Research on load state prediction model of electric vehicle lithium battery based on Kalman filter algorithm

被引：0
|
作者：
Zheng, Xiu [1 ]
Nie, Zhen [1 ]
Yang, Jie [1 ]
Li, Qiqi [1 ]
Li, Fenglin [1 ]
机构：
[1] School of Electrical Engineering & Automation, Henan Institute of Technology, Henan, Xinxiang, China
来源：
International Journal of Vehicle Information and Communication Systems | 2024年 / 9卷 / 03期
关键词：
Battery management systems - Electric loads - Electric vehicles - Equivalent circuits - Errors - Extended Kalman filters - Forecasting - Iterative methods - Least squares approximations - Lithium batteries;
D O I：
10.1504/IJVICS.2024.139760
中图分类号：
学科分类号：
摘要：
The monitoring and protection of the power core lithium battery of electric vehicles is closely related to whether the lithium battery can output energy efficiently. To predict the charging state of lithium batteries, this study determined the online identification method, namely Recursive Least Square Method with Forcing Fact (FRLS), for model parameters based on the equivalent circuit model of lithium batteries, namely Thevenin. The overall prediction error of SVD-UKF is lower than that of Adaptive Extended Kalman Filter (AEKF) and Iterative Extended Kalman Filter (IEKF), which are about 20% and 30%, respectively. FRLS&Singular Value Decomposition-Unscented Kalman Filter (FRLS&SVD-UKF) has low prediction error of lithium battery load state under the same working condition and temperature, and the prediction error of lithium battery load state under the same working condition shows a gradually increasing trend with the increase of temperature. The FRLS&SVD-UKF joint prediction model can accurately predict the load state of lithium battery in electric vehicles in real time, and can improve the recycling performance of lithium battery. Copyright © 2024 Inderscience Enterprises Ltd.
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页码：276 / 291
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