Liuhui Zhao

Liuhui Zhao, A M Ishtiaque Mahbub, Andreas A. Malikopoulos

The implementation of connected and automated vehicle technologies enables opportunities for a novel computational framework for real-time control actions aimed at optimizing energy consumption and associated benefits. In this paper, we present a two-level control architecture for a connected and automated plug-in hybrid electric vehicle to optimize simultaneously its speed profile and powertrain efficiency. We evaluate the proposed architecture through simulation in a network of vehicles.

Liuhui Zhao, Andreas A. Malikopoulos, Jackeline Rios-Torres

The implementation of connected and automated vehicle (CAV) technologies enables a novel computational framework for real-time control actions aimed at optimizing energy consumption and associated benefits. Several research efforts reported in the literature to date have proposed decentralized control algorithms to coordinate CAVs in various traffic scenarios, e.g., highway on-ramps, intersections, and roundabouts. However, the impact of optimally coordinating CAVs on the performance of a transportation network has not been thoroughly analyzed yet. In this paper, we apply a decentralized optimal control framework in a transportation network and compare its performance to a baseline scenario consisting of human-driven vehicles. We show that introducing of CAVs yields radically improved roadway capacity and network performance.

Adam Stager, Luke Bhan, Andreas Malikopoulos et al.

The common thread that characterizes energy efficient mobility systems for smart cities is their interconnectivity which enables the exchange of massive amounts of data; this, in turn, provides the opportunity to develop a decentralized framework to process this information and deliver real-time control actions that optimize energy consumption and other associated benefits. To seize these opportunities, this paper describes the development of a scaled smart city providing a glimpse that bridges the gap between simulation and full scale implementation of energy efficient mobility systems. Using this testbed, we can quickly, safely, and affordably experimentally validate control concepts aimed at enhancing our understanding of the implications of next generation mobility systems.