Optimal Health Indicator Selection and Validation for Improving SOH Rapid Diagnosis Accuracy in Second-Life Applications of Used Batteries

The increasing occurrence of used batteries has highlighted the need for rapid and accurate state-of-health (SOH) estimation. However, rapid diagnostics can greatly diminish estimation accuracy. To address this challenge, this study introduces a health indicator (HI) that effectively reflects battery degradation during high-rate discharge processes. Voltage data from discharges at two different C-rate conditions were used to extract HI values for identifying the optimal HI, and multiple HIs were combined to enhance accuracy. Furthermore, the SOH estimation performance was evaluated by inputting the single and combined HIs with the highest correlation coefficients for each C-rate into a long short-term memory model.