IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v373y2024ics0306261924012819.html
   My bibliography  Save this article

Deep learning-assisted design for battery liquid cooling plate with bionic leaf structure considering non-uniform heat generation

Author

Listed:
  • Zheng, Aodi
  • Gao, Huan
  • Jia, Xiongjie
  • Cai, Yuhao
  • Yang, Xiaohu
  • Zhu, Qiang
  • Jiang, Haoran

Abstract

Liquid cooling is a promising approach for battery thermal management due to its compact construction and high heat transfer coefficient. However, the conventional liquid cooling plates are generally designed by trial-and-error approaches regarding the battery as a uniform heat generating body during charge and discharge processes, limiting the cooling performance of the battery thermal management systems. This paper introduces a deep learning framework that considers non-uniform heat generation to design a liquid cooling plate with a bionic leaf structure. The framework links the structural features of the cooling plate to performance using deep learning and optimizes these features with a non-dominated sorting genetic algorithm II. The results show that the maximum temperature difference of the battery can be up to 11°C in natural cooling condition at 4C. The average value of of the three outputs of the established ANN neural network model is 0.98, and the model is capable of predicting results with high accuracy. More significantly, the temperature difference and pressure drop are 2.26°C and 2562.62 Pa compared to traditional structure, which are reduced by 60.17% and 25.06%, respectively. The proposed deep learning-based multi-objective optimization framework can guide the design of liquid cooling plates for battery thermal management systems.

Suggested Citation

  • Zheng, Aodi & Gao, Huan & Jia, Xiongjie & Cai, Yuhao & Yang, Xiaohu & Zhu, Qiang & Jiang, Haoran, 2024. "Deep learning-assisted design for battery liquid cooling plate with bionic leaf structure considering non-uniform heat generation," Applied Energy, Elsevier, vol. 373(C).
    • Handle: RePEc:eee:appene:v:373:y:2024:i:c:s0306261924012819
    DOI: 10.1016/j.apenergy.2024.123898
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261924012819
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2024.123898?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. Satyam Panchal & Krishna Gudlanarva & Manh-Kien Tran & Roydon Fraser & Michael Fowler, 2020. "High Reynold’s Number Turbulent Model for Micro-Channel Cold Plate Using Reverse Engineering Approach for Water-Cooled Battery in Electric Vehicles," Energies, MDPI, vol. 13(7), pages 1-25, April.
    2. Zuo, Wei & Li, Dexin & Li, Qingqing & Cheng, Qianju & Zhou, Kun & E, Jiaqiang, 2023. "Multi-objective optimization of multi-channel cold plate under intermittent pulsating flow by RSM and NSGA-Ⅱ for thermal management of electric vehicle lithium-ion battery pack," Energy, Elsevier, vol. 283(C).
    3. Shan, Shuai & Li, Li & Xu, Qiang & Ling, Lei & Xie, Yajun & Wang, Hongkang & Zheng, Keqing & Zhang, Lanchun & Bei, Shaoyi, 2023. "Numerical investigation of a compact and lightweight thermal management system with axially mounted cooling tubes for cylindrical lithium-ion battery module," Energy, Elsevier, vol. 274(C).
    4. Han, Jie & Liu, Wenxue & Zheng, Yusheng & Khalatbarisoltani, Arash & Yang, Yalian & Hu, Xiaosong, 2023. "Health-conscious predictive energy management strategy with hybrid speed predictor for plug-in hybrid electric vehicles: Investigating the impact of battery electro-thermal-aging models," Applied Energy, Elsevier, vol. 352(C).
    5. Zhang, Furen & Lu, Fu & Liang, Beibei & Zhu, Yilin & Gou, Huan & Xiao, Kang & He, Yanxiao, 2023. "Thermal performance analysis of a new type of branch-fin enhanced battery thermal management PCM module," Renewable Energy, Elsevier, vol. 206(C), pages 1049-1063.
    6. Cai, Yuhao & Qian, Xin & Su, Ruihang & Jia, Xiongjie & Ying, Jinhui & Zhao, Tianshou & Jiang, Haoran, 2024. "Thermo-electrochemical modeling of thermally regenerative flow batteries," Applied Energy, Elsevier, vol. 355(C).
    7. Panchal, S. & Dincer, I. & Agelin-Chaab, M. & Fraser, R. & Fowler, M., 2016. "Experimental and simulated temperature variations in a LiFePO4-20Ah battery during discharge process," Applied Energy, Elsevier, vol. 180(C), pages 504-515.
    8. Xu, Xiaobin & Su, Yanghan & Kong, Jizhou & Chen, Xing & Wang, Xiaolin & Zhang, Hengyun & Zhou, Fei, 2024. "Performance analysis of thermal management systems for prismatic battery module with modularized liquid-cooling plate and PCM-negative Poisson's ratio structural laminboard," Energy, Elsevier, vol. 286(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. 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).
    2. An, Zhiguo & Liu, Huaixi & Gao, Weilin & Zhang, Jianping, 2024. "A triple-hybrid battery thermal management system with drop-shaped fin channels for improving weather tolerance," Energy, Elsevier, vol. 307(C).
    3. Luo, Ding & Wu, Zihao & Jiang, Li & Yan, Yuying & Chen, Wei-Hsin & Cao, Jin & Cao, Bingyang, 2024. "Realizing rapid cooling and latent heat recovery in the thermoelectric-based battery thermal management system at high temperatures," Applied Energy, Elsevier, vol. 370(C).
    4. Bamdezh, M.A. & Molaeimanesh, G.R., 2024. "The effect of active and passive battery thermal management systems on energy consumption, battery degradation, and carbon emissions of an electric vehicle," Energy, Elsevier, vol. 304(C).
    5. Jianxun Zhang & Xiao He & Xiaosheng Si & Changhua Hu & Donghua Zhou, 2017. "A Novel Multi-Phase Stochastic Model for Lithium-Ion Batteries’ Degradation with Regeneration Phenomena," Energies, MDPI, vol. 10(11), pages 1-24, October.
    6. Jilte, Ravindra & Afzal, Asif & Panchal, Satyam, 2021. "A novel battery thermal management system using nano-enhanced phase change materials," Energy, Elsevier, vol. 219(C).
    7. Saeed, Ali & Karimi, Nader & Paul, Manosh C., 2021. "Analysis of the unsteady thermal response of a Li-ion battery pack to dynamic loads," Energy, Elsevier, vol. 231(C).
    8. Gandoman, Foad H. & Jaguemont, Joris & Goutam, Shovon & Gopalakrishnan, Rahul & Firouz, Yousef & Kalogiannis, Theodoros & Omar, Noshin & Van Mierlo, Joeri, 2019. "Concept of reliability and safety assessment of lithium-ion batteries in electric vehicles: Basics, progress, and challenges," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    9. Chen-Lung Wang & Jik Chang Leong, 2024. "Analysis of Thermal Management Strategies for 21700 Lithium-Ion Batteries Incorporating Phase Change Materials and Porous Copper Foam with Different Battery Orientations," Energies, MDPI, vol. 17(7), pages 1-27, March.
    10. Raijmakers, L.H.J. & Danilov, D.L. & Eichel, R.-A. & Notten, P.H.L., 2019. "A review on various temperature-indication methods for Li-ion batteries," Applied Energy, Elsevier, vol. 240(C), pages 918-945.
    11. Hongya Zhang & Hao Chen & Haisheng Fang, 2022. "Cooling Optimization Strategy for a 6s4p Lithium-Ion Battery Pack Based on Triple-Step Nonlinear Method," Energies, MDPI, vol. 16(1), pages 1-31, December.
    12. Qian, Cheng & Guan, Hongsheng & Xu, Binghui & Xia, Quan & Sun, Bo & Ren, Yi & Wang, Zili, 2024. "A CNN-SAM-LSTM hybrid neural network for multi-state estimation of lithium-ion batteries under dynamical operating conditions," Energy, Elsevier, vol. 294(C).
    13. Carlo Cunanan & Manh-Kien Tran & Youngwoo Lee & Shinghei Kwok & Vincent Leung & Michael Fowler, 2021. "A Review of Heavy-Duty Vehicle Powertrain Technologies: Diesel Engine Vehicles, Battery Electric Vehicles, and Hydrogen Fuel Cell Electric Vehicles," Clean Technol., MDPI, vol. 3(2), pages 1-16, June.
    14. Jiabin Duan & Jiapei Zhao & Xinke Li & Satyam Panchal & Jinliang Yuan & Roydon Fraser & Michael Fowler, 2021. "Modeling and Analysis of Heat Dissipation for Liquid Cooling Lithium-Ion Batteries," Energies, MDPI, vol. 14(14), pages 1-19, July.
    15. Pan, Mingzhang & Fu, Changcheng & Cao, Xinxin & Guan, Wei & Liang, Lu & Li, Ding & Gu, Jinkai & Tan, Dongli & Zhang, Zhiqing & Man, Xingjia & Ye, Nianye & Qin, Haifeng, 2024. "An energy management strategy for fuel cell hybrid electric vehicle based on HHO-BiLSTM-TCN-self attention speed prediction," Energy, Elsevier, vol. 307(C).
    16. Liu, Qian & Liu, Yingying & Zhang, Mingjie & Wang, Shuping & Li, Wenlong & Zhu, Xiaoqing & Ju, Xing & Xu, Chao & Wei, Bin, 2024. "Comprehensive investigation of the electro-thermal performance and heat transfer mechanism of battery system under forced flow immersion cooling," Energy, Elsevier, vol. 298(C).
    17. Hartmut Popp & Gregor Glanz & Karoline Alten & Irina Gocheva & Wernfried Berghold & Alexander Bergmann, 2018. "Mechanical Frequency Response Analysis of Lithium-Ion Batteries to Disclose Operational Parameters," Energies, MDPI, vol. 11(3), pages 1-13, March.
    18. Wang, Haimin & Shi, Weijie & Hu, Feng & Wang, Yufei & Hu, Xuebin & Li, Huanqi, 2021. "Over-heating triggered thermal runaway behavior for lithium-ion battery with high nickel content in positive electrode," Energy, Elsevier, vol. 224(C).
    19. Gianluca Brando & Adolfo Dannier & Ivan Spina & Pietro Tricoli, 2017. "Integrated BMS-MMC Balancing Technique Highlighted by a Novel Space-Vector Based Approach for BEVs Application," Energies, MDPI, vol. 10(10), pages 1-14, October.
    20. Sui, Zengguang & Lin, Haosheng & Sun, Qin & Dong, Kaijun & Wu, Wei, 2024. "Multi-objective optimization of efficient liquid cooling-based battery thermal management system using hybrid manifold channels," Applied Energy, Elsevier, vol. 371(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:appene:v:373:y:2024:i:c:s0306261924012819. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.