4.5 Article

A Novel State of Charge Estimation Algorithm for Lithium-Ion Battery Packs of Electric Vehicles

期刊

ENERGIES
卷 9, 期 9, 页码 -

出版社

MDPI
DOI: 10.3390/en9090710

关键词

daptive extended Kalman filter (AEKF); electric vehicle (EV); state of charge (SOC); weighed coefficients

资金

  1. National Science Foundation of China [51567012]
  2. key project of education department of Yunnan province [2015Z023]
  3. talent training program of Yunnan province [KKSY201302084]
  4. innovation fund of advanced techniques for new energy vehicles of Kunming University of Science and Technology [14078368]

向作者/读者索取更多资源

This paper focuses on state of charge (SOC) estimation for the battery packs of electric vehicles (EVs). By modeling a battery based on the equivalent circuit model (ECM), the adaptive extended Kalman filter (AEKF) method can be applied to estimate the battery cell SOC. By adaptively setting different weighed coefficients, a battery pack SOC estimation algorithm is established based on the single cell estimation. The proposed method can not only precisely estimate the battery pack SOC, but also effectively prevent the battery pack from overcharge and over-discharge, thus providing safe operation. Experiment results verify the feasibility of the proposed algorithm.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据