State of Charge Estimation of Lithium-Ion Batteries Based on Grey Wolf Optimizer-Adaptive New information Square-Root Cubature Kalman Filter Algorithm

State of charge (SOC) estimation of lithium-ion batteries is one of the key technologies in battery management systems. Accurate SOC estimation is crucial for improving battery efficiency, extending battery lifespan, and ensuring safety. To enhance the capability and accuracy of SOC estimation, we propose an adaptive modified innovation square-root cubature Kalman filter algorithm based on grey wolf optimization (GWO-AMISRCKF). This method optimizes the sliding window parameters using the grey wolf algorithm and incorporates an adaptive mechanism and innovation theory to improve the estimation accuracy and stability of the filter. During the experimental phase, data from 18650 batteries under different temperatures and operating conditions were collected and used for simulation validation. The results demonstrate that the proposed method achieves high-precision estimation under various temperatures and operating conditions while exhibiting strong adaptability and estimation accuracy in scenarios involving battery aging and different battery models. Finally, compared with two advanced models, the GWO-AMISRCKF demonstrates superior estimation accuracy, proving its enhanced estimation capability.