Rudramoorthy, R., 2010. "Review of design considerations and technological challenges for successful development and deployment of plug-in hybrid electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(3), pages 1104-1110, April.
    14. Qi, Chunyang & Zhu, Yiwen & Song, Chuanxue & Yan, Guangfu & Xiao, Feng & Da wang, & Zhang, Xu & Cao, Jingwei & Song, Shixin, 2022. "Hierarchical reinforcement learning based energy management strategy for hybrid electric vehicle," Energy, Elsevier, vol. 238(PA).
    15. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
    16. Zhou, Jianhao & Xue, Siwu & Xue, Yuan & Liao, Yuhui & Liu, Jun & Zhao, Wanzhong, 2021. "A novel energy management strategy of hybrid electric vehicle via an improved TD3 deep reinforcement learning," Energy, Elsevier, vol. 224(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hongtu Yang & Yan Sun & Changgao Xia & Hongdang Zhang, 2022. "Research on Energy Management Strategy of Fuel Cell Electric Tractor Based on Multi-Algorithm Fusion and Optimization," Energies, MDPI, vol. 15(17), pages 1-15, September.
    2. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Liu, Teng & Tan, Wenhao & Tang, Xiaolin & Zhang, Jinwei & Xing, Yang & Cao, Dongpu, 2021. "Driving conditions-driven energy management strategies for hybrid electric vehicles: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    3. Wu, Yuankai & Tan, Huachun & Peng, Jiankun & Zhang, Hailong & He, Hongwen, 2019. "Deep reinforcement learning of energy management with continuous control strategy and traffic information for a series-parallel plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 247(C), pages 454-466.
    4. Qi, Chunyang & Zhu, Yiwen & Song, Chuanxue & Yan, Guangfu & Xiao, Feng & Da wang, & Zhang, Xu & Cao, Jingwei & Song, Shixin, 2022. "Hierarchical reinforcement learning based energy management strategy for hybrid electric vehicle," Energy, Elsevier, vol. 238(PA).
    5. Xiao, B. & Ruan, J. & Yang, W. & Walker, P.D. & Zhang, N., 2021. "A review of pivotal energy management strategies for extended range electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    6. 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).
    7. 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.
    8. Qi, Chunyang & Song, Chuanxue & Xiao, Feng & Song, Shixin, 2022. "Generalization ability of hybrid electric vehicle energy management strategy based on reinforcement learning method," Energy, Elsevier, vol. 250(C).
    9. Liu, Yonggang & Wu, Yitao & Wang, Xiangyu & Li, Liang & Zhang, Yuanjian & Chen, Zheng, 2023. "Energy management for hybrid electric vehicles based on imitation reinforcement learning," Energy, Elsevier, vol. 263(PC).
    10. Du, Guodong & Zou, Yuan & Zhang, Xudong & Liu, Teng & Wu, Jinlong & He, Dingbo, 2020. "Deep reinforcement learning based energy management for a hybrid electric vehicle," Energy, Elsevier, vol. 201(C).
    11. Lian, Renzong & Peng, Jiankun & Wu, Yuankai & Tan, Huachun & Zhang, Hailong, 2020. "Rule-interposing deep reinforcement learning based energy management strategy for power-split hybrid electric vehicle," Energy, Elsevier, vol. 197(C).
    12. Zhou, Jianhao & Xue, Siwu & Xue, Yuan & Liao, Yuhui & Liu, Jun & Zhao, Wanzhong, 2021. "A novel energy management strategy of hybrid electric vehicle via an improved TD3 deep reinforcement learning," Energy, Elsevier, vol. 224(C).
    13. 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).
    14. Du, Guodong & Zou, Yuan & Zhang, Xudong & Guo, Lingxiong & Guo, Ningyuan, 2022. "Energy management for a hybrid electric vehicle based on prioritized deep reinforcement learning framework," Energy, Elsevier, vol. 241(C).
    15. Das, Himadry Shekhar & Tan, Chee Wei & Yatim, A.H.M., 2017. "Fuel cell hybrid electric vehicles: A review on power conditioning units and topologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 268-291.
    16. Miranda, Matheus H.R. & Silva, Fabrício L. & Lourenço, Maria A.M. & Eckert, Jony J. & Silva, Ludmila C.A., 2022. "Vehicle drivetrain and fuzzy controller optimization using a planar dynamics simulation based on a real-world driving cycle," Energy, Elsevier, vol. 257(C).
    17. Chen, Jiaxin & Shu, Hong & Tang, Xiaolin & Liu, Teng & Wang, Weida, 2022. "Deep reinforcement learning-based multi-objective control of hybrid power system combined with road recognition under time-varying environment," Energy, Elsevier, vol. 239(PC).
    18. Sun, Wenjing & Zou, Yuan & Zhang, Xudong & Guo, Ningyuan & Zhang, Bin & Du, Guodong, 2022. "High robustness energy management strategy of hybrid electric vehicle based on improved soft actor-critic deep reinforcement learning," Energy, Elsevier, vol. 258(C).
    19. Wu, Changcheng & Ruan, Jiageng & Cui, Hanghang & Zhang, Bin & Li, Tongyang & Zhang, Kaixuan, 2023. "The application of machine learning based energy management strategy in multi-mode plug-in hybrid electric vehicle, part I: Twin Delayed Deep Deterministic Policy Gradient algorithm design for hybrid ," Energy, Elsevier, vol. 262(PB).
    20. Zhuang, Weichao & Li (Eben), Shengbo & Zhang, Xiaowu & Kum, Dongsuk & Song, Ziyou & Yin, Guodong & Ju, Fei, 2020. "A survey of powertrain configuration studies on hybrid electric vehicles," Applied Energy, Elsevier, vol. 262(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:242:y:2022:i:c:s0360544221027973. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.