Robust design optimization of electrical machines for electric and hybrid vehicles

Following the rapid emergence of electric vehicles in the automotive market, manufacturers are seeking to regularly improve their product’s performance to stand out from the competition. The electrical machine's design is considered as a significant phase, and designers often use multi-objective optimization methods coupled with multi-physical models describing the machine's behavior with great precision. These models are based on the design's geometrical parameters and the physical characteristics of the materials given by the suppliers. However, in a prototype, the real values of these geometrical parameters and the characteristics of the materials may deviate from those used in simulations. 
Indeed, the manufacturing method's tolerances can cause deviations between the dimensions of the geometrical parameters given by the optimization and those in the real machine. These same manufacturing methods (sheet metal cutting, magnet molding...) also impact the material’s characteristics : often decreasing their performance compared to their initial characteristics. Finally, the assembly techniques of the machine can also impact its electromagnetic performances. 
How then to optimize an electrical machine in the presence of all these uncertain parameters?
The proposed thesis subject aims at developing new design optimization methods making the final solution more robust to any uncertainty related to the input parameters, thus ensuring the quality of the proposed solution.

Keywords: Electrical machines, uncertainties, optimization, response surface / metamodels

• Academic supervisor    Professor HLIOUI Sami, Laboratoire SATIE, ORCID : 0000-0002-3992-8266
• Doctoral School    ED575 - Electrical, Optical, Bio-physics and Engineering (EOBE)
• IFPEN supervisor    DR. NASR Andre, Electrified systems department, andre.nasr@ifpen.fr (ORCID)/Dr. SINOQUET Delphine, Applied mathematics department, delphine.sinoquet@ifpen.fr (personal pages)
• PhD location    IFP Energies nouvelles, Rueil-Malmaison, France
• Duration and start date    3 years, starting in fourth quarter 2021
• Employer    IFP Energies nouvelles, Rueil-Malmaison, France
• Academic equirements    University Master degree in electrical engineering or applied mathematics
• Language equirements    Fluency in French or English
• Other requirements    knowledge required in programming (Matlab, python, R), statistics, optimization, and an interest in electrical engineering