Modeling and Design Optimization of Plug-In Hybrid Electric Vehicle Powertrains

dc.contributor.authorChehresaz, Maryyeh
dc.date.accessioned2013-09-26T16:43:45Z
dc.date.available2014-06-04T05:00:42Z
dc.date.issued2013-09-26T16:43:45Z
dc.date.submitted2013
dc.description.abstractHybrid electric vehicles (HEVs) were introduced in response to rising environmental challenges facing the automotive sector. HEVs combine the benefits of electric vehicles and conventional internal combustion engine vehicles, integrating an electrical system (a battery and an electric motor) with an engine to provide improved fuel economy and reduced emissions, while maintaining adequate driving range. By comparison with conventional HEVs, plug-in hybrid electric vehicles (PHEVs) have larger battery storage systems and can be fully charged via an external electric power source such as the electrical grid. Of the three primary PHEV architectures, power-split architectures tend to provide greater efficiencies than parallel or series systems; however, they also demonstrate more complicated dynamics. Thus, in this research project, the problem of optimizing the component sizes of a power-split PHEV was addressed in an effort to exploit the flexibility of this powertrain system and further improve the vehicle's fuel economy, using a Toyota plug-in Prius as the baseline vehicle. Autonomie software was used to develop a vehicle model, which was then applied to formulate an optimization problem for which the main objective is to minimize fuel consumption over standard driving cycles. The design variables considered were: the engine's maximum power, the number of battery cells and the electric motor's maximum power. The genetic algorithm approach was employed to solve the optimization problem for various drive cycles and an acceptable reduction in fuel consumption was achieved thorough the sizing process. The model was validated against a MapleSim model. This research project successfully delivered a framework that integrates an Autonomie PHEV model and genetic algorithm optimization and can be used to address any HEV parameter optimization problem, with any objective, constraints, design variables and optimization parameters.en
dc.description.embargoterms1 yearen
dc.identifier.urihttp://hdl.handle.net/10012/7923
dc.language.isoenen
dc.pendingtrueen
dc.publisherUniversity of Waterlooen
dc.subjectPowertrainen
dc.subjectPHEVen
dc.subjectComponent Sizingen
dc.subjectOptimizationen
dc.subjectModelingen
dc.subjectPower-Spliten
dc.subject.programSystem Design Engineeringen
dc.titleModeling and Design Optimization of Plug-In Hybrid Electric Vehicle Powertrainsen
dc.typeMaster Thesisen
uws-etd.degreeMaster of Applied Scienceen
uws-etd.degree.departmentSystems Design Engineeringen
uws.peerReviewStatusUnrevieweden
uws.scholarLevelGraduateen
uws.typeOfResourceTexten

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