Shahbazi, Z.; Nowaczyk, S. Enhancing Energy Efficiency in Connected Vehicles for Traffic Flow Optimization. Smart Cities2023, 6, 2574-2592.
Shahbazi, Z.; Nowaczyk, S. Enhancing Energy Efficiency in Connected Vehicles for Traffic Flow Optimization. Smart Cities 2023, 6, 2574-2592.
Shahbazi, Z.; Nowaczyk, S. Enhancing Energy Efficiency in Connected Vehicles for Traffic Flow Optimization. Smart Cities2023, 6, 2574-2592.
Shahbazi, Z.; Nowaczyk, S. Enhancing Energy Efficiency in Connected Vehicles for Traffic Flow Optimization. Smart Cities 2023, 6, 2574-2592.
Abstract
The predominance of traffic lights in urban settings often induces fluctuations in traffic patterns and energy utilization among vehicles. To counteract the adverse effects of traffic lights on the energy efficiency of electric vehicles (EVs), a Multi-Intersections-Based Eco-Approach and Departure strategy (M-EAD) is proposed. This strategy aims to enhance vehicle energy efficiency, traffic flow, and battery longevity, all while upholding satisfactory driving comfort. The M-EAD strategy unfolds in two distinct stages: the optimization of an eco-friendly green signal window and the refinement of speed trajectories. The initial stage tackles the optimization of traffic light green signal windows, underpinned by the minimization of travel delays via solving the shortest path problem. In the subsequent stage, a receding horizon framework takes center stage, leveraging an iterative dynamic programming algorithm to tackle the speed optimization challenge. The objective here is to curtail energy consumption and reduce battery wear by finding an optimal speed trajectory. Furthermore, the real-world efficacy of this approach is substantiated through on-road vehicle tests, attesting to its viability in actual road scenarios.
Keywords
Energy efficiency; optimization; electric vehicle; M-EAD
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
