Learning More With Less: A Generalizable, Self-Supervised Framework for Privacy-Preserving Capacity Estimation With EV Charging Data

作者：Anushiya Arunan, Yan Qin, Xiaoli Li, U-Xuan Tan, H. Vincent Poor, Chau Yuen · 发表于：IEEE Transactions on Industrial Informatics · 年份：2025 · DOI：10.1109/tii.2025.3613385 · 被引用次数：2 · 研究领域：Autonomous Vehicle Technology and Safety、Traffic Prediction and Management Techniques、Adversarial Robustness in Machine Learning

Accurate battery capacity estimation is key to alleviating consumer concerns about battery performance and reliability of electric vehicles (EVs). However, practical data limitations imposed by stringent privacy regulations and labeled data shortages hamper the development of generalizable capacity estimation models that remain robust to real-world data distribution shifts. While self-supervised learning can leverage unlabeled data, existing techniques are not particularly designed to learn effectively from challenging field data—let alone from privacy-friendly data, which are often less feature-rich and noisier. In this work, we propose a first-of-its-kind capacity estimation model based on self-supervised pretraining, developed on a large-scale dataset of privacy-friendly charging data snippets from real-world EV operations. Our pre-training framework,snippet similarity-weighted masked input reconstruction, is designed to learn rich, generalizable representations even from less feature-rich and fragmented privacy-friendly data. Our key innovation lies in harnessing contrastive learning to first capture high-level similarities among fragmented snippets that otherwise lack meaningful context. With our snippet-wise contrastive learning and subsequent similarity-weighted masked reconstruction, we are able to learn rich representations of both granular charging patterns within individual snippets and high-level associative relationships across different snippets. Bolstered by th...