An Adaptive Tracking-Extended Kalman Filter for SOC Estimation of Batteries with Model Uncertainty and Sensor Error

• DOI: 10.3390/en15103499
• Publication Date: 2022-05-10T12:31:55Z
• Authors: Deng Ma; Kai Gao; Yutao Mu; Ziqi Wei; Ronghua Du
• Keywords: Robustness

Accurate state of charge (SOC) plays a vital role in battery management systems (BMSs). Among several developed SOC estimation methods, the extended Kalman filter (EKF) has been extensively applied. However, EKF cannot achieve valid when model accuracy is inadequate, noise covariance matrix uncertain, and sensor large errors. This paper makes two contributions to overcome these drawbacks: (1) A variable forgetting factor recursive least squares (VFFRLS) proposed accomplish parameters identification. method updates according innovation sequence, which superior (FFRLS); (2) an adaptive tracking (ATEKF) estimate battery. In ATEKF, error adaptively corrected sequence correction factor. The value related actual error. Proposed algorithms are validated with publicly available dataset from University Maryland. experimental results indicate that identification VFFRLS can be reduced 0.05% 0.018%. Additionally, ATEKF better robustness than having errors uncertainty matrix, case it reduce 1.09% 0.15%.