Soc Estimation of Li-ion Battery Based on Adaptive CKF Algorithm
Zhengjun Huang, Yu Chen and Meifang Zhou* Author for corresponding; e-mail address: 465074730@qq.com
Volume: Vol.50 No.6 (November 2023)
Research Article
DOI: https://doi.org/10.12982/CMJS.2023.063
Received: 17 March 2023, Revised: 6 July 2023, Accepted: 1 September 2023, Published: -
Citation: Huang Z., Chen Y. and Zhou M., Soc Estimation of Li-ion Battery Based on Adaptive CKF Algorithm, Chiang Mai Journal of Science, 2023; 50(6): e2023063. DOI 10.12982/CMJS.2023.063.
Abstract
A second-or der RC equivalent circuit model was established to improve the estimation accuracy of state of charge (SOC) of power Li-ion batteries, and the model parameters were identified by the recursive least square method with forgetting factor (FFRLS). On this basis, an adaptive cubature kalman filter (ACKF) algorithm was proposed to adaptively modify the process noise covariance matrix and the measurement noise covariance matrix to improve the SOC estimation accuracy. Finally, the SOC estimation algorithm was verified by MATLAB simulations. The results show that compared with UKF and CKF algorithms, the proposed algorithm has higher estimation accuracy and robustness, and can meet the application requirements.