Alternate Adaptive Extended Kalman Filter and Ampere-hour Counting Method to Estimate the State of Charge

Abstract

Accurate SOC estimation is a significant important parameter for the battery management system (BMS) and it is the basis for adjusting the battery usage strategy and charging strategy. Both Adaptive Kalman Filter and Ampere-hour counting method are the current methods to estimate SOC. Adaptive Kalman Filter based on the model-based method provide the robust performance for the SOC estimation due to their sustained error correction mechanism through the closed-loop feedback. Adaptive Kalman Filter (AEKF) method can quickly obtain the accurate SOC estimation even with large initial SOC error and high drift of current measurement. Ampere-hour counting (AHC) method is simplest and popular method to estimate SOC. However, both methods have some drawbacks. For example, AEKF requires more computational resources and AHC requires accurate initial SOC and high-precision of current measurement. These shortcomings limit their application in practice. In this paper, alternate AEKF and AHC method to estimate SOC method is proposed. The alternating method can combine the advantage of quickly correction SOC estimation and robustness of the AEKF and the less computing resources of AHC method. Finally, the proposed SOC estimation using alternative method is verified with large drift measurement error condition. The results show that alternative method is more accurate and robustness than AEKF and AHC method.