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Les impératifs écologiques imposent des changements de pratiques et la mobilité électrique remplit les critères de réduction des émissions de gaz à effet de serre. Ces véhicules doivent être rechargés et ce de manière intelligente afin de satisfaire aux exigences environnementales et aux préférences de chaque acteur, les gestionnaires des réseaux de distribution et de transmission, le conducteur du véhicule ou le gestionnaire de flotte. Ce travail analyse de manière globale les composantes d’une recharge de véhicule et aborde une technique de contrôle des recharges : la commande prédictive.

Commande prédictive, véhicules électriques, station de recharge, Matlab-Simulink --- Ingénierie, informatique & technologie > Energie

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Electrical machines and drives dominate our everyday lives. This is due to their numerous applications in industry, power production, home appliances, and transportation systems such as electric and hybrid electric vehicles, ships, and aircrafts. Their development follows rapid advances in science, engineering, and technology. Researchers around the world are extensively investigating electrical machines and drives because of their reliability, efficiency, performance, and fault-tolerant structure. In particular, there is a focus on the importance of utilizing these new trends in technology for energy saving and reducing greenhouse gas emissions. This Special Issue will provide the platform for researchers to present their recent work on advances in the field of electrical machines and drives, including special machines and their applications; new materials, including the insulation of electrical machines; new trends in diagnostics and condition monitoring; power electronics, control schemes, and algorithms for electrical drives; new topologies; and innovative applications.

Technology: general issues --- History of engineering & technology --- fault size --- inter-laminar fault --- localized losses --- thermographic measurement --- thermal-electric coupling --- axial flux --- demagnetization --- finite element analysis --- permanent magnet --- static eccentricity --- synchronous generator --- power loss minimization --- speed control drive systems --- efficiency measurement --- IPMSM --- additive manufacturing --- three-dimensional printing --- topology optimization --- magnetic materials --- soft magnetic materials --- permanent magnets --- electrical machines --- torque ripple --- 6th harmonic --- induction motor --- AC machine --- PWM inverter --- space phasor modulation --- electric motor --- interior permanent magnet --- reluctance --- MMF-permeance --- winding function --- predictive maintenance --- digital twin --- artificial intelligence --- Industry 4.0 --- data handling --- life cycle --- electric machines --- electromagnetic analysis --- electromagnetic measurements --- core losses --- rotor flux linkage --- modular stator --- oriented steel --- flux-injecting probes --- insulation system --- partial discharges --- capacitive model --- MATLAB/Simulink --- flashover voltage --- epoxy resin --- matrix-converter --- input AC filter design --- PMSM --- predictive control --- electrical drives --- energy efficiency --- energy-saving --- power quality --- current signals --- stray flux signals --- LDA --- automatic fault diagnosis --- broken rotor bars --- soft-starters --- fault size --- inter-laminar fault --- localized losses --- thermographic measurement --- thermal-electric coupling --- axial flux --- demagnetization --- finite element analysis --- permanent magnet --- static eccentricity --- synchronous generator --- power loss minimization --- speed control drive systems --- efficiency measurement --- IPMSM --- additive manufacturing --- three-dimensional printing --- topology optimization --- magnetic materials --- soft magnetic materials --- permanent magnets --- electrical machines --- torque ripple --- 6th harmonic --- induction motor --- AC machine --- PWM inverter --- space phasor modulation --- electric motor --- interior permanent magnet --- reluctance --- MMF-permeance --- winding function --- predictive maintenance --- digital twin --- artificial intelligence --- Industry 4.0 --- data handling --- life cycle --- electric machines --- electromagnetic analysis --- electromagnetic measurements --- core losses --- rotor flux linkage --- modular stator --- oriented steel --- flux-injecting probes --- insulation system --- partial discharges --- capacitive model --- MATLAB/Simulink --- flashover voltage --- epoxy resin --- matrix-converter --- input AC filter design --- PMSM --- predictive control --- electrical drives --- energy efficiency --- energy-saving --- power quality --- current signals --- stray flux signals --- LDA --- automatic fault diagnosis --- broken rotor bars --- soft-starters

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In recent years, the industrial environment has been changing radically due to the introduction of concepts and technologies based on the fourth industrial revolution, also known as Industry 4.0. After the introduction of Industry 4.0 in large enterprises, SMEs have moved into the focus, as they are the backbone of many economies. Small organizations are increasingly proactive in improving their operational processes, which is a good starting point for introducing the new concepts of Industry 4.0. The readiness of SME-adapted Industry 4.0 concepts and the organizational capability of SMEs to meet this challenge exist only in some areas. This reveals the need for further research and action plans for preparing SMEs in a technical and organizational direction. Therefore, special research and investigations are needed for the implementation of Industry 4.0 technologies and concepts in SMEs. SMEs will only achieve Industry 4.0 by following SME-customized implementation strategies and approaches and realizing SME-adapted concepts and technological solutions. Thus, this Special Issue represents a collection of theoretical models as well as practical case studies related to the introduction of Industry 4.0 concepts in small- and medium-sized enterprises.

History of engineering & technology --- latent semantic analysis --- virtual quality management --- concept investigation --- concept disambiguation --- knowledge discovery --- sustainable methodologies --- small and medium sized enterprises --- material handling systems --- simulation --- ARENA®, time study --- overall equipment effectiveness --- manufacturing performance --- Industry 4.0 --- manufacturing sustainability --- manufacturing process model --- business process management --- hierarchical clustering --- similarity --- BPMN --- human factors --- cyber-physical systems --- cyber-physical production systems --- anthropocentric design --- Operator 4.0 --- human–machine interaction --- energy efficient operation --- manufacturing system --- stochastic event --- digital twin --- Max-plus Algebra --- MATLAB-Simulink --- advanced manufacturing --- industry 4.0 --- SME --- technology adoption model --- assembly supply chain --- sustainability --- complexity indicators --- testing criteria --- SMEs --- e-business modelling --- LSP Lifecycle Model --- Quality Function Deployment --- Best-Worst Method --- Internet of Things --- India --- awareness --- small and medium-sized enterprises --- assessment model --- collaborative robotics --- physical ergonomics --- human-robot collaboration --- human-centered design --- assembly --- small and medium sized enterprise --- positive complexity --- negative complexity --- infeasible configurations --- product platform --- customer’s perception --- assessment --- field study --- smart manufacturing --- cloud platform --- artificial intelligence --- machine learning --- deep learning --- smart logistics --- logistics 4.0 --- smart technologies --- sustainable agriculture --- plant factory --- latent semantic analysis --- virtual quality management --- concept investigation --- concept disambiguation --- knowledge discovery --- sustainable methodologies --- small and medium sized enterprises --- material handling systems --- simulation --- ARENA®, time study --- overall equipment effectiveness --- manufacturing performance --- Industry 4.0 --- manufacturing sustainability --- manufacturing process model --- business process management --- hierarchical clustering --- similarity --- BPMN --- human factors --- cyber-physical systems --- cyber-physical production systems --- anthropocentric design --- Operator 4.0 --- human–machine interaction --- energy efficient operation --- manufacturing system --- stochastic event --- digital twin --- Max-plus Algebra --- MATLAB-Simulink --- advanced manufacturing --- industry 4.0 --- SME --- technology adoption model --- assembly supply chain --- sustainability --- complexity indicators --- testing criteria --- SMEs --- e-business modelling --- LSP Lifecycle Model --- Quality Function Deployment --- Best-Worst Method --- Internet of Things --- India --- awareness --- small and medium-sized enterprises --- assessment model --- collaborative robotics --- physical ergonomics --- human-robot collaboration --- human-centered design --- assembly --- small and medium sized enterprise --- positive complexity --- negative complexity --- infeasible configurations --- product platform --- customer’s perception --- assessment --- field study --- smart manufacturing --- cloud platform --- artificial intelligence --- machine learning --- deep learning --- smart logistics --- logistics 4.0 --- smart technologies --- sustainable agriculture --- plant factory