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.

Even if Electrochemical Storage (ES) devices are nowadays commonly used in a wide range of applications of different power, one application cannot be clearly addressed by only one specific technology of ES. Moreover, operating conditions such as charge/discharge rates or ambient temperatures have a huge influence on both ES real-time performance and lifetime. In this article, a new indicator of the energetic reserve, the State-of-Energy (SoE), is proposed to deal with modern Battery Management Systems (BMS) attendees: easy-to-implement, reliable given the operating conditions, using the power as a direct input parameter to manage the battery reserve. After a complete description of the SoE and the experimental characterization procedures, the paper presents some detailed examples of the SoE applications like storage technology comparison, smoothing of solar or wind production and finally on-line forecasting in advanced applications such as smart grids or electrical vehicle.Electrochemical Storage (ES) devices are now available in all fields of our daily lives, from portable electronic devices to health implants, in households for autonomous power supply, rural electrification, satellites for telecommunications and aerospace, defense and military applications. In particular, in the few past decades, ES devices have become a huge factor of development for intermittent renewable energies, for electric and hybrid vehicles and also for smart grids. 1,2 Electrochemical storage is thus one field within which the technologies are making constant progress to match the needs of the applications. Nowadays, storage systems provide a wide range of energy, power and cycling resistance ability, necessarily involving a wide diversity of packaging, assembling and materials. ES devices can now match applications from a few mWh to several MWh. 3 Today, as systems require more and more intelligence, predictability, precision and accuracy (e.g. electric vehicles and smartgrids), improvements have to be made to develop energetic models of ES that -are easy-to-implement, -use the power as a direct parameter of control, -have an acceptable reliability given the operating conditions, -can be easily upgraded during the lifetime.In the first section, the authors underline the mismatch of usual modeling approaches, mainly based on a double modeling of the capacity and the voltage, and the real needs for Energy Demand Size (EDS) and Energy Management Systems (EMS). The second section presents the State-of-Energy (SoE), as an indicator designed to address these requirements. In particular, the characterization procedures used to parameterize this SoE indicator are detailed. The third section describes how to operate this indicator in an easy-to-implement algorithm that is able to forecast the available energy for any power and temperature conditions. Dynamic Stress Tests (DST), with several severities operated, allowed the validation of this methodology, with an error inferior to +/− 3%, on commercially-available Li-ion batteries. Finally, th...

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