Optimal vehicle speed and gear position control for connected and autonomous vehicles

Yunli Shao, Zongxuan Sun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


For a connected and autonomous vehicle (CAV), co-optimization of vehicle speed and powertrain operation maximizes the fuel benefits. For an internal combustion engine based vehicle (ICV), the transmission gear position can be optimized to adapt to anticipated future vehicle speed and power demand. It is necessary to consider drivability when optimizing the gear shift to ensure a satisfactory acceleration capability and to avoid the shift busyness. This work proposes a first-of-its-kind real-time implementable optimal control strategy to optimize vehicle speed and gear position simultaneously for ICVs while considering both fuel efficiency and drivability. The control strategy is developed upon a unified CAV framework so that it is widely applicable to various CAV applications. The optimal control problem is formulated and simplified to a mixed integer programming problem with a convex quadratic objective function and linear constraints. An efficient numerical solver is applied to obtain the optimal solutions for an eco-drive application in a model predictive control (MPC) fashion. The control is real-time implementable with an average computational time of 0.33 seconds and maximum computational time of 0.79 seconds. Results from simulation and experiment show that by co-optimizing vehicle speed and gear position, the target vehicle can achieve 16% fuel benefits compared to a baseline vehicle with constant speed cruising control. In addition, experimental results show that the optimal control can also significantly reduce emissions.

Original languageEnglish (US)
Title of host publication2019 American Control Conference, ACC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781538679265
StatePublished - Jul 2019
Event2019 American Control Conference, ACC 2019 - Philadelphia, United States
Duration: Jul 10 2019Jul 12 2019

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Conference2019 American Control Conference, ACC 2019
Country/TerritoryUnited States

Bibliographical note

Funding Information:
Yunli Shao is supported by the University of Minnesota Doctoral Dissertation Fellowship (DDF).

Publisher Copyright:
© 2019 American Automatic Control Council.


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