Choose an application

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

This book focuses on both fundamental and applied research on nanogenerators. The triboelectric nanogenerator (TENG) is based on expanded Maxwell’s equations for a mechano-driven system, including the polarization density term Ps in a displacement vector owing to the electrostatic charges on medium surfaces as produced by effects such as triboelectrification. The TENGs have potential applications in blue energy, wearable devices, environmental protectioin, medical science, and security. Hybridized and coupled nanogenerators further expand the application of nanogenerators in energy stability and multi-functional sensing.

Technology: general issues --- triboelectric nanogenerator --- network --- blue energy --- wave energy --- energy harvesting --- surface engineering --- surface morphology --- surface modification --- enhanced performance --- human–machine interface (HMI) --- triboelectric nanogenerator (TENG) --- artificial intelligence (AI) --- robot perception --- wearable sensor --- Internet of things (IoT) --- Beaufort scale monitoring --- near-zero power --- wake-up system --- triboelectric sensor --- ferroelectric materials --- nanogenerators --- piezoelectricity --- triboelectricity --- pyroelectricity --- bulk ferroelectric photovoltaic effect (BPVE) --- harvesting --- coupled effects --- mechanical conversion --- mechanical transmission --- triboelectric nanogenerators (TENGs) --- external mechanical system control --- regulated output --- uniform output --- stretchable electronic skin --- self-powered sensing --- human motion monitoring --- thermoplastic polyurethane fibers --- biosensors --- hybridization --- piezoelectric nanogenerator --- electromechanical conversion --- self-powered --- cell modulation --- smart textiles --- triboelectric nanogenerators --- electricity generation --- output enhancement --- air breakdown --- lubricant liquid --- mechanical lifespan --- triboelectric nanogenerator --- network --- blue energy --- wave energy --- energy harvesting --- surface engineering --- surface morphology --- surface modification --- enhanced performance --- human–machine interface (HMI) --- triboelectric nanogenerator (TENG) --- artificial intelligence (AI) --- robot perception --- wearable sensor --- Internet of things (IoT) --- Beaufort scale monitoring --- near-zero power --- wake-up system --- triboelectric sensor --- ferroelectric materials --- nanogenerators --- piezoelectricity --- triboelectricity --- pyroelectricity --- bulk ferroelectric photovoltaic effect (BPVE) --- harvesting --- coupled effects --- mechanical conversion --- mechanical transmission --- triboelectric nanogenerators (TENGs) --- external mechanical system control --- regulated output --- uniform output --- stretchable electronic skin --- self-powered sensing --- human motion monitoring --- thermoplastic polyurethane fibers --- biosensors --- hybridization --- piezoelectric nanogenerator --- electromechanical conversion --- self-powered --- cell modulation --- smart textiles --- triboelectric nanogenerators --- electricity generation --- output enhancement --- air breakdown --- lubricant liquid --- mechanical lifespan

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.

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 --- 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

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