Degradation Curve Prediction of Lithium-Ion Batteries Based on Knee Point Detection Algorithm and Convolutional Neural Network

Estimating the capacity degradation curve and the remaining useful life (RUL) of lithium-ion batteries is of great importance for battery manufacturers and customers. Lithium iron phosphate (LiFePO4) and lithium nickel manganese cobalt (NMC) batteries exhibit a slow degradation of the capacity up to the knee point, after which the degradation accelerates rapidly until the end of life (EOL). In the existing literature, data-driven methods require higher percentages of training data for predicting the lithium-ion batteries' RUL with reasonable accuracy. This study first presents a novel online and offline knee detection algorithm to detect the knee in the capacity degradation curve. Compared with the existing knee detection algorithms, the proposed algorithm has better algorithmic efficiency and superior performance. Using the knee point, we present a novel method to estimate the complete degradation curve using the data of the first cycle with the help of the convolutional neural network (CNN). This study also presents the aging precursors of lithium-ion batteries, which are used as features for the CNN degradation curve estimation model. The proposed method successfully predicts the degradation curve using data of the first cycle with root-mean-squared error (RMSE) and mean absolute percentage error (MAPE) as low as 0.005 and 0.416, respectively.