Next Electric Drives for Future Electric Cars

未来电动汽车的下一代电力驱动

• 批准号: RGPIN-2020-05997
• 负责人: Kar, Narayan
• 金额: $ 4.01万
• 依托单位: University of Windsor
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=718146
• 关键词: Next; Electric; Drives; Future; Cars

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.

电动汽车(EVS)大规模采用的最大障碍是为工业和消费者创造出负担得起、有吸引力和功能性的电动汽车设计。要消除这一障碍，就需要设计和开发具有成本效益、高效率并提供更高扭矩密度和性能的创新牵引电机(EMachines)和驱动器。申请人在实现电动汽车应用的增强型电动机械和驱动方面取得了重大进展。电机领域的创新包括：1)改进铁心材料、绕组涂层和绝缘，2)几何设计和优化，3)电机控制和性能分析，4)热模型。在电机驱动领域，取得的进展包括新的逆变器拓扑结构、降低损耗和总谐波失真以及优化控制算法。申请者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。这些进步带来了卓越的电驱动性能和更大的电动汽车行驶里程。通过开发更快、更准确的新测试方法，以及建立用户友好的高性能数据分析界面，在电子机械和驱动器的测试方面也取得了重大改进。 这项提案将支持正在进行和计划在未来进行的几项创新举措，这将进一步巩固申请人在电子机械和控制领域已经取得的成功。这些措施包括： 申请人将与合作伙伴合作，继续研究利用轻质和新兴材料制造磁性和非磁性机械部件，以提高电气和机械性能，并改善热性能。 利用多功能、基于多约束的迭代优化技术和随机进化方法，将进行几何优化，从而在降低材料消耗和成本的同时，产生易于制造的多用途机器，具有更高的性能。 该方案将通过采用新的参数独立自适应控制算法，在响应速度、降低扭矩脉动和容错方面继续提高机床的控制性能。所提出的增强型多用途模型参考自适应控制算法将改善充电操作，实现均衡电流，提高充电效率。 所开发的多功能电动机床将在电磁、热和结构性能以及所提出的控制方法方面进行测试和验证。