عنوان مقاله [English]
The state of charge (SOC) estimation of battery in the lithium-ion batteries is of great importance for ensuring its safe operation and preventing it from over-charging or over-discharging. Despite the high importance of the SOC parameter, this parameter cannot be directly measured from battery terminals. So it needs to be estimated. So far, various methods have been introduced for the state of charge estimation of lithium-ion batteries. This paper presents the identification of the battery model and the SOC estimation algorithm for lithium-ion batteries in electric vehicles based on an Fuzzy Adaptive Cubature Kalman Filter (FACKF). In this method, firstly the lithium-ion battery is modeled by a second order RC circuit and then, the Cubature Kalman Filter method is used to estimate the battery parameters and the state of battery charge. One of the requirements of the Cubature Kalman filter is to know the measurement of covariance matrices and process. However, these matrices are generally unknown in practice. In the case of inaccurate selection of Q and R matrices, the performance of the filter is affected and the accuracy of the state of charge estimation is reduced and even divergence may occurs. To solve this problem, in this paper a fuzzy system is designed to monitor the performance of a Cubature Kalman filter. The fuzzy system of R and Q matrices adjusts the filter to have optimal performance. To evaluate the performance of the proposed method, this method is compared with classical methods. The results show the effective performance of the proposed method in comparison with other methods.