Assessing the Energy Impacts of Connected and Automated Vehicles: A Comprehensive Validation via Simulation, Dynamometer, and Track Tests

Document Type

Conference Proceeding

Publication Date

12-13-2024

Department

Department of Mechanical and Aerospace Engineering

Abstract

Connected and automated vehicles (CAVs) have the potential to enhance energy efficiency by leveraging advanced environmental data, a capability beyond the reach of traditional vehicles. Nevertheless, quantifying the precise energy impacts of CAVs remains challenging due to their complex interactions with dynamic environments. This challenge is further underscored by conventional simulations and experiments that utilize predefined speed profiles to emulate vehicle behavior. To address these gaps, our study adopts a comprehensive approach involving simulations, dynamometer usage, and track tests to assess the energy effects of CAVs more accurately. Our simulation method integrates sophisticated environmental models, such as road regulations and surrounding traffic, with both human and automated driver models equipped with an eco-driving control algorithm. This algorithm adjusts vehicle operations based on real-time environmental inputs, while the vehicle’s powertrain model provides feedback, creating a closed-loop system that mirrors real-world conditions. The eco-driving algorithm is further evaluated in the dynamometer and track tests to confirm its effectiveness and to examine the vehicle’s behavior under real conditions. By integrating “anything-in-the-loop” (XIL) workflows, our approach enables real-time interaction between the vehicle and simulated environments during these tests, thus avoiding the limitations of the predefined speed profiles used in traditional experiments. This paper primarily focuses on comparing these methodologies to thoroughly investigate and highlight their distinct characteristics, providing insights into factors that influence discrepancies in energy consumption and the accuracy of energy impact assessments. Our findings validate the reliability of our method, revealing less than a 5% discrepancy between simulation predictions and actual test outcomes under various conditions. Ultimately, this comprehensive evaluation not only confirms the effectiveness of the proposed simulation model but also offers invaluable insights for developing more energy-efficient CAV technologies.

Publication Title

2024 IEEE International Automated Vehicle Validation Conference (IAVVC)

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