TELL-Me: T time-series-decomposition-based ensembled lightweight learning model for diverse battery prognosis and diagnosis

An ensembled lightweight learning model for battery prognosis and diagnosis was proposed, utilizing a dual-module framework to accurately predict degradation trajectories with minimal early-cycle data and offer insights into aging mechanisms across diverse datasets. As batteries become increasingly essential for energy storage technologies, battery prognosis, and diagnosis remain central to ensure reliable operation and effective management, as well as to aid the in-depth investigation of degradation mechanisms. However, dynamic operating conditions, cell-to-cell inconsistencies, and limited availability of labeled data have posed significant challenges to accurate and robust prognosis and diagnosis. Herein, we introduce a time-series-decomposition-based ensembled lightweight learning model (TELL-Me), which employs a synergistic dual-module framework to facilitate accurate and reliable forecasting. The feature module formulates features with physical implications and sheds light on battery aging mechanisms, while the gradient module monitors capacity degradation rates and captures aging trend. TELL-Me achieves high accuracy in end-of-life prediction using minimal historical data from a single battery without requiring offline training dataset, and demonstrates impressive generality and robustness across various operating conditions and battery types. Additionally, by correlating feature contributions with degradation mechanisms across different datasets, TELL-Me is endowed with the diagnostic ability that not only enhances prediction reliability but also provides critical insights into the design and optimization of next-generation batteries.