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The prevalence of smart devices and cloud computing has led to an explosion in the amount of data generated by IoT devices. Moreover, emerging IoT applications, such as augmented and virtual reality (AR/VR), intelligent transportation systems, and smart factories require ultra-low latency for data communication and processing. Fog/edge computing is a new computing paradigm where fully distributed fog/edge nodes located nearby end devices provide computing resources. By analyzing, filtering, and processing at local fog/edge resources instead of transferring tremendous data to the centralized cloud servers, fog/edge computing can reduce the processing delay and network traffic significantly. With these advantages, fog/edge computing is expected to be one of the key enabling technologies for building the IoT infrastructure. Aiming to explore the recent research and development on fog/edge computing technologies for building an IoT infrastructure, this book collected 10 articles. The selected articles cover diverse topics such as resource management, service provisioning, task offloading and scheduling, container orchestration, and security on edge/fog computing infrastructure, which can help to grasp recent trends, as well as state-of-the-art algorithms of fog/edge computing technologies.

cloud computing --- container orchestration --- custom metrics --- Docker --- edge computing --- Horizontal Pod Autoscaling (HPA) --- Kubernetes --- Prometheus --- resource metrics --- fog computing --- task allocation --- multi-objective optimization --- evolutionary genetics --- hyper-angle --- crowding distance --- containers --- leader election --- load balancing --- stateful --- multi-access edge computing --- orchestrator --- task offloading --- fuzzy logic --- 5G --- fog/edge computing --- service provisioning --- service placement --- service offloading --- Internet of Things (IoT) --- task scheduling --- markov decision process (MDP) --- deep reinforcement learning (DRL) --- resource management --- algorithm classification --- evaluation framework --- web --- Web Assembly --- OpenCL --- LWC --- fast implementation --- Internet of things --- IoT actor --- data manager --- GDPR --- computing --- computational offloading --- dynamic offloading threshold --- minimizing delay --- minimizing energy consumption --- maximizing throughputs --- n/a

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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.

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