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Energy Storage Systems

D18.10 - State-of-Health Estimation in NCA Lithium-Ion Batteries Using Incremental Capacity Analysis and Gaussian Process Regression While Considering Capacity Recovery

Thursday, March 26, 2026
12:00 PM - 1:45 PM CT
Location: Hemisfair Ballroom
Accurate state-of-health (SOH) estimation is critical for the safe and efficient use of lithium-ion batteries, particularly in electric vehicles and energy storage systems. Conventional SOH-estimation approaches often neglect the capacity recovery effect, which can significantly impact prediction accuracy under real-world conditions. In this study, a hybrid method integrating incremental capacity analysis (ICA) with Gaussian process regression (GPR) to account for the capacity recovery phenomenon in lithium nickel cobalt aluminum oxide cells is proposed. Health features were extracted from IC curve peaks obtained under different storage state-of-charge conditions (0-100%) at 40 °C. The proposed model demonstrated robust predictive performance, achieving mean absolute error below 0.12% and root mean square error below 0.18% across all tested conditions. These findings highlight the importance of incorporating capacity recovery into SOH-estimation and demonstrate the potential of GPR-based frameworks for reliable battery management in practical applications.