Lithium-ion batteries have become the focus of research interest, thanks to their numerous benets for vehicle applications. One main limitation of these technologies resides in the battery ageing. The eects of battery ageing limit its performance and occur throughout its whole life, even if the battery is used or not, which is a major drawback on real usage. Furthermore, degradations take place in every condition, but in dierent proportions as usage and external conditions interact to provoke degradations. The ageing phenomena are highly complicated to characterize due to the factors cross-dependence. This paper reviews various aspects of recent research and developments, from dierent elds, on Lithium-ion battery ageing mechanisms and estimations. A summary of techniques, models and algorithms used for battery ageing estimation (SOH, RUL), going from a detailed electrochemical approach to statistical methods based on data, are presented.

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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.