Connected Vehicle Technologies for Efficient Urban Transportation

Prasad, Ajay; Advani, Suresh; Shin, Hyeon-Shic · 2016 · ROSA P / Mid-Atlantic Transportation Sustainability University Transportation Center

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Summary

This research addresses the dual challenges of urban traffic congestion and vehicle inefficiency by leveraging connected vehicle technologies to optimize the power management of hybrid fuel cell buses. The study was motivated by the limitations of historical traffic data, which cannot account for real-time fluctuations caused by incidents or weather, and the need to improve both fuel economy and the durability of fuel cell stacks, which degrade under frequent startup/shutdown cycles. The primary objective was to develop a two-way communication system that allows vehicles to upload operational data to a Traffic Management Center (TMC) and download real-time traffic information to inform intelligent, real-time control strategies. The methodology involved modeling a hybrid fuel cell bus using a Matlab/Simulink simulation tool called LFM, which was validated against experimental data from the University of Delaware’s fleet. The researchers developed a dynamic programming (DP) optimization algorithm to determine optimal power management strategies. This algorithm utilized cost functions that minimized fuel consumption while penalizing frequent fuel cell on/off cycles and power fluctuations to enhance stack durability. Historical trip data from a shuttle bus (bus2) were analyzed to understand velocity profiles and energy usage. Additionally, a two-way communication system was implemented on a newer bus (bus3) using a Raspberry Pi, GPS unit, and CAN-bus interface to enable real-time data exchange and remote control of the fuel cell power. The results demonstrated that the DP-based optimization significantly outperformed the original rule-based control system. When optimizing solely for fuel consumption, the DP model reduced hydrogen usage by 3.1% compared to the rule-based strategy, though it resulted in excessive fuel cell cycling. By incorporating a penalty for on/off cycles into the cost function, the system suppressed these cycles entirely, reducing fuel consumption by 2.4% while ensuring smoother workload distribution and improved durability. The study also highlighted that while historical data show patterns, they remain stochastic, confirming the necessity of real-time traffic data for robust power management. The communication system on bus3 successfully transmitted real-time operational metrics, validating the feasibility of the integrated hardware and software platform. The significance of this work lies in its demonstration of how connected vehicle technologies can simultaneously optimize traffic flow and vehicle efficiency. By integrating real-time traffic data with intelligent power management, the system offers a pathway to reduce congestion, lower emissions, and extend the lifespan of fuel cell components. The findings provide a foundational framework for future intelligent transportation systems, suggesting that such technologies can be adapted for broader application in hybrid vehicles and integrated into regional traffic management infrastructure to create more efficient urban transportation networks.

Key finding

Dynamic programming-based power management reduced fuel consumption by 3.1 percent compared to rule-based control, and by 2.4 percent when durability penalties were included.

Methodology

modeling

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

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• v2x connected vehicle