State of charge estimation for lithium-ion batteries based on extended Kalman filter optimized by fuzzy neural network
SHANG Yun-long
ZHANG Cheng-hui
CUI Na-xin
ZHANG Qi
Abstract：The accurate estimation for state of charge (SOC) is the important basis to prevent overcharge or overdis-charge of batteries, and is the important guarantee for the electric vehicle safety and reliability. In the traditional SOC estimation methods based on extended Kalman filter (EKF), the SOC estimation precision was highly dependent on an accurate battery model. To solve the above problems, an error prediction model was built based on fuzzy neural network (FNN), by which the measurement noise covariance of EKF was real-time revised. When the predicted model error was small, the measurement model was updated, otherwise, the process model was updated only. The simulation and experi-mental results show that the proposed algorithm can effectively eliminate the SOC estimation error caused by the model error and the uncertain noise statistical properties, with the maximum error of less than 1.2%. The proposed algorithm has good convergence and robustness, and is applicable to various complicated driving cycles for electric vehicles, with high application value.
Keywords：power batterySOC estimationmodel errorfuzzy neural networksextended Kalman filters
Publication Date：2016-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：9( 212-220 )