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

• Published in: Energy (Oxford) 2022-05, Vol.247, p.123556, Article 123556
• Main Authors: Lin, Chuanping, Xu, Jun, Shi, Mingjie, Mei, Xuesong
• Format: Article
• Language: English
• Subjects: Adaptability; algorithms; Batteries; Charging; Charging time; data collection; electric potential difference; Feature extraction; Incremental capacity peak; Lithium; Lithium-ion batteries; Lithium-ion battery; management systems; prediction; Random forest regression; Rechargeable batteries; State of health
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2022.123556

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. The Constant Current Charging Time based fast SOH estimation method of lithium-ion battery. [Display omitted] •Charging time can perfectly replace incremental capacity peak area is proved.•More accurate SOH estimation is realized by using only one feature.•The model training and prediction time is less than 1 s.•The universality of SOH estimation is discussed.