Grant: Discovery Grants Program - Individual

The largest obstacle to the mass adoption of electric vehicles (EVs) is the creation of affordable, attractive and functional EV designs for industry and consumers. Eliminating this obstacle involves the design and development of innovative traction electric machines (emachines) and drives that are cost effective, highly efficient, and provide improved torque density and performance. The applicant has made significant progress in achieving enhanced emachines and drives for EV applications. Innovations made in the emachine area include: 1) improvement of core materials and winding coating and insulation, 2) geometrical design and optimization, 3) emachine control and performance analysis, and 4) thermal modeling. In the area of motor drives, progress made includes new inverter topology, reduced losses and reduced total harmonic distortion, and optimized control algorithms. The applicant is currently investigating wide band gap device based motor drives through ongoing research projects. These advancements have resulted in superior electric drive performance and increased EV driving range. Significant improvement was also achieved in the testing of emachines and drives through the development of faster and accurate new testing methods, and the creation of a user friendly data analysis interface with high capability. Several innovative initiatives, both ongoing and planned for the future, will be supported by this proposal which will further build upon the success already achieved by the applicant in the field of emachines and controls. These initiatives include: In collaboration with his partners the applicant will continue to investigate the utilization of lightweight and emerging materials for magnetic and nonmagnetic emachine parts for enhanced electrical and mechanical properties and improved thermal performance. Utilizing multifunctional, multiconstraint based iterative optimization techniques and stochastic evolutionary methods, geometrical optimization will be conducted that will result in an easily manufactured multipurpose emachine with improved performance while reducing material consumption and cost. The proposed program will continue to improve the emachine control performance in terms of responsiveness, torque ripple reduction, and fault tolerance by employing novel parameter independent adaptive control algorithms. The proposed enhanced multipurpose model reference adaptive control algorithm will result in improved charging operation with balanced currents and increased efficiency. The developed multipurpose emachines will be tested and verified in terms of electromagnetic, thermal, and structural performance along with the proposed control methods.