IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i24p6238-d1541295.html
   My bibliography  Save this article

Research on Energy Management Strategies Based on Bargaining Game for Range-Extended Electric Vehicle Considering Battery Life

Author

Listed:
  • Zhenhai Gao

    (Xinxiang Vocational and Technical College, Xinxiang 453000, China)

  • Jiewen Liu

    (College of Automotive Engineering, Jilin University, Changchun 130022, China)

  • Shiqing Long

    (College of Transportation, Jilin University, Changchun 130022, China)

  • Zihang Su

    (College of Transportation, Jilin University, Changchun 130022, China)

  • Hanwu Liu

    (College of Transportation, Jilin University, Changchun 130022, China)

  • Cheng Chang

    (School of Vehicle and Traffic Engineering, Henan Institute of Technology, Xinxiang 453003, China)

  • Wang Song

    (College of Materials Science and Engineering, Changchun University of Science and Technology, Changchun 130022, China)

Abstract

Effective energy management techniques are essential for the full utilization of energy in the field of extended-range electric vehicle research, with the goals of lowering energy consumption and exhaust emissions, enhancing driving comfort, and extending battery life. To achieve optimal comprehensive usage costs, this article uses bargaining game theory to design an adaptive energy management strategy ( EMS ad-bg ) that focuses on battery life. In the study, a power system model was first built based on AVL/Cruise software and MATLAB/Simulink software. The impact of discount factors on strategy results was analyzed through simulation experiments. The results showed that the discount factor for auxiliary power unit (APU) focused more on energy optimization, while the discount factor for battery focused more on optimizing the degradation of battery life. When the initial state of charge (SoC) is high, the specific value of the discount factor also has a significant impact on the battery SoC value at the end of the trip. To improve the strategy’s adaptability to various initial SoC values, a fuzzy controller was created that can adaptively modify the discount factor based on the battery SoC. The results of the simulation experiment demonstrate that the bargaining game strategy taking SoC into account has more pronounced advantages in terms of overall usage cost when compared to the strategy of the fixed discount factor. The creation of an EMS ad-bg that takes battery life into account based on a bargaining game can serve as a helpful model for the creation of a clever EMS that lowers the total cost of operating a vehicle.

Suggested Citation

  • Zhenhai Gao & Jiewen Liu & Shiqing Long & Zihang Su & Hanwu Liu & Cheng Chang & Wang Song, 2024. "Research on Energy Management Strategies Based on Bargaining Game for Range-Extended Electric Vehicle Considering Battery Life," Energies, MDPI, vol. 17(24), pages 1-21, December.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:24:p:6238-:d:1541295
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/24/6238/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/24/6238/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Li, Junqiu & Jin, Xin & Xiong, Rui, 2017. "Multi-objective optimization study of energy management strategy and economic analysis for a range-extended electric bus," Applied Energy, Elsevier, vol. 194(C), pages 798-807.
    2. Tarroja, Brian & Shaffer, Brendan & Samuelsen, Scott, 2015. "The importance of grid integration for achievable greenhouse gas emissions reductions from alternative vehicle technologies," Energy, Elsevier, vol. 87(C), pages 504-519.
    3. Peng, Jiankun & He, Hongwen & Xiong, Rui, 2017. "Rule based energy management strategy for a series–parallel plug-in hybrid electric bus optimized by dynamic programming," Applied Energy, Elsevier, vol. 185(P2), pages 1633-1643.
    4. Chen, Bo-Chiuan & Wu, Yuh-Yih & Tsai, Hsien-Chi, 2014. "Design and analysis of power management strategy for range extended electric vehicle using dynamic programming," Applied Energy, Elsevier, vol. 113(C), pages 1764-1774.
    5. Sun, Fengchun & Xiong, Rui & He, Hongwen, 2016. "A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique," Applied Energy, Elsevier, vol. 162(C), pages 1399-1409.
    6. Wang, Hong & Huang, Yanjun & Khajepour, Amir & Song, Qiang, 2016. "Model predictive control-based energy management strategy for a series hybrid electric tracked vehicle," Applied Energy, Elsevier, vol. 182(C), pages 105-114.
    7. Zhang, Yahui & Wei, Zeyi & Wang, Zhong & Tian, Yang & Wang, Jizhe & Tian, Zhikun & Xu, Fuguo & Jiao, Xiaohong & Li, Liang & Wen, Guilin, 2024. "Hierarchical eco-driving control strategy for connected automated fuel cell hybrid vehicles and scenario-/hardware-in-the loop validation," Energy, Elsevier, vol. 292(C).
    Full references (including those not matched with items on IDEAS)

    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. Liu, Hanwu & Lei, Yulong & Fu, Yao & Li, Xingzhong, 2022. "A novel hybrid-point-line energy management strategy based on multi-objective optimization for range-extended electric vehicle," Energy, Elsevier, vol. 247(C).
    2. Liu, Hanwu & Lei, Yulong & Sun, Wencai & Chang, Cheng & Jiang, Wei & Liu, Yuwei & Hu, Jianlong, 2024. "Research on approximate optimal energy management and multi-objective optimization of connected automated range-extended electric vehicle," Energy, Elsevier, vol. 306(C).
    3. Gye-Seong Lee & Dong-Hyun Kim & Jong-Ho Han & Myeong-Hwan Hwang & Hyun-Rok Cha, 2019. "Optimal Operating Point Determination Method Design for Range-Extended Electric Vehicles Based on Real Driving Tests," Energies, MDPI, vol. 12(5), pages 1-17, March.
    4. Xu, Nan & Kong, Yan & Yan, Jinyue & Zhang, Yuanjian & Sui, Yan & Ju, Hao & Liu, Heng & Xu, Zhe, 2022. "Global optimization energy management for multi-energy source vehicles based on “Information layer - Physical layer - Energy layer - Dynamic programming” (IPE-DP)," Applied Energy, Elsevier, vol. 312(C).
    5. Ye Yang & Youtong Zhang & Jingyi Tian & Si Zhang, 2018. "Research on a Plug-In Hybrid Electric Bus Energy Management Strategy Considering Drivability," Energies, MDPI, vol. 11(8), pages 1-22, August.
    6. Rezaei, A. & Burl, J.B. & Solouk, A. & Zhou, B. & Rezaei, M. & Shahbakhti, M., 2017. "Catch energy saving opportunity (CESO), an instantaneous optimal energy management strategy for series hybrid electric vehicles," Applied Energy, Elsevier, vol. 208(C), pages 655-665.
    7. Bizon, Nicu, 2017. "Energy optimization of fuel cell system by using global extremum seeking algorithm," Applied Energy, Elsevier, vol. 206(C), pages 458-474.
    8. Bizon, Nicu, 2019. "Real-time optimization strategies of Fuel Cell Hybrid Power Systems based on Load-following control: A new strategy, and a comparative study of topologies and fuel economy obtained," Applied Energy, Elsevier, vol. 241(C), pages 444-460.
    9. Bizon, Nicu, 2019. "Efficient fuel economy strategies for the Fuel Cell Hybrid Power Systems under variable renewable/load power profile," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    10. Hou, Daizheng & Sun, Qun & Bao, Chunjiang & Cheng, Xingqun & Guo, Hongqiang & Zhao, Ying, 2019. "An all-in-one design method for plug-in hybrid electric buses considering uncertain factor of driving cycles," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    11. Zeyu Chen & Jiahuan Lu & Bo Liu & Nan Zhou & Shijie Li, 2020. "Optimal Energy Management of Plug-In Hybrid Electric Vehicles Concerning the Entire Lifespan of Lithium-Ion Batteries," Energies, MDPI, vol. 13(10), pages 1-15, May.
    12. Li, Junqiu & Wang, Yihe & Chen, Jianwen & Zhang, Xiaopeng, 2017. "Study on energy management strategy and dynamic modeling for auxiliary power units in range-extended electric vehicles," Applied Energy, Elsevier, vol. 194(C), pages 363-375.
    13. Kong, Yan & Xu, Nan & Liu, Qiao & Sui, Yan & Jia, Yifan, 2024. "Variable horizon-based predictive energy management strategy for plug-in hybrid electric vehicles and determination of a suitable predictive horizon," Energy, Elsevier, vol. 294(C).
    14. Wang, Zhiguo & Wei, Hongqian & Xi, Yecheng & Xiao, Gongwei, 2024. "Data-driven energy utilization for plug-in hybrid electric bus with driving patten application and battery health considerations," Energy, Elsevier, vol. 310(C).
    15. Zhou, Yu & Ong, Ghim Ping & Meng, Qiang, 2023. "The road to electrification: Bus fleet replacement strategies," Applied Energy, Elsevier, vol. 337(C).
    16. Zhang, Shuo & Xiong, Rui & Sun, Fengchun, 2017. "Model predictive control for power management in a plug-in hybrid electric vehicle with a hybrid energy storage system," Applied Energy, Elsevier, vol. 185(P2), pages 1654-1662.
    17. Changqing Du & Shiyang Huang & Yuyao Jiang & Dongmei Wu & Yang Li, 2022. "Optimization of Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles Based on Dynamic Programming," Energies, MDPI, vol. 15(12), pages 1-25, June.
    18. Nicu Bizon & Mihai Oproescu, 2018. "Experimental Comparison of Three Real-Time Optimization Strategies Applied to Renewable/FC-Based Hybrid Power Systems Based on Load-Following Control," Energies, MDPI, vol. 11(12), pages 1-32, December.
    19. Li, Xiaolong & Xie, Changjun & Quan, Shuhai & Huang, Liang & Fang, Wei, 2018. "Energy management strategy of thermoelectric generation for localized air conditioners in commercial vehicles based on 48 V electrical system," Applied Energy, Elsevier, vol. 231(C), pages 887-900.
    20. 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).

    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:gam:jeners:v:17:y:2024:i:24:p:6238-:d:1541295. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.