Unlocking the potential of unlabeled data: Self-supervised machine learning for battery aging diagnosis with real-world field data

Abstract

Accurate aging diagnosis is crucial for the health and safety management of lithium-ion batteries in electric vehicles. Despite significant advancements achieved by data-driven methods, diagnosis accuracy remains constrained by the high costs of check-up tests and the scarcity of labeled data. This paper presents a framework utilizing self-supervised machine learning to harness the potential of unlabeled data for diagnosing battery aging in electric vehicles during field operations. We validate our method using battery degradation datasets collected over more than two years from twenty real-world electric vehicles. Our analysis comprehensively addresses cell inconsistencies, physical interpretations, and charging uncertainties in real-world applications. This is achieved through self-supervised feature extraction using random short charging sequences in the main peak of incremental capacity curves. By leveraging inexpensive unlabeled data in a self-supervised approach, our method demonstrates improvements in average root mean square errors of 74.54% and 60.50% in the best and worst cases, respectively, compared to the supervised benchmark. This work underscores the potential of employing low-cost unlabeled data with self-supervised machine learning for effective battery health and safety management in real-world scenarios.