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Future Powertrain Technologies
| Future Powertrain Technologies |
| Autore | Rinderknecht Stephan |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 electronic resource (266 p.) |
| Soggetto topico | History of engineering & technology |
| Soggetto non controllato |
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 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557443503321 |
Rinderknecht Stephan
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Stochastic Range Estimation Algorithms for Electric Vehicles using Data-Driven Learning Models
| Stochastic Range Estimation Algorithms for Electric Vehicles using Data-Driven Learning Models |
| Autore | Scheubner Stefan |
| Pubbl/distr/stampa | Karlsruhe, : KIT Scientific Publishing, 2022 |
| Descrizione fisica | 1 electronic resource (192 p.) |
| Collana | Karlsruher Schriftenreihe Fahrzeugsystemtechnik |
| Soggetto topico | Mechanical engineering & materials |
| Soggetto non controllato |
Elektromobilität
Vorhersagen Algorithmen Fahrzeugtechnik Energiemanagement E-Mobility Forecasting Algorithms Vehicle Technology Energy Management |
| ISBN | 1000143200 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910576868503321 |
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Scheubner Stefan
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| Karlsruhe, : KIT Scientific Publishing, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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