针对如何提取容量增量(IC)曲线上更有效的特征参数进行锂电池健康状态(SOH)估计问题，提出了一种基于修正的洛伦兹电压容量(RL-VC)模型。首先使用传统滤波方法对锂电池进行容量增量分析(ICA)。然后使用RL-VC模型进行对比，获得相应的特征参数并计算容量建模误差。在基于自主搭建的试验平台上获得的试验数据与开源数据集NASA中的动态数据集NCM中分别进行试验。VC容量建模的误差分别在0.23%和0.16%以内。RL-VC模型拟合的IC曲线提取的特征参数与锂电池容量高度线性相关，为后续SOH工作奠定了基础。基于RL-VC模型的IC分析方法相较于传统滤波方法，不仅在电池老化方面具有更高的鲁棒性，同时在特征参数提取方面避免了主观性和不确定性。

Aiming at the problem of how to extract more effective characteristic parameters from the capacity increment (IC) curve for state of health (SOH) estimation of lithium batteries, a modified Lorentz voltage-capacity (RL-VC) based model is proposed. The capacity increment analysis (ICA) of lithium batteries is first performed using the traditional filtering method. Then the RL-VC model is used for comparison to obtain the corresponding feature parameters and calculate the capacity modeling error. The experimental data obtained based on the self-constructed experimental platform and the dynamic dataset NCM from the open-source dataset NASA are carried out separately. The errors of VC capacity modeling are within 0.23% and 0.16%, respectively. The feature parameters extracted from the IC curves fitted by the RL-VC model are highly linearly correlated with the capacity of Li-ion batteries, which lays the foundation for the subsequent SOH work. The IC analysis method based on the RL-VC model proposed in this paper not only has higher robustness in battery aging compared with the traditional filtering method, but also avoids subjectivity and uncertainty in feature parameter extraction.

航空科学基金资助项目(20183352030, 201933052001)