Optimal eco-approach control with traffic prediction for connected vehicles

Yunli Shao, Zongxuan Sun

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

1 Citation (Scopus)

Abstract

This work proposes a unified framework for the eco-approach application that integrates traffic prediction, vehicle optimization, and implementation. The eco-approach application is formulated as either a car-following optimization problem or a single vehicle optimization problem, depending on whether a preceding vehicle exists. The traffic prediction scheme anticipates future traffic conditions and describes the traffic dynamics on the road segment of interest using state variables: traffic flow, density, and speed. With the information enabled by connectivity, the traffic state estimation is updated using an observer. Uncertainties in the traffic prediction are considered using a robust optimization approach. The robust optimization problem is discretized and solved by an efficient nonlinear programming solver. The proposed eco-approach framework is implemented to a single lane single intersection scenario for 12, 8, 4, and 1 connected vehicle scenarios. The fuel benefits vary from 11.0% to 6.7% as the penetration rates of connectivity decrease. The performance is satisfactory compared to the 12.0% fuel benefits with perfection traffic prediction.

Original languageEnglish (US)
Title of host publicationControl and Optimization of Connected and Automated Ground Vehicles; Dynamic Systems and Control Education; Dynamics and Control of Renewable Energy Systems; Energy Harvesting; Energy Systems; Estimation and Identification; Intelligent Transportation and Vehicles; Manufacturing; Mechatronics; Modeling and Control of IC Engines and Aftertreatment Systems; Modeling and Control of IC Engines and Powertrain Systems; Modeling and Management of Power Systems
PublisherAmerican Society of Mechanical Engineers (ASME)
Volume2
ISBN (Electronic)9780791851906
DOIs
StatePublished - Jan 1 2018
EventASME 2018 Dynamic Systems and Control Conference, DSCC 2018 - Atlanta, United States
Duration: Sep 30 2018Oct 3 2018

Other

OtherASME 2018 Dynamic Systems and Control Conference, DSCC 2018
CountryUnited States
CityAtlanta
Period9/30/1810/3/18

Fingerprint

Nonlinear programming
State estimation
Railroad cars
Uncertainty

Cite this

Shao, Y., & Sun, Z. (2018). Optimal eco-approach control with traffic prediction for connected vehicles. In Control and Optimization of Connected and Automated Ground Vehicles; Dynamic Systems and Control Education; Dynamics and Control of Renewable Energy Systems; Energy Harvesting; Energy Systems; Estimation and Identification; Intelligent Transportation and Vehicles; Manufacturing; Mechatronics; Modeling and Control of IC Engines and Aftertreatment Systems; Modeling and Control of IC Engines and Powertrain Systems; Modeling and Management of Power Systems (Vol. 2). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/DSCC2018-9059

Optimal eco-approach control with traffic prediction for connected vehicles. / Shao, Yunli; Sun, Zongxuan.

Control and Optimization of Connected and Automated Ground Vehicles; Dynamic Systems and Control Education; Dynamics and Control of Renewable Energy Systems; Energy Harvesting; Energy Systems; Estimation and Identification; Intelligent Transportation and Vehicles; Manufacturing; Mechatronics; Modeling and Control of IC Engines and Aftertreatment Systems; Modeling and Control of IC Engines and Powertrain Systems; Modeling and Management of Power Systems. Vol. 2 American Society of Mechanical Engineers (ASME), 2018.

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

Shao, Y & Sun, Z 2018, Optimal eco-approach control with traffic prediction for connected vehicles. in Control and Optimization of Connected and Automated Ground Vehicles; Dynamic Systems and Control Education; Dynamics and Control of Renewable Energy Systems; Energy Harvesting; Energy Systems; Estimation and Identification; Intelligent Transportation and Vehicles; Manufacturing; Mechatronics; Modeling and Control of IC Engines and Aftertreatment Systems; Modeling and Control of IC Engines and Powertrain Systems; Modeling and Management of Power Systems. vol. 2, American Society of Mechanical Engineers (ASME), ASME 2018 Dynamic Systems and Control Conference, DSCC 2018, Atlanta, United States, 9/30/18. https://doi.org/10.1115/DSCC2018-9059
Shao Y, Sun Z. Optimal eco-approach control with traffic prediction for connected vehicles. In Control and Optimization of Connected and Automated Ground Vehicles; Dynamic Systems and Control Education; Dynamics and Control of Renewable Energy Systems; Energy Harvesting; Energy Systems; Estimation and Identification; Intelligent Transportation and Vehicles; Manufacturing; Mechatronics; Modeling and Control of IC Engines and Aftertreatment Systems; Modeling and Control of IC Engines and Powertrain Systems; Modeling and Management of Power Systems. Vol. 2. American Society of Mechanical Engineers (ASME). 2018 https://doi.org/10.1115/DSCC2018-9059
Shao, Yunli ; Sun, Zongxuan. / Optimal eco-approach control with traffic prediction for connected vehicles. Control and Optimization of Connected and Automated Ground Vehicles; Dynamic Systems and Control Education; Dynamics and Control of Renewable Energy Systems; Energy Harvesting; Energy Systems; Estimation and Identification; Intelligent Transportation and Vehicles; Manufacturing; Mechatronics; Modeling and Control of IC Engines and Aftertreatment Systems; Modeling and Control of IC Engines and Powertrain Systems; Modeling and Management of Power Systems. Vol. 2 American Society of Mechanical Engineers (ASME), 2018.
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