IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v265y2023ics0360544222032170.html
   My bibliography  Save this article

A module-level charging optimization method of lithium-ion battery considering temperature gradient effect of liquid cooling and charging time

Author

Listed:
  • Fan, Zhaohui
  • Fu, Yijie
  • Liang, Hong
  • Gao, Renjing
  • Liu, Shutian

Abstract

The contradiction between fast charging and battery lifetime has become one of the main obstacles for the development of electric vehicles. The large currents of fast charging protocols will bring about a high temperature rise of battery, which can be controlled by the liquid-cooled battery thermal management system. However, the temperature difference of the battery module is unavoidable due to the temperature gradient effect of liquid cooling. In this study, considering temperature gradient effect of liquid cooling, a charging optimization strategy at a battery module level is proposed to balance the charging time and temperature difference. Genetic algorithm is utilized to determine the specific charging protocol by evaluating the fitness function. Compared with the multistage constant current-constant voltage charging strategy, the temperature difference of the battery module using the optimal charging strategy reduces by 37.9%. Besides, the charging time of the optimal charging strategy decreases by 11.9% than that of the constant current-constant voltage charging strategy. The experimental results demonstrate that the optimal charging strategy can weigh the charging time and temperature difference of the battery module. In addition, the different charging protocols can be determined by adjusting the weighting coefficients of battery temperature difference and charging time.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:energy:v:265:y:2023:i:c:s0360544222032170
    DOI: 10.1016/j.energy.2022.126331
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222032170
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2022.126331?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. 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).
    2. 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.
    3. Abdel-Monem, Mohamed & Trad, Khiem & Omar, Noshin & Hegazy, Omar & Van den Bossche, Peter & Van Mierlo, Joeri, 2017. "Influence analysis of static and dynamic fast-charging current profiles on ageing performance of commercial lithium-ion batteries," Energy, Elsevier, vol. 120(C), pages 179-191.
    4. Fan, Zhaohui & Gao, Renjing & Liu, Shutian, 2022. "Thermal conductivity enhancement and thermal saturation elimination designs of battery thermal management system for phase change materials based on triply periodic minimal surface," Energy, Elsevier, vol. 259(C).
    5. Zachary P. Cano & Dustin Banham & Siyu Ye & Andreas Hintennach & Jun Lu & Michael Fowler & Zhongwei Chen, 2018. "Batteries and fuel cells for emerging electric vehicle markets," Nature Energy, Nature, vol. 3(4), pages 279-289, April.
    6. Wang, Yujie & Zhou, Caijie & Chen, Zonghai, 2022. "Optimization of battery charging strategy based on nonlinear model predictive control," Energy, Elsevier, vol. 241(C).
    7. 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).
    8. Wu, Weixiong & Wu, Wei & Wang, Shuangfeng, 2019. "Form-stable and thermally induced flexible composite phase change material for thermal energy storage and thermal management applications," Applied Energy, Elsevier, vol. 236(C), pages 10-21.
    9. Sieg, Johannes & Schmid, Alexander U. & Rau, Laura & Gesterkamp, Andreas & Storch, Mathias & Spier, Bernd & Birke, Kai Peter & Sauer, Dirk Uwe, 2022. "Fast-charging capability of lithium-ion cells: Influence of electrode aging and electrolyte consumption," Applied Energy, Elsevier, vol. 305(C).
    10. Lin, Qian & Wang, Jun & Xiong, Rui & Shen, Weixiang & He, Hongwen, 2019. "Towards a smarter battery management system: A critical review on optimal charging methods of lithium ion batteries," Energy, Elsevier, vol. 183(C), pages 220-234.
    11. Su, Shaosen & Li, Wei & Garg, Akhil & Gao, Liang, 2022. "An adaptive boosting charging strategy optimization based on thermoelectric-aging model, surrogates and multi-objective optimization," Applied Energy, Elsevier, vol. 312(C).
    12. Peter M. Attia & Aditya Grover & Norman Jin & Kristen A. Severson & Todor M. Markov & Yang-Hung Liao & Michael H. Chen & Bryan Cheong & Nicholas Perkins & Zi Yang & Patrick K. Herring & Muratahan Ayko, 2020. "Closed-loop optimization of fast-charging protocols for batteries with machine learning," Nature, Nature, vol. 578(7795), pages 397-402, February.
    13. Yangying Zhu & Jin Xie & Allen Pei & Bofei Liu & Yecun Wu & Dingchang Lin & Jun Li & Hansen Wang & Hao Chen & Jinwei Xu & Ankun Yang & Chun-Lan Wu & Hongxia Wang & Wei Chen & Yi Cui, 2019. "Fast lithium growth and short circuit induced by localized-temperature hotspots in lithium batteries," Nature Communications, Nature, vol. 10(1), pages 1-7, December.
    14. Xu, Meng & Wang, Xia & Zhang, Liwen & Zhao, Peng, 2021. "Comparison of the effect of linear and two-step fast charging protocols on degradation of lithium ion batteries," Energy, Elsevier, vol. 227(C).
    15. Mathieu, Romain & Briat, Olivier & Gyan, Philippe & Vinassa, Jean-Michel, 2021. "Comparison of the impact of fast charging on the cycle life of three lithium-ion cells under several parameters of charge protocol and temperatures," Applied Energy, Elsevier, vol. 283(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. Huang, Huiyu & Liu, Pengzhan & Ma, Qiuxia & Tang, Zihao & Wang, Mu & Hu, Junhui, 2023. "Airborne ultrasound catalyzed saltwater Al/Mg-air flow batteries," Energy, Elsevier, vol. 270(C).
    2. Yetik, Ozge & Morali, Ugur & Karakoc, Tahir Hikmet, 2023. "A numerical study of thermal management of lithium-ion battery with nanofluid," Energy, Elsevier, vol. 284(C).
    3. He, Xitian & Sun, Bingxiang & Zhang, Weige & Su, Xiaojia & Ma, Shichang & Li, Hao & Ruan, Haijun, 2023. "Inconsistency modeling of lithium-ion battery pack based on variational auto-encoder considering multi-parameter correlation," Energy, Elsevier, vol. 277(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. 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).
    2. Ruixue Liu & Guannan He & Xizhe Wang & Dharik Mallapragada & Hongbo Zhao & Yang Shao-Horn & Benben Jiang, 2024. "A cross-scale framework for evaluating flexibility values of battery and fuel cell electric vehicles," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    3. Xu, Meng & Wang, Xia & Zhang, Liwen & Zhao, Peng, 2021. "Comparison of the effect of linear and two-step fast charging protocols on degradation of lithium ion batteries," Energy, Elsevier, vol. 227(C).
    4. 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).
    5. Pan, Yue & Kong, Xiangdong & Yuan, Yuebo & Sun, Yukun & Han, Xuebing & Yang, Hongxin & Zhang, Jianbiao & Liu, Xiaoan & Gao, Panlong & Li, Yihui & Lu, Languang & Ouyang, Minggao, 2023. "Detecting the foreign matter defect in lithium-ion batteries based on battery pilot manufacturing line data analyses," Energy, Elsevier, vol. 262(PB).
    6. Lv, Haichao & Kang, Lixia & Liu, Yongzhong, 2023. "Analysis of strategies to maximize the cycle life of lithium-ion batteries based on aging trajectory prediction," Energy, Elsevier, vol. 275(C).
    7. Jia Guo & Yaqi Li & Kjeld Pedersen & Daniel-Ioan Stroe, 2021. "Lithium-Ion Battery Operation, Degradation, and Aging Mechanism in Electric Vehicles: An Overview," Energies, MDPI, vol. 14(17), pages 1-22, August.
    8. Brindha Ramasubramanian & Rayavarapu Prasada Rao & Vijila Chellappan & Seeram Ramakrishna, 2022. "Towards Sustainable Fuel Cells and Batteries with an AI Perspective," Sustainability, MDPI, vol. 14(23), pages 1-27, November.
    9. Ouyang, Quan & Fang, Ruyi & Xu, Guotuan & Liu, Yonggang, 2022. "User-involved charging control for lithium-ion batteries with economic cost optimization," Applied Energy, Elsevier, vol. 314(C).
    10. Wang, Yujie & Zhou, Caijie & Chen, Zonghai, 2022. "Optimization of battery charging strategy based on nonlinear model predictive control," Energy, Elsevier, vol. 241(C).
    11. Feng, Xuning & Zheng, Siqi & Ren, Dongsheng & He, Xiangming & Wang, Li & Cui, Hao & Liu, Xiang & Jin, Changyong & Zhang, Fangshu & Xu, Chengshan & Hsu, Hungjen & Gao, Shang & Chen, Tianyu & Li, Yalun , 2019. "Investigating the thermal runaway mechanisms of lithium-ion batteries based on thermal analysis database," Applied Energy, Elsevier, vol. 246(C), pages 53-64.
    12. Zhang, Chen & Wang, Hongmin & Wu, Lifeng, 2023. "Life prediction model for lithium-ion battery considering fast-charging protocol," Energy, Elsevier, vol. 263(PE).
    13. Zou, Changfu & Hu, Xiaosong & Wei, Zhongbao & Tang, Xiaolin, 2017. "Electrothermal dynamics-conscious lithium-ion battery cell-level charging management via state-monitored predictive control," Energy, Elsevier, vol. 141(C), pages 250-259.
    14. 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.
    15. Stančin, H. & Mikulčić, H. & Wang, X. & Duić, N., 2020. "A review on alternative fuels in future energy system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 128(C).
    16. Pietro Iurilli & Luigi Luppi & Claudio Brivio, 2022. "Non-Invasive Detection of Lithium-Metal Battery Degradation," Energies, MDPI, vol. 15(19), pages 1-14, September.
    17. Sun, Li & Sun, Wen & You, Fengqi, 2020. "Core temperature modelling and monitoring of lithium-ion battery in the presence of sensor bias," Applied Energy, Elsevier, vol. 271(C).
    18. Li, Guanzheng & Li, Bin & Li, Chao & Wang, Shuai, 2023. "State-of-health rapid estimation for lithium-ion battery based on an interpretable stacking ensemble model with short-term voltage profiles," Energy, Elsevier, vol. 263(PE).
    19. Zheng Huang & Laisuo Su & Yunjie Yang & Linsong Gao & Xinyu Liu & Heng Huang & Yubai Li & Yongchen Song, 2023. "Three-Dimensional Simulation on the Effects of Different Parameters and Pt Loading on the Long-Term Performance of Proton Exchange Membrane Fuel Cells," Sustainability, MDPI, vol. 15(4), pages 1-22, February.
    20. Lucio Ciabattoni & Stefano Cardarelli & Marialaura Di Somma & Giorgio Graditi & Gabriele Comodi, 2021. "A Novel Open-Source Simulator Of Electric Vehicles in a Demand-Side Management Scenario," Energies, MDPI, vol. 14(6), pages 1-16, March.

    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:265:y:2023:i:c:s0360544222032170. 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.