Lithium-ion battery future degradation trajectory early description amid data-driven end-of-life point and knee point co-prediction

This study develops a novel lithium-ion battery future degradation trajectory early description method amid data-driven end-of-life (EOL) point and knee point (KP) co-prediction. Inspired by the concept of KP, the proposed method firstly employs two-stage curve fitting to offline locate KP with minimization total absolute error between the original trajectory and fitted secants as object. Secondly, from partial multi-step fast-charging curves lying in high state-of-charge (SOC) level with only approximately 0.09 SOC interval, our method mines three new aging-dependent features for EOL and knee cycle co-prediction via two optimized Gaussian process regression models. Afterwards, considering the highly linear characteristics of slow degradation stage, knee capacity is further calculated by the linear function fitted via early capacity degradation sequence. Finally, with early capacity degradation sequence, predicted KP and predicted EOL point, the developed method adopts piece-wise cubic Hermite interpolating polynomial to directly generate future degradation trajectory. The verification results using a 140-cell aging dataset demonstrate that the proposed method is powerful for future degradation trajectory early prediction with high accuracy and extremely low real-time computational cost, where most root-mean-square-error and mean-absolute-percentage-error of trajectory prediction results are below 0.03 Ah and 3%, respectively. Our work, for the first time, reveals the possibility of feature extraction from partial multi-step fast-charging curves, and also provides a low-complexity universal framework for accurate future degradation trajectory early prediction.