Abstract: In view of that　the single SOC estimation algorithm cannot concurrently meet the requirements of multi-indicators, an algorithm combining the extended Kalman filtering (EKF) and support vector machine (SVM) is proposed. By dynamically tracking the model parameters and estimating the open-circuit voltage in real-time, the preliminary SOC estimation is obtained by using EKF algorithm. Furthermore, by training the DST condition data output from EKF algorithm, SVM model is obtained and its regression prediction ability is utilized to perform error compensation on preliminary estimation, further reducing the error of SOC estimation. The results of simulation show that compared with EKF and EKF-BP algorithms,the proposed EKF-SVM algorithm has better robustness and adaptability and can achieve accurate estimation of battery SOC,with the maximum absolute error of about 1%

Key words: lithium-ion battery, SOC, extended Kalman filtering, support vector machine, combination algorithm