A State-of-Charge Estimation Method Based on Multi-Algorithm Fusion

Lithium-ion power batteries are widely used in the electric vehicle (EV) industry due to their high working voltage, high energy density, long cycle life, low self-discharge rate, and environmental protection. A multi-algorithm fusion method is proposed in this paper to estimate the battery state of charge (SOC), establishing the Thevenin model and collecting the terminal voltage residuals when the extended Kalman filter (EKF), adaptive extended Kalman filter (AEKF), and H infinite filter (HIF) estimate the SOC separately. The residuals are fused by Bayesian probability and the weight is obtained, and then the SOC estimated value of the fusion algorithm is obtained from the weight. A comparative analysis of the estimation accuracy of a single algorithm and a fusion algorithm under two different working conditions is made. Experimental results show that the fusion algorithm is more robust in the whole process of SOC estimation, and its estimation accuracy is better than the EKF algorithm. The estimation result for the fusion algorithm under a Dynamic Stress Test (DST) is better than that under a Hybrid Pulse Power Characterization (HPPC) test. With the emergence of cloud batteries, the fusion algorithm is expected to realize real vehicle online application.