Predicting the lifetime of Lithium–Ion batteries: Integrated feature extraction and modeling through sequential Unsupervised-Supervised Projections (USP)

Abstract

Lithium-ion batteries are among the most used rechargeable batteries in the market, from portable electronics, and mobility solutions to process industry and aerospace. The ability to accurately predict battery lifetime and health status is of great interest for R&D activities, quality control & product release, predictive maintenance and recycling, among others. However, the complexity and multiplicity of degradation modes and usage/charging conditions raise considerable challenges to the construction of robust predictive models for battery lifecycle prediction. In this work, we propose an integrated methodology that is able to extract operational features and use them in the scope of a predictive model. Results demonstrate the superior feature extraction and accuracy of the proposed approach based only on the discharge information collected in the early usage periods (40–60 cycles), an aspect of practical interest. Data used regard commercial high-power LFP/graphite A123 cells, with nominal capacity of 1.1Ah and nominal voltage of 3.3 V.