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Title: Using Global Positioning System Travel Data to Assess Real-World Energy Use of Plug-In Hybrid Electric Vehicles

Abstract

Plug-in hybrid electric vehicles (PHEVs) have received considerable recent attention for their potential to reduce petroleum consumption significantly and quickly in the transportation sector. Analysis to aid the design of such vehicles and predict their real-world performance and fuel displacement must consider the driving patterns the vehicles will typically encounter. This paper goes beyond consideration of standardized certification cycless by leveraging state-of-the-art travel survey techniques that use Global Positioning System (GPS) technology to obtain a large set of real-world drive cycles from the surveyed vehicle fleet. This study specifically extracts 24-h, second-by-second driving profiles from a set of 227 GPS-instrumented vehicles in the St. Louis, Missouri, metropolitan area. The performance of midsize conventional, hybrid electric, and PHEV models is then simulated over the 227 full-day driving profiles to assess fuel consumption and operating characteristics of these vehicle technologies over a set of real-world usage patterns. In comparison to standard cycles used for certification procedures, the travel survey duty cycles include significantly more aggressive acceleration and deceleration events across the velocity spectrum, which affect vehicle operation and efficiency. Even under these more aggressive operating conditions, PHEVs using a blended charge-depleting energy management strategy consume less than 50% of the petroleum usedmore » by similar conventional vehicles. Although true prediction of the widespread real-world use of these vehicles requires expansion of the vehicle sample size and a refined accounting for the possible interaction of several variables with the sampled driving profiles, this study demonstrates a cutting-edge use of available GPS travel survey data to analyze the (highly drive cycle-dependent) performance of advanced technology PHEVs. This demonstration highlights new opportunities for using innovative GPS travel survey techniques and sophisticated vehicle system simulation tools to guide vehicle design improvements and to maximize the benefits offered by energy efficiency technologies.« less

Authors:
; ; ;
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
981954
DOE Contract Number:
AC36-08GO28308
Resource Type:
Journal Article
Resource Relation:
Journal Name: Transportation Research Record: Journal of the Transportation Research Board; Journal Volume: 2017; Journal Issue: 2007
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; 33 ADVANCED PROPULSION SYSTEMS; 47 OTHER INSTRUMENTATION; ACCELERATION; CERTIFICATION; DATA; DESIGN; EFFICIENCY; ENERGY; ENERGY EFFICIENCY; ENERGY MANAGEMENT; EXPANSION; FORECASTING; FUEL CONSUMPTION; FUELS; GLOBAL POSITIONING SYSTEM; HYBRIDIZATION; PERFORMANCE; PETROLEUM; POTENTIALS; TRANSPORTATION SECTOR; URBAN AREAS; VEHICLES; VELOCITY; Transportation

Citation Formats

Gonder, J., Markel, T., Thornton, M., and Simpson, A. Using Global Positioning System Travel Data to Assess Real-World Energy Use of Plug-In Hybrid Electric Vehicles. United States: N. p., 2007. Web. doi:10.3141/2017-04.
Gonder, J., Markel, T., Thornton, M., & Simpson, A. Using Global Positioning System Travel Data to Assess Real-World Energy Use of Plug-In Hybrid Electric Vehicles. United States. doi:10.3141/2017-04.
Gonder, J., Markel, T., Thornton, M., and Simpson, A. Mon . "Using Global Positioning System Travel Data to Assess Real-World Energy Use of Plug-In Hybrid Electric Vehicles". United States. doi:10.3141/2017-04.
@article{osti_981954,
title = {Using Global Positioning System Travel Data to Assess Real-World Energy Use of Plug-In Hybrid Electric Vehicles},
author = {Gonder, J. and Markel, T. and Thornton, M. and Simpson, A.},
abstractNote = {Plug-in hybrid electric vehicles (PHEVs) have received considerable recent attention for their potential to reduce petroleum consumption significantly and quickly in the transportation sector. Analysis to aid the design of such vehicles and predict their real-world performance and fuel displacement must consider the driving patterns the vehicles will typically encounter. This paper goes beyond consideration of standardized certification cycless by leveraging state-of-the-art travel survey techniques that use Global Positioning System (GPS) technology to obtain a large set of real-world drive cycles from the surveyed vehicle fleet. This study specifically extracts 24-h, second-by-second driving profiles from a set of 227 GPS-instrumented vehicles in the St. Louis, Missouri, metropolitan area. The performance of midsize conventional, hybrid electric, and PHEV models is then simulated over the 227 full-day driving profiles to assess fuel consumption and operating characteristics of these vehicle technologies over a set of real-world usage patterns. In comparison to standard cycles used for certification procedures, the travel survey duty cycles include significantly more aggressive acceleration and deceleration events across the velocity spectrum, which affect vehicle operation and efficiency. Even under these more aggressive operating conditions, PHEVs using a blended charge-depleting energy management strategy consume less than 50% of the petroleum used by similar conventional vehicles. Although true prediction of the widespread real-world use of these vehicles requires expansion of the vehicle sample size and a refined accounting for the possible interaction of several variables with the sampled driving profiles, this study demonstrates a cutting-edge use of available GPS travel survey data to analyze the (highly drive cycle-dependent) performance of advanced technology PHEVs. This demonstration highlights new opportunities for using innovative GPS travel survey techniques and sophisticated vehicle system simulation tools to guide vehicle design improvements and to maximize the benefits offered by energy efficiency technologies.},
doi = {10.3141/2017-04},
journal = {Transportation Research Record: Journal of the Transportation Research Board},
number = 2007,
volume = 2017,
place = {United States},
year = {Mon Jan 01 00:00:00 EST 2007},
month = {Mon Jan 01 00:00:00 EST 2007}
}
  • Highlights opportunities using GPS travel survey techniques and systems simulation tools for plug-in hybrid vehicle design improvements, which maximize the benefits of energy efficiency technologies.
  • Plug-in hybrid electric vehicles (PHEVs) show great promise in reducing transportation-related fossil fuel consumption and greenhouse gas emissions. Designing an efficient energy management system (EMS) for PHEVs to achieve better fuel economy has been an active research topic for decades. Most of the advanced systems rely either on a priori knowledge of future driving conditions to achieve the optimal but not real-time solution (e.g., using a dynamic programming strategy) or on only current driving situations to achieve a real-time but nonoptimal solution (e.g., rule-based strategy). This paper proposes a reinforcement learning–based real-time EMS for PHEVs to address the trade-off betweenmore » real-time performance and optimal energy savings. The proposed model can optimize the power-split control in real time while learning the optimal decisions from historical driving cycles. Here, a case study on a real-world commute trip shows that about a 12% fuel saving can be achieved without considering charging opportunities; further, an 8% fuel saving can be achieved when charging opportunities are considered, compared with the standard binary mode control strategy.« less
  • The benefit of using a PHEV comes from its ability to substitute gasoline with electricity in operation. Defined as the proportion of distance traveled in the electric mode, the utility factor (UF) depends mostly on the battery capacity, but also on many other factors, such as travel pattern and recharging pattern. Conventionally, the UFs are calculated based on the daily vehicle miles traveled (DVMT) by assuming motorists leave home in the morning with a full battery, and no charge occurs before returning home in the evening. Such an assumption, however, ignores the impact of the heterogeneity in both travel andmore » charging behavior, such as going back home more than once in a day, the impact of available charging time, and the price of gasoline. In addition, the conventional UFs are based on the National Household Travel Survey (NHTS) data, which are one-day travel data of each sample vehicle. A motorist's daily distance variation is ignored. This paper employs the GPS-based longitudinal travel data (covering 3-18 months) collected from 403 vehicles in the Seattle metropolitan area to investigate how such travel and charging behavior affects UFs. To do this, for each vehicle, we organized trips to a series of home and work related tours. The UFs based on the DVMT are found close to those based on home-to-home tours. However, it is seen that the workplace charge opportunities significantly increase UFs if the CD range is no more than 40 miles.« less
  • As vehicle powertrain efficiency increases through electrification, consumer travel and driving behavior have significantly more influence on the potential fuel consumption of these vehicles. Therefore, it is critical to have a good understanding of in-use or 'real world' driving behavior if accurate fuel consumption estimates of electric drive vehicles are to be achieved. Regional travel surveys using Global Positioning System (GPS) equipment have been found to provide an excellent source of in-use driving profiles. In this study, a variety of vehicle powertrain options were developed and their performance was simulated over GPS-derived driving profiles for 783 vehicles operating in Texas.more » The results include statistical comparisons of the driving profiles versus national data sets, driving performance characteristics compared with standard drive cycles, and expected petroleum displacement benefits from the electrified vehicles given various vehicle charging scenarios.« less
  • The green routing strategy instructing a vehicle to select a fuel-efficient route benefits the current transportation system with fuel-saving opportunities. This paper introduces a navigation API route fuel-saving evaluation framework for estimating fuel advantages of alternative API routes based on large-scale, real-world travel data for conventional vehicles (CVs) and hybrid electric vehicles (HEVs). The navigation APIs, such Google Directions API, integrate traffic conditions and provide feasible alternative routes for origin-destination pairs. This paper develops two link-based fuel-consumption models stratified by link-level speed, road grade, and functional class (local/non-local), one for CVs and the other for HEVs. The link-based fuel-consumption modelsmore » are built by assigning travel from a large number of GPS driving traces to the links in TomTom MultiNet as the underlying road network layer and road grade data from a U.S. Geological Survey elevation data set. Fuel consumption on a link is calculated by the proposed fuel consumption model. This paper envisions two kinds of applications: 1) identifying alternate routes that save fuel, and 2) quantifying the potential fuel savings for large amounts of travel. An experiment based on a large-scale California Household Travel Survey GPS trajectory data set is conducted. The fuel consumption and savings of CVs and HEVs are investigated. At the same time, the trade-off between fuel saving and time saving for choosing different routes is also examined for both powertrains.« less