Plug-in Hybrid Electric Vehicle (PHEV)

Edited by
August 2019
230 pages
  • ISBN978-3-03921-453-2 (Paperback)
  • ISBN978-3-03921-454-9 (PDF)

This book is a reprint of the Special Issue Plug-in Hybrid Electric Vehicle (PHEV) that was published in

Biology & Life Sciences
Chemistry & Materials Science
Computer Science & Mathematics
Environmental & Earth Sciences
Physical Sciences

Climate 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.

  • Paperback
License and Copyright
© 2019 by the authors; CC BY-NC-ND license
battery power; convex optimization; dynamic programming; engine-on power; plug-in hybrid electric vehicle; simulated annealing; electric vehicle; open-end winding; dual inverter; voltage vector distribution; power sharing; energy management; range-extender; CO2; air quality; mobility needs; LCA; Paris Agreement; hybrid energy storage system; lithium-ion battery; lithium-ion capacitor; lifetime model; power distribution; state of health estimation; adaptive neuron-fuzzy inference system (ANFIS); group method of data handling (GMDH); artificial neural network (ANN); electric vehicles (EVs); capacity degradation; lithium-ion battery; time-delay input; interleaved multiport converte; multi-objective genetic algorithm; hybrid electric vehicles; losses model; wide bandgap (WBG) technologies; Energy Storage systems; LCA; Well-to-Wheel; electric vehicle; plug-in hybrid; electricity mix; consequential; attributional; marginal; system modelling; energy system; meta-analysis; parallel hybrid electric vehicle; regenerative braking; fuel consumption characteristics; energy efficiency; state of charge; lithium polymer battery; electric vehicle; Plugin Hybrid electric vehicle; Li-ion battery; modelling; measurements; state of charge; strong track filter; modified one-state hysteresis model; Li(Ni1/3Co1/3Mn1/3)O2 battery; energy management strategy; Markov decision process (MDP); plug-in hybrid electric vehicles (PHEVs); Q-learning (QL); reinforcement learning (RL); novel propulsion systems; emerging power electronics; including wide bandgap (WBG) technology; emerging electric machines; efficient energy management strategies for hybrid propulsion systems; energy storage systems; life-cycle assessment (LCA)