Electric Machine Differential For Vehicle Traction Control And Stability Control

<p>Evolving requirements in energy efficiency and tightening regulations for reliable electric drivetrains drive the advancement of the hybrid electric (HEV) and full electric vehicle (EV) technology. Different configurations of EV and HEV architectures are evaluated for their performance. The future technology is trending towards utilizing distinctive properties in electric machines to not only to improve efficiency but also to realize advanced road adhesion controls and vehicle stability controls. Electric machine differential (EMD) is such a concept under current investigation for applications in the near future. Reliability of a power train is critical. Therefore, sophisticated fault detection schemes are essential in guaranteeing reliable operation of a complex system such as an EMD. The research presented here emphasize on implementation of a 4kW electric machine differential, a novel single open phase fault diagnostic scheme, an implementation of a real time slip optimization algorithm and an electric machine differential based yaw stability improvement study. The proposed d-q current signature based SPO fault diagnostic algorithm detects the fault within one electrical cycle. The EMD based extremum seeking slip optimization algorithm reduces stopping distance by 30% compared to hydraulic braking based ABS.</p>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Technology

Year

2013

Author dc:creator

• Kuruppu, Sandun Shivantha

Contributors dc:contributor

• N. Athula Kulatunga
• Kartik B. Ariyur
• Steven D. Pekarek
• John M. Starkey
• Anthony B. Will

• electric machine control
• electric machine differential
• extremum seeking optimization
• sm-pmsm fault diagnostics
• traction control
• vehicle stability control
• Electrical and Computer Engineering
• Mechanical Engineering
• Oil, Gas, and Energy

Repository record dc:identifier

https://docs.lib.purdue.edu/open_access_dissertations/135

OAI identifier oai:identifier

oai:docs.lib.purdue.edu:open_access_dissertations-1080