A Physics-Based Framework for Estimating Real-Time Platoon Energy Savings

Abstract

Platooning is a strategy that aims to reduce aerodynamic drag and fuel consumption by driving trucks closely behind each other. However, the advantages of platooning can be compromised by braking loss, which refers to the extra braking needed to follow trucks on hills and in traffic. Conducting experiments to determine the benefits of platooning is time-consuming and resource-intensive, prompting the search for a method to estimate these benefits.

This thesis introduces a framework that allows operators to predict energy savings during platooning, even when braking is involved, providing immediate feedback to platoon operators. The physics-based framework applies to various types of road vehicles. The dissertation outlines practical approaches to implement the framework in real-time, such as a new adaptive estimation of braking loss based on vehicle wheelspeed and a method to query hyperlocal wind data, although the latter did not enhance correlation.

The validity of the framework is confirmed through robust regression analysis of more than 8000 different pairwise comparisons from experimental platoon trials, with the estimated energy change often closely matching the actual energy change and an R-Squared value exceeding 0.68 in the model. The proposed methods for estimating braking losses are relatively resistant to errors in drag and rolling resistance, but errors in mass can skew the results by a factor of two. Furthermore, a logistic regression classification approach is introduced to use the framework for making go/no-go decisions, enabling users to specify their desired confidence level. The classification approach demonstrated a 73.5% accuracy when applied to unseen on-road platooning data.

The framework effectively distinguishes the energy impacts of platooning from other vehicle energy consumption. It is grounded in physics, adaptable, capable of real-time operation without the need for a simultaneous reference, sensitive to various parameterizations and signal inaccuracies, and can provide clear feedback to platoon operators on energy savings with a binary outcome. Ultimately, this research can help platoon operators optimize their energy efficiency by offering realistic fuel savings expectations and guiding decisions about when to engage in platooning.