LEADER 04867nam 2201141z- 450 001 9910367565103321 005 20231214133722.0 010 $a3-03921-454-3 035 $a(CKB)4100000010106095 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/56419 035 $a(EXLCZ)994100000010106095 100 $a20202102d2019 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPlug-in Hybrid Electric Vehicle (PHEV) 210 $cMDPI - Multidisciplinary Digital Publishing Institute$d2019 215 $a1 electronic resource (230 p.) 311 $a3-03921-453-5 330 $aClimate 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. 517 $aPlug-in Hybrid Electric Vehicle 610 $ahybrid energy storage system 610 $aplug-in hybrid electric vehicle 610 $aLi-ion battery 610 $aemerging electric machines 610 $alithium-ion capacitor 610 $aelectric vehicles (EVs) 610 $aefficient energy management strategies for hybrid propulsion systems 610 $aplug-in hybrid 610 $aattributional 610 $aelectric vehicle 610 $aenergy system 610 $aenergy efficiency 610 $amodified one-state hysteresis model 610 $aair quality 610 $aadaptive neuron-fuzzy inference system (ANFIS) 610 $aMarkov decision process (MDP) 610 $asimulated annealing 610 $aParis Agreement 610 $amobility needs 610 $ainterleaved multiport converte 610 $adynamic programming 610 $astate of health estimation 610 $astrong track filter 610 $aLCA 610 $amodelling 610 $aconsequential 610 $alosses model 610 $avoltage vector distribution 610 $aparallel hybrid electric vehicle 610 $aelectricity mix 610 $atime-delay input 610 $aconvex optimization 610 $alifetime model 610 $aartificial neural network (ANN) 610 $aLi(Ni1/3Co1/3Mn1/3)O2 battery 610 $abattery power 610 $aCO2 610 $acapacity degradation 610 $aregenerative braking 610 $aopen-end winding 610 $anovel propulsion systems 610 $agroup method of data handling (GMDH) 610 $astate of charge 610 $aWell-to-Wheel 610 $aenergy storage systems 610 $aincluding wide bandgap (WBG) technology 610 $awide bandgap (WBG) technologies 610 $amarginal 610 $alithium polymer battery 610 $alife-cycle assessment (LCA) 610 $aenergy management 610 $adual inverter 610 $alithium-ion battery 610 $ameasurements 610 $aplug-in hybrid electric vehicles (PHEVs) 610 $aemerging power electronics 610 $aQ-learning (QL) 610 $afuel consumption characteristics 610 $aPlugin Hybrid electric vehicle 610 $aEnergy Storage systems 610 $ameta-analysis 610 $arange-extender 610 $aengine-on power 610 $areinforcement learning (RL) 610 $amulti-objective genetic algorithm 610 $apower sharing 610 $aenergy management strategy 610 $apower distribution 610 $ahybrid electric vehicles 610 $asystem modelling 700 $aVAN Mierlo$b Joeri$4auth$01305963 906 $aBOOK 912 $a9910367565103321 996 $aPlug-in Hybrid Electric Vehicle (PHEV)$93028071 997 $aUNINA