Accurate state-of-health (SOH) estimation for lithium-ion batteries is of great significance for future intelligent battery management systems. This study proposes a novel method combining voltage-capacity (VC)-model-based incremental capacity analysis (ICA) with support vector regression (SVR) for battery SOH estimation. For accurate and efficient capture of IC curves, 18 VC models are first compared, and then, suitable models are selected for two types of batteries with different chemistries, enabling multitype health features to be obtained by parameterizing the VC models. After correlation analysis of these extracted health features with the reference battery capacity, the SVR algorithm is adopted to construct SOH estimation models. Finally, four aging datasets are employed for validation of the proposed method. The experimental results show that the SVR models achieve high accuracy in SOH estimation, i.e., the respective mean absolute errors (MAEs) and root mean square errors (RMSEs) of all batteries are limited to within 1.1%. Moreover, the method is robust against different initial aging statuses and cycle conditions of the batteries: after migration and fine-tuning, both the MAEs and RMSEs can be confined to within 2.3% by utilizing the established SVR models.

锂离子电池的准确健康状态（SOH）估计对于未来的智能电池管理系统具有重要意义。本研究提出了一种将基于电压-容量 (VC) 模型的增量容量分析 (ICA) 与支持向量回归 (SVR) 相结合的新方法，用于电池 SOH 估计。为了准确高效地捕捉IC曲线，首先比较18个VC模型，然后针对两种不同化学成分的电池选择合适的模型，通过参数化VC模型来获得多类型的健康特征。在对这些提取的健康特征与参考电池容量进行相关分析后，采用SVR算法构建SOH估计模型。最后，采用四个老化数据集来验证所提出的方法。实验结果表明，SVR模型在SOH估计中实现了高精度，即所有电池各自的平均绝对误差（MAE）和均方根误差（RMSE）均限制在1.1%以内。此外，该方法对电池的不同初始老化状态和循环条件具有鲁棒性：在迁移和微调后，利用建立的 SVR 模型，MAE 和 RMSE 都可以限制在 2.3% 以内。