Eduardo Redondo-Iglesias

In our increasingly electrified society, lithium–ion batteries are a key element. To design, monitor or optimise these systems, data play a central role and are gaining increasing interest. This article is a review of data in the battery field. The authors are experimentalists who aim to provide a comprehensive overview of battery data. From data generation to the most advanced analysis techniques, this article addresses the concepts, tools and challenges related to battery informatics with a holistic approach. The different types of data production techniques are described and the most commonly used analysis methods are presented. The cost of data production and the heterogeneity of data production and analysis methods are presented as major challenges for the development of data-driven methods in this field. By providing an understandable description of battery data and their limitations, the authors aim to bridge the gap between battery experimentalists, modellers and data scientists. As a perspective, open science practices are presented as a key approach to reduce the impact of data heterogeneity and to facilitate the collaboration between battery scientists from different institutions and different branches of science.

Battery lifetime is an important parameter in the life cycle assessment (LCA) of a plug-in hybrid-electric vehicle (PHEV). This paper aims to study the impact of various parameters on the battery aging of a PHEV. For this purpose, model-based use cases are generated, the outputs of which are the daily driven distances for a period of one year, recharge scenarios, and battery temperature. A combined aging model (calendar and cycling aging) is used to calculate the capacity lost by the battery at the end of one year of use. The thermal model of the battery is using an electro-thermal coupling equation, for which the ambient temperature is modeled using daily minimum and maximum temperature data varying throughout the year for different cities. Finally, a sensitivity analysis is carried out using the conditioned variance method to identify the most important input parameters which largely affect the output of this study. The results of this study show that battery size, annual mileage, external temperature, and charging behavior are the most important parameters to be considered in the aging study of the battery of a PHEV personal car.