Abstract
An accurate state of charge (SOC) estimation is vital for safe operation and efficient management of lithium-ion batteries. To improve the accuracy and robustness, an adaptive and robust square root cubature Kalman filter based on variational Bayesian approximation and Huber’s M-estimation (VB-HASRCKF) is proposed. The variational Bayesian (VB) approximation is used to improve the adaptivity by simultaneously estimating the measurement noise covariance and the SOC, while Huber’s M-estimation is employed to enhance the robustness with respect to the outliers in current and voltage measurements caused by adverse operating conditions. A constant-current discharge test and an urban dynamometer driving schedule (UDDS) test are performed to verify the effectiveness and superiority of the proposed algorithm by comparison with the square root cubature Kalman filter (SRCKF), the VB-based SRCKF, and the Huber-based SRCKF. The experimental results show that the proposed VB-HASRCKF algorithm outperforms the other three filters in terms of SOC estimation accuracy and robustness, with a little higher computation complexity.
Highlights
Lithium-ion batteries have been prevalently employed as energy storage devices in electric vehicles (EVs) and renewable energy storage systems owing to their high energy density, low self-discharge rate, and long cycle life [1]
For the battery energy storage system (BESS) of the grid, accurate estimation of state of charge (SOC) is crucial for controlling the SOC level within a certain range in order to reserve a certain amount of energy for load leveling as well as minimize battery health degradation
The outliers in current and voltage measurements caused by adverse operating conditions are accounted for by Huber’s M-estimation
Summary
Lithium-ion batteries have been prevalently employed as energy storage devices in electric vehicles (EVs) and renewable energy storage systems owing to their high energy density, low self-discharge rate, and long cycle life [1]. To cope with the numerical unstability of the CKF, square root cubature Kalman filter (SRCKF) [22,23] was developed, which can ensure the positive definiteness of the covariance These KF-based methods have some accuracy limitations. Is proposed for SOC estimation of lithium-ion batteries; (2) Compared with the existing models, the case that there are both modeling errors and outliers in SOC estimation is firstly taken into account and the VB-HASRCKF is used to handle this problem; (3) A constant-current discharge test and an UDDS test are performed to verify the effectiveness and superiority of the proposed algorithm by comparison with the square root cubature Kalman filter (SRCKF), the VB-based SRCKF, and the.
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