4.8 Article

Temperature rise prediction of lithium-ion battery suffering external short circuit for all-climate electric vehicles application

期刊

APPLIED ENERGY
卷 213, 期 -, 页码 375-383

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2018.01.068

关键词

All-climate electric vehicles; Battery safety; Abusing test; External short circuit; Temperature prediction; Fault detection

资金

  1. Joint Funds of the National Natural Science Foundation of China [U1564206]
  2. National Natural Science Foundation of China [51607030]
  3. Beijing Municipal Science and Technology Project [Z171100000917013]
  4. Fundamental Research Funds for the Central Universities [N160304001]

向作者/读者索取更多资源

External short circuit (ESC) is a severe fault that can cause the large current and high temperature of lithium-ion batteries (LiBs) immediately. Temperature rise prediction is crucial for LiB safety management in an all-climate electric vehicles application because many disastrous consequences are caused by high temperature. This study mainly investigates the ESC-caused temperature rise characteristics of LiB, and proposes an online prediction approach of the maximum temperature rise. Three original contributions are made: (1) Abusing tests of LiBs under ESC are conducted at varying ambient temperatures, and the influences of battery state of charge (SOC) and ambient temperature on the maximum temperature rise are revealed. (2) Characteristics of temperature rises are analysed, therein finding that the heat generation of LiBs caused by ESC presents two modes: Joule heat dominant mode and reaction heat/Joule heat blended mode; leakage is an external manifestation of the latter. (3) Two heat generation modes are proved to be linearly separable at temperature rise discharge capacity plane, and then a two-step prediction approach of maximum temperature rise is proposed based on support vector machine. Finally, the presented approach is validated by the experimental data. The maximum temperature rise can be predicted up to 22.3 s ahead of time and very precise prediction results are obtained, where the mean prediction error for the eight test cells is 3.05%.

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

4.8
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