No abstract

This article proposes a state‐of‐charge (SOC) estimation method to eliminate the influence of the hysteresis effect and the ambient temperature. First, an improved dual‐polarization (DP) model considering the hysteresis effect and the ambient temperature is established. A hysteresis voltage source is connected in series with a couple of resistance–capacitance pairs in the improved DP model, all the parameters of which are related to the ambient temperature to depict the temperature characteristics of the battery. Second, the forgetting factor recursive least squares method is utilized to identify the parameters under the battery dynamic test data at different temperatures. The proposed model and parameterization scheme integrate the effects of hysteresis and temperature, greatly enhancing the performance of the proposed method at different temperatures. Finally, an extended Kalman filter algorithm for SOC estimation is adopted to verify the improved DP model and the simulation indicates that the error of SOC estimation is within 1.5% at different ambient temperatures. The proposed method can improve the precision of the SOC estimation even if the temperature is below −10 or above 50 °C.

Accurate estimations of the temperature and the state-of-charge (SOC) are of extreme importance for the safety of lithium-ion battery operation. Traditional battery temperature and SOC estimation methods often omit the relation between battery temperature and SOC, which may lead to significant errors in the estimations. This study presents a coupled electrothermal battery model and a coestimation method for simultaneously estimating the temperature and SOC of lithium-ion batteries. The coestimation method is performed by a coupled model-based dual extended Kalman filter (DEKF). The coupled estimators utilizing electrochemical impedance spectroscopy (EIS) measurements, rather than utilizing direct battery surface measurements, are adopted to estimate the battery temperature and SOC, respectively. The information being exchanged between the temperature estimator and the SOC estimator effectively improves the estimation accuracy. Extensive experiments show that, in contrast with the EKF-based separate estimation method, the DEKF-based coestimation method is more favorable in reducing errors for estimating both the temperature and SOC even if the battery core temperature has increased by 17°C or more during the process of test.