Data-driven model development to predict the aging of a Li-ion battery pack in electric vehicles representative conditions

• Published in: Journal of energy storage Vol. 39; p. 102592
• Main Authors: Mingant, Rémy, Petit, Martin, Belaïd, Sofiane, Bernard, Julien
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2021; Elsevier
• Subjects: Aging; Computer Science; Easy training; Electric power; Engineering Sciences; Environmental Sciences; Generic model; Li-ion battery; Modeling and Simulation; State of health; Aging; Generic model; Easy training; Li-ion battery; State of health; State of Health; Generic Model
• ISSN: 2352-152X; 2352-1538
• DOI: 10.1016/j.est.2021.102592

•A generic aging model of lithium-ion batteries has been developed and presented.•This model is independent of the aging mechanism's knowledge.•The calibration of the model is easy and fully automated.•The model has been validated on several profiles including PHEV. An empirical generic Li-ion aging model, compatible with a large number of aging mechanisms without their a priori knowledge has been developed as well as a calibration methodology allowing its fast and automated parameter setting. This model has been applied to simulate the aging behavior of a 26 Ah cell. To train this model, a large aging test campaign has been conducted dedicated to both calibration and validation purposes. This one takes into account calendar, cycling, and their combinations. Based on the design of the aging campaign it is able to account for the effect of State Of Charge, temperature and current on aging. As its calibration is based on an automated process, it can be trained automatically and does not need expert knowledge for operation. Simulation data are validated to a 2% error in comparison to experimental data and is then validated for automotive applications.