State-of-Charge Estimation Using Triple Forgetting Factor Adaptive Extended Kalman Filter for Battery Energy Storage Systems in Electric Bus Applications

Abstract

The transport sector has been moving toward electrification due to the significant advancement in E-mobility technology. This prioritizes reliable and safe battery energy storage system (BESS) operation. Therefore, accurate battery state-of-charge (SoC) estimation is essential in effectively monitoring and controlling the BESS stability. Many studies have been conducted to estimate the BESS SoC and improve the estimation accuracy. Nevertheless, considering system complexity and computational efforts, the suggested SoC estimate techniques fall short of providing optimal filtering performance with high noise levels. In this regard, this article introduces SoC estimation using the triple forgetting factor adaptive extended Kalman filter (TFF-AEKF) to provide better SoC estimation accuracy and faster convergence considering the high measurement noise levels and environmental circumstances encountered by the operation of electric buses (EBs). The performance of the proposed TFF-AEKF is evaluated and compared to the conventional adaptive extended Kalman filter (AEKF) and the dual forgetting factor AEKF (DFF-AEKF), considering low and high measurement noise levels. It has been validated that the proposed algorithm can provide faster convergence and better accuracy when considering a high measurement noise level. In addition, the three filters are evaluated using four performance indicators, namely, maximum absolute error (MaxAE), mean absolute error (MAE), root mean square error (RMSE), and convergence time. It is concluded that the presented method offers faster convergence and lower error. Results have demonstrated that the proposed algorithm provides an RMSE of 0.3%, an MAE of 0.01%, and a MaxAE of 1.7% for SoC estimation.