Driving data pattern recognition for intelligent energy management of plug-in hybrid electric vehicles

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dc.contributor.authorMunthikodu, Sreejith
dc.contributor.supervisorDong, Zuomin
dc.date.accessioned2019-08-19T23:21:21Z
dc.date.available2019-08-19T23:21:21Z
dc.date.copyright2019en_US
dc.date.issued2019-08-19
dc.degree.departmentDepartment of Mechanical Engineering
dc.degree.levelMaster of Applied Science M.A.Sc.en_US
dc.description.abstractThis work focuses on the development and testing of new driving data pattern recognition intelligent system techniques to support driver adaptive, real-time optimal power control and energy management of hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs). A novel, intelligent energy management approach that combines vehicle operation data acquisition, driving data clustering and pattern recognition, cluster prototype based power control and energy optimization, and real-time driving pattern recognition and optimal energy management has been introduced. The method integrates advanced machine learning techniques and global optimization methods form the driver adaptive optimal power control and energy management. Fuzzy C-Means clustering algorithm is used to identify the representative vehicle operation patterns from collected driving data. Dynamic Programming (DA) based off-line optimization is conducted to obtain the optimal control parameters for each of the identified driving patterns. Artificial Neural Networks (ANN) are trained to associate each of the identified operation patterns with the optimal energy management plan to support real-time optimal control. Implementation and advantages of the new method are demonstrated using the 2012 California household travel survey data, and driver-specific data collected from the city of Victoria, BC Canada.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/11052
dc.languageEnglisheng
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectIntelligent Energy Management for HEV/PHEVen_US
dc.subjectHEV Powertrain Control and Energy Managementen_US
dc.subjectDriving Data Pattern Recognitionen_US
dc.subjectArtificial Neural Networks for Real-time Driving Data Pattern Identificationen_US
dc.subjectFuzzy C Means Clustering for Driving Data Pattern Recognitionen_US
dc.subjectClean Transportationen_US
dc.titleDriving data pattern recognition for intelligent energy management of plug-in hybrid electric vehiclesen_US
dc.typeThesisen_US

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