Prognostics and Health Management System for Electric Vehicles with a Hierarchy Fusion Framework: Concepts, Architectures, and Methods
Cheng Wang, Tongtong Ji, Feng Mao, Zhenpo Wang, Zhiheng Li
Abstract
The prognostics and health management (PHM) of electric vehicles is an important guarantee for their safety and long‐term development. At present, there are few studies researching about life cycle PHM system of electric vehicles. In this paper, we first summarize the research progress and key methods of PHM. Then, we propose a three‐level PHM system with a hierarchy fusion architecture for electric vehicles based on the structure, data source of them. In the PHM system, we introduce a database consisting of the factory data, real‐time data, and detection data. The electric vehicle′s factory parameters are used for determining the life curve of the electric vehicle and its components, the real‐time data are used for predicting the remaining useful lifetime (RUL) of the electric vehicle and its components, and the detection data are used for fault diagnosis. This health management database is established to help make condition‐based maintenance decisions for electric vehicles. In this way, a complete electric vehicle PHM system is formed, which can realize the whole‐life‐cycle life prediction and fault diagnosis of electric vehicles.