Online estimation of model parameters and state of charge of LiFePO₄ batteries using a novel open-circuit voltage at various ambient temperatures

This study describes an online estimation of the model parameters and state of charge (SOC) of lithium iron phosphate batteries in electric vehicles. A widely used SOC estimator is based on the dynamic battery model with predeterminate parameters. However, model parameter variances that follow with their varied operation temperatures can result in errors in estimating battery SOC. To address this problem, a battery online parameter estimator is presented based on an equivalent circuit model using an adaptive joint extended Kalman filter algorithm. Simulations based on actual data are established to verify accuracy and stability in the regression of model parameters. Experiments are also performed to prove that the proposed estimator exhibits good reliability and adaptability under different loading profiles with various temperatures. In addition, open-circuit voltage (OCV) is used to estimate SOC in the proposed algorithm. However, the OCV based on the proposed online identification includes a part of concentration polarization and hysteresis, which is defined as parametric identification-based OCV (OCV_PI). Considering the temperature factor, a novel OCV-SOC relationship map is established by using OCV_PI under various temperatures. Finally, a validating experiment is conducted based on the consecutive loading profiles. Results indicate that our method is effective and adaptable when a battery operates at different ambient temperatures.