Learning the Ageing Behaviour of Lithium-Ion Batteries Using Electric Vehicle Fleet Analysis

Abstract

This article presents the results of the Febal project, where the aim was to parametrize a stress-factor-based ageing model for Lithium-ion batteries using operation data of an electric fleet. Contrary to state-of-the-art methods, this approach does not rely on laboratory ageing tests only. Instead, a novel physics-informed learning procedure is used to combine the accuracy and flexibility of data-driven approaches with the extrapolation properties of physical models. The ageing model is parameterized in a two-stage process. In order to cover data ranges not present in operation, a laboratory ageing test campaign is used as a baseline. In the second stage, transfer learning is used to adjust a subset of the model parameters to fit data of different cells. This approach is not only applied to laboratory measurements but also validated by a series of capacity checkup tests performed with a fleet of electric vehicles. Results show the improved state-of-health (SOH) prediction of the proposed model parameterization method.