Exploring the integration of multi-speed gearboxes in electric vehicle (EV) drivetrains, this research presents a comprehensive analysis through detailed gearbox modelling, empirical traction machine testing, and analytical drive unit loss evaluations. The study utilizes two distinct automotive-grade electric machines – an axial-flux permanent magnet synchronous machine and an interior permanent magnet machine, the latter coupled with a single-speed gearbox – to demonstrate how multi-speed gearboxes can enhance drivetrain efficiency and performance for a subcompact EV. Extensive dynamometer testing, incorporating a variety of electrical and thermal conditions, characterizes both traction machines. Findings reveal that despite the incremental churning losses from additional gear pairs, two-speed gearboxes facilitate a more efficient operation of the electric machine, inverter, and gearbox, particularly when optimized through strategic gear ratio selection. Dynamometer testing under no-load conditions and at different temperatures underscores the impact of gearbox churning and bearing drag losses and the potential for their reduction. Detailed examinations of load-dependent and independent losses within the drive unit elucidate the interactions among drivetrain components across various gear ratios. Optimized two-speed gearboxes are shown to reduce vehicle energy consumption by up to 9% and increase driving range compared to conventional single-speed configurations, supported by strategic gear ratio selections and optimizations aimed at achieving vehicle performance targets, such as acceleration, gradeability, and top speed. This research contributes to advancing the field of electric vehicle technology by illustrating the complex trade-offs and potential enhancements achievable with multi-speed drivetrains, setting a precedent for future studies to refine gearbox performance and explore novel technologies to optimize powertrain performance across diverse operational landscapes. / Thesis / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/30001 |
| Date | January 2024 |
| Creators | Machado, Fabricio |
| Contributors | Emadi, Ali, Mechanical Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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