SOC estimation of Li-ion battery based on fractional order modeling
Accurate state of charge(SOC)estimation helps to extend battery life and ensure battery safety.Due to the different time constants corresponding to charge transfer impedance and diffusion impedance,the battery model parameters are different.The fractional-order model-based adaptive forgetting factor recursive least squares(FOM-AFFRLS)method is investigated for parameter identification in order to capture the variation of forgetting factor and parameters in real time,and to estimate the SOC using extended Kalman filtering.The error of FOM-AFFRLS algorithm is 1%,which is smaller than that of fractional-order forgetting-factor-based recursive least squares(FOM-FFRLS),integer-order adaptive forgetting-factor recursive least squares(IOM-AFFRLS)and integer-order forgetting-factor recursive least squares(IOM-FFRLS).It verifies that the proposed method has high SOC estimation accuracy under dynamic operating conditions and normal operation.The method can overcome the dispersion caused by the wrong initial value,the average absolute error is less than 0.068 when the initial value of SOC is 0.7,the robustness is good.
Li-ion batteryparameter identificationadaptive forgetting factor recursive least squares(AFFRLS)methodstate of charge(SOC)estimation
万方数据
维普
