Early-stage degradation trajectory prediction for lithium-ion batteries: A generalized method across diverse operational conditions

Abstract

Accurate early prediction of the degradation trajectory of lithium-ion batteries (LIBs) can accelerate battery development, production, and design optimization. However, existing early-stage prediction methods for degradation trajectory prediction face challenges in dealing with insufficient data and the generalization under different operational conditions. To address this issue, a novel transfer learning based data-driven method is proposed, which integrates convolutional neural network (CNN) and long short-term memory (LSTM) neural networks. Additionally, we incorporate a temporal attention (TA) mechanism to selectively focus on informative capacity fade patterns and leverage Bayesian Optimization (BO) for hyper-parameters optimization. The proposed method is validated using experimental degradation data from six 5Ah low-temperature batteries (−20 °C, −10 °C) and public datasets. Results demonstrate high accuracy leveraging merely 10% of initial battery data. For the low-temperature aging experimental data, the best prediction result proposed is 0.025 Ah root mean square error (RMSE) and 0.019 Ah mean absolute error (MAE). Compared to baseline CNN-LSTM models, our framework achieved an average reduction of 62.6% in RMSE for early battery capacity trajectory prediction, while maintaining high accuracy across diverse operational conditions. The proposed framework puts forward a promising solution toward enabling adaptive data-driven capacity monitoring under different practical application conditions.