Energy Efficiency and Emission Testing for Connected and Automated Vehicles Using Real-World Driving Data

TL;DR

This paper introduces a novel evaluation framework using unsupervised learning and real-world driving data to assess energy efficiency and emissions of connected and automated vehicles, aiming to standardize testing methods.

Contribution

It proposes a new method combining driving primitive analysis and linear weighted estimation for fair evaluation of CAV energy and emission impacts.

Findings

Successfully identifies typical driving primitives

Couples evaluated vehicle primitives with large dataset primitives

Enhances standard development for CAV energy and emission testing

Abstract

By using the onboard sensing and external connectivity technology, connected and automated vehicles (CAV) could lead to improved energy efficiency, better routing, and lower traffic congestion. With the rapid development of the technology and adaptation of CAV, it is more critical to develop the universal evaluation method and the testing standard which could evaluate the impacts on energy consumption and environmental pollution of CAV fairly, especially under the various traffic conditions. In this paper, we proposed a new method and framework to evaluate the energy efficiency and emission of the vehicle based on the unsupervised learning methods. Both the real-world driving data of the evaluated vehicle and the large naturalistic driving dataset are used to perform the driving primitive analysis and coupling. Then the linear weighted estimation method could be used to calculate the testing result of the evaluated vehicle. The results show that this method can successfully identify the typical driving primitives. The couples of the driving primitives from the evaluated vehicle and the typical driving primitives from the large real-world driving dataset coincide with each other very well. This new method could enhance the standard development of the energy efficiency and emission testing of CAV and other off-cycle credits.