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.
- Dynamic Systems and Control Division
Optimal Eco-Approach Control With Traffic Prediction for Connected Vehicles
- Views Icon Views
- Share Icon Share
- Search Site
Shao, Y, & Sun, Z. "Optimal Eco-Approach Control With Traffic Prediction for Connected Vehicles." Proceedings of the ASME 2018 Dynamic Systems and Control Conference. Volume 2: 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. Atlanta, Georgia, USA. September 30–October 3, 2018. V002T22A002. ASME. https://doi.org/10.1115/DSCC2018-9059
Download citation file: