Accurate estimation of the degree of battery aging is essential to ensure safe operation of electric vehicles. In this paper, using real-world vehicles and their operational data, a battery aging estimation method is proposed based on a dual-polarization equivalent circuit (DPEC) model and multiple data-driven models. The DPEC model and the forgetting factor recursive least-squares method are used to determine the battery system’s ohmic internal resistance, with outliers being filtered using boxplots. Furthermore, eight common data-driven models are used to describe the relationship between battery degradation and the factors influencing this degradation, and these models are analyzed and compared in terms of both estimation accuracy and computational requirements. The results show that the gradient descent tree regression, XGBoost regression, and light GBM regression models are more accurate than the other methods, with root mean square errors of less than 6.9 mΩ. The AdaBoost and random forest regression models are regarded as alternative groups because of their relative instability. The linear regression, support vector machine regression, and k-nearest neighbor regression models are not recommended because of poor accuracy or excessively high computational requirements. This work can serve as a reference for subsequent battery degradation studies based on real-time operational data.

准确估计电池老化程度对于确保电动汽车的安全运行至关重要。在本文中，使用真实世界的车辆及其运行数据，提出了一种基于双极化等效电路（DPEC）模型和多个数据驱动模型的电池老化估计方法。DPEC 模型和遗忘因子递归最小二乘法用于确定电池系统的欧姆内阻，并使用箱线图过滤异常值。此外，使用八个常见的数据驱动模型来描述电池退化与影响退化的因素之间的关系，并从估计精度和计算要求两方面对这些模型进行分析和比较。结果表明，梯度下降树回归、XGBoost回归、和轻型 GBM 回归模型比其他方法更准确，均方根误差小于 6.9 mΩ。AdaBoost 和随机森林回归模型因其相对不稳定性而被视为替代组。线性回归、支持向量机回归和由于精度差或计算要求过高，不推荐k -最近邻回归模型。该工作可为后续基于实时运行数据的电池退化研究提供参考。