Eco-efficient driving pattern evaluation for sustainable road transport based on contextually enriched automotive data

Vdović, Hrvoje; Babić, Jurica; Podobnik, Vedran

doi:10.1016/j.jclepro.2021.127564

Cited by 8 publications

(2 citation statements)

References 30 publications

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“…It is easy to get the dashboard FE data of various cars, but it is hard to connect VBOX to the OBD port and to set VBOX to extract the FC data. It is very difficult to get the FC data for cars whose OBD signal protocol is not open [21]. That is why there are just two reference vehicles with VBOX-measured FC data and eight test cars with dashboard-displayed FE data.…”

Section: Driving Test Resultsmentioning

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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Section: Driving Test Resultsmentioning

confidence: 99%

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

Lee

Jeong³

2021

Sustainability

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“…The consequences of distracted driving are far-reaching, contributing to a substantial number of deadly accidents, injuries, and fatalities annually. In addition, it also amplifies the carbon footprint associated with transportation due to inefficiencies caused by sudden braking and erratic driving behavior [3], [4]. Yet, avoiding accidents and minimizing harsh driving behaviors can extend the lifespan of a vehicle, reducing the overall environmental impact of the automotive industry.…”

Section: Introductionmentioning

confidence: 99%