Online parameter identification and state estimation of lithium batteries based on real-time forgetting factor
The accuracy of State of Charge(SOC)is an important basis for energy management,range estimation,power system control,and other functions of electric vehicles.The accurate model parameters are the very foundations for correctly determining the SOC of power batteries.Traditional offline parameter identification uses fixed model parameters to describe the performance and response of batteries.However,under the influence of different discharge rates and durations,some internal parameters of the batteries experience changes accordingly.If fixed model parameters are employed,significant deviations may occur in predicting and estimating the battery state.We propose a self-adjusting forgetting factor recursive least squares method to identify the various parameters of the battery model online,and the obtained model parameters are imported into the extended Kalman filtering algorithm for real-time estimation of the battery's SOC.Through comparative analysis and verification,our method converges to within 1％of SOC estimation error under different operating conditions,demonstrating fairly good model parameter identification accuracy and robustness,and significantly improving SOC estimation accuracy.
国家科技期刊平台
NSTL
万方数据
