Immediate remaining capacity estimation of heterogeneous second-life lithium-ion batteries via deep generative transfer learning

Abstract

The reuse of second-life lithium-ion batteries (LIBs) retired from electric vehicles is critical for energy storage in underdeveloped regions, where power infrastructures are weak or absent. However, estimating the relative remaining capacity (RRC) of second-life batteries using field-accessible data stream remains challenging due to its scarcity and heterogeneity, despite efforts in battery passports and other initiatives to secure data integrity. This study proposes a deep generative transfer learning framework to address these two-fold challenges by generating voltage dynamics across state-of-charge (SOC) and using deep correlation alignment (CORAL) to align heterogeneities resulting from different aging patterns (domains) of second-life LIBs. We generate voltage response dynamics data across various SOC conditions from 20 160 samples under 10 SOC values, demonstrating high statistical similarities and confidence. The model estimates the RRC with minimal field data availability, specifically 2% of the full sample size, achieving a mean absolute percentage error of 7.2% and 3.6% for second-life batteries with different degradation behaviors, respectively. The model preserves established knowledge in the available domain while reducing RRC estimation risks in new domains where data availability is limited. The maximum RRC estimation risk is reduced by 49% at a 95% confidence level. This unified data generation and transfer learning paradigm outperforms the state-of-the-art machine learning and equivalent circuit model-method across all data availability conditions. The “generate and transfer” paradigm enlightens many potential applications in other predictive management tasks by preferentially generalizing in-distribution data and then adapting to out-of-distribution conditions under guidance of limited field data.