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Hierarchical Energy Management Control of Connected Hybrid Electric Vehicles on Urban Roads With Efficiencies Feedback

[+] Author Affiliations
Zhiyuan Du, Pierluigi Pisu

Clemson University, Greenville, SC

Lihong Qiu

Hefei University of Technology, Hefei, China

Paper No. DSCC2016-9641, pp. V001T16A002; 8 pages
  • ASME 2016 Dynamic Systems and Control Conference
  • Volume 1: Advances in Control Design Methods, Nonlinear and Optimal Control, Robotics, and Wind Energy Systems; Aerospace Applications; Assistive and Rehabilitation Robotics; Assistive Robotics; Battery and Oil and Gas Systems; Bioengineering Applications; Biomedical and Neural Systems Modeling, Diagnostics and Healthcare; Control and Monitoring of Vibratory Systems; Diagnostics and Detection; Energy Harvesting; Estimation and Identification; Fuel Cells/Energy Storage; Intelligent Transportation
  • Minneapolis, Minnesota, USA, October 12–14, 2016
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5069-5
  • Copyright © 2016 by ASME


In this paper, we present a fuel efficient control strategy for a group of connected hybrid electric vehicles (HEVs) in urban road conditions. A hierarchical control architecture is proposed where the higher level controller is located at traffic signal light while the lower level controllers are equipped on each HEV. The higher level controller utilizes Signal Phase and Timing (SPAT) information from the traffic lights to generate target velocities for every HEV, which allows a maximum number of vehicles pass the intersection at given green light window. Model Predictive Control (MPC) is used to track the target velocity and evaluate the energy efficient velocity profile for every vehicle for a given horizon. Each lower level controller then follows the velocity profile (from the higher level controller) in a fuel efficient fashion using adaptive equivalent consumption minimization strategy (A-ECMS). The lower level controller also feeds the average recuperation efficiency in a certain time window back to the higher level controller, thus affects the future velocity profile evaluation from the higher level controller, which is the major contribution of this paper. In this paper, the HEV model is developed based on Autonomie software and the simulation results show the effectiveness of our proposed approach.

Copyright © 2016 by ASME



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