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Optimal performance of the electric machine/drive system is mandatory to improve the energy consumption and reliability. To achieve this goal, mathematical models of the electric machine/drive system are necessary. Hence, this motivated the editors to instigate the Special Issue “Mathematical Approaches to Modeling, Optimally Designing, and Controlling Electric Machine”, aiming to collect novel publications that push the state-of-the art towards optimal performance for the electric machine/drive system. Seventeen papers have been published in this Special Issue. The published papers focus on several aspects of the electric machine/drive system with respect to the mathematical modelling. Novel optimization methods, control approaches, and comparative analysis for electric drive system based on various electric machines were discussed in the published papers.

Technology: general issues --- demagnetization --- electric machine --- flux reversal machine --- high-speed electrical machine --- high-speed electrical motor --- Nelder–Mead method --- optimal design --- switched reluctance motor --- direct instantaneous torque control --- numerical analysis --- optimization --- current angle --- design of electric motors --- flux-barriers --- synchronous reluctance motor --- torque ripple --- induction motor --- model predictive --- sensorless --- high performance --- switched reluctance machine --- NSGA-II optimization --- finite element analysis --- direct-drive --- electric machine analysis computing --- interior permanent magnet machine --- mathematical model --- optimal-design --- permanent magnet flux-switching machine --- wind generator --- doubly fed induction generator --- DC-link voltage regulation --- second-order sliding mode control --- extended state observer --- fuzzy gain scheduling --- advanced metaheuristics --- MO-Jaya optimization --- centrifugal pump --- energy efficiency --- parallel pumps --- throttling --- variable speed pump --- synchronous homopolar machine --- synchronous homopolar motor --- traction drives --- traction motor --- high-harmonic injection --- brushless field excitation --- wound field synchronous machines --- Axial flux permanent magnet machine --- 3D FEA --- Genetic algorithm --- hexagonal-shaped PMs --- PM overhang --- brushless topology --- third harmonic flux --- dc offset --- direct-on-line permanent magnet synchronous motor --- direct-on-line synchronous reluctance motor --- permanent magnet motor --- reactive power compensation --- carbon dioxide emissions --- climate change mitigation --- electric motors --- energy conversion --- energy efficiency class --- energy policy and regulation --- energy saving --- sustainable utilization of resources --- synchronous motor --- adaptive control --- MTPA control --- parameter variation --- constraints design --- mining dump truck --- traction drive --- demagnetization --- electric machine --- flux reversal machine --- high-speed electrical machine --- high-speed electrical motor --- Nelder–Mead method --- optimal design --- switched reluctance motor --- direct instantaneous torque control --- numerical analysis --- optimization --- current angle --- design of electric motors --- flux-barriers --- synchronous reluctance motor --- torque ripple --- induction motor --- model predictive --- sensorless --- high performance --- switched reluctance machine --- NSGA-II optimization --- finite element analysis --- direct-drive --- electric machine analysis computing --- interior permanent magnet machine --- mathematical model --- optimal-design --- permanent magnet flux-switching machine --- wind generator --- doubly fed induction generator --- DC-link voltage regulation --- second-order sliding mode control --- extended state observer --- fuzzy gain scheduling --- advanced metaheuristics --- MO-Jaya optimization --- centrifugal pump --- energy efficiency --- parallel pumps --- throttling --- variable speed pump --- synchronous homopolar machine --- synchronous homopolar motor --- traction drives --- traction motor --- high-harmonic injection --- brushless field excitation --- wound field synchronous machines --- Axial flux permanent magnet machine --- 3D FEA --- Genetic algorithm --- hexagonal-shaped PMs --- PM overhang --- brushless topology --- third harmonic flux --- dc offset --- direct-on-line permanent magnet synchronous motor --- direct-on-line synchronous reluctance motor --- permanent magnet motor --- reactive power compensation --- carbon dioxide emissions --- climate change mitigation --- electric motors --- energy conversion --- energy efficiency class --- energy policy and regulation --- energy saving --- sustainable utilization of resources --- synchronous motor --- adaptive control --- MTPA control --- parameter variation --- constraints design --- mining dump truck --- traction drive

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Optimal performance of the electric machine/drive system is mandatory to improve the energy consumption and reliability. To achieve this goal, mathematical models of the electric machine/drive system are necessary. Hence, this motivated the editors to instigate the Special Issue “Mathematical Approaches to Modeling, Optimally Designing, and Controlling Electric Machine”, aiming to collect novel publications that push the state-of-the art towards optimal performance for the electric machine/drive system. Seventeen papers have been published in this Special Issue. The published papers focus on several aspects of the electric machine/drive system with respect to the mathematical modelling. Novel optimization methods, control approaches, and comparative analysis for electric drive system based on various electric machines were discussed in the published papers.

demagnetization --- electric machine --- flux reversal machine --- high-speed electrical machine --- high-speed electrical motor --- Nelder–Mead method --- optimal design --- switched reluctance motor --- direct instantaneous torque control --- numerical analysis --- optimization --- current angle --- design of electric motors --- flux-barriers --- synchronous reluctance motor --- torque ripple --- induction motor --- model predictive --- sensorless --- high performance --- switched reluctance machine --- NSGA-II optimization --- finite element analysis --- direct-drive --- electric machine analysis computing --- interior permanent magnet machine --- mathematical model --- optimal-design --- permanent magnet flux-switching machine --- wind generator --- doubly fed induction generator --- DC-link voltage regulation --- second-order sliding mode control --- extended state observer --- fuzzy gain scheduling --- advanced metaheuristics --- MO-Jaya optimization --- centrifugal pump --- energy efficiency --- parallel pumps --- throttling --- variable speed pump --- synchronous homopolar machine --- synchronous homopolar motor --- traction drives --- traction motor --- high-harmonic injection --- brushless field excitation --- wound field synchronous machines --- Axial flux permanent magnet machine --- 3D FEA --- Genetic algorithm --- hexagonal-shaped PMs --- PM overhang --- brushless topology --- third harmonic flux --- dc offset --- direct-on-line permanent magnet synchronous motor --- direct-on-line synchronous reluctance motor --- permanent magnet motor --- reactive power compensation --- carbon dioxide emissions --- climate change mitigation --- electric motors --- energy conversion --- energy efficiency class --- energy policy and regulation --- energy saving --- sustainable utilization of resources --- synchronous motor --- adaptive control --- MTPA control --- parameter variation --- constraints design --- mining dump truck --- traction drive

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Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens.

Technology: general issues --- History of engineering & technology --- star image --- image denoising --- reinforcement learning --- maximum likelihood estimation --- mixed Poisson–Gaussian likelihood --- machine learning-based classification --- non-uniform foundation --- stochastic analysis --- vehicle–pavement–foundation interaction --- forest growing stem volume --- coniferous plantations --- variable selection --- texture feature --- random forest --- red-edge band --- on-shelf availability --- semi-supervised learning --- deep learning --- image classification --- machine learning --- explainable artificial intelligence --- wildfire --- risk assessment --- Naïve bayes --- transmission-line corridors --- image encryption --- compressive sensing --- plaintext related --- chaotic system --- convolutional neural network --- color prior model --- object detection --- piston error detection --- segmented telescope --- BP artificial neural network --- modulation transfer function --- computer vision --- intelligent vehicles --- extrinsic camera calibration --- structure from motion --- convex optimization --- temperature estimation --- BLDC --- electric machine protection --- touchscreen --- capacitive --- display --- SNR --- stylus --- laser cutting --- quality monitoring --- artificial neural network --- burr formation --- cut interruption --- fiber laser --- semi-supervised --- fuzzy --- noisy --- real-world --- plankton --- marine --- activity recognition --- wearable sensors --- imbalanced activities --- sampling methods --- path planning --- Q-learning --- neural network --- YOLO algorithm --- robot arm --- target reaching --- obstacle avoidance --- star image --- image denoising --- reinforcement learning --- maximum likelihood estimation --- mixed Poisson–Gaussian likelihood --- machine learning-based classification --- non-uniform foundation --- stochastic analysis --- vehicle–pavement–foundation interaction --- forest growing stem volume --- coniferous plantations --- variable selection --- texture feature --- random forest --- red-edge band --- on-shelf availability --- semi-supervised learning --- deep learning --- image classification --- machine learning --- explainable artificial intelligence --- wildfire --- risk assessment --- Naïve bayes --- transmission-line corridors --- image encryption --- compressive sensing --- plaintext related --- chaotic system --- convolutional neural network --- color prior model --- object detection --- piston error detection --- segmented telescope --- BP artificial neural network --- modulation transfer function --- computer vision --- intelligent vehicles --- extrinsic camera calibration --- structure from motion --- convex optimization --- temperature estimation --- BLDC --- electric machine protection --- touchscreen --- capacitive --- display --- SNR --- stylus --- laser cutting --- quality monitoring --- artificial neural network --- burr formation --- cut interruption --- fiber laser --- semi-supervised --- fuzzy --- noisy --- real-world --- plankton --- marine --- activity recognition --- wearable sensors --- imbalanced activities --- sampling methods --- path planning --- Q-learning --- neural network --- YOLO algorithm --- robot arm --- target reaching --- obstacle avoidance

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Among the various factors greatly influencing the development process of future powertrain technologies, the trends in climate change and digitalization are of huge public interest. To handle these trends, new disruptive technologies are integrated into the development process. They open up space for diverse research which is distributed over the entire vehicle design process. This book contains recent research articles which incorporate results for selecting and designing powertrain topology in consideration of the vehicle operating strategy as well as results for handling the reliability of new powertrain components. The field of investigation spans from the identification of ecologically optimal transformation of the existent vehicle fleet to the development of machine learning-based operating strategies and the comparison of complex hybrid electric vehicle topologies to reduce CO2 emissions.

History of engineering & technology --- degree of hybridization --- energy management --- hybrid propulsion --- proton exchange membrane fuel cell --- simulink, supercapacitor --- fleet transition --- optimization --- life-cycle assessment --- greenhouse gas --- global warming potential --- vehicle powertrain concepts --- dedicated hybrid transmission --- benchmarking --- hybrid electric vehicle --- efficiency --- topology optimization --- drive train optimization --- powertrain concepts --- structural reliability --- uncertainties --- ensemble learning --- fault diagnosis --- VFS --- GA --- input feedforward --- fault observation --- pressure sensor --- aftermarket hybridization kit --- emissions mitigation --- local driving cycle --- plug-in hybrid electric vehicles --- vehicle efficiency --- plug-in hybrid electric vehicle --- electromechanical coupling --- electrified mechanical transmission --- multi-purpose vehicle --- machine learning --- powertrain control --- automatic re-training --- hybrid electric vehicles --- dynamic programming --- transmission --- vehicle emissions --- particle measurement programme (PMP) --- portable emissions measurement systems (PEMS) --- volatile removal efficiency --- non-volatiles --- solid particle number --- catalytic stripper --- evaporation tube --- artefact --- E-Mobility --- powertrain design --- high-speed --- electric machine design --- transmission design --- gearbox --- electric vehicles --- range extenders --- zinc–air battery --- lithium-ion battery --- electric vehicle transition --- Arrhenius model --- losses --- mission profile --- inverter --- powertrain --- Rainflow algorithm --- reliability --- thermal network --- electric vehicle --- n/a --- zinc-air battery

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Among the various factors greatly influencing the development process of future powertrain technologies, the trends in climate change and digitalization are of huge public interest. To handle these trends, new disruptive technologies are integrated into the development process. They open up space for diverse research which is distributed over the entire vehicle design process. This book contains recent research articles which incorporate results for selecting and designing powertrain topology in consideration of the vehicle operating strategy as well as results for handling the reliability of new powertrain components. The field of investigation spans from the identification of ecologically optimal transformation of the existent vehicle fleet to the development of machine learning-based operating strategies and the comparison of complex hybrid electric vehicle topologies to reduce CO2 emissions.

degree of hybridization --- energy management --- hybrid propulsion --- proton exchange membrane fuel cell --- simulink, supercapacitor --- fleet transition --- optimization --- life-cycle assessment --- greenhouse gas --- global warming potential --- vehicle powertrain concepts --- dedicated hybrid transmission --- benchmarking --- hybrid electric vehicle --- efficiency --- topology optimization --- drive train optimization --- powertrain concepts --- structural reliability --- uncertainties --- ensemble learning --- fault diagnosis --- VFS --- GA --- input feedforward --- fault observation --- pressure sensor --- aftermarket hybridization kit --- emissions mitigation --- local driving cycle --- plug-in hybrid electric vehicles --- vehicle efficiency --- plug-in hybrid electric vehicle --- electromechanical coupling --- electrified mechanical transmission --- multi-purpose vehicle --- machine learning --- powertrain control --- automatic re-training --- hybrid electric vehicles --- dynamic programming --- transmission --- vehicle emissions --- particle measurement programme (PMP) --- portable emissions measurement systems (PEMS) --- volatile removal efficiency --- non-volatiles --- solid particle number --- catalytic stripper --- evaporation tube --- artefact --- E-Mobility --- powertrain design --- high-speed --- electric machine design --- transmission design --- gearbox --- electric vehicles --- range extenders --- zinc–air battery --- lithium-ion battery --- electric vehicle transition --- Arrhenius model --- losses --- mission profile --- inverter --- powertrain --- Rainflow algorithm --- reliability --- thermal network --- electric vehicle --- n/a --- zinc-air battery