IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i19p7038-d924697.html
   My bibliography  Save this article

Fast Charging Optimization for Lithium-Ion Batteries Based on Improved Electro-Thermal Coupling Model

Author

Listed:
  • Ran Li

    (Automotive Electronic Drive Control and System Integration Engineering Research Center, Ministry of Education, Harbin 150080, China
    School of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin 150080, China)

  • Xue Wei

    (Automotive Electronic Drive Control and System Integration Engineering Research Center, Ministry of Education, Harbin 150080, China
    School of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin 150080, China)

  • Hui Sun

    (Automotive Electronic Drive Control and System Integration Engineering Research Center, Ministry of Education, Harbin 150080, China
    School of Automation, Harbin University of Science and Technology, Harbin 150080, China)

  • Hao Sun

    (College of Artificial Intelligence, Nankai University, Tianjin 300110, China)

  • Xiaoyu Zhang

    (College of Artificial Intelligence, Nankai University, Tianjin 300110, China)

Abstract

New energy automobiles possess broad application prospects, and the charging technology of vehicle power batteries is one of the key technologies in the development of new energy automobiles. Traditional lithium battery charging mostly adopts the constant current-constant voltage method, but continuous and frequent charging application conditions will cause temperature rise and accelerated capacity decay, which easily bring about safety problems. Aiming at the above-mentioned problems related to the charging process of lithium-ion batteries, this paper proposes an optimization strategy and charging method for lithium-ion batteries based on an improved electric-thermal coupling model. Through the HPPC experiment, the parameter identification of the second-order RC equivalent circuit model was completed, and the electric-thermal coupling model of the lithium battery was established. Taking into account the two factors of charging time and charging temperature rise, the multi-stage charging strategy of the lithium-ion battery is optimized by the particle swarm optimization algorithm. The experimental results show that the multi-stage constant current charging method proposed in this paper not only reduces the maximum temperature during the charging process by an average of 0.83% compared with the maximum temperature of the battery samples charged with the traditional constant current-constant voltage (CC-CV) charging method but also reduces the charging time by an average of 13.87%. Therefore, the proposed optimized charging strategy limits the charging temperature rise to a certain extent on the basis of ensuring fast charging and provides a certain theoretical basis for the thermal management of the battery system and the design and safe charging method of the battery charging system.

Suggested Citation

  • Ran Li & Xue Wei & Hui Sun & Hao Sun & Xiaoyu Zhang, 2022. "Fast Charging Optimization for Lithium-Ion Batteries Based on Improved Electro-Thermal Coupling Model," Energies, MDPI, vol. 15(19), pages 1-19, September.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7038-:d:924697
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/19/7038/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/19/7038/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhang, Caiping & Jiang, Jiuchun & Gao, Yang & Zhang, Weige & Liu, Qiujiang & Hu, Xiaosong, 2017. "Charging optimization in lithium-ion batteries based on temperature rise and charge time," Applied Energy, Elsevier, vol. 194(C), pages 569-577.
    2. Haitao Min & Weiyi Sun & Xinyong Li & Dongni Guo & Yuanbin Yu & Tao Zhu & Zhongmin Zhao, 2017. "Research on the Optimal Charging Strategy for Li-Ion Batteries Based on Multi-Objective Optimization," Energies, MDPI, vol. 10(5), pages 1-15, May.
    3. Xiaogang Wu & Wenwen Shi & Jiuyu Du, 2017. "Multi-Objective Optimal Charging Method for Lithium-Ion Batteries," Energies, MDPI, vol. 10(9), pages 1-18, August.
    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. Boshi Wang & Haitao Min & Weiyi Sun & Yuanbin Yu, 2021. "Research on Optimal Charging of Power Lithium-Ion Batteries in Wide Temperature Range Based on Variable Weighting Factors," Energies, MDPI, vol. 14(6), pages 1-21, March.
    2. Wang, Bin & Wang, Shifeng & Tang, Yuanyuan & Tsang, Chi-Wing & Dai, Jinchuan & Leung, Michael K.H. & Lu, Xiao-Ying, 2019. "Micro/nanostructured MnCo2O4.5 anodes with high reversible capacity and excellent rate capability for next generation lithium-ion batteries," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    3. Fan, Zhaohui & Fu, Yijie & Liang, Hong & Gao, Renjing & Liu, Shutian, 2023. "A module-level charging optimization method of lithium-ion battery considering temperature gradient effect of liquid cooling and charging time," Energy, Elsevier, vol. 265(C).
    4. Haitao Min & Boshi Wang & Weiyi Sun & Zhaopu Zhang & Yuanbin Yu & Yanzhou Zhang, 2020. "Research on the Combined Control Strategy of Low Temperature Charging and Heating of Lithium-Ion Power Battery Based on Adaptive Fuzzy Control," Energies, MDPI, vol. 13(7), pages 1-21, April.
    5. Yan, Xiaohe & Gu, Chenghong & Li, Furong & Xiang, Yue, 2018. "Network pricing for customer-operated energy storage in distribution networks," Applied Energy, Elsevier, vol. 212(C), pages 283-292.
    6. Jian Yang & Yu Liu & Shangguang Jiang & Yazhou Luo & Nianzhang Liu & Deping Ke, 2022. "A Method of Probability Distribution Modeling of Multi-Dimensional Conditions for Wind Power Forecast Error Based on MNSGA-II-Kmeans," Energies, MDPI, vol. 15(7), pages 1-21, March.
    7. Landini, S. & O’Donovan, T.S., 2021. "Novel experimental approach for the characterisation of Lithium-Ion cells performance in isothermal conditions," Energy, Elsevier, vol. 214(C).
    8. Xiaogang Wu & Wenwen Shi & Jiuyu Du, 2017. "Multi-Objective Optimal Charging Method for Lithium-Ion Batteries," Energies, MDPI, vol. 10(9), pages 1-18, August.
    9. Yin, Yilin & Choe, Song-Yul, 2020. "Actively temperature controlled health-aware fast charging method for lithium-ion battery using nonlinear model predictive control," Applied Energy, Elsevier, vol. 271(C).
    10. In-Ho Cho & Pyeong-Yeon Lee & Jong-Hoon Kim, 2019. "Analysis of the Effect of the Variable Charging Current Control Method on Cycle Life of Li-ion Batteries," Energies, MDPI, vol. 12(15), pages 1-11, August.
    11. Yu Ji & Xiaogang Hou & Lingfeng Kou & Ming Wu & Ying Zhang & Xiong Xiong & Baodi Ding & Ping Xue & Junlong Li & Yue Xiang, 2019. "Cost–Benefit Analysis of Energy Storage in Distribution Networks," Energies, MDPI, vol. 12(17), pages 1-23, September.
    12. Tian, Jiaqiang & Wang, Yujie & Liu, Chang & Chen, Zonghai, 2020. "Consistency evaluation and cluster analysis for lithium-ion battery pack in electric vehicles," Energy, Elsevier, vol. 194(C).
    13. Li, Xiaoyu & Chen, Le & Hua, Wen & Yang, Xiaoguang & Tian, Yong & Tian, Jindong & Xiong, Rui, 2024. "Optimal charging for lithium-ion batteries to avoid lithium plating based on ultrasound-assisted diagnosis and model predictive control," Applied Energy, Elsevier, vol. 367(C).
    14. Yang, Lin & Cai, Yishan & Yang, Yixin & Deng, Zhongwei, 2020. "Supervisory long-term prediction of state of available power for lithium-ion batteries in electric vehicles," Applied Energy, Elsevier, vol. 257(C).
    15. Jiang, Benben & Berliner, Marc D. & Lai, Kun & Asinger, Patrick A. & Zhao, Hongbo & Herring, Patrick K. & Bazant, Martin Z. & Braatz, Richard D., 2022. "Fast charging design for Lithium-ion batteries via Bayesian optimization," Applied Energy, Elsevier, vol. 307(C).
    16. Joris De Hoog & Joris Jaguemont & Mohamed Abdel-Monem & Peter Van Den Bossche & Joeri Van Mierlo & Noshin Omar, 2018. "Combining an Electrothermal and Impedance Aging Model to Investigate Thermal Degradation Caused by Fast Charging," Energies, MDPI, vol. 11(4), pages 1-15, March.
    17. Tang, Aihua & Gong, Peng & Huang, Yukun & Xiong, Rui & Hu, Yuanzhi & Feng, Renhua, 2024. "Orthogonal design based pulse preheating strategy for cold lithium-ion batteries," Applied Energy, Elsevier, vol. 355(C).
    18. Veneri, Ottorino & Capasso, Clemente & Patalano, Stanislao, 2018. "Experimental investigation into the effectiveness of a super-capacitor based hybrid energy storage system for urban commercial vehicles," Applied Energy, Elsevier, vol. 227(C), pages 312-323.
    19. 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.
    20. Zhao, Yang & Wang, Zhenpo & Shen, Zuo-Jun Max & Sun, Fengchun, 2021. "Data-driven framework for large-scale prediction of charging energy in electric vehicles," Applied Energy, Elsevier, vol. 282(PB).

    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:15:y:2022:i:19:p:7038-:d:924697. 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.