Impact of drivers on real-driving fuel consumption and emissions performance

Huang, Yuhan; Ng, Elvin C.Y.; Zhou, John L.; Surawski, Nicholas C.; Lü, Xingcai; Du, Bo; Forehead, Hugh; Perez, Pascal; Chan, Eric

doi:10.1016/j.scitotenv.2021.149297

Cited by 25 publications

(6 citation statements)

References 34 publications

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“…Comparing different sensory devices in dynamic guidance, it is shown that haptic touch feedback is less distractive with higher users’ acceptance than auditory and visualization devices. In terms of drivers’ characteristics, inexperienced and unprofessional drivers are more likely to adopt driving suggestions and change their behaviour, although exceptions exist (see [ 104 ]). Socio-demographic factors are associated with the adaptiveness of the eco-driving guidance, while the influence is varied, and the intentions are unclear.…”

Section: Discussionmentioning

confidence: 99%

Effective and Acceptable Eco-Driving Guidance for Human-Driving Vehicles: A Review

et al. 2022

IJERPH

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Eco-driving guidance refers to courses, warnings, or suggestions provided to human drivers to improve driving behaviour to enable less energy use and emissions. This paper reviews existing eco-driving guidance studies and identifies challenges to tackle in the future. We summarize two categories of current guidance systems, static and dynamic, distinguished by whether real-world driving records are used to generate behaviour guidance or not. We find that influencing factors, such as the content of suggestions, the display methods, and drivers’ socio-demographic characteristics, have varied effects on the guidance results across studies. Drivers are reported to have basic eco-driving knowledge, while the question of how to motivate the acceptance and practice of such behaviour, especially in the long term, is overlooked. Adaptive driving suggestions based on drivers’ individual habits can improve the effectiveness and acceptance while this field is under investigation. In-vehicle assistance presents potential safety issues, and visualized in-vehicle assistance is reported to be most distractive. Given existing studies focusing on the operational level, a common agreement on the guidance design and associated influencing factors has yet to be reached. Research on the systematic and tactical design of eco-driving guidance and in-vehicle interaction is advised.

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Section: Discussionmentioning

confidence: 99%

Effective and Acceptable Eco-Driving Guidance for Human-Driving Vehicles: A Review

et al. 2022

IJERPH

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“…However, fuel injection stops when the fuel-cut is activated. The fuel-cut is a basic function adopted in most modern vehicles [14,15]. It is live when the gas pedal is released with the gear engaged in drive position and engine speed kept higher than about 1200 rpm.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Method for Monitoring Vehicle Energy Consumption with GPS Data

Lee

Jeong³

2021

Sustainability

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A monitoring method for energy consumption of vehicles is proposed in the study. It is necessary to have parameters estimating fuel economy with GPS data obtained while driving in the proposed method. The parameters are trained by fuel consumption data measured with a data logger for the reference cars. The data logger is equipped with a GPS sensor and OBD connection capability. The GPS sensor measures vehicle speed, acceleration rate and road gradient. The OBD connector gathers the fuel consumption signaled from OBD port built in the car. The parameters are trained by a 5-layer deep-learning construction with input data (speed, acceleration, gradient) and labels (fuel consumption data) in the typical classification approach. The number of labels is about 6–8 and the number of neurons for hidden layers increases in proportionate to the label numbers. There are about 160–200 parameters. The parameters are calibrated to consider the wide range of fuel efficiency and deterioration degree in age for various test cars. The calibration factor is made from the certified fuel economy and model year taken from the car registration form. The error range of the estimated fuel economy from the measured value is about −6% to +7% for the eight test cars. It is accurate enough to capture the vehicle dynamics for using the input and output data in point-to-point classification style for training steps. Further, it is simple enough to hit fuel economy of the other test cars because fuel economy is a kind of averaged value of fuel consumption for the time period or driven distance for monitoring steps. You can predict or monitor energy consumption for any vehicle with the GPS-measured speed/acceleration/gradient data by the pre-trained parameters and calibration factors of the reference vehicles according to fuel types such as gasoline, diesel and electric. The proposed method requires just a GPS sensor that is cheap and common, and the calculating procedure is so simple that you can monitor energy consumption of various vehicles in real-time with ease. However, it does not consider weight, weather and auxiliary changes and these effects will be addressed in the future works with a monitoring service system under preparation.

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“…In the U.S., traffic consumes 28% of total energy [1] and emits 29% of total emissions [2] . Recent studies show that the energy consumption and carbon emissions of a vehicle are greatly influenced by road contexts [3] , [4] and driving behavior [5] , [6] . Although the driver experience is an important factor that influence the fuel consumption rate of a vehicle, it is crucial to anticipate surrounding road traffic conditions and driving states (including information on curvatures and surface conditions) to drive in an efficient-efficient manner.…”

Section: Introductionmentioning

confidence: 99%

Optimal eco-driving scheme for reducing energy consumption and carbon emissions on curved roads

Bakibillah,

Kamal,

Tan

et al. 2024

Heliyon

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Impact of drivers on real-driving fuel consumption and emissions performance

Cited by 25 publications

References 34 publications

Effective and Acceptable Eco-Driving Guidance for Human-Driving Vehicles: A Review

Effective and Acceptable Eco-Driving Guidance for Human-Driving Vehicles: A Review

A Deep Learning Method for Monitoring Vehicle Energy Consumption with GPS Data

Optimal eco-driving scheme for reducing energy consumption and carbon emissions on curved roads

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