Impact of Two-Way Communication of Traffic Light Signal-to-Vehicle on the Electric Vehicle State of Charge

Alturiman, Amal; Alsabaan, Maazen · 2019 · Crossref

DOI: 10.1109/access.2018.2890081

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Summary

This paper addresses the limited driving range of electric vehicles (EVs), a primary drawback linked to battery state of charge (SOC) depletion caused by high speeds and accelerations. The authors propose utilizing two-way vehicular network communication between EVs and adaptive traffic light signals (TLS) at isolated intersections to minimize power consumption. By enabling bidirectional interaction, the TLS can adjust its green light duration based on incoming vehicle data, while vehicles can adapt their speeds based on TLS timing information. The study aims to determine the optimal green duration and vehicle speed that allow an EV to cross an intersection with maximum SOC. The methodology employs a microscopic SOC estimation model based on power consumption, which accounts for rolling resistance, aerodynamic drag, hill climbing, and acceleration forces. The system assumes an ideal communication channel using Dedicated Short Range Communications (DSRC) where EVs are equipped with on-board units and application units for location tracking. The traffic model considers an isolated suburban intersection with adaptive TLS control, where green light duration is variable within defined limits. The authors develop an optimization model to calculate the optimal recommended speed ($S_R^*$) and optimal green duration ($T_g^*$). The analysis evaluates three distinct scenarios for an approaching EV: crossing directly without deceleration, crossing after decelerating without stopping, and stopping at the intersection. The model calculates power consumption across various phases of acceleration, deceleration, and constant speed to determine the SOC impact. The analytical results demonstrate that the proposed two-way communication approach, which adapts both vehicle speed and traffic signal timing, achieves a higher SOC compared to strategies that adapt only speed or only the traffic signal. The study confirms that avoiding unnecessary high speeds and accelerations at intersections significantly reduces power consumption. Specifically, the optimization model successfully identifies parameters that maximize the battery's remaining capacity upon crossing the intersection. The findings indicate that coordinating both the infrastructure (TLS timing) and the vehicle (speed adaptation) yields superior energy efficiency compared to one-way communication schemes or non-adaptive strategies. The significance of this work lies in its contribution to intelligent transportation systems and EV efficiency. By integrating vehicular networks with adaptive traffic control, the approach offers a practical method to extend EV driving range without requiring hardware changes to the vehicles themselves. The study highlights the potential of two-way communication to optimize energy usage at signalized intersections, addressing a critical barrier to EV adoption. The proposed optimization model provides a framework for maximizing SOC, suggesting that future ITS implementations should consider bidirectional data exchange to enhance both traffic flow and environmental sustainability.

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• v2x connected vehicle