IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v249y2022ics0360544222005369.html

Validation of a data-driven fast numerical model to simulate the immersion cooling of a lithium-ion battery pack

Author

Listed:
  • Solai, Elie
  • Guadagnini, Maxime
  • Beaugendre, Héloïse
  • Daccord, Rémi
  • Congedo, Pietro

Abstract

Thermal management of Lithium-ion batteries is a key element to the widespread of electric vehicles. In this study, we illustrate the validation of a data-driven numerical method permitting to evaluate fast the behavior of the Immersion Cooling of a Lithium-ion Battery Pack. First, we illustrate an experiment using a set up of immersion cooling battery pack, where the temperatures, voltage and electrical current evolution of the Li-ion batteries are monitored. The impact of different charging/discharging cycles on the thermal behavior of the battery pack is investigated. Secondly, we introduce a numerical model, that simulates the heat transfer and electrical behavior of an immersion cooling Battery Thermal Management System. The deterministic numerical model is compared against the experimental measurements of temperatures. Then, we perform a Bayesian calibration of the multi-physics input parameters using the experimental measurements directly. The informative distributions outcoming of this process are used to validate the model in different experimental conditions and reduce the uncertainty in the model's temperatures predictions. Finally, the learned distributions of inputs and the numerical model are used to design the system under realistic conditions representing a realistic racing car operation. A Sobol indices based sensitivity analysis is performed to get further analysis elements on the behavior of the BTMS.

Suggested Citation

  • Solai, Elie & Guadagnini, Maxime & Beaugendre, Héloïse & Daccord, Rémi & Congedo, Pietro, 2022. "Validation of a data-driven fast numerical model to simulate the immersion cooling of a lithium-ion battery pack," Energy, Elsevier, vol. 249(C).
    • Handle: RePEc:eee:energy:v:249:y:2022:i:c:s0360544222005369
    DOI: 10.1016/j.energy.2022.123633
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222005369
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.123633?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Chang, Chia-Chin & Huang, Sin-Yi & Chen, Wei-Hsin, 2019. "Thermal and solid electrolyte interphase characterization of lithium-ion battery," Energy, Elsevier, vol. 174(C), pages 999-1011.
    2. Perez Estevez, Manuel Antonio & Calligaro, Sandro & Bottesi, Omar & Caligiuri, Carlo & Renzi, Massimiliano, 2021. "An electro-thermal model and its electrical parameters estimation procedure in a lithium-ion battery cell," Energy, Elsevier, vol. 234(C).
    3. Saw, Lip Huat & Ye, Yonghuang & Tay, Andrew A.O. & Chong, Wen Tong & Kuan, Seng How & Yew, Ming Chian, 2016. "Computational fluid dynamic and thermal analysis of Lithium-ion battery pack with air cooling," Applied Energy, Elsevier, vol. 177(C), pages 783-792.
    4. Li, Xiaoyu & Yuan, Changgui & Li, Xiaohui & Wang, Zhenpo, 2020. "State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression," Energy, Elsevier, vol. 190(C).
    5. Chen, Jingwei & E, Jiaqiang & Kang, Siyi & Zhao, Xiaohuan & Zhu, Hao & Deng, Yuanwang & Peng, Qingguo & Zhang, Zhiqing, 2019. "Modeling and characterization of the mass transfer and thermal mechanics of the power lithium manganate battery under charging process," Energy, Elsevier, vol. 187(C).
    6. Lu, Chen & Zhang, Lipin & Ma, Jian & Chen, Zihan & Tao, Laifa & Su, Yuzhuan & Chong, Jin & Jin, Haizu & Lin, Yongshou, 2017. "Li-ion battery capacity cycling fading dynamics cognition: A stochastic approach," Energy, Elsevier, vol. 137(C), pages 251-259.
    7. Menale, Carla & D'Annibale, Francesco & Mazzarotta, Barbara & Bubbico, Roberto, 2019. "Thermal management of lithium-ion batteries: An experimental investigation," Energy, Elsevier, vol. 182(C), pages 57-71.
    8. Zhang, Xinghui & Li, Zhao & Luo, Lingai & Fan, Yilin & Du, Zhengyu, 2022. "A review on thermal management of lithium-ion batteries for electric vehicles," Energy, Elsevier, vol. 238(PA).
    9. Hongwen He & Rui Xiong & Jinxin Fan, 2011. "Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach," Energies, MDPI, vol. 4(4), pages 1-17, March.
    10. Marc C. Kennedy & Anthony O'Hagan, 2001. "Bayesian calibration of computer models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 425-464.
    11. Wang, Qian & Jiang, Bin & Li, Bo & Yan, Yuying, 2016. "A critical review of thermal management models and solutions of lithium-ion batteries for the development of pure electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 106-128.
    12. Li, Xiaoyu & Yuan, Changgui & Wang, Zhenpo, 2020. "State of health estimation for Li-ion battery via partial incremental capacity analysis based on support vector regression," Energy, Elsevier, vol. 203(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. Wang, Bing-Chuan & He, Yan-Bo & Liu, Jiao & Luo, Biao, 2024. "Fast parameter identification of lithium-ion batteries via classification model-assisted Bayesian optimization," Energy, Elsevier, vol. 288(C).
    2. Rajib Mahamud & Chanwoo Park, 2022. "Theory and Practices of Li-Ion Battery Thermal Management for Electric and Hybrid Electric Vehicles," Energies, MDPI, vol. 15(11), pages 1-45, May.
    3. Wahab, Abdul & Najmi, Aezid-Ul-Hassan & Senobar, Hossein & Amjady, Nima & Kemper, Hans & Khayyam, Hamid, 2025. "Immersion cooling innovations and critical hurdles in Li-ion battery cooling for future electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 211(C).
    4. Wu, Tingting & Wang, Changhong & Hu, Yanxin & Liang, Zhixuan & Fan, Changxiang, 2023. "Research on electrochemical characteristics and heat generating properties of power battery based on multi-time scales," Energy, Elsevier, vol. 265(C).
    5. Zha, Yunfei & Meng, Xianfeng & Qin, Shuaishuai & Hou, Nairen & He, Shunquan & Huang, Caiyuan & Zuo, Hongyan & Zhao, Xiaohuan, 2023. "Performance evaluation with orthogonal experiment method of drop contact heat dissipation effects on electric vehicle lithium-ion battery," Energy, Elsevier, vol. 271(C).
    6. Zhang, Xiaoxi & Pan, Yongjun & Xiong, Yue & Zhang, Yongzhi & Tang, Mao & Dai, Wei & Liu, Binghe & Hou, Liang, 2024. "Deep learning-based vibration stress and fatigue-life prediction of a battery-pack system," Applied Energy, Elsevier, vol. 357(C).
    7. Liu, Jiahao & Tao, Liyanyu & Yang, Qinyuan & Wang, Jinhui, 2026. "Recent advances in immersion cooling for thermal management of lithium-ion batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 226(PE).

    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. Yang, Yue & Chen, Lei & Yang, Lijun & Du, Xiaoze & Yang, Yongping, 2020. "Capacity fade characteristics of lithium iron phosphate cell during dynamic cycle," Energy, Elsevier, vol. 206(C).
    2. Wu, Chunxia & Sun, Yalong & Tang, Heng & Zhang, Shiwei & Yuan, Wei & Zhu, Likuan & Tang, Yong, 2024. "A review on the liquid cooling thermal management system of lithium-ion batteries," Applied Energy, Elsevier, vol. 375(C).
    3. Di Giorgio, Paolo & Di Ilio, Giovanni & Jannelli, Elio & Conte, Fiorentino Valerio, 2022. "Innovative battery thermal management system based on hydrogen storage in metal hydrides for fuel cell hybrid electric vehicles," Applied Energy, Elsevier, vol. 315(C).
    4. 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).
    5. Zhang, Yajun & Liu, Yajie & Wang, Jia & Zhang, Tao, 2022. "State-of-health estimation for lithium-ion batteries by combining model-based incremental capacity analysis with support vector regression," Energy, Elsevier, vol. 239(PB).
    6. Corentin Renais & Benjamin Mercier-Guyon & David Wasylowski & Morian Sonnet & Phillip Dechent & Maxime Servajon & Nils Blanc & Sandrine Lyonnard & Dirk Uwe Sauer & Claire Villevieille, 2025. "Exploring electrochemical dynamics in graphite||LiNi0.8Mn0.1Co0.1O2 cells via operando ultrasound and multiprobe approaches," Nature Communications, Nature, vol. 16(1), pages 1-10, December.
    7. Lei, Deyong & Wang, Yun & Fu, Jingfei & Zhu, Xiaobao & Shi, Jing & Wang, Yachao, 2024. "Electrochemical-thermal analysis of large-sized lithium-ion batteries: Influence of cell thickness and cooling strategy in charging," Energy, Elsevier, vol. 307(C).
    8. Li, Yong & Wang, Liye & Feng, Yanbiao & Liao, Chenglin & Yang, Jue, 2024. "An online state-of-health estimation method for lithium-ion battery based on linear parameter-varying modeling framework," Energy, Elsevier, vol. 298(C).
    9. Fu, Shiyi & Tao, Shengyu & Fan, Hongtao & He, Kun & Liu, Xutao & Tao, Yulin & Zuo, Junxiong & Zhang, Xuan & Wang, Yu & Sun, Yaojie, 2024. "Data-driven capacity estimation for lithium-ion batteries with feature matching based transfer learning method," Applied Energy, Elsevier, vol. 353(PA).
    10. Liu, Yunpeng & Hou, Bo & Ahmed, Moin & Mao, Zhiyu & Feng, Jiangtao & Chen, Zhongwei, 2024. "A hybrid deep learning approach for remaining useful life prediction of lithium-ion batteries based on discharging fragments," Applied Energy, Elsevier, vol. 358(C).
    11. Jin, Xianrong & Duan, Xiting & Jiang, Wenjuan & Wang, Yan & Zou, Youlan & Lei, Weixin & Sun, Lizhong & Ma, Zengsheng, 2021. "Structural design of a composite board/heat pipe based on the coupled electro-chemical-thermal model in battery thermal management system," Energy, Elsevier, vol. 216(C).
    12. Tang, Ting & Yuan, Huimei, 2022. "A hybrid approach based on decomposition algorithm and neural network for remaining useful life prediction of lithium-ion battery," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    13. Wudil, Yakubu Sani & Gondal, M.A. & Al-Osta, Mohammed A., 2025. "Designing fire-retardant polymer-based electrolytes and separators for high-energy-density lithium-ion batteries via combustion calorimetry and machine learning," Energy, Elsevier, vol. 335(C).
    14. Shen, Dongxu & Wu, Lifeng & Kang, Guoqing & Guan, Yong & Peng, Zhen, 2021. "A novel online method for predicting the remaining useful life of lithium-ion batteries considering random variable discharge current," Energy, Elsevier, vol. 218(C).
    15. Wei, Meng & Balaya, Palani & Ye, Min & Song, Ziyou, 2022. "Remaining useful life prediction for 18650 sodium-ion batteries based on incremental capacity analysis," Energy, Elsevier, vol. 261(PA).
    16. Shi, Mingjie & Xu, Jun & Lin, Chuanping & Mei, Xuesong, 2022. "A fast state-of-health estimation method using single linear feature for lithium-ion batteries," Energy, Elsevier, vol. 256(C).
    17. Wang, Anci & Yin, Xiang & Xin, Zhicheng & Cao, Feng & Wu, Zan & Sundén, Bengt & Xiao, Di, 2023. "Performance optimization of electric vehicle battery thermal management based on the transcritical CO2 system," Energy, Elsevier, vol. 266(C).
    18. Suresh, C. & Awasthi, Abhishek & Kumar, Binit & Im, Seong-kyun & Jeon, Yongseok, 2025. "Advances in battery thermal management for electric vehicles: A comprehensive review of hybrid PCM-metal foam and immersion cooling technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 208(C).
    19. Ospina Agudelo, Brian & Zamboni, Walter & Monmasson, Eric, 2021. "Application domain extension of incremental capacity-based battery SoH indicators," Energy, Elsevier, vol. 234(C).
    20. Juan Carlos Ríos-Fernández & M. Inmaculada Álvarez Fernández, 2025. "New Use of LiMn 2 O 4 Batteries Under Renewable Overvoltage as Thermal Power Generators: Energy and Exergy Analysis," Sustainability, MDPI, vol. 17(21), pages 1-21, October.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:249:y:2022:i:c:s0360544222005369. 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.