State of Charge Estimation of Li-Ion Batteries Based on Sage-Husa High-Degree Cubature Kalman Filter

Abstract Accurate estimation of the state of charge (SOC) is crucial for efficient energy management in Li-ion batteries. This paper addresses the challenge of SOC estimation in Li-ion batteries with unknown statistical characteristics of the noises in battery systems. Initially, a state space model for Li-ion batteries is established for identifying model parameters using online parameter identification method. Subsequently, a noise estimator is designed based on Sage-Husa to estimate the means and variances of the unknown noises. Additionally, an adaptive high-degree cubature Kalman filter is developed to achieve highly accurate SOC estimation. Finally, the effectiveness and high accuracy of the proposed algorithm are validated through several battery experiments.