Multi-modal framework for battery state of health evaluation using open-source electric vehicle data
Hongao Liu, Chang Li, Xiaosong Hu, Jinwen Li, Kai Zhang, Yang Xie, Ranglei Wu, Ziyou Song
Abstract
Accurate, practical, and robust evaluation of the battery state of health is crucial to the efficient and reliable operation of electric vehicles. However, the limited availability of large-scale, high-quality field data hinders the development of the battery management system for state of health estimation, lifetime prediction, and fault detection in various applications. In this work, to gain insights into underlying factors limiting battery management system performance in real-world vehicles, we analyze the operational data of 300 diverse electric vehicles over three years to understand the disparities between field data and laboratory battery test data and their effect on state of health estimation. Furthermore, we propose a deep learning-based multi-modal framework to effectively leverage historical vehicle data for efficient, accurate, and cost-effective state of health estimation. The proposed paradigm exhibits considerable potential for numerous applications in state estimation and diagnostics in multi-sensor systems. Furthermore, we make the field data of these electric vehicles publicly available aiming to promote further research on the development of effective and reliable battery management systems for real-world vehicles. Electric vehicle batteries deteriorate with usage, which subsequently affects vehicle performance and range. Here, authors demonstrate a deep learning framework that integrates extensive vehicle field data to enable an efficient and accurate assessment of battery state of health.