Zhang, Guanchen - Energy Management Strategies and Evaluation for Plug-in Electric Vehicles - On and Off the Road...

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This thesis has been submitted to the Library for purposes of graduation, but needs to be audited for technical details related to publication in order to be approved for inclusion in the Library collection.
Summer 2017
Degree type: 
School of Mechatronic Systems Engineering
Applied Sciences
Senior supervisor: 
Gary Wang
Co-supervisor, if any: 
Hassan Farhangi, Ali Palizban
Thesis title: 
Energy Management Strategies and Evaluation for Plug-in Electric Vehicles - On and Off the Road
Given Names: 
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.
Smart grid; electric vehicle; smart charging; vehicle-to-grid; load disaggregation; load forecast
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