IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v269y2023ics0360544223002529.html
   My bibliography  Save this article

Double deep Q-network guided energy management strategy of a novel electric-hydraulic hybrid electric vehicle

Author

Listed:
  • Zhang, Zhen
  • Zhang, Tiezhu
  • Hong, Jichao
  • Zhang, Hongxin
  • Yang, Jian
  • Jia, Qingxiao

Abstract

The configuration and development of energy management strategies (EMSs) are generating considerable interest regarding vehicles due to the rapid blossoming momentum of electric vehicles. Battery state of charge is one of the main characterization parameters for evaluating EMSs, so the practical advancement of the range is critical to the evolution of electric vehicles. A novel electric-hydraulic hybrid electric vehicle (EHHEV) is investigated in this paper, which has the characteristics of various working modes and multi-energy sources. According to the hybrid system's energy flow, a rule-based control strategy is established, and the superiority of EHHEV in energy management is verified by steady-state simulation. Further, this paper combines Q-learning with deep neural networks to construct a double deep Q-network (DDQN)-guided EMS to solve traditional control strategy and reinforcement learning issues. After appropriate hyperparameters setting and batch training, the EMS can make EHHEV realize the optimal switching among working modes. Experimental results showcase that the EMS can significantly enhance vehicle economy. This is the first of its kind to apply the DDQN to developing EMS for EHHEV.

Suggested Citation

  • Zhang, Zhen & Zhang, Tiezhu & Hong, Jichao & Zhang, Hongxin & Yang, Jian & Jia, Qingxiao, 2023. "Double deep Q-network guided energy management strategy of a novel electric-hydraulic hybrid electric vehicle," Energy, Elsevier, vol. 269(C).
    • Handle: RePEc:eee:energy:v:269:y:2023:i:c:s0360544223002529
    DOI: 10.1016/j.energy.2023.126858
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223002529
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.126858?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. 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).
    2. Randive, Vaibhav & Subramanian, Shankar C. & Thondiyath, Asokan, 2021. "Design and analysis of a hybrid electric powertrain for military tracked vehicles," Energy, Elsevier, vol. 229(C).
    3. Loïc Joud & Rui Da Silva & Daniela Chrenko & Alan Kéromnès & Luis Le Moyne, 2020. "Smart Energy Management for Series Hybrid Electric Vehicles Based on Driver Habits Recognition and Prediction," Energies, MDPI, vol. 13(11), pages 1-17, June.
    4. Junwoo Song & Simon Hu & Ke Han & Chaozhe Jiang, 2020. "Nonlinear Decision Rule Approach for Real-Time Traffic Signal Control for Congestion and Emission Mitigation," Networks and Spatial Economics, Springer, vol. 20(3), pages 675-702, September.
    5. Geng, Wenran & Lou, Diming & Wang, Chen & Zhang, Tong, 2020. "A cascaded energy management optimization method of multimode power-split hybrid electric vehicles," Energy, Elsevier, vol. 199(C).
    6. Li, Shuangqi & He, Hongwen & Zhao, Pengfei, 2021. "Energy management for hybrid energy storage system in electric vehicle: A cyber-physical system perspective," Energy, Elsevier, vol. 230(C).
    7. Sziroczak, David & Jankovics, Istvan & Gal, Istvan & Rohacs, Daniel, 2020. "Conceptual design of small aircraft with hybrid-electric propulsion systems," Energy, Elsevier, vol. 204(C).
    8. 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).
    9. Xu, Bin & Rathod, Dhruvang & Zhang, Darui & Yebi, Adamu & Zhang, Xueyu & Li, Xiaoya & Filipi, Zoran, 2020. "Parametric study on reinforcement learning optimized energy management strategy for a hybrid electric vehicle," Applied Energy, Elsevier, vol. 259(C).
    10. Hong, Jichao & Wang, Zhenpo & Yao, Yongtao, 2019. "Fault prognosis of battery system based on accurate voltage abnormity prognosis using long short-term memory neural networks," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    11. Hong, Jichao & Wang, Zhenpo & Qu, Changhui & Zhou, Yangjie & Shan, Tongxin & Zhang, Jinghan & Hou, Yankai, 2022. "Investigation on overcharge-caused thermal runaway of lithium-ion batteries in real-world electric vehicles," Applied Energy, Elsevier, vol. 321(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. Li, Lin & Zhang, Tiezhu & Lu, Liqun & Zhang, Hongxin & Yang, Jian & Zhang, Zhen, 2023. "An energy active regulation management strategy based on driving mode recognition for electro-hydraulic hybrid vehicles," Energy, Elsevier, vol. 285(C).
    2. Tian, Weiyong & Liu, Li & Zhang, Xiaohui & Shao, Jiaqi, 2024. "Flight trajectory and energy management coupled optimization for hybrid electric UAVs with adaptive sequential convex programming method," Applied Energy, Elsevier, vol. 364(C).
    3. Zhiwen Zhang & Jie Tang & Jiyuan Zhang & Tianci Zhang, 2024. "Research on Energy Hierarchical Management and Optimal Control of Compound Power Electric Vehicle," Energies, MDPI, vol. 17(6), pages 1-22, March.
    4. Hong, Jichao & Zhang, Tiezhu & Zhang, Zhen & Zhang, Hongxin, 2023. "Investigation of energy management strategy for a novel electric-hydraulic hybrid vehicle: Self-adaptive electric-hydraulic ratio," Energy, Elsevier, vol. 278(C).

    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. Li, Lin & Zhang, Tiezhu & Lu, Liqun & Zhang, Hongxin & Yang, Jian & Zhang, Zhen, 2023. "An energy active regulation management strategy based on driving mode recognition for electro-hydraulic hybrid vehicles," Energy, Elsevier, vol. 285(C).
    2. 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).
    3. Wilberforce, Tabbi & Anser, Afaaq & Swamy, Jangam Aishwarya & Opoku, Richard, 2023. "An investigation into hybrid energy storage system control and power distribution for hybrid electric vehicles," Energy, Elsevier, vol. 279(C).
    4. Zhiwen Zhang & Jie Tang & Jiyuan Zhang & Tianci Zhang, 2024. "Research on Energy Hierarchical Management and Optimal Control of Compound Power Electric Vehicle," Energies, MDPI, vol. 17(6), pages 1-22, March.
    5. Hong, Jichao & Zhang, Tiezhu & Zhang, Zhen & Zhang, Hongxin, 2023. "Investigation of energy management strategy for a novel electric-hydraulic hybrid vehicle: Self-adaptive electric-hydraulic ratio," Energy, Elsevier, vol. 278(C).
    6. Hong, Jichao & Zhang, Huaqin & Zhang, Xinyang & Yang, Haixu & Chen, Yingjie & Wang, Facheng & Huang, Zhongguo & Wang, Wei, 2024. "Online accurate voltage prediction with sparse data for the whole life cycle of Lithium-ion batteries in electric vehicles," Applied Energy, Elsevier, vol. 369(C).
    7. Guo, Xiaokai & Yan, Xianguo & Chen, Zhi & Meng, Zhiyu, 2022. "Research on energy management strategy of heavy-duty fuel cell hybrid vehicles based on dueling-double-deep Q-network," Energy, Elsevier, vol. 260(C).
    8. 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).
    9. Huang, Ying & Wang, Shilong & Li, Ke & Fan, Zhuwei & Xie, Haiming & Jiang, Fachao, 2023. "Multi-parameter adaptive online energy management strategy for concrete truck mixers with a novel hybrid powertrain considering vehicle mass," Energy, Elsevier, vol. 277(C).
    10. Alcázar-García, Désirée & Romeral Martínez, José Luis, 2022. "Model-based design validation and optimization of drive systems in electric, hybrid, plug-in hybrid and fuel cell vehicles," Energy, Elsevier, vol. 254(PA).
    11. Li, Jie & Wu, Xiaodong & Xu, Min & Liu, Yonggang, 2022. "Deep reinforcement learning and reward shaping based eco-driving control for automated HEVs among signalized intersections," Energy, Elsevier, vol. 251(C).
    12. 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).
    13. Huaqin Zhang & Jichao Hong & Zhezhe Wang & Guodong Wu, 2022. "State-Partial Accurate Voltage Fault Prognosis for Lithium-Ion Batteries Based on Self-Attention Networks," Energies, MDPI, vol. 15(22), pages 1-14, November.
    14. Li, Lin & Zhang, Tiezhu & Sun, Binbin & Wu, Kaiwei & Sun, Zehao & Zhang, Zhen & Lin, Lianhua & Xu, Haigang, 2023. "Research on electro-hydraulic ratios for a novel mechanical-electro-hydraulic power coupling electric vehicle," Energy, Elsevier, vol. 270(C).
    15. Yang, Kun & Zhang, Benjun & Chu, Yongkun & Wang, Zhongwei & Shao, Changjiang & Ma, Chao, 2024. "Research on the configuration design and energy management of a novel plug-in hybrid electric vehicle based on the double-rotor motor and hybrid energy storage system," Energy, Elsevier, vol. 302(C).
    16. Serrano, José Ramón & Piqueras, Pedro & De la Morena, Joaquín & Gómez-Vilanova, Alejandro & Guilain, Stéphane, 2021. "Methodological analysis of variable geometry turbine technology impact on the performance of highly downsized spark-ignition engines," Energy, Elsevier, vol. 215(PB).
    17. Shi, Dehua & Liu, Sheng & Cai, Yingfeng & Wang, Shaohua & Li, Haoran & Chen, Long, 2021. "Pontryagin’s minimum principle based fuzzy adaptive energy management for hybrid electric vehicle using real-time traffic information," Applied Energy, Elsevier, vol. 286(C).
    18. Cui, Binghan & Wang, Han & Li, Renlong & Xiang, Lizhi & Zhao, Huaian & Xiao, Rang & Li, Sai & Liu, Zheng & Yin, Geping & Cheng, Xinqun & Ma, Yulin & Huo, Hua & Zuo, Pengjian & Lu, Taolin & Xie, Jingyi, 2024. "Ultra-early prediction of lithium-ion battery performance using mechanism and data-driven fusion model," Applied Energy, Elsevier, vol. 353(PA).
    19. Ma, Zhikai & Huo, Qian & Wang, Wei & Zhang, Tao, 2023. "Voltage-temperature aware thermal runaway alarming framework for electric vehicles via deep learning with attention mechanism in time-frequency domain," Energy, Elsevier, vol. 278(C).
    20. Sun, Alexander Y., 2020. "Optimal carbon storage reservoir management through deep reinforcement learning," Applied Energy, Elsevier, vol. 278(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:269:y:2023:i:c:s0360544223002529. 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.