Abstract
An accurate state of charge (SOC) estimation is of great importance for the battery management systems of electric vehicles. To improve the accuracy and robustness of SOC estimation, lithium-ion battery SOC is estimated using an adaptive square root unscented Kalman filter (ASRUKF) method. The square roots of the variance matrices of the SOC and noise can be calculated directly by the ASRUKF algorithm, which ensures the symmetry and nonnegative definiteness of the matrices. The process values and measurement noise covariance can be adaptively adjusted, which greatly improves the accuracy, stability, and self-adaptability of the filter. The effectiveness of the proposed method has been verified through experiments under different operating conditions. The obtained results were compared with those of extended Kalman filter (EKF) and unscented Kalman filter (UKF) , which indicates that the ASRUKF method provides better accuracy, robustness and convergence in the estimation of battery SOC for electric vehicles. The proposed method has a mean SOC estimation error of 0.5% and a maximum SOC estimation error of 0.8%. These errors are lower than those of other methods.
Highlights
A battery’s state of charge (SOC), which describes the charge remaining in the battery, is an important parameter in the process of battery utilization
EKFmore and accurately results results show that The it can estimatemethod the SOCwas of lithium-ion battery than theThe other two show that it can estimate the of lithium-ion battery more accurately than the other two methods
Since the non-negative qualitative and symmetry of Pk could be ensured and process values and measurement noise covariance can be adaptively adjusted with the proposed method in this paper, we have found a maximum SOC
Summary
A battery’s state of charge (SOC), which describes the charge remaining in the battery, is an important parameter in the process of battery utilization. As one of the important decision-making factors involved in the battery management systems of electric vehicles, accurate estimation of the SOC plays a crucial role in improving the utilization of batteries’ capacity and energy. Several methods for estimating SOC have been demonstrated, which improve the performance of SOC estimation in battery management systems. The ampere-hour method is the most popular method being used to estimate battery SOC [3]. The ampere-hour method has the shortcoming of accumulating errors over time, and its estimation of SOC is likely undermined by measurement errors and noise. F and Tong, S describe a mathematical model for estimating SOC by modifying open-circuit voltage parameters, and the SOC estimation error of this method is less than 3% [4,5]
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