Towards real-world state of health estimation: Part 2, system level method using electric vehicle field data

Abstract

Accurate battery health estimation is pivotal for ensuring the safety and performance of electric vehicles (EVs). While predominant research has centered on laboratory-level single cells, the accurate estimation of battery system capacity using real-world data remains a challenge, due to the vast diversity in battery types, operating conditions, data recordings, etc. To this end, we release three large-scale field datasets of 464 EVs from three manufacturers, comprising over 1.2 million charging snippets. The EVs’ capacity and State of Health (SOH) are effectively labeled using K-means to cluster and concatenate charging snippets. A robust data-driven framework integrating a Gated Convolutional Neural Network (GCNN) for estimating battery capacity is proposed, and the results outperform other machine learning models. In addition, a fine-tuning technique is employed to further enhance model efficacy on new datasets and with limited training data. This research not only advances battery health estimations but also paves the way for broader applications in battery management systems (BMSs), offering a scalable solution to real-world challenges in battery technology.