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New ANN results on a major benchmark for the prediction of RUL of Lithium Ion batteries in electric vehicles

Lithium Ion batteries are a core component of many lately designed devices. It is of crucial importance to be able to fully master the behaviour of batteries in order to meet the requirements in terms of safety, and performances. Predicting the Remaining Useful Life of batteries can help preventing a failure before it occurs, with an increased safety for the user and reduced costs linked to maintenance.The work described in this paper is an attempt to accurately predict the Remaining Useful Life of Li-Ion batteries using machine learning regression methods applied to a new set of ageing data published by the department of chemical engineering of the Massachusetts Institute of Technology. By changing the usual approach applied to data and feature management, very encouraging results were obtained. Compared with previous approaches in the literature using linear regression or Convolutional Neural Networks on the same dataset, our work on how to build a more efficient representation of ageing data combined with Artificial Neural Networks leads to more accurate predicting performances.