A Novel Hybrid Physics-Based and Data-Driven Approach for Degradation Trajectory Prediction in Li-Ion Batteries

Lithium-ion batteries have been widely used in electric vehicles. To ensure safety and reliability, accurate prediction of the battery's future degradation trajectory is critical. However, early prediction capability and adaptive prediction capability under various battery aging conditions remain two main challenges. Either physics-based or data-driven methods have their advantages and limitations. In this study, a novel hybrid method that combines the physics-based and data-driven approaches is proposed to achieve early prediction of the battery capacity degradation trajectory. This framework consists of three steps. First, to improve the generality of the method, a hybrid feature is extracted using an electrochemical model and measured voltage data. Second, the clustering algorithm is adopted to divide battery degradation data into different clusters, and the data augmentation technique is used to enrich the training dataset. Finally, the training dataset in each cluster is used to train the sequence-to-sequence deep neural network, and the future degradation trajectory can be predicted. The proposed method provides accurate predictions using only 20% of training data, and it has strong robustness under noisy input. Validation results under different aging conditions show that the mean absolute percentage errors of capacity degradation trajectory and remaining useable cycle life are below 2.5% and 6.5%, respectively.