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

Author: 
Date created: 
2017-08-28
Identifier: 
etd10358
Keywords: 
Smart grid
Electric vehicle
Smart charging
Vehicle-to-grid
Load disaggregation
Load forecast
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 type: 
Thesis
Rights: 
This thesis may be printed or downloaded for non-commercial research and scholarly purposes. Copyright remains with the author.
File(s): 
Senior supervisor: 
Gary Wang
Hassan Farhangi, Ali Palizban
Department: 
Applied Sciences: School of Mechatronic Systems Engineering
Thesis type: 
(Thesis) M.A.Sc.
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