State of charge estimation for electric vehicle lithium-ion batteries based on model parameter adaptation

Abstract

Accurate state of charge (SOC) estimation is the basis of the battery management system in electric vehicles. In order to reduce the influence of time-varying model parameters on SOC estimation accuracy for lithium-ion batteries under complex operating conditions, this paper proposes an improved method for online model parameter identification using variable forgetting factor recursive least squares (VFFRLS), which is based on the sliding window time-varying forgetting factor theory. The VFFRLS was used for online parameter identification at the macroscopic timescale, and extended Kalman filter (EKF) was used for estimating the battery SOC at the microscopic timescale. The accuracy of parameter identification was verified using pulsed discharging and urban dynamometer driving schedule (UDDS) tests, and recursive least squares (RLS) was used to identify the parameters of lithium-ion batteries under UDDS test and estimate the SOC of lithium-ion batteries in combination with EKF, and the experimental results verify the accuracy and robustness of VFFRLS-EKF.