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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 online resource (266 p.) |
| Soggetto topico | History of engineering and technology |
| Soggetto non controllato |
aftermarket hybridization kit
Arrhenius model artefact automatic re-training benchmarking catalytic stripper dedicated hybrid transmission degree of hybridization drive train optimization dynamic programming E-Mobility efficiency electric machine design electric vehicle electric vehicle transition electric vehicles electrified mechanical transmission electromechanical coupling emissions mitigation energy management ensemble learning evaporation tube fault diagnosis fault observation fleet transition GA gearbox global warming potential greenhouse gas high-speed hybrid electric vehicle hybrid electric vehicles hybrid propulsion input feedforward inverter life-cycle assessment lithium-ion battery local driving cycle losses machine learning mission profile multi-purpose vehicle n/a non-volatiles optimization particle measurement programme (PMP) plug-in hybrid electric vehicle plug-in hybrid electric vehicles portable emissions measurement systems (PEMS) powertrain powertrain concepts powertrain control powertrain design pressure sensor proton exchange membrane fuel cell Rainflow algorithm range extenders reliability simulink, supercapacitor solid particle number structural reliability thermal network topology optimization transmission transmission design uncertainties vehicle efficiency vehicle emissions vehicle powertrain concepts VFS volatile removal efficiency zinc-air battery |
| 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 online resource (192 p.) |
| Collana | Karlsruher Schriftenreihe Fahrzeugsystemtechnik |
| Soggetto topico | Mechanical engineering & materials |
| Soggetto non controllato |
Algorithmen
Algorithms E-Mobility Elektromobilität Energiemanagement Energy Management Fahrzeugtechnik Forecasting Vehicle Technology Vorhersagen |
| 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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