• Journal of Biosystems Engineering
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• v.35 no.1
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• pp.1-9
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• 2010

A mathematical model was developed to predict the fuel consumption rate consumed by agricultural tractors under arbitrary loaded conditions. The model utilizes the measured data on the fuel consumptions at the full load and at the rated engine speed with partial loads, which can easily be obtained from the official OECD tractor test reports. It was found from the analysis of the measured fuel consumption data that the fuel consumptions at two different speeds does not change with power. The model was developed based on this fact and validated with the measured data of the 159 tractor test reports. The fuel consumptions predicted by the model were compared with those measured under the partially loaded conditions specified in the official OECD tractor test code II. The percent errors of the predicted fuel consumptions were in a range from 0.36 to 2.86% which assured that the developed fuel consumption model can be used practically to predict the fuel consumptions at any speed and power combinations. It was also shown that the developed model predicts the fuel consumption rate better than the Grisso's model.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.4
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• pp.48-56
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• 2014

Even though vehicle types, gradient, pavement conditions and types of pavement should be considered for estimating fuel consumption, existing models were developed as a function of vehicle types and vehicle speed. Therefore in this study, the model of fuel consumption was developed using field test data in order that effect analysis on the passenger vehicle fuel consumption by road gradient. At first, fuel consumption was measured in second-based, using GPS device and fuel consumption measurement device for development of fuel consumption model considered road gradient. The road gradient was classified as flatland, up-hill and down-hill. Development of model was using by regression model which vehicle speed(km/h) and fuel consumption(${\ell}/km$). The on-road test proved that fuel consumption of passenger vehicle is affected by road gradient.

• Journal of the Korean Society of Mechanical Technology
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• v.13 no.3
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• pp.17-23
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• 2011

To estimate fuel consumption of a vehicle, a car can be tested on chassis dynamometer. In this case, test causes a lot of time and money. To predict the fuel efficiency of vehicles in the design stage or early stage of development, the development of computer simulation model is necessary. Using simulation to predict the fuel consumption, the driving model which consists of time-velocity profile and time-grade profile is necessary In this study, vehicle model is developed in MatLab/simulink to estimate real driving fuel consumption rate with time-velocity profile, time-shift gear profile and time-grade profile. Vehicle model consists of driver model, engine model, power train model, and so on. On-road vehicle tests to verify the vehicle model are carried out for analyzing the result of simulation and comparing with those of the experiments.

• Journal of ILASS-Korea
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• v.22 no.2
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• pp.47-54
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• 2017

$CO_2$ emission regulation will be prescribed and main issue in automotive industry. Mostly, vehicle's fuel efficiency deeply related to $CO_2$ emission is regulated by qualified driving test cycle by using chassis dynamometer and exhaust gas analyser. But, real driving fuel consumption rate depends so much on the individual usage profile and where it is being driven: city traffic, road conditions. In this study, vehicle model of fuel consumption rate for ICEV and PHEV was developed through co-simulation with CRUISE model and Simulink based on driving control model. The simulation results of fuel consumption rate were analysed with on-road vehicle data and compared with its official level.

• Transactions of the Korean Society of Automotive Engineers
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• v.23 no.3
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• pp.326-335
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• 2015

In this paper, is a study that analyzed the fuel consumption of hybrid tractor. Testing and analysis in order to evaluate the fuel consumption was performed. Analysis model was developed by using the SimulationX that is a commercial software. Also, map of the analysis model was modeled on the basis of test data. Test was performed using a dynamo device. The engine was tested the fuel consumption in accordance with the conditions on the load and throttle opening. The battery was tested the discharge and charge in accordance with the current amount. We verified the reliability of the analysis model by comparing the analysis results with the rest results. After considering the reliability of each analysis model was extended to the entire hybrid tractor system. To evaluate the efficiency using the analysis model, compared the fuel consumption of general tractor with hybrid tractor in the same load conditions.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.28 no.2
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• pp.29-35
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• 2020

In this paper, we established a prediction model of fuel consumption at the aircraft's taxi operation. To look for countermeasures to reduce fuel consumption and carbon emissions, Airbus A380's actual ground taxi data was used. As a result, the number of stops or turnings during the taxi operation was not related to fuel consumption. It was confirmed that the amount of fuel consumption in the taxi operation was the taxi time and the thrust change. It can be confirmed that ground control optimization, which is the result of close cooperation between the control organization and the airline, is absolutely necessary to reduce taxi time and minimize the occurrence of thrust change events.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.6_2
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• pp.949-956
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• 2020

It's important to measure fuel consumption of vehicles. It's possible to monitor green house gas from vehicles for various traffic conditions with the measured data. It's effective to eco-drive for drivers with fuel consumption data also. There's a display of fuel consumption in the modern vehicles, but it's not useful to get the data from the display. An estimating method for fuel consumption of a vehicle is suggested in the study. It's a simple but an effective method using GPS data. The GPS data(speed, acceleration, road slope) and vehicle data(weight, frontal area, model year, certified fuel economy) is necessary to estimate the fuel consumption for the method. It calculates driving resistance force to estimate engine power. Then it estimates the necessary fuel consumption to maintain the engine power with fuel-power conversion factor. The conversion factor is corrected with certified fuel economy, model year and rated power. The precision of the methods is checked with road test data. The test driving data was measured with GPS and OBD. The error of the estimated fuel consumption for the measured one is about 1.8%. But the error is large for the 1000 and 100 data number from the total data number of about 10,000. The error is from the larger change range of the GPS data than the one of the measured fuel consumption data. But the proposed estimating method is useful to percept the fuel consumption change for better fuel economy with simple gadget like smart phone or other GPS instruments.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.6
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• pp.14-20
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• 2015

The Korea ministry of land, infrastructure and transport set the goal of cutting greenhouse gas emissions from the transport sector by 34.3% relative to the business as usual scenario by 2020. In order to achieve this goal, support is being given to education and information regarding eco-driving. As a practical measure, however, a vehicle control strategy for decreasing fuel consumptions and emissions is necessary. Therefore, this paper presents an optimized driving model in order to decrease fuel consumption. Scenarios were established by driving mode. The speed profile for each scenario applied to Comprehensive Modal Emission Model and then each fuel consumption was estimated. Scenarios and speed variation with the least fuel consumption were derived by comparing the fuel consumptions of scenarios. The optimized driving model was developed by the derived the results. The speed profiles of general driver were collected by field test. The speed profile of the developed model and the speed profile of general driver were compared and then fuel consumptions for each speed profile were analyzed. The fuel consumptions for optimized driving were decreased by an average of 11.8%.

• Journal of Korean Society of Transportation
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• v.29 no.4
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• pp.65-71
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• 2011

It is practically hard to measure vehicle fuel consumption required to evaluate the energy-related governmental policies and traffic management strategies; the existing methods are too simplified due to the limited field data available. Existing methods are even unable to reflect the amount of fuel consumed when vehicles accelerate and decelerate, and such technical limitations have reduced the quality of the policy evaluation. This study proposes a new fuel consumption model that simultaneously considers the effects of both cruising speed and acceleration/deceleration of vehicles. A new fuel consumption model was developed based on the simulation data generated by AVL Cruise, a vehicle simulation program. The estimated by the proposed model was compared against the one from the existing method. Comparison results showed that the proposed model provided much reliable estimate (fuel consumption) than the other did.

• Environmental and Resource Economics Review
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• v.20 no.2
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• pp.335-356
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• 2011

In this paper, we attempted to suggest a way to evaluate appropriateness and efficiency for the energy consumption structure. For this, based on Markowitz (1952)' mean-variance portfolio model, we constructed an optimal fossil fuel mix. In constructing the optimal mix, we first defined returns on fossil fuels (oil, coal and natural gas) as TOE (Ton of Oil Equivalent) per $1. Then, by using the dynamic latent common factor model, we decomposed the growth rates of the returns on each fossil fuel into two parts : the common part and the idiosyncratic part. Finally, based on the results from the dynamic latent common factor model, we constructed the optimal fossil fuel mix implied by the mean-variance portfolio model. Our results indicate that for the fossil fuel mix to be on the efficient frontier, it is crucial to reduce oil consumption as low as possible. Moreover, our results imply that it is more efficient to increase natural gas consumption rather than coal consumption in reducing oil consumption. These results are in line with the strategies for the future energy consumption structure pursued by Korea and indicate that reduction in oil use can improve overall efficiency in energy consumption.