Optimal velocity prediction for fuel economy improvement of connected vehicles

With the advancement of vehicle-to-vehicle and vehicle-to-infrastructure technologies, more and more real-time information regarding traffic and transportation system will be available to vehicles. This paper presents the development of a novel algorithm that uses available velocity bounds and power...

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Veröffentlicht in:	IET intelligent transport systems Jg. 12; H. 10; S. 1329 - 1335
Hauptverfasser:	Barik, Biswajit, Krishna Bhat, Pradeep, Oncken, Joseph, Chen, Bo, Orlando, Joshua, Robinette, Darrell
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States The Institution of Engineering and Technology 01.12.2018 Institution of Engineering and Technology (IET)
Schlagworte:	ADVANCED PROPULSION SYSTEMS Autonomie connected vehicles dynamic loss reduction energy management energy management systems fuel consumption reduction fuel economy fuel economy improvement nonlinearly constrained optimisation problem optimal velocity prediction optimal velocity trajectory generation powertrain information prediction horizon quadratic programming Research Article rule‐based mode selection sequential quadratic programming method tractive force requirement traffic system transportation system travel time trip distance completion vehicle‐to‐infrastructure technology vehicle‐to‐vehicle technology vehicular ad hoc networks velocity bounds sequential quadratic programming method connected vehicles transportation system rule-based mode selection optimal velocity trajectory generation nonlinearly constrained optimisation problem optimal velocity prediction energy management vehicle-to-infrastructure technology dynamic loss reduction velocity bounds vehicular ad hoc networks traffic system vehicle-to-vehicle technology tractive force requirement quadratic programming powertrain information Autonomie prediction horizon energy management systems fuel economy trip distance completion fuel consumption reduction travel time fuel economy improvement
ISSN:	1751-956X, 1751-9578
Online-Zugang:	Volltext
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