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.
Highlights
Compared with traditional automobiles, electric vehicles are different in electric drive and control systems due to different energy consumption types
A complete electric vehicle fault prediction and health management system is established based on the system structure of electric vehicles and the sources of data information. e system mainly includes three levels: (1) Life prediction of electric vehicles at the factory stage: at the factory stage, a physical model method or a data-driven method is used to obtain the life curve of a single component
A linear and nonlinear fusion method is used to obtain the life curve of a system composed of multiple components. e simulation method and the data-driven method are used to obtain the vehicle life prediction curve composed of multiple systems
Summary
Prognostics is to predict the future status based on the current and historical status of the system, including the health state, remaining useful lifetime, and faults of the system and components. Health management is to make decisions on maintenance based on fault prediction information, maintenance resources, and application requirements. For complex equipment and systems, PHM can achieve comprehensive diagnosis and fault prediction at different levels. Is centralized architecture and data processing technology can be applied to the electric vehicle PHM system design. Electric vehicle is a complex system composed of the battery system, motor system, and electronic control system, and the safety performance is closely related to each system. E PHM system has been successfully applied to some complex system such as the aircraft system It has advantages over the whole-life cycle health management. It has advantages over the whole-life cycle health management. erefore, introducing the idea of PHM to the electric vehicle field is a promising idea
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