State of Health Estimation of Lithium-Ion Battery Based on Constant Current Charging Time Feature Extraction and Internal Resistance Compensation

Abstract

Existing state of health (SOH) estimation methods for lithium-ion batteries typically require complete charge-discharge curves or involve complex algorithms and computational processes. To address this issue, a lithium-ion battery health state estimation method considering internal resistance compensation is proposed in this paper. The method proposes the feature of Constant Current Charging Time (CCCT)which takes the place of commonly used feature of increment capacity under the voltage peak, thereby eliminating the need for complex calculations. By compensating for the battery charging voltage curve using internal resistance, the method mitigates the impact of Incremental Capacity(IC) curve shifts caused by different charging rates on the CCCT feature. Pearson correlation coefficients are applied to optimize the length and position of voltage segments in the charging voltage curve. Additionally, the gradient boosting regression tree algorithm is utilized to achieve SOH estimation. The effectiveness of the proposed SOH estimation method is validated. Experimental results show that the Mean Absolute Error (MAE) values of the proposed SOH estimation method are 3.31%, 2.67%, 1.79%, and 1.28% for voltage segments of 10mV, 25mV, 50mV, and 100mV, respectively.

Keywords: lithium-ion battery, State of health, Constant current charging time, Incremental capacity, Internal resistance compensation