State-of-Charge Hstimation for Li-Ion Batteries Based on Multi-Strategy Probabilities Fusion

The estimation of state-of-charge (SOC) is the fundamental technology to improve battery working status and life. But severe external environment, dynamic nonlinearity of battery model, and measurement errors make SOC estimation challengable. In this paper, a multi-strategy probabilities based fusion method is proposed. It can combine the dynamic tracking capability of Proportional-Integral Observer (PIO) and noise tolerance capability of extend Kalman filter (EKE). The different abnormal issues, such as internal resistance change, capacity attenuation, data saturation and initialization error, may have a serious impact on the estimates. While the proposed method can guarantee high accuracy estimation and robustness under these issues. It tracks the changes of residual sequence dynamically, and the weighting coefficient can be real-time adjusted according to Bayesian analysis. In simulation, we test the algorithm performance under a variety of current load conditions and abnormal issues. Compared to other model-based methods, the proposed approach performs higher accuracy and robustness with moderate computational complexity.