A prototype fuel-efficiency support tool

Voort, Mascha C. van der; Dougherty, Mark; Maarseveen, Martin van

doi:10.1016/s0968-090x(00)00038-3

Cited by 133 publications

(68 citation statements)

References 5 publications

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“…Concerning the eco-driving advisory, The University of Twente (Netherlands) in collaboration with the School of Transportation and Society (Sweden) have developed a fuel-efficiency support tool capable of performing real-time control of consumption and providing both positive and negative video feedback while driving. The support tool includes the so-called normative model, which back-calculates the minimal fuel consumption for maneuvers carried out [28]. Similarly, [29] developed an Acceleration Advisor (AA) able to signal the driver, causing a resistance on the gas pedal if s/he is accelerating too quickly.…”

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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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%

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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“…Studies suggest that simply asking drivers to drive economically can reduce fuel consumption by 10-15% (van der Voort et al, 2001;Waters and Laker, 1980). When drivers are asked to drive more efficiently, they generally interpret this as to drive slower.…”

Section: Green Drivingmentioning

confidence: 99%

“…This system benefitted following vehicles, resulting in not only faster reaction times to the braking event, but also a smoother deceleration thus offers theoretical improvements in fuel consumption. Moreover, there is evidence that such advice could influence both current behaviour as well as potentially inducing longer term behavioural adaptations (van der Voort et al, 2001;Waters and Laker, 1980). …”

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confidence: 99%

Safe driving in a green world: A review of driver performance benchmarks and technologies to support ‘smart’ driving

2011

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Road transport is a significant source of both safety and environmental concerns.With climate change and fuel prices increasingly prominent on social and political agendas, many drivers are turning their thoughts to fuelenvironmentally friendly) driving practices. Many vehicle manufacturers are satisfying this demand by offering green driving feedback or advice tools. However, there is a legitimate concern regarding the effects of such devices on road safety both from the point of view of change in driving styles, as well as potential distraction caused by the in-vehicle feedback. In this paper, we appraise the benchmarks for safe and green driving, concluding that whilst they largely overlap, there are some specific circumstances in which the goals are in conflict. We go on to review current and emerging in-vehicle information systems which purport to affect safe and/or green driving, and discuss some fundamental ergonomics principles for the design of such devices. The results of the review are being used in the Foot-LITE project, aimed at that is safe and green driving.

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“…Timing of gear changes [6]-not a focus in the present study due to the current market dominance of automatic transmission vehicles. [6]-metric defined as the following distance from one car to another divided by the speed difference (when the second car is overtaking the first).…”

mentioning

confidence: 99%

“…[6]-metric defined as the following distance from one car to another divided by the speed difference (when the second car is overtaking the first). The study observed that drivers following an efficiency regimen tended to have fewer instances of small TTC values.…”

mentioning

confidence: 99%

Analyzing Vehicle Fuel Saving Opportunities through Intelligent Driver Feedback

Gonder¹,

Earleywine²,

Sparks³

2012

SAE Int. J. Passeng. Cars - Electron. Electr. Syst.

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While it is well known that "MPG will vary" based on how one drives, little independent research exists on the aggregate fuel savings potential of improving driver efficiency and on the best ways to motivate driver behavior changes. This paper finds that reasonable driving style changes could deliver significant national petroleum savings, but that current feedback approaches may be insufficient to convince many people to adopt efficient driving habits. To quantify the outer bound fuel savings for drive cycle modification, the project examines completely eliminating stop-and-go driving plus unnecessary idling, and adjusting acceleration rates and cruising speeds to ideal levels. Even without changing the vehicle powertrain, such extreme adjustments result in dramatic fuel savings of over 30%, but would in reality only be achievable through automated control of vehicles and traffic flow. Considering the effects of real-world driving conditions, efficient driving behaviors could reduce fuel use by 20% on aggressively driven cycles and by 5-10% on more moderately driven trips.To evaluate potential receptiveness to changing driving habits, the project team conducted a literature survey of driver behavior influences and observed pertinent factors from on-road experiments with different driving styles. This effort highlighted important driver influences such as surrounding vehicle behavior, anxiety over trying to get somewhere quickly, and the power/torque available from the vehicle. Existing feedback approaches often effectively deliver efficiency information and instruction, but do not always do so in an easy way that avoids unintended consequences and helps trump other driving behavior influences. Based on these findings the report details three recommendations for maximizing fuel savings from potential drive cycle improvement: (1) Leverage applications with enhanced incentives, (2) Use an approach that makes it easy and is widely-deployable to motivated drivers, and (3) Utilize connected vehicle and automation technologies to achieve large and widespread efficiency improvements.

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A prototype fuel-efficiency support tool

Cited by 133 publications

References 5 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

Safe driving in a green world: A review of driver performance benchmarks and technologies to support ‘smart’ driving

Analyzing Vehicle Fuel Saving Opportunities through Intelligent Driver Feedback

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