Statistical analysis for understanding and predicting battery degradations in real-life electric vehicle use

被引:80
作者
Barre, Anthony [1 ,2 ]
Suard, Frederic [1 ]
Gerard, Mathias [2 ]
Montaru, Maxime [2 ]
Riu, Delphine [3 ]
机构
[1] CEA, LIST, F-91191 Gif Sur Yvette, France
[2] CEA Grenoble, LITEN, F-38054 Grenoble 9, France
[3] G2Elab, UMR 5269, F-38402 St Martin Dheres, France
关键词
Li-ion battery; Battery degradation; Ageing; Prediction; Statistics; Electric vehicle; LITHIUM-ION BATTERIES; STATE-OF-HEALTH; AGING MECHANISMS; CAPACITY FADE; CALENDAR LIFE; CYCLE LIFE; CELLS; REGRESSION; SELECTION; SEI;
D O I
10.1016/j.jpowsour.2013.07.052
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070305 [高分子化学与物理];
摘要
This paper describes the statistical analysis of recorded data parameters of electrical battery ageing during electric vehicle use. These data permit traditional battery ageing investigation based on the evolution of the capacity fade and resistance raise. The measured variables are examined in order to explain the correlation between battery ageing and operating conditions during experiments. Such study enables us to identify the main ageing factors. Then, detailed statistical dependency explorations present the responsible factors on battery ageing phenomena. Predictive battery ageing models are built from this approach. Thereby results demonstrate and quantify a relationship between variables and battery ageing global observations, and also allow accurate battery ageing diagnosis through predictive models. (C) 2013 Elsevier B.V. All rights reserved.
引用
收藏
页码:846 / 856
页数:11
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