Lithium Battery SOC Estimation Based on Improved Unscented Kalman Filter

摘要

The accurate estimation of the State of Charge (SOC) of a battery is an essential function in battery management system. Kalman filter algorithm is commonly used for SOC estimation. However, the existing Kalman filter algorithm has drawbacks such as low frequency and poor calculation stability when performing SOC estimation. In this paper, an Improved Unscented Kalman Filter (IUKF) which based on Singular Value Decomposition (SVD) is used, this algorithm uses traceless transformation for accuracy improving, while using Singular Value Decomposition for calculation stability. Experiments indicate the accuracy of the battery SOC can be within 3%, and it has good stability which meets the actual demands