Abstract

Accurate and reliable global positioning system (GPS)-based vehicle use data are highly valuable for many transportation, analysis, and automotive considerations. Model-based design, real-world fuel economy analysis, and the growing field of autonomous and connected technologies (including predictive powertrain control and self-driving cars) all have a vested interest in high-fidelity estimation of powertrain loads and vehicle usage profiles. Unfortunately, road grade can be a difficult property to extract from GPS data with consistency. In this report, we present a methodology for appending high-resolution elevation data to GPS speed traces via a static digital elevation model. Anomalous data points in the digital elevation model are addressed during a filtration/smoothing routine, resulting in an elevation profile that can be used to calculate road grade. This process is evaluated against a large, commercially available height/slope dataset from the Navteq/Nokia/HERE Advanced Driver Assistance Systems product. Results will show good agreement with the Advanced Driver Assistance Systems data in the ability to estimate road grade between any two consecutive points in the contiguous United States. Document type: Report

Abstract

The benefits of electrified powertrains for light-duty vehicles are well understood, however sufficient published information is not available on the benefits of advanced powertrains on the various types of medium and heavy duty vehicles. Quantifying the benefits of powertrain electrification will help fleet operators understand the advantages or limitations in adopting electrified powertrains in their truck fleets. Trucks vary in size and shape, as they are designed for specific applications. It is necessary to model each kind of truck separately to understand what kind of powertrain architecture will be feasible for their daily operations. This paper examines 11 types of vehicles and 5 powertrain technology choices to quantify the fuel saving potential of each design choice. This study uses the regulatory cycles proposed by the US Environmental Protection Agency (EPA) for measuring fuel consumption. Document type: Article