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Even though extensive studies have developed various eco-driving strategies for vehicle platoon to travel on urban roads with traffic signals, most of them focus on vehicle-level trajectory planning or speed advisory rather than real-time platoon-level closed-loop control. In addition, majority of existing efforts neglect the traffic and vehicle dynamic uncertainties to avoid the modeling and solution complexity. To make up these research gaps, this study develops a system optimal vehicle platooning control for eco-driving (SO-ED), which can guide a mixed flow platoon to smoothly run on the urban roads and pass the signalized intersections without sudden deceleration or red idling. The SO-ED is mathematically implemented by a hybrid model predictive control (MPC) system, including three MPC controllers and an MINLP platoon splitting switching signal. Based on the features of the system, this study uses active set method to solve the large-scale MPC controllers in real time. The numerical experiments validate the merits of the proposed SO-ED in smoothing the traffic flow and reducing energy consumption and emission at urban signalized intersections.
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Wireless charging utility maximization and intersection control delay minimization framework for electric vehicles
Abstract This study presents the Wireless Charging Utility Maximization (WCUM) framework, which aims to maximize the utility of Wireless Charging Units (WCUs) for electric vehicle (EV) charging through the optimal WCU deployment at signalized intersections. Furthermore, the framework aims to minimize the control delay at all signalized intersections of the network. The framework consists of a two‐step optimization formulation, a dynamic traffic assignment model to calculate the user equilibrium, a traffic microsimulator to formulate the objective functions, and a global Mixed Integer Non‐Linear Programming (MINLP) optimization solver. An optimization problem is formulated for each intersection, and another for the entire network. The performance of the WCUM framework is tested using the Sioux Falls network. We perform a comparative study of 12 global MINLP solvers with a case study. Based on solution quality and computation time, we choose the Couenne solver for this framework.
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- Award ID(s):
- 1647361
- PAR ID:
- 10087900
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Computer-Aided Civil and Infrastructure Engineering
- Volume:
- 34
- Issue:
- 7
- ISSN:
- 1093-9687
- Page Range / eLocation ID:
- p. 547-568
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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