The development of technology on reduces the SOC error rate using Hybrid Kalman Filter(HKF) and the demonstration its performance using BMS platform

Generally, the BMS (Battery Management System) is core component of ESS and EV. And the news of BMS is very hot in relevant field. For BMS system, the SOC (State of Charge) algorithm is one of the most important core algorithms. In this paper, a battery SOC estimation method based on the hybrid Kalman filter is proposed. We combined ampere-hour counting method and Kalman filter-battery equivalent circuit method as the hybrid Kalman filter to estimate SOC value of batteries, and the error rate was smaller than using ampere-hour counting method and Kalman filter- battery equivalent circuit method separately. We did the demonstration experiments on the BMS platform which we have developed. The experimental results verify the effectiveness of the proposed method