Online identification of characteristic change points in capacity degradation trajectories of lithium-ion batteries

To elucidate the evolutionary characteristics of the current available capacity of lithium-ion batteries, this study proposes an online identification framework of characteristic change points (CCPs) for capacity degradation curves. Firstly, health indicators are extracted from a short period of data before the end of constant current charging. The interaction of three data-driven algorithms and multiple feature intervals in terms of capacity estimation accuracy is systematically investigated. Then, the parameter combinations for the improved cumulative sum (CUSUM) algorithm are selected and validated through single-factor analysis, orthogonal experimental design, and main effect analysis. Specifically, the improved CUSUM algorithm exhibits an average error of only 16.42 cycles and an average latency of merely 4.20 cycles. Finally, the proposed improved CUSUM algorithm is applied to identify the CCPs in the battery capacity degradation curves of different morphological types. The validation performance of the proposed algorithm demonstrates its capability of identifying both routine degradation patterns and anomalous transitions. The identified CCPs can serve as evaluation indicators for multiple application scenarios, such as battery design, usage strategy optimization, and second-life utilization.