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

Aodi Zheng, Huan Gao, Xiongjie Jia, Yuhao Cai, Xiaohu Yang, Qiang Zhu and Haoran Jiang

Applied Energy, 2024, vol. 373, issue C, No S0306261924012819

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.

Keywords: Lithium-ion battery; Non-uniform heat generation; Liquid cooling plate; Neural network; Multi-objective optimization (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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DOI: 10.1016/j.apenergy.2024.123898

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