Eco-driving: Strategic, tactical, and operational decisions of the driver that influence vehicle fuel economy

Sivak, Michael; Schoettle, Brandon

doi:10.1016/j.tranpol.2012.05.010

Cited by 263 publications

(157 citation statements)

References 4 publications

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“…It is defined as a decision-making process which will influence the fuel economy and emissions intensity of a vehicle to reduce its environmental impact [8]. This technique can operate at network or single vehicle level.…”

Section: Problem Formulationmentioning

confidence: 99%

“…At the transportation network level, eco-driving acts by implementing proper instruments for the evaluation and implementation of strategies for the reduction of traffic congestion [9,10] and for optimum route choice in order to limit CO2 emission and fuel consumption (eco-route, [8]). Eco-driving at the single vehicle/driver level can be achieved through an understanding what primarily affects fuel consumption, and by developing ADAS that help drivers in adopting efficient driving styles in terms of emissions and energy consumption.…”

Section: Problem Formulationmentioning

confidence: 99%

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A Linear Model for the Estimation of Fuel Consumption and the Impact Evaluation of Advanced Driving Assistance Systems

et al. 2015

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Abstract:Reduction of the environmental impact of cars represents one of the biggest transport industry challenges. Beyond more efficient engines, a promising approach is to use eco-driving technologies that help drivers achieve lower fuel consumption and emission levels. In this study, a real-time microscopic fuel consumption model was developed. It was designed to be integrated into simulation platforms for the design and testing of Advanced Driving Assistance Systems (ADAS), aimed at keeping the vehicle within the environmentally friendly driving zone and hence reducing harmful exhaust gases. To allow integration in platforms employed at early stages of ADAS development and testing, the model was kept very simple and dependent on a few easily computable variables. To show the feasibility of the identification of the model (and to validate it), a large experiment involving more than 100 drivers and about 8000 km of driving was carried out using an instrumented vehicle. An instantaneous model was identified based on vehicle speed, acceleration level and gas pedal excursion, applicable in an extra-urban traffic context. Both instantaneous and aggregate validation was performed and the model was shown to estimate vehicle fuel consumption consistently with in-field instantaneous measurements. Very accurate estimations were also shown for the aggregate consumption of each driving session.

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Section: Problem Formulationmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

A Linear Model for the Estimation of Fuel Consumption and the Impact Evaluation of Advanced Driving Assistance Systems

et al. 2015

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show abstract

“…However, this simple definition includes many concepts and strategic actions, depending on the level of decision-making considered [11]:…”

Section: Introductionmentioning

confidence: 99%

Green Eco-Driving Effects in Non-Congested Cities

et al. 2017

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Despite technological advances in engines and fuels, the transportation sector is still one of the largest emitters of greenhouse gas (GHG). Driving patterns, including eco-driving techniques, are a complementary measure for saving GHG emissions. Most eco-driving studies so far have been conducted in large cities suffering chronic congestion problems. The aim of this research is therefore to analyse the potential of driver behaviour for reducing emissions in a small non-congested city. Driver performance parameters such as travel speeds, number of stops, revolutions per minute, and maximum acceleration-deceleration are also studied. The methodology is designed to measure the effect of both eco-driving and eco-routing under real traffic conditions. A campaign was carried out in the city of Caceres (Spain) to collect data on various types of roads under different traffic conditions. This research concludes that eco-driving leads to CO 2 savings on all routes and road types of 17% in gasoline engines and 21% in diesel, although travel times are increased by 7.5% on average. The shortest route is also the most ecological, regardless of the traffic volume and characteristics, implying that consumption in non-congested cities depends mainly on distance travelled rather than driving patterns in terms of number of stops, speed and acceleration.

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“…At the peak engine torque, the fuel efficiency is lower. However, at higher RPM (Revolutions per minute) (low engine torque), acceleration is maximized and thus, fuel efficiency is higher [63][64][65]. Nevertheless, quick acceleration is still recommended by experts, although this should be a smooth transition.…”

Section: Literature Content Analysis and Synthesizementioning

confidence: 99%

Green Driver: Travel Behaviors Revisited on Fuel Saving and Less Emission

et al. 2018

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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.

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Eco-driving: Strategic, tactical, and operational decisions of the driver that influence vehicle fuel economy

Cited by 263 publications

References 4 publications

A Linear Model for the Estimation of Fuel Consumption and the Impact Evaluation of Advanced Driving Assistance Systems

A Linear Model for the Estimation of Fuel Consumption and the Impact Evaluation of Advanced Driving Assistance Systems

Green Eco-Driving Effects in Non-Congested Cities

Green Driver: Travel Behaviors Revisited on Fuel Saving and Less Emission

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