An adaptive and interpretable SOH estimation method for lithium-ion batteries based-on relaxation voltage cross-scale features and multi-LSTM-RFR2

Accurate estimation of state of health (SOH) is crucial for ensuring reliable and safe operations of lithium-ion batteries across diverse engineering scenarios. However, current SOH estimation methods face challenges of reliance on specific working conditions, inadequate quality of degradation features, and limited precision and interpretability of estimation models. To address these issues, this paper presents an adaptive and interpretable SOH estimation method based on cross-scale features and a framework with multiple long short-term memory processes and two-level random forest regression (Multi-LSTM-RFR2). First, relaxation voltage, adapting to multiple charging-discharging conditions, is utilized as a source for degradation feature construction. In this aspect, multiple value features, variation features, statistic features, and parameter features are created from four scales: point data, local data, global data, and extended data. Then, the Multi-LSTM-RFR2 interpretable framework employs multi-LSTM processes to handle time-series inputs from multiple features, incorporating a two-level RFR mechanism to fuse and optimize pre-estimated SOH results. Finally, the importance of features is studied from both single and category feature perspectives for interpretability analysis. Degradation experiments with 24 commercial 18,650 battery cells under multiple charging-discharging conditions are used to validate effectiveness of the proposed method. The average values of four evaluation indicators in terms of mean absolute error, mean absolute percentage error, root mean squared error, and R-squared are 1.07 %, 1.33 %, 1.28 %, and 0.9654, respectively.