Revealing effects of pouch Li-ion battery structure on fast charging ability through numerical simulation

Jixiang Cai, Xuezhe Wei, Xueyuan Wang, Jiangong Zhu, Bo Jiang, Zhe Tao, Mengshu Tian and Haifeng Dai

Applied Energy, 2025, vol. 377, issue PA, No S030626192401821X

Abstract: Lithium plating may occur in lithium-ion batteries (LIBs) under extreme charging conditions, such as high C-rates and low temperatures, which compromises both the lifespan and safety of the batteries. Avoiding lithium plating is a critical topic in the design and management of LIBs. In the present work, a three-dimensional electrochemical model is established and validated to optimize the battery structure. Using the onset time of lithium plating as a quantitative index, the particle swarm optimization (PSO) algorithm is employed to identify the best and worst combinations of battery structures, including tab type, cell aspect ratio, and tab attachment positions. The results demonstrate that the onset time of lithium plating for the best structures in the three tab types is significantly delayed compared to the worst structures. Taking 2C charging as an example, there is an improvement of 28.57 %, 30.88 %, and 42.19 % for the normal type (NT), counter type (CT), and L-shaped type (LT), respectively. The differences in the degree of optimization among the three tab types are primarily observed in the worst structures, particularly in the worst structure of the LT type. Furthermore, the distributions of lithium plating overpotential and standard deviations are presented, indicating that optimizing battery structures can also significantly enhance the uniformity of lithium plating within the anode. Finally, the sensitivity analysis is conducted and the tab attachment positions are proved to be the most important factor among these variables. This work provides valuable insights into battery design and manufacturing with a focus on improving fast charging ability.

Keywords: lithium ion battery; Fast charging; Structure optimization; Electrochemical model; Particle swarm optimization (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2024.124438

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