Open AccessOptimal velocity prediction for fuel economy improvement of connected vehicles
Author(s) -
Barik Biswajit,
Krishna Bhat Pradeep,
Oncken Joseph,
Chen Bo,
Orlando Joshua,
Robinette Darrell
Publication year - 2018
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2018.5110
Subject(s) - fuel efficiency , trajectory , powertrain , dynamic programming , sequential quadratic programming , automotive engineering , energy management , driving cycle , quadratic programming , computer science , trajectory optimization , time horizon , reduction (mathematics) , engineering , simulation , optimal control , mathematical optimization , energy (signal processing) , electric vehicle , torque , algorithm , mathematics , power (physics) , statistics , physics , geometry , quantum mechanics , astronomy , thermodynamics
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.