Methodology exploration for predictive maintenance of synchronous electric machines and their inverter, integrated in the electrified vehicle

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Methodology exploration for predictive maintenance of synchronous electric machines and their inverter, integrated in the electrified vehicle
Due to the rapid and massive introduction of electrified vehicles (Hybrid Electric Vehicle, Plug-In Hybrid Electric Vehicle, Electric Vehicle) in the road transport market in the near future, the reliability of operation of electric powertrains (electric machines) and their power electronics becomes a major industrial issue. Indeed, any malfunction can lead to excessive energy consumption, requires time and immobilization costs for diagnosis and repairs, and even can endanger the safety of passengers. Moreover, the operating environments of the electrical systems in these vehicles are generally very constrained by the vibratory and thermal aspects. In addition, electrical systems are subject to frequent and repeated transient cycles, which solicit them mechanically and thermally. Predictive Maintenance (PM) has been proposed in a variety of industrial areas to improve the reliability of normal operation and reduce costs associated with unplanned downtime. It is based on the crossing of a predefined threshold (incipient fault indicator) that makes it possible to give the state of degradation of the components of a system before their complete deterioration. Most of the proposed methods are developed under favorable laboratory conditions and with a very high spectral resolution (for those based on frequency analysis), and are generally not compliant with onboard operation constrained by the limited computing capability of embedded controllers.
In this context, the objective of this thesis is to develop a methodology for the development and deployment of predictive maintenance techniques for synchronous electrical machines (with permanent magnets) and their associated power electronics (inverter), specifically suitable for use in a road vehicle for the electrification of the propulsion system, and to undertake a first validation using representative use scenarios (standardized cycle WLTC, RDE cycles). This methodology is expected to optimize the structure of the monitoring system.

Keywords: Predictive maintenance, Permanent magnet synchronous machine (PMSM), Voltage source inverter, Fault detection, Fault tolerant control, Condition monitoring, signal and data processing, Statistical analysis

  • Academic supervisor    Prof. DIALLO Demba, Laboratoire Génie électrique et électronique de Paris (GeePs - UMR8507, CentraleSupélec, CNRS, Paris-Saclay, Sorbonne Université) – Dr. DELPHA Claude, Laboratoire des Signaux et Systèmes (L2S - UMR8506, CentraleSupélec, CNRS, Paris-Saclay)
  • Doctoral School    (ED 575) ELECTRICAL, OPTICAL, BIO: PHYSICS_AND_ENGINEERING (EOBE) lien sur le site
  • IFPEN supervisor    SARABI Siyamak, PhD. Research Engineer on power electronics and drive control, Electric Systems Department, siyamak.sarabi@ifpen.fr 
  • PhD location    IFP Energies nouvelles, Rueil-Malmaison, France - GeePs, Gif-sur-Yvette, France 
  • Duration and start date    3 years, starting not earlier than October 2020
  • Employer    IFP Energies nouvelles, Rueil-Malmaison, France
  • Academic requirements    University Master degree or engineering school in Electrical engineering
  • Language requirements    Fluency in English or French, 
  • Other requirements    Skills on electric machine and voltage source inverters’ modelling and control, statistical analysis, signal and data processing. Good knowledge in Matlab/Simulink.
     
Contact
Encadrant IFPEN 
Dr. SARABI Siyamak
Research Engineer on power electronics and drive control, Electric Systems Department
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IFP Energies nouvelles is a French public-sector research, innovation and training center. Its mission is to develop efficient, economical, clean and sustainable technologies in the fields of energy, transport and the environment. For more information, see https://www.ifpen.com. 
IFPEN offers a stimulating research environment, with access to first in class laboratory infrastructures and computing facilities. IFPEN offers competitive salary and benefits packages. All PhD students have access to dedicated seminars and training sessions.