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In this work, a multibody system dynamics model of a battery pack is constructed based on the recursive idea, which can characterize the state information of each cell, such as velocity, acceleration, deformation, etc., during extrusion. By utilizing machine learning techniques, it is possible to achieve both the forward and reverse design of the adhesive for the battery pack. This enables accurate prediction of battery deformation under various adhesive stiffness and damping coefficients, as well as different battery SOCs. Consequently, the design of the battery adhesive can be guided, resulting in minimal distortion of the battery pack during extrusion and reducing the risk of internal short circuits.

本研究基于递推思想构建了电池包多体系统动力学模型，可表征挤压过程中每个电芯的速度、加速度、变形量等状态信息。结合机器学习技术，可实现电池包粘接胶的正向与逆向设计。该方法可精准预测不同粘接胶刚度、阻尼系数以及不同荷电状态（State of Charge, SOC）下的电芯变形情况，进而可指导电池粘接胶的优化设计，使电池包在挤压过程中变形量降至最低，同时降低内部短路风险。