Abstract
State of charge (SOC) is a key parameter for lithium-ion battery management systems. The square root cubature Kalman filter (SRCKF) algorithm has been developed to estimate the SOC of batteries. SRCKF calculates 2n points that have the same weights according to cubature transform to approximate the mean of state variables. After these points are propagated by nonlinear functions, the mean and the variance of the capture can achieve third-order precision of the real values of the nonlinear functions. SRCKF directly propagates and updates the square root of the state covariance matrix in the form of Cholesky decomposition, guarantees the nonnegative quality of the covariance matrix, and avoids the divergence of the filter. Simulink models and the test bench of extended Kalman filter (EKF), Unscented Kalman filter (UKF), cubature Kalman filter (CKF) and SRCKF are built. Three experiments have been carried out to evaluate the performances of the proposed methods. The results of the comparison of accuracy, robustness, and convergence rate with EKF, UKF, CKF and SRCKF are presented. Compared with the traditional EKF, UKF and CKF algorithms, the SRCKF algorithm is found to yield better SOC estimation accuracy, higher robustness and better convergence rate.
Highlights
Along with the fossil energy depletion, air pollution and more and more serious global climate changes, people have begun to realize the great importance of the utilization and development of non-fossil energy [1]
We proposed a new method for state of charge (SOC) estimation using the square root cubature Kalman filter (SRCKF) algorithm in this paper
At a temperature of 25 ◦ C and state of health (SOH) = 1, by using Figure 3 as the test bench, and the results of the battery parameters shown in Table 2, the extended Kalman filter (EKF), Unscented Kalman filter (UKF), CKF, and SRCKF algorithms were compared in the experiment
Summary
Along with the fossil energy depletion, air pollution and more and more serious global climate changes, people have begun to realize the great importance of the utilization and development of non-fossil energy [1]. Since transportation consumes a large amount of energy, it is necessary to develop and utilize electric vehicles (EVs) to realize green mobility. Lithium-ion batteries (LIBs) have many advantages, such as high power density and durability, and they have been widely used in EVs. when overcharging occurs, the LIBs are more likely to burn and explode than other batteries, necessitating higher requirements for battery management system (BMS) [7,8,9,10]. The most important index for a BMS is state of charge (SOC). Because of the inherently time-varying and non-linearity characteristics of LIB under working conditions, accurate estimation of SOC remains a challenge [16,17,18,19]
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