Regression-Based OCV Table Selection for Accurate SOC Correction Under Post-Discharge Small-Charge Conditions in Lithium-Ion Batteries

Accurate state-of-charge (SOC) correction using open-circuit-voltage (OCV)–SOC tables is challenging under post-discharge small-charge conditions because the OCV characteristics of lithium-ion batteries depend on their charge–discharge history. This study proposes a regression-based method that predicts the SOC errors associated with the charging and discharging OCV–SOC tables and selects the table with the smaller predicted absolute error. Experiments at 25 °C used 21700 cells under 60 conditions varying preceding discharge C-rate, SOC, pre-charge rest time, and small-charge C-rate. Linear, reduced second-order polynomial, and SOC piecewise-linear models were compared; the piecewise-linear models were selected and integrated into a Coulomb-counting SOC correction algorithm. Independent validation comprised 15 new profiles: 13 at original factor levels and two under intermediate 0.35C discharge and 90 min pre-charge rest conditions. Predicted and measured selections agreed for all 15 profiles. For six profiles where the proposed and last-current-direction-based methods selected different tables, mean absolute SOC error decreased from 1.732% to 0.657% (62.1%); across all 15 profiles, it decreased from 1.169% to 0.739% (36.8%). Selections also agreed for all 10 dynamic discharge profiles. At 30–120 min post-charge rest, the proposed method yielded mean absolute errors of 0.648–0.662%, 45.3–45.8% below the last-current-direction baseline, demonstrating that performance was maintained with shorter rest.