An adaptive cubature Kalman filter algorithm based on singular value decomposition for joint estimation of state of charge and state of power for lithium-ion batteries under wide temperature range

Accurately estimating the state of charge (SOC) and state of power (SOP) of the battery is essential for optimizing the use of electric quantity and ensuring the safe and efficient operation and energy management of the battery system of electric vehicles. In this paper, a particle swarm optimization algorithm is used to identify the model parameters of lithium-ion batteries under wide temperature range, and a SOC estimation method of adaptive cubature Kalman filter algorithm based on singular value decomposition (SVD-ACKF) is proposed. The Cholesky decomposition of covariance of state variables is replaced by singular value decomposition, which successfully avoids the problem of the non-positive definite matrix during the adaptive updating of the cubature Kalman filter algorithm, and improves the convergence stability of the iterative computation process. Based on accurate SOC estimation at each temperature, the key constraints in this study are composed of the combination of the SOC, voltage, and current of the battery, and changes in battery model parameters due to ambient temperature are considered, developing an SOP estimation strategy under multi-constraint conditions, realizing the joint estimation of SOC and SOP, verifying the feasibility of the proposed state estimation algorithm in different ambient temperatures. The results show that the maximum error of SOC estimation under different ambient temperatures is less than 0.015, and the SOC estimation error of the proposed method is the smallest compared with the extended Kalman filter (EKF) and the cubature Kalman filter (CKF), and the average relative errors of peak charge power and peak discharge power estimation with a duration of 30 s at 25 °C can be kept within 2.5% and 1.5%, respectively. It is proved that the proposed method has good accuracy and adaptability.