Early-Stage Prediction of Lithium-Ion Battery RUL and Capacity Degradation Trajectory Using CNN-LSTM and Stretched Exponential Model

Sustainable development in the energy storage sector requires batteries to be graded before leaving the factory so that their performance matches the requirements of intended applications, thereby maximizing service potential and minimizing resource waste caused by premature retirement. However, conventional grading relies primarily on initial performance and does not account for the future capacity degradation trajectory or service life, and limited early-cycle data are typically available. This paper therefore proposes a method that uses only two early aging cycles to estimate both the capacity degradation trajectory and the remaining useful life (RUL). The proposed method, termed CNN-LSTM-SE, combines a convolutional neural network-long short-term memory (CNN-LSTM) architecture with a stretched exponential (SE) model. The network takes the resampled voltage-capacity curves obtained during the constant-current charging segments of the first and third cycles as input and estimates the parameters of the SE model. The predicted parameters are then used to reconstruct the capacity degradation trajectory, from which the battery RUL is estimated. Experimental results show that the proposed method achieves mean absolute percentage errors (MAPE) of 5.06% and 0.73% for RUL estimation and capacity degradation trajectory prediction, respectively, with corresponding root mean square errors (RMSEs) of 4.54 cycles and 19.96 mAh. Compared with TCN-SE, RF-SE, SVR-SE, and GPR-SE, the proposed method achieves the lowest MAPE and RMSE for both RUL estimation and degradation trajectory prediction. Overall, the proposed method enables accurate estimation of the degradation trajectory and RUL using only two early cycles, showing promise for sustainable battery deployment.