Online estimation of battery power state based on improved equivalent circuit model

Abstract

Abstract The state-of-power (SOP) of lithium-ion batteries is an important parameter for safety control and energy recovery of electric vehicles. In the battery management system (BMS), the Equivalent-Circuit Model (ECM) is commonly used to simulate battery dynamics. However, there is always a contradiction between the complexity and accuracy of the model. A simple model usually cannot reflect all the dynamic effects of the battery, which may bring error identification to the parameters. A complex model always has too many parameters that cannot be identified, and there may be parameter divergence problems. In order to solve this problem, this paper proposes a new equivalent circuit model, that is, the equivalent circuit model of the vehicle power battery based on the Auto Regression (AR) model. Based on this model, some inaccurate parameters were found in the parameter identification process, so a parameter identification method based on the extended Kalman filter algorithm and Recursive Least Squares (RLS) was used, and a composite model estimation method was used Comprehensive consideration of a series of restrictions such as State of Charge (SOC) limit and factory setting limit, combined with other methods such as equivalent circuit model, requires the estimated result to meet all the restrictions, to make up for some of the shortcomings of other methods, and make SOP estimation more accurate and reliable.