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dc.contributor.authorVAN Mierlo, Joeri*
dc.date.accessioned2021-02-11T23:08:50Z
dc.date.available2021-02-11T23:08:50Z
dc.date.issued2019*
dc.date.submitted2019-12-09 11:49:15*
dc.identifier42500*
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/56419
dc.description.abstractClimate change, urban air quality, and dependency on crude oil are important societal challenges. In the transportation sector especially, clean and energy efficient technologies must be developed. Electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) have gained a growing interest in the vehicle industry. Nowadays, the commercialization of EVs and PHEVs has been possible in different applications (i.e., light duty, medium duty, and heavy duty vehicles) thanks to the advances in energy storage systems, power electronics converters (including DC/DC converters, DC/AC inverters, and battery charging systems), electric machines, and energy efficient power flow control strategies. This book is based on the Special Issue of the journal Applied Sciences on “Plug-In Hybrid Electric Vehicles (PHEVs)”. This collection of research articles includes topics such as novel propulsion systems, emerging power electronics and their control algorithms, emerging electric machines and control techniques, energy storage systems, including BMS, and efficient energy management strategies for hybrid propulsion, vehicle-to-grid (V2G), vehicle-to-home (V2H), grid-to-vehicle (G2V) technologies, and wireless power transfer (WPT) systems.*
dc.languageEnglish*
dc.subjectTA1-2040*
dc.subjectT1-995*
dc.subject.otherhybrid energy storage system*
dc.subject.otherplug-in hybrid electric vehicle*
dc.subject.otherLi-ion battery*
dc.subject.otheremerging electric machines*
dc.subject.otherlithium-ion capacitor*
dc.subject.otherelectric vehicles (EVs)*
dc.subject.otherefficient energy management strategies for hybrid propulsion systems*
dc.subject.otherplug-in hybrid*
dc.subject.otherattributional*
dc.subject.otherelectric vehicle*
dc.subject.otherenergy system*
dc.subject.otherenergy efficiency*
dc.subject.othermodified one-state hysteresis model*
dc.subject.otherair quality*
dc.subject.otheradaptive neuron-fuzzy inference system (ANFIS)*
dc.subject.otherMarkov decision process (MDP)*
dc.subject.othersimulated annealing*
dc.subject.otherParis Agreement*
dc.subject.othermobility needs*
dc.subject.otherinterleaved multiport converte*
dc.subject.otherdynamic programming*
dc.subject.otherstate of health estimation*
dc.subject.otherstrong track filter*
dc.subject.otherLCA*
dc.subject.othermodelling*
dc.subject.otherconsequential*
dc.subject.otherlosses model*
dc.subject.othervoltage vector distribution*
dc.subject.otherparallel hybrid electric vehicle*
dc.subject.otherelectricity mix*
dc.subject.othertime-delay input*
dc.subject.otherconvex optimization*
dc.subject.otherlifetime model*
dc.subject.otherartificial neural network (ANN)*
dc.subject.otherLi(Ni1/3Co1/3Mn1/3)O2 battery*
dc.subject.otherbattery power*
dc.subject.otherCO2*
dc.subject.othercapacity degradation*
dc.subject.otherregenerative braking*
dc.subject.otheropen-end winding*
dc.subject.othernovel propulsion systems*
dc.subject.othergroup method of data handling (GMDH)*
dc.subject.otherstate of charge*
dc.subject.otherWell-to-Wheel*
dc.subject.otherenergy storage systems*
dc.subject.otherincluding wide bandgap (WBG) technology*
dc.subject.otherwide bandgap (WBG) technologies*
dc.subject.othermarginal*
dc.subject.otherlithium polymer battery*
dc.subject.otherlife-cycle assessment (LCA)*
dc.subject.otherenergy management*
dc.subject.otherdual inverter*
dc.subject.otherlithium-ion battery*
dc.subject.othermeasurements*
dc.subject.otherplug-in hybrid electric vehicles (PHEVs)*
dc.subject.otheremerging power electronics*
dc.subject.otherQ-learning (QL)*
dc.subject.otherfuel consumption characteristics*
dc.subject.otherPlugin Hybrid electric vehicle*
dc.subject.otherEnergy Storage systems*
dc.subject.othermeta-analysis*
dc.subject.otherrange-extender*
dc.subject.otherengine-on power*
dc.subject.otherreinforcement learning (RL)*
dc.subject.othermulti-objective genetic algorithm*
dc.subject.otherpower sharing*
dc.subject.otherenergy management strategy*
dc.subject.otherpower distribution*
dc.subject.otherhybrid electric vehicles*
dc.subject.othersystem modelling*
dc.titlePlug-in Hybrid Electric Vehicle (PHEV)*
dc.typebook
oapen.identifier.doi10.3390/books978-3-03921-454-9*
oapen.relation.isPublishedBy46cabcaa-dd94-4bfe-87b4-55023c1b36d0*
virtual.oapen_relation_isPublishedBy.publisher_nameMDPI - Multidisciplinary Digital Publishing Institute
virtual.oapen_relation_isPublishedBy.publisher_websitewww.mdpi.com/books
oapen.relation.isbn9783039214549*
oapen.relation.isbn9783039214532*
oapen.pages230*
oapen.edition1st*


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