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The automotive industry is today one of the most prominent and broad in the world. Since the 1980s electric vehicles have attracted much interest and experienced intense growth thanks to the numerous advantages they provide compared to traditional ones. Nevertheless, they show some problems related to their energy storage device, being a very complex electrochemical system. In fact, the prevention and control of battery ageing are key issues in the widespread battery adoption by the automotive sector. The main aim of this work is to provide a comprehensive and updated overview of battery ageing. Specifically, after a general introduction of the lithium-ion batteries, the study offers a focus on the degradation mechanisms affecting cell electrodes and their consequences from a global cell level point of view, that are the degradation modes. Then, insights on the diagnosis/prognosis methodologies are presented and the use of differential curve as a non-invasive and straightforward diagnostic tool is detailed described. These curves show to be very powerful instruments for data analysis and therefore they can be used for the development of artificial intelligence/automatic procedures for ageing analysis in energy storage devices, useful for both academic and industrial application. One possible application is the development of an intelligent algorithm to find patterns in curves changes useful to understand the ageing mechanisms affecting cells also without knowing their actual usage. • Comprehensive review on Li-ion battery ageing and degradation mechanisms. • Modeling ageing mechanisms serves as a proxy for diagnostic and prognostic methods. • Focus on applications in energy storage and automotive battery management systems. • Incremental capacity and differential voltage analysis explored for ageing insights. • Differential curves are effective for machine learning-based ageing analysis.
Santis et al. (Thu,) studied this question.