Research Article
SOC Estimation of Ternary Lithium Battery Based on Interpolation Method and Online Parameter Identification
@INPROCEEDINGS{10.1007/978-3-030-62483-5_5, author={Dawei Wang and Ying Yang and Weiguang Zhao and Tianyang Yu and Dongni Zhang}, title={SOC Estimation of Ternary Lithium Battery Based on Interpolation Method and Online Parameter Identification}, proceedings={Green Energy and Networking. 7th EAI International Conference, GreeNets 2020, Harbin, China, June 27-28, 2020, Proceedings}, proceedings_a={GREENETS}, year={2020}, month={11}, keywords={SOC estimation Spline interpolation Recursive least squares Extended kalman filter}, doi={10.1007/978-3-030-62483-5_5} }- Dawei Wang
Ying Yang
Weiguang Zhao
Tianyang Yu
Dongni Zhang
Year: 2020
SOC Estimation of Ternary Lithium Battery Based on Interpolation Method and Online Parameter Identification
GREENETS
Springer
DOI: 10.1007/978-3-030-62483-5_5
Abstract
SOC estimation is currently a function of the energy management system for new energy vehicles. Based on the SOC of batteries, the remaining available capacity of batteries can be directly determined to determine the remaining driving range of electric vehicles. Aiming at this problem, this paper use the two Resistance and Capacitance equivalent circuit model for the ternary lithium-ion battery, and then obtains the OCV-SOC curve by using spline interpolation. The improved recursive least squares (FFRLS) method with forgetting factor is used to identify parameters of the battery model. Due to the nonlinear state of the external characteristics of the battery, the linear kalman filter would lead to a large error in the estimation, which cannot meet the need for accuracy. Therefore, in this paper, EKF is improved in this paper, and spline interpolation is used to optimize the relationship between open circuit voltage and SOC in data processing, thus improving the estimation accuracy.
