Energy Management Strategies and Evaluation for Plug-in Electric Vehicles - On and Off the Road

Resource type
Thesis type
(Thesis) M.A.Sc.
Date created
2017-08-28
Authors/Contributors
Abstract
Electric vehicle (EV) industries are driven by new technologies in batteries and powertrains. This thesis studies the cutting-edge Formula E racing vehicles with vehicle simulation and optimization for energy efficiency. On the consumer side, a new challenge EVs introduce is the need for large-scale charging infrastructure with minimum grid impact. This thesis studies EV charging management on the daily basis, featuring practical smart charging solutions at public locations and bi-directional (dis)charging at workplace and residence. Techniques that support smart charging are also studied. A data-mining based load disaggregation approach is developed to evaluate the general energy usage in the residential context. A machine-learning based load forecasting model is proposed to predict short-term residential loads in ultra-small scales. Overall, this thesis anticipates every aspect of EVs' daily activities, whether it is on or off the road, and suggests solutions to maximizing EV utilization for both drivers and the smart grid.
Document
Identifier
etd10358
Copyright statement
Copyright is held by the author.
Permissions
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Scholarly level
Supervisor or Senior Supervisor
Thesis advisor: Wang, Gary
Attachment Size
etd10358_GZhang.pdf 16.93 MB