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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Vydáno v:	IET intelligent transport systems Ročník 12; číslo 10; s. 1329 - 1335
Hlavní autoři:	Barik, Biswajit, Krishna Bhat, Pradeep, Oncken, Joseph, Chen, Bo, Orlando, Joshua, Robinette, Darrell
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States The Institution of Engineering and Technology 01.12.2018 Institution of Engineering and Technology (IET)
Témata:	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
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Abstract	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 powertrain information to generate an optimal velocity trajectory over a prediction horizon. When utilised by a vehicle, this optimal velocity trajectory reduces fuel consumption. The objective of this optimisation problem is to reduce dynamic losses, required tractive force, and completing trip distance with a given travel time. Sequential quadratic programming method is employed for this nonlinearly constrained optimisation problem. When applied to a GM Volt-2, the generated velocity trajectory saves fuel compared to a real-world drive cycle. The simulation results confirm the fuel consumption reduction with the rule-based mode selection and the energy management strategy of a GM Volt 2 model in Autonomie.
AbstractList	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 powertrain information to generate an optimal velocity trajectory over a prediction horizon. When utilised by a vehicle, this optimal velocity trajectory reduces fuel consumption. The objective of this optimisation problem is to reduce dynamic losses, required tractive force, and completing trip distance with a given travel time. Sequential quadratic programming method is employed for this nonlinearly constrained optimisation problem. When applied to a GM Volt‐2, the generated velocity trajectory saves fuel compared to a real‐world drive cycle. The simulation results confirm the fuel consumption reduction with the rule‐based mode selection and the energy management strategy of a GM Volt 2 model in Autonomie. 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 powertrain information to generate an optimal velocity trajectory over a prediction horizon. When utilised by a vehicle, this optimal velocity trajectory reduces fuel consumption. The objective of this optimisation problem is to reduce dynamic losses, required tractive force, and completing trip distance with a given travel time. Sequential quadratic programming method is employed for this nonlinearly constrained optimisation problem. When applied to a GM Volt-2, the generated velocity trajectory saves fuel compared to a real-world drive cycle. The simulation results confirm the fuel consumption reduction with the rule-based mode selection and the energy management strategy of a GM Volt 2 model in Autonomie.
Author	Barik, Biswajit Krishna Bhat, Pradeep Robinette, Darrell Chen, Bo Oncken, Joseph Orlando, Joshua
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Keywords	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
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Snippet	With the advancement of vehicle-to-vehicle and vehicle-to-infrastructure technologies, more and more real-time information regarding traffic and transportation... With the advancement of vehicle‐to‐vehicle and vehicle‐to‐infrastructure technologies, more and more real‐time information regarding traffic and transportation...
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SubjectTerms	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
Title	Optimal velocity prediction for fuel economy improvement of connected vehicles
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