Trip Energy Estimation Methodology and Model Based on Real-World Driving Data for Green-Routing Applications

Journal Article · 21 September 2018 · Transportation Research Record

DOI: https://doi.org/10.1177/0361198118798286 · OSTI ID:1477948

Holden, Jacob ^[1]; Van Til, Harrison ^[1]; Wood, Eric ^[1]; Zhu, Lei ^[1]; Gonder, Jeffrey ^[1]; Shirk, Matthew ^[2]

---

1. National Renewable Energy Lab. (NREL), Golden, CO (United States)
2. Idaho National Lab. (INL), Idaho Falls, ID (United States)

A data-informed model to predict energy use for a proposed vehicle trip has been developed in this paper. The methodology leverages roughly one million miles of real-world driving data to generate the estimation model. Driving is categorized at the sub-trip level by average speed, road gradient, and road network geometry, then aggregated by category. An average energy consumption rate is determined for each category, creating an energy rate look-up table. Proposed vehicle trips are then categorized in the same manner, and estimated energy rates are appended from the look-up table. The methodology is robust and applicable to a wide range of driving data. The model has been trained on vehicle travel profiles from the Transportation Secure Data Center at the National Renewable Energy Laboratory and validated against on-road fuel consumption data from testing in Phoenix, Arizona. When compared against the detailed on-road conventional vehicle fuel consumption test data, the energy estimation model accurately predicted which route would consume less fuel over a dozen different tests. When compared against a larger set of real-world origin-destination pairs, it is estimated that implementing the present methodology should accurately select the route that consumes the least fuel 90% of the time. The model results can be used to inform control strategies in routing tools, such as change in departure time, alternate routing, and alternate destinations to reduce energy consumption. This work provides a highly extensible framework that allows the model to be tuned to a specific driver or vehicle type.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V)
• Grant/Contract Number: AC36-08GO28308
• OSTI ID: 1477948
• Report Number(s): NREL/JA-5400-71111
• Journal Information: Transportation Research Record, Vol. 2672, Issue 24; ISSN 0361-1981
• Publisher: National Academy of Sciences, Engineering and MedicineCopyright Statement
• Country of Publication: United States
• Language: English