Energy saving potentials of connected and automated vehicles

Vahidi, Ardalan; Sciarretta, Antonio · 2018 · Crossref

DOI: 10.1016/j.trc.2018.09.001

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Summary

This review paper investigates the energy-saving potentials of Connected and Automated Vehicles (CAVs), addressing a gap in literature that has historically prioritized safety and comfort over efficiency. The authors argue that while CAVs are marketed for safety and time savings, their enhanced situational awareness and precision control offer unprecedented opportunities for eco-driving. The study focuses on first-principles motion analysis and optimal control theory to quantify how connectivity (Vehicle-to-Vehicle, Vehicle-to-Infrastructure, Vehicle-to-Cloud) and automation can reduce energy consumption, disregarding secondary effects like increased vehicle miles traveled. The paper establishes the fundamentals of energy-efficient driving through Newton’s laws of motion, identifying aerodynamic drag and frictional losses as the primary variables controllable by driving behavior. It demonstrates that minimizing drag losses requires maintaining low, constant velocities, while avoiding braking events preserves kinetic energy. The authors categorize energy-saving strategies into two main areas: anticipative driving for individual vehicles and cooperative driving for fleets. For individual CAVs, the paper details how prior knowledge of road geometry (grade, curvature), traffic conditions, and signal phase and timing (SPaT) allows for predictive powertrain control and velocity optimization. For example, knowing upcoming road grades enables vehicles to adjust speed and gear selection to minimize fuel use, with literature showing gains of 2–11% for heavy-duty trucks. Similarly, accessing SPaT data allows vehicles to adjust speeds to arrive at intersections during green lights, avoiding unnecessary stops and idling, with reported savings of 5–29% depending on traffic density and automation level. The study further explores cooperative driving strategies, such as platooning and cooperative adaptive cruise control, which reduce aerodynamic drag through drafting and harmonize traffic flow. The authors note that even in mixed traffic, automated vehicles can positively influence the energy efficiency of surrounding human-driven vehicles by smoothing traffic flow. The paper synthesizes scattered experimental and simulation results from the past decade, highlighting that while human drivers can benefit from connectivity advisories, only automated vehicles can reliably execute the precise, complex maneuvers required for maximum energy efficiency. The significance of this work lies in its comprehensive framework for understanding CAV energy efficiency, providing a basis for policy-making and regulatory guidance. The authors conclude that proactive policy is essential to steer CAV development toward energy efficiency, as the technology’s impact could either halve or double energy use depending on implementation. By formalizing eco-driving as an optimal control problem, the paper offers researchers and engineers a structured approach to designing algorithms that maximize the environmental benefits of connected and automated transportation systems.

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• eco driving