An Economic Calibration Method for Fuel Consumption Model in HDM4

Ko, Kwang-Ho; Moon, Byung-Koo; Lee, Tong-Won; Lee, Wonho; Yoo, Ink-Yoon; Lee, Soohyung; Han, Daeseok; Jeong, Seung-Hyun

doi:10.1007/s11277-016-3353-2

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…There are two categories in these fields. The first one is the mechanical or physics-based method using the driving resistance forces [11][12][13]. The force consists of four resistance terms.…”

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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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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“…Among the existing models which have been proposed to estimate fuel consumption rate, the HDM-4 fuel consumption model is one of the most widely utilized. Many studies have used this model and then carried out calibration, which is a necessary step in this methodology [5,6]. The accuracy of the HDM-4 fuel consumption model and the need for further calibration were discussed in [7].…”

Section: Literature Reviewmentioning

confidence: 99%

Estimation of Real-World Fuel Consumption Rate of Light-Duty Vehicles Based on the Records Reported by Vehicle Owners

et al. 2021

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Private vehicle travel is the most basic mode of transportation, so that an effective way to control the real-world fuel consumption rate of light-duty vehicles plays a vital role in promoting sustainable economic growth as well as achieving a green low-carbon society. Therefore, the factors impacting individual carbon emissions must be elucidated. This study builds five different models to estimate the real-world fuel consumption rate of light-duty vehicles in China. The results reveal that the light gradient boosting machine (LightGBM) model performs better than the linear regression, naïve Bayes regression, neural network regression, and decision tree regression models, with a mean absolute error of 0.911 L/100 km, a mean absolute percentage error of 10.4%, a mean square error of 1.536, and an R-squared (R2) value of 0.642. This study also assesses a large pool of potential factors affecting real-world fuel consumption, from which the three most important factors are extracted, namely, reference fuel-consumption-rate value, engine power, and light-duty vehicle brand. Furthermore, a comparative analysis reveals that the vehicle factors with the greatest impact are the vehicle brand, engine power, and engine displacement. The average air pressure, average temperature, and sunshine time are the three most important climate factors.

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“…This study classifies the sentiments of words and assesses the sentiment weights by analyzing the sentiments of a large volume of lexical resources collected from social media. The paper by Ko et al [16] proposes an economic calibration method for a fuel consumption model in HDM4. This study is a fuel-consumption testing method for quick and easy determination of the calibration factors in HDM4 vehicle operating cost calculation.…”

mentioning

confidence: 99%

Convergence Interaction for Communication

Jeong¹,

Ryu

2016

Wireless Pers Commun

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Welcome to this special issue of Wireless Personal Communications. This issue contains a collection of the best papers out of various authors who have been submitted to this issue. The main goal for this issue is to be a timely vehicle for publishing selected research papers from practitioners and academia in convergence industries on this emerging topic. This issue covers some of the hottest topics in Convergence Interaction for Communication, including: convergence; interactive applications; cloud service; P2P architectures and wireless protocols; data and index structures; motion recognition; future communication system; hybrid networking system; spectrum resource allocation; personal networking and architectures; multiple access techniques; multicasting and computer communications.The paper by Mun et al. [1] proposes the injecting of a subject policy into access control to strengthen the protection of personal information. This study provides two confidential access control models that apply individually established policies to the role-based access control model (RBAC) and the mandatory access control model (MAC) technologies. In the SpRBAC model, a user's right to access would follow organizational policy, and accessing personal information would be restricted by the subject policy. In the SpMAC model, users would have to satisfy the subject policy established by the provider of the information, in addition to the requirements of the normal MAC policy. The paper by Lee et al.[2] presents variability change management using orthogonal variability model-based traceability. The proposed method is an approach to tracing variability based on explicit & Joong-Kyung Ryu

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An Economic Calibration Method for Fuel Consumption Model in HDM4

Cited by 3 publications

References 8 publications

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

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

Estimation of Real-World Fuel Consumption Rate of Light-Duty Vehicles Based on the Records Reported by Vehicle Owners

Convergence Interaction for Communication

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