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Data-DrivenModel Development to Predict the Aging of a Li-ion Battery Pack in Electric Vehicles Representative Conditions

Abstract : 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.
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https://hal-ifp.archives-ouvertes.fr/hal-03312439
Contributor : Catherine Belli Connect in order to contact the contributor
Submitted on : Monday, August 2, 2021 - 2:15:13 PM
Last modification on : Thursday, October 21, 2021 - 3:32:57 AM

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Rémy Mingant, Martin Petit, Sofiane Belaïd, Julien Bernard. Data-DrivenModel Development to Predict the Aging of a Li-ion Battery Pack in Electric Vehicles Representative Conditions. Journal of Energy Storage, Elsevier, 2021, 39, pp.102592. ⟨10.1016/j.est.2021.102592⟩. ⟨hal-03312439⟩

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