Stress-informed transfer learning: Accelerated battery life evaluation model across diverse operating conditions and cell mechanisms

Life evaluation for newly developed lithium-ion batteries is often constrained by the time-intensive and costly nature of battery testing. This is particularly true in the aerospace industry, where limited comprehensive data availability significantly hampers life evaluations. Data collected from batteries under diverse operating conditions and cell mechanisms provides valuable insights for constructing degradation models. Nevertheless, the nonlinearity in battery degradation across operating conditions, combined with data distribution discrepancies among different cell mechanisms, presents significant challenges in developing degradation models for newly designed batteries. In this study, a stress-informed transfer learning methodology is proposed to accelerate the life evaluation process. Firstly, a stochastic model is employed to capture the nonlinear dynamics inherent in battery degradation under diverse operating conditions. Model migration is implemented to adapt stochastic models to unique degradation trends, ensuring precision under varying stresses. Secondly, a Transformer-based model is developed to accommodate variations in data distributions across different cell mechanisms. Domain-adaptive fine-tuning with specified loss function is then incorporated to address the challenge of limited target degradation features. Finally, a hybrid model is devised by integrating these foundational components, realizing accelerated life evaluation through the utilization of multi-modal data. Experimental results demonstrate that the proposed methodology achieves improvements of 63.40 % in MAE and 58.55 % in RMSE with 30 % training data length compared to mainstream benchmark methods. This highlights the method's potential as an early-stage screening and assessment tool for newly developed space lithium-ion batteries, complementing conventional cycle life evaluation protocols with accelerated evaluations from limited degradation data.