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Optimizing Engine Downsizing and Driving Behavior in Conventional and Hybrid Powertrains for Autonomous Driving Applications

Matteo Spano

Optimizing Engine Downsizing and Driving Behavior in Conventional and Hybrid Powertrains for Autonomous Driving Applications.

Rel. Daniela Anna Misul, Giovanni Belingardi. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Meccanica (Mechanical Engineering), 2019


The continuous increase in global CO2 emissions, together with more stringent vehicle emissions and fuel economy regulations around the world require innovative solutions and technologies to address these challenges. To this end, Connected and Automated Vehicles (CAV) are one pathway by which significant fuel economy improvements and emissions reductions may be simultaneously achieved. In this study, the potential fuel economy improvements stemming from improved driving behaviors, as well as dedicated engine rightsizing possible with CAVs have been considered. The study has focused on two different powertrain configurations: a conventional gasoline engine driven powertrain, and a hybrid electric vehicle. Simulations were performed in GT-Suite for a model year 2019 Jeep Compass with a baseline 2.4 L I4 engine on three different driving schedules: the FTP75, the US06 and a real world driving cycle. To investigate the fuel economy improvement potential of CAVs via driving behavior modifications, each cycle was gradually modified by smoothing speed fluctuations at several stages representing a range of driving behaviors from those which would be possible on-road today, to those which would require a fully connected automated vehicle network. In addition to this, the optimal engine displacement with respect to fuel economy was found for each of the different scenarios by neglecting conventional performance metrics, and focusing only on drive cycle requirements. The results showed that it was possible to downsize the engine to 1.0 L while still meeting the FTP75 cycle, whereas the US06 and real world cycle required engine displacements of 1.8 L and 1.9 L respectively. Simulation results demonstrated potential fuel economy improvements for both conventional and hybrid powertrains, from 5% to 14% derived from improved driving behavior, while improvements from 1% to 28% where possible with engine rightsizing, and improvements of 6% to 38% were possible by combining improved driving behavior with engine rightsizing depending on the drive cycle considered. For driving scenarios requiring a fully connected autonomous network, simulation results predicted even larger potential fuel economy improvements, indicating significant benefits derived from the implementation of CAVs in terms of fuel economy and vehicle CO2 emissions.

Relators: Daniela Anna Misul, Giovanni Belingardi
Academic year: 2019/20
Publication type: Electronic
Number of Pages: 102
Additional Information: Tesi secretata. Fulltext non presente
Corso di laurea: Corso di laurea magistrale in Ingegneria Meccanica (Mechanical Engineering)
Classe di laurea: New organization > Master science > LM-33 - MECHANICAL ENGINEERING
Aziende collaboratrici: UNSPECIFIED
URI: http://webthesis.biblio.polito.it/id/eprint/12214
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