Estimating Energy Efficiency of Connected and Autonomous Vehicles in a Mixed Fleet

(2018) Estimating Energy Efficiency of Connected and Autonomous Vehicles in a Mixed Fleet. Transportation, Department of

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Abstract

Connected and autonomous vehicle (CAV) technologies are likely to be gradually implemented over time and in a traffic environment consisting of a significant share of alternative fuel vehicles, such as flexible-fuel, plug-in electric, and fuel cell vehicles. This work proposes the use of rule-based ecological adaptive cruise control strategies—the ecological smart driver model (Eco-SDM) for gasoline CAVs and the energy-efficient electric driving model (E3DM) for electric CAVs (e-CAVs)—to improve the energy efficiency of individual vehicles and traffic flow. By adjusting the spacing between the leading and the following vehicles, the Eco-SDM provides smoother deceleration and acceleration than the adaptive cruise control strategies based on intelligent driver model-adaptive cruise control (IDM-ACC) and the Nissan model (Nissan-ACC). The E3DM is able to maintain high energy efficiency of regenerative braking by adjusting the spacing between the leading and the following vehicles. To estimate vehicle energy consumption in a mixed traffic stream, the Virginia Polytechnic Institute and State University (Virginia Tech) microscopic energy and emission (VT-Micro) model was calibrated for gasoline vehicles and a power-based electricity consumption model that considers the impact of ambient temperature on auxiliary load was proposed for battery electric vehicles (BEVs). Single-lane vehicle dynamics in a traffic stream with a mix of CAVs and human-driven vehicles were simulated. Results showed that the Eco-SDM and E3DM outperform IDM-ACC and Nissan-ACC in terms of energy consumption. For Eco-SDM-based CAVs, the fuel saving benefit was greatest when a CAV is at the front of a platoon. For E3DM-based e-CAVs, higher market penetration of e-CAVs may not result in higher energy efficiency of the entire fleet. Considering mixed traffic streams with BEVs and gasoline vehicles, the energy consumption of the entire fleet decreased when the market penetration of BEVs (which contains both e-CAVs and manual BEVs) increased. A higher ratio of e-CAVs to manual BEVs resulted in higher energy efficiency.