Next Electric Drives for Future Electric Cars

大规模采用电动汽车（EV）的最大障碍是为工业和消费者创造价格合理、有吸引力和功能齐全的电动汽车设计。为了消除这一障碍，需要设计和开发具有成本效益、高效率、提高扭矩密度和性能的创新型牵引电机（emachines）和驱动器。申请人在实现电动汽车应用的增强型机器和驱动器方面取得了重大进展。机床领域的创新包括：1)芯材和绕组涂层和绝缘的改进，2)几何设计和优化，3)机床控制和性能分析，4)热建模。在电机驱动领域，取得的进展包括新的逆变器拓扑结构，减少损耗和减少总谐波失真，以及优化的控制算法。申请人目前正在通过正在进行的研究项目研究基于宽带隙器件的电机驱动器。这些进步带来了卓越的电力驱动性能，并增加了电动汽车的行驶里程。通过开发更快，更准确的新测试方法，以及创建具有高功能的用户友好数据分析界面，在机器和驱动器的测试方面也取得了重大改进。

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.