Development and analysis of eco-driving metrics for naturalistic instrumented vehicles (original) (raw)
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2017
Keyhole Markup Language LAMDA Learning Algorithm for Multivariate Data Analysis LDCV Light Duty Commercial Vehicle MAF Mass Air Flow mini MPV Mini MultiPurpose Vehicles MLR Multivariable Linear Regression model MOVES Motor Vehicle and Equipment Emission System software MPG Miles per Gallon NEDC New European Driving Cycle NFDA The National Franchised Dealers Association NHTSA US National Highway Traffic Safety Administration OBD On-Board Diagnostic OGL Open Government Licence PAYD Pay as You Drive Plan PEMS Portable Emissions Measurement System PHYD Pay How You Drive PREVIEW Portable Real-Time Emissions Vehicle Integrated Engineering Workstation PROLOGUE Promoting real Life Observations for Gaining Understanding of road user behaviour in Europe RPM Revolutions per Minute RTA Road Traffic Act 26 SA Selective Availability SAE Society of Automotive Engineers SARTRE Social Attitudes to Road Traffic Risk in Europe SMP Sustainable Mobility Project SWOT Strengths, Weaknesses, Opportunities, and Threats SWOV Netherlands Institute for Road Safety Research TRL Transport Research Laboratory TRRL Transport and Road Research Laboratory TWC Three-Way Catalyst UDC Urban Driving Cycle UTC Universal Time Coordinated VISSIM Microscopic Traffic Model software package VITO Flemish Institute for Technological Research VOEM VITO on-the-road emission and energy measurement system VSP Vehicle Specific Power VSP-SFC The Vehicle Specific Power-Specific Fuel Consumption VTI Swedish Road and Traffic Research Institute WBCSD World Business Council for Sustainable Development WHO World Health Organisation WLTC Worldwide Harmonised Light Duty Driving Test Cycle WLTP The Worldwide Harmonised Light Vehicles Test Procedures WMW Wilcoxon-Mann-Whitney U test method Ford Focus Peugeot Bipper Tepee Citroen C1-1.0 VTi Touch 3dr Volkswagen Golf Renault Twingo Dacia Sandero-1.2 Access 5dr
The many reasons your mileage may vary: Toward a unifying typology of eco-driving behaviors
Transportation Research Part D: Transport and Environment, 2017
The role of vehicle driver behavior has been ignored in prior energy and environmental policy making. Laboratory procedures that produce the fuel economy estimates posted on every new car sold in the US are designed to preclude the effects of differences between drivers. Yet, every vehicle states the caveat, ''Actual results will vary for many reasons, including driving conditions and how you drive and maintain your vehicle." Eco-driving as means of strategically taking advantage of this variability has been inconsistently defined in conceptual analyses and variously operationalized in empirical analyses. The present research clarifies, synthesizes, and expands on prior definitions of eco-driving to develop a comprehensive and precise definition and typology of eco-driving behaviors. The resultant typology includes six mutually exclusive classes of behavior: driving, cabin comfort, trip planning, load management, fueling, and maintenance. This typology establishes a basis for systematic research to determine energy and climate impacts and develop effective policies and interventions for different types of eco-driving.
Using statistical models to characterize eco-driving style with an aggregated indicator
2012 IEEE Intelligent Vehicles Symposium, 2012
This paper presents the construction of an aggregated indicator of a fuelefficient driving style, in order to construct an efficient Ecological Driving Assistance System (EDAS). Such an eco-index can be used to detect eco-driving behaviour, but also to give to the driver useful advices to help him improving his driving efficiency without deteriorating safety. The logistic regression is used to model our experimental dataset of twenty subjects driving twice the same route: normally or following the golden rules of eco-driving. Depending on some driving indicators, the estimated probability of being an eco-driver is used as an eco-index to characterize that driving pattern. This work show how such a simple aggregated indicator, related to driving dynamics rather than fuel consumption, can be useful for driver monitoring and information. Two models, from the simplest to the most complicated, are compared, and their performances analysed.
Influence of driver characteristics on emissions and fuel consumption
Transportation Research Procedia, 2017
Fuel consumption and atmospheric pollution emissions of vehicles depend on driving conditions, the characteristics of the driver and the car. The influence of driving style on the environmental aspects of a car journey has been investigated. Driver characteristics were determined by a Driver Behaviour Questionnaire and observed acceleration and deceleration behaviour. That results in four types of drivers with similar characteristics within a type group. We measured 56 trajectories of 28 drivers using GPS devices. The measurements were done on a route of 8.4 km in an urban environment in Chengdu (PR China). From the trajectories, the emissions and fuel consumption were determined with the Comprehensive Modal Emissions Model. The results were related to the traffic control along the journey resulting in fuel consumption and emissions per stop and per second idling. There are significant differences in saturation flow, emissions and fuel consumption between different driver types. Cautious, novice drivers have the lowest emission and fuel consumption and give the lowest saturation flow and have the lowest cruise speed; experienced smooth driving drivers give a high saturation flow while keeping fuel consumption and emissions also low. Aggressive experienced drivers have a high saturation flow and fuel consumption / emissions. Therefore, microscopic traffic models that simulate emissions and fuel consumption should take the differences between driver types into account.
Assessing the impact of driving behavior on instantaneous fuel consumption
2015 12th Annual IEEE Consumer Communications and Networking Conference (CCNC), 2015
Despite the recent technological improvements in vehicles and engines, and the introduction of better fuels, road transportation is still responsible for air pollution in urban areas due to the increasing number of circulating vehicles, and their relative travelled distances. We develop a methodology to calculate, in real-time, the consumption and environmental impact of spark ignition and diesel vehicles from a set of variables such as Engine Fuel Rate, Speed, Mass Air Flow, Absolute Load, and Manifold Absolute Pressure, all of them obtained from the vehicle's Electronic Control Unit (ECU). Our platform is able to assist drivers in correcting their bad driving habits, while offering helpful recommendations to improve fuel economy. In this paper we will demonstrate through data mining, to what extent does the driving style really affect (negatively or positively) the fuel consumption, as well as the increase or reduction of greenhouse gas emissions generated by vehicles.
Environmental Sciences Europe, 2020
Background Divergence in fuel consumption (FC) between the type-approval tests and real-world driving trips, known also as the FC gap, is a well-known issue and Europe is preparing the field for tackling it. The present study focuses on the monitoring of the FC of a single vehicle throughout 1 year with 20 different drivers and almost 14,000 km driven with the aim to analyze and quantify the true intrinsic variability in the FC gap coming from environmental and traffic conditions and driving factors. In addition, the regression model has been developed to evaluate the importance of these different factors on the FC gap’s variability. Results The 1-year FC gap measured in this study was 29% while driver’s averages were in the range from 16 to 106%. The regression model developed had R^{2}$$R2 equal to 90.4 meaning that more than 90% of the FC gap’s variance can be explained with this model and factors measured in this study. The results of the model showed that among all factors an...
2000
The transportation sector is the dominant source of U.S. fuel consumption and emissions. Specifically, highway travel accounts for nearly 75 percent of total transportation energy use and slightly more than 33 percent of national emissions of EPA's six Criteria pollutants. Enactment of the Clean Air Act Amendment of 1990 (CAAA) and the Intermodal Surface Transportation Efficiency Act of 1991 (ISTEA) have changed the ways that most states and local governments deal with transportation problems. Transportation planning is geared to improve air quality as well as mobility. It is required that each transportation activity be analyzed in advance using the most recent mobile emission estimate model to ensure not to violate the Conformity Regulation. Several types of energy and emission models have been developed to capture the impact of a number of factors on vehicle fuel consumption and emissions. Specifically, the current state-ofpractice in emission modeling (i.e. Mobile5 and EMFAC7) uses the average speed as a single explanatory variable. However, up to date there has not been a systematic attempt to quantify the impact of various travel and driver-related factors on vehicle fuel consumption and emissions. This thesis first systematically quantifies the impact of various travel-related and driver-related factors on vehicle fuel consumption and emissions. The analysis indicates that vehicle fuel consumption and emission rates increase considerably as the number of vehicle stops increases especially at high cruise speed. However, vehicle fuel consumption is more sensitive to the cruise speed level than to vehicle stops. The aggressiveness of a vehicle stop, which represents a vehicle's acceleration and deceleration level, does have an impact on vehicle fuel consumption and emissions. Specifically, the HC and CO emission rates are highly sensitive to the level of acceleration when compared to cruise speed in the range of 0 to 120 km/h. The impact of the deceleration level on all MOEs is relatively small. At high speeds the introduction of vehicle stops that involve extremely mild acceleration levels can actually reduce vehicle emission rates. Consequently, the thesis demonstrated that the use of average speed as a sole explanatory variable is inadequate for estimating vehicle fuel consumption and emissions, and the addition of speed variability as an explanatory variable results in better models. Second, the thesis identifies a number of critical variables as potential explanatory variables for estimating vehicle fuel consumption and emission rates. These explanatory variables include the average speed, the speed variance, the number of vehicle stops, the acceleration noise associated with positive acceleration and negative acceleration noise, the kinetic energy, and the power exerted. Statistical models are developed using these critical variables. The statistical models predict the vehicle fuel consumption rate and emission rates of HC, CO, and NO x (per unit of distance) within an accuracy of 88%-96% when compared to instantaneous microscopic models (Ahn and Rakha, 1999), and predict emission rates of HC, CO, and NO x within 95 percentile confidence limits of chassis dynamometer tests conducted by EPA. I would like to give my special thanks to my former advisor Dr. Michel Van Aerde, who I respect sincerely and will be in my memory for my whole life, for his generous guidance, advice, and help.
Green Driver: Travel Behaviors Revisited on Fuel Saving and Less Emission
Road transportation is the main energy consumer and major contributor of ever-increasing hazardous emissions. Transportation professionals have raised the idea of applying the green concept in various areas of transportation, including green highways, green vehicles and transit-oriented designs, to tackle the negative impact of road transportation. This research generated a new dimension called the green driver to remediate urgently the existing driving assessment models that have intensified emissions and energy consumption. In this regard, this study aimed to establish the green driver's behaviors related to fuel saving and emission reduction. The study has two phases. Phase one involves investigating the driving behaviors influencing fuel saving and emission reduction through a systematic literature review and content analysis, which identified twenty-one variables classified into four clusters. These clusters included the following: (i) FE f1 , which is driving style; (ii) FE f2 , which is driving behavior associated with vehicle transmission; (iii) FE f3 , which is driving behavior associated with road design and traffic rules; and (iv) FE f4 , which is driving behavior associated with vehicle operational characteristics. The second phase involves validating phase one findings by applying the Grounded Group Decision Making (GGDM) method. The results of GGDM have established seventeen green driving behaviors. The study conducted the Green Value (GV) analysis for each green behavior on fuel saving and emission reduction. The study found that aggressive driving (GV = 0.16) interferes with the association between fuel consumption, emission and driver's personalities. The research concludes that driver's personalities (including physical, psychological and psychosocial characteristics) have to be integrated for advanced in-vehicle driver assistance system and particularly, for green driving accreditation.
Driving style and traffic measures-influence on vehicle emissions and fuel consumption
Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 2004
This paper describes the influence on vehicle emissions and energy consumption of different vehicle parameters and driving style as well as of traffic measures taken in order to increase transport safety or to reduce traffic jams. This should allow the Flemish Regional Government to perform more realistic modelling of the impact of transport on air pollution. The methodology is based on on-road measurements, roll-bench emission tests, vehicle simulations and regional emission modelling (for the Flemish Region, which encompasses the northern part of Belgium and is one of three entities that constitute the Federal Kingdom of Belgium). A vehicle simulation programme (VSP) has assisted in the assessment of the individual vehicle parameters (weight, gear shifts, tyre pressure, etc.). Different drive styles (sportive, EcoDriving, etc.) were measured on-road and evaluated on a roll-bench. Typical speed profiles corresponding to different traffic measures such as roundabouts, phased traffic...
Review of Driving Behavior Towards Fuel Consumption and Road Safety
2018
One of the main concerns for automobile researchers is establish a driving method that is efficient to the engine and ensures road safety. Most studies categorized driver’s behavior based on aggressiveness while driving. It was found that aggressive drivers tend to provoke fast start and quick acceleration, driving at high engine revolution, and causing sudden speed change that are prone to road accidents. On the other hand, eco-driving which consists of gentle acceleration, coast-down deceleration, maintaining a steady speed and avoidance of high speed is much safer than the aggressive driving. At the same time smooth, experienced pattern in the eco-driving consumes lesser fuel in a statistically significant way. Driver’s aid system in modern cars have been invented to assist the driver into eco-driving were claimed to be effective. However, in the absence or failure of such system, drivers are suggested to drive by maintaining steady speed, avoiding sudden stop and harsh accelerat...