基于低频阻抗谱和健康特征融合的锂离子电池健康状态主动探测方法研究

An active detection method of li-ion battery health state based on low-frequency EIS and health feature fusion

准确的健康状态估计对锂离子电池管理具有重要意义。健康状态通常用衰退后的容量来表征,传统的容量估计主要被动采集电池的电压、电流和温度三种信号,进而提取与容量相关的特征,对充放电曲线的完整性和规则性要求较高。所提出的方法基于充电过程中探测的低频阻抗谱,提取五个健康特征,其中包含三个新的具有物理意义的健康特征,分别为修正的Warburg因子、伪锂离子扩散状态以及其经验模态分解后的残值,在锂离子电池内部动力学特征与外部老化特征之间架起了一座桥梁并且与容量具有强相关性。锂离子电池容量估计模型优化前决定系数R^(2)不到0.6。通过健康特征融合,从整体角度考量变量组间的相关性,能够大幅度提高模型估计精度,决定系数R^(2)可以达到0.935 7,RMSE为0.374 9 mAh和MAPE为0.836 2 mAh。

Accurate health state estimation is of great importance for li-ion battery management. The health state is usually characterized by the post-decay capacity. Traditional capacity estimation mainly passively collects three signals of battery voltage, current and temperature, and then extracts the capacity-related features. This requires high integrity and regularity of the charging and discharging curves. The method in this paper extracts five health features based on the low-frequency electrochemical impedance spectroscopy detected during charging, including three new physically significant health features, namely the modified Warburg factor, the pseudo lithium-ion diffusion state and the residual value after its empirical mode decomposition. This bridges the gap between the internal kinetic features and the external aging features of li-ion batteries and has a strong correlation with capacity. The determination coefficient R^(2)of the li-ion battery capacity estimation model before optimization is less than 0.6. By fusing the health features and considering the correlation between the variable groups from a holistic perspective, the model estimation accuracy can be significantly improved, and R^(2)can reach 0.935 7. RMSE is 0.374 9 mAh and MAPE is 0.836 2 mAh.