A parameter identification and state of charge estimation method of lithium-ion battery considering temperature bias

Abstract

To eliminate the impact of inaccurate initial parameter value on the parameter identification results of lithium-ion battery (LIB) model, a method for parameter identification of LIB combining Matlab and 1stOpt is proposed, fully utilizing the powerful global optimization ability of 1stOpt to obtain accurate initial parameter value. Moreover, this method can also efficiently heighten the precision of parameter identification results. The hybrid pulse power characteristic (HPPC) experiment is carried out on a LIB in the laboratory to obtain the relationship among state of charge (SOC), open circuit voltage (OCV), and ambient temperature, and the data required for parameter identification. To enhance the accuracy of SOC estimation, based on the obtained high-precision identification parameters, a dual-polarization dynamic thermal model of coupling temperature and SOC of the LIB is established. We find that the battery temperature tested is inconsistent with the ambient temperature, which causes inaccurate temperature input of the battery model, thus affecting the accuracy of the battery model. In this work, a particle swarm optimization (PSO) algorithm is introduced to obtain exact temperature differences to rectify the temperature input of the battery model. Then, the simulated terminal voltage of the battery is compared and validated against the experimental data. It is obtained that the maximum and minimum average errors considering temperature bias are only 0.962 % and 0.0075 %, respectively, while the maximum and minimum average errors without temperature bias are 3.622 % and 0.109 %, respectively, which commendably prove the accuracy of the established model. Finally, the extended Kalman filter (EKF) is adopted to estimate the SOC under the dynamic stress test (DST), and the maximum error is less than 0.8 %. The results strongly show that the established model in this work still has high accuracy in a wide temperature range. The results provide a theoretical reference for the related research on parameter identification and modeling of batteries.