【摘要】 锂电池热管理是确保电池热安全的关键，虽然传统的有限元分析方法被广泛应用于锂电池热管理研究，但存在计算效率低、参数设置复杂等局限性。本文提出了一种结合特征工程和有限元分析结果的机器学习模型，通过正交设计方法有效减小所需的有限元仿真数据量；利用支持向量回归（SVR）模型准确预测混合电池包的温度特征；采用非支配排序遗传算法Ⅱ（NSGA-Ⅱ）系统分析了电池结构参数与冷却策略的协同优化关系，提出了兼顾散热性能与能耗效率的最佳方案。与传统方法相比，本方法在保持预测精度的同时大幅提升了计算效率，为电池热管理系统的智能化设计提供了新思路。本研究构建的“特征提取-机器学习建模-多目标优化”技术框架，不仅能够准确预测电池温度特性，还能为不同应用场景下的热管理方案优化提供决策支持。该方法在电动汽车和储能系统等领域具有重要的工程应用价值，有助于提升电池系统的安全性与能效。更多还原

【Abstract】 Battery thermal management is critical to ensure the thermal safety of lithium-ion batteries. Although traditional finite element analysis methods have been widely applied in battery thermal management research, they have limitations, such as low computational efficiency and complex parameter settings. This paper presented a machine learning model that combines feature engineering with finite element analysis results. By employing an orthogonal design method, the required finite element simulation data volume was effectively reduced. The support vector regression(SVR) model was used to accurately predict the temperature characteristics of a hybrid battery pack. The non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ) was applied to systematically analyze the synergistic optimization relationship between battery structural parameters and cooling strategies, proposing an optimal solution that balanced heat dissipation performance and energy consumption efficiency. Compared with traditional methods, the proposed approach significantly enhanced computational efficiency while maintaining prediction accuracy, providing a novel approach for the intelligent design of battery thermal management systems. The “feature extraction-machine learning modeling-multi-objective optimization” framework constructed in this study not only accurately predicteed battery temperature characteristics but also provided decision support for optimizing thermal management solutions in various application scenarios. This method has significant engineering application value in fields such as electric vehicles and energy storage systems, contributing to the improvement of battery system safety and energy efficiency.更多还原