Maximum capacity and state of health estimation based on equivalent circuit model for degraded battery

Abstract

In real-time systems, state of health (SOH) and maximum capacity need to be updated regularly as battery degrades with time. Incorrect estimation of SOH or maximum capacity leads to inaccurate state of charge (SOC) estimation, especially for degraded batteries. Maximum capacity or SOH is usually obtained by constant-current discharging test, which is impractical in real-time battery management system (BMS). Therefore, it is meaningful to find an adaptive method to estimate SOH or maximum capacity in real-time BMS instead of discharging test. This paper proposes a two-step approach to estimate SOC and SOH. In the first step, SOC and battery electrical parameters (such as resistance, capacitor, etc.) are estimated simultaneously with fixed maximum capacity by using (dual) extended Kalman filter model. In the second step, the maximum capacity of degraded battery is estimated based on estimated electrical parameters using (dual) unscented Kalman filter, which rending estimated SOH. The above two step could be deployed on real-time applications to improve the accuracy of SOC estimation even when battery degrades.