Constant current charging time based fast state-of-health estimation for lithium-ion batteries

The state of health (SOH) estimation is critical for a battery management system's safe operation. Considering feature extraction, time-consuming, model/calculation complexity problems, a battery SOH estimation method based on constant current charging time (CCCT) is proposed in this paper. Unlike previous works, it is proved that CCCT can perfectly replace incremental capacity peak area. Since no filtering process is required in this method, the validity of the feature is maximally preserved. The random forest regression is combined to form accurate and fast SOH estimation. The proposed method is validated with the Oxford and CALCE datasets, collected from different batteries under different conditions. The average root-mean-square error of 8 cells for SOH estimation is 0.52%. Compared with the incremental capacity analysis (ICA)-based SOH estimation method, the prediction accuracy of the proposed method is improved by 41.6%, and fewer data are utilized. Besides, the time needed for the model training and prediction of the proposed method is less than 1 s. Additionally, the proposed method is proved to have good adaptability to different voltage ranges and charging/discharging conditions.(c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

This paper proposes a battery state of health estimation method based on constant current charging time, which can accurately and quickly estimate the health status of the battery. Compared with traditional methods, this method has higher prediction accuracy, requires less data, and has shorter training and prediction time.