Data driven discovery of an analytic formula for the life prediction of Lithium-ion batteries
Jie Xiong, Tong-Xing Lei, Da-Meng Fu, Junwei Wu, Tong‐Yi Zhang
Abstract
Predicting the cycle life of Lithium-Ion Batteries (LIBs) remains a great challenge due to their complicated degradation mechanisms. The present work employs an interpretative machine learning of symbolic regression (SR) to discover an analytic formula for LIB life prediction with newly defined features. The novel features are based on the discharging energies under the constant-current (CC) and constant-voltage (CV) modes at every five cycles alternately. The cycle life is affected by the CC-discharging energy at the 15th cycle (E15−CCD) and the difference between the CC-discharging energies at the 45th cycle and 95th cycle (Δ45−95). The cycle life highly correlates with a simple indicator (E15−CCD−3)/Δ45−95 with a Pearson correlation coefficient of 0.957. The machine learning tools provide a rapid and accurate prediction of cycle life at the early stage.