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

Jacob Holden, Eric Wood, Jeffrey Gonder, Lei Zhu, Matthew Shirk

Research output: Contribution to conferencePaper

Abstract

A data-informed model to predict energy use for a proposed vehicle trip has been developed in this paper. The methodology leverages nearly 1 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 rates 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 almost any type of driving data. The model has been trained on vehicle global positioning system data 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. The estimation model has demonstrated an error range of 8.6% to 13.8%. 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.
Original languageAmerican English
Number of pages11
StatePublished - 2018
Event97th Annual Meeting of the Transportation Research Board - Washington, D. C.
Duration: 7 Jan 201811 Jan 2018

Conference

Conference97th Annual Meeting of the Transportation Research Board
CityWashington, D. C.
Period7/01/1811/01/18

NREL Publication Number

  • NREL/CP-5400-70512

Keywords

  • drive cycles
  • energy estimation
  • GPS trajectories
  • green routing
  • transportation data
  • trip energy

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