The battery state of charge (SOC) and state of power capability (SOP) are the core elements of a battery management system (BMS) for ensuring efficient and safe driving in electric vehicles. In this paper, the SOC and the SOP are jointly estimated under multiple constraints by using the dual polarization (DP) model for a ternary lithium-ion battery, and the extended Kalman filter (EKF) algorithm has been used to improve the accuracy of state estimation and the calculational simplicity. The calculation equations for the multiple-constraint parameters are deduced. The power capability can be calculated rapidly based on the constraints on the current, voltage and SOC. The simulation and experimental results demonstrate that for both the SOC and the SOP, highly satisfactory prediction accuracy is realized under various operating conditions. The maximum SOP estimation error in the DP model is less than 2.1% for a battery with various SOC states, which corresponds to higher estimation accuracy than that of the Thevenin model. In addition, the SOP estimation model has strong robustness, which renders the joint estimation of the SOC and the SOP more reliable in the practical application of electric vehicles.

The state of charge (SOC) of a lithium-ion battery plays a key role in ensuring the charge and discharge energy control strategy, and SOC estimation is the core part of the battery management system for safe and efficient driving of electric vehicles. In this paper, a model-based SOC estimation strategy based on the Adaptive Cubature Kalman filter (ACKF) is studied for lithium-ion batteries. In the present study, the dual polarization (DP) model is employed for SOC estimation and the vector forgetting factor recursive least squares (VRLS) method is utilized for model parameter online identification. The ACKF is then designed to estimate the battery’s SOC. Finally, the Urban Dynamometer Driving Schedule and Dynamic Stress Test are utilized to evaluate the performance of the proposed method by comparing with results obtained using the extended Kalman filter (EKF) and the cubature Kalman filter (CKF) algorithms. The simulation and experimental results show that the proposed ACKF algorithm combined with VRLS-based model identification is a promising SOC estimation approach. The proposed algorithm is found to provide more accurate SOC estimation with satisfying stability than the extended EKF and CKF algorithms.