• 한국방재학회:학술대회논문집
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• 2010.02a
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• pp.45.1-45.1
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• 2010

포장도로의 노화로 인해 도로 표면의 평탄성이 높아지면 차량 주행 시 연료소모량이 증가하는 것으로 알려져 있다. 본 연구에서는 소형/중형/대형의 3개 승용차량에 대해 3가지 평탄도의 도로에서 40~100km/h 의 정속주행 연료소모량을 측정하여 도로 평탄성의 변화에 따른 연료소모량의 변화율을 계산할 수 있었다. 시험결과, 평탄성 증가에 따라 연료소모량이 직선적으로 증가하였으며, 평탄성에 대한 l차 직선방정식으로 연료소모량을 표현할 수 있었다. 평탄성 1m/km 증가 시 연료소모량은 약 80mL/100km 정도의 비율로 증가함을 알 수 있었다. 추후 본 시험의 결과를 이용하여 도로 노화에 따른 연료소모량 증가의 정도를 추정하여 다양한 도로 복구 작업 등에 이용하여 도로에서 발생할 수 있는 사고 예방 등에 활용할 수 있을 것으로 판단된다.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.12
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• pp.2582-2589
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• 2011

This Paper proposed the prediction method of fuel consumption from vehicle diagnosis informations through OBD-II Interface. We assumed mass air flow (MAF), shor-term fuel trim (STFT), and long-term fuel trim (LTFT) had a relationship with fuel consumption. We got the output as fuel-consumption from MAF, STFT, and LTFT as input variables. We had modelling as combustion reaction equation with OBD-II data and fuel consumption data supported by automotive company in real. In order to verify the effectiveness of proposed method, 5 km real road-test was performed. The results showed that the proposed method can estimate precisely the fuel consumption from vehicle data.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2015.11a
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• pp.201-202
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• 2015

과거 연료소모량과 오염물질 배출량을 추정하기 위한 연구에서는 주로 속도변수를 이용하였으나, 속도의 변화에 따른 연료소모량 및 오염물질 배출량의 변화를 올바르게 반영하지 못하는 문제점이 대두되었다. 이러한 문제점을 극복할 수 있는 대안으로 평가받는 것이 가속도이다. 이처럼 가속도 변수가 중요하게 다루어지고 있으나 여전히 연료소모량이나 오염물질 배출량과 관련하여 급가속을 판단할 만한 기준이 모호하다. 이에 본 연구에서는 연료소모 및 $CO_2$ 배출량을 증가시켜 급가속으로 판단할 수 있는 가속도 임계치를 추정하고자 하였다. 가속도 임계치 및 모형추정을 위해 LPG 중형 승용차량에 장착한 차량 정보 저장장치로부터 가속 주행실험시 수집한 실시간 데이터를 수집 분석하였다. 가속의 특성상 동일한 가속도라 할지라도 정지상태인지 여부에 따라 동일한 가속도에 대한 연료소모량, $CO_2$ 배출량이 상이하게 나타난다. 따라서 실험을 통해 정지상태에서 가속시 관성을 극복하기 위한 동력이 요구되는 속도의 범위를 확인하고 이중 출발 가속주행시 임계가속도를 도출하였다. 가속 주행실험 결과 연료소모 및 $CO_2$ 배출 증가량이 급격히 증가되는 임계가속도를 도출하기 위해 CART 분석을 이용하였으며, 그 결과 정지 상태에서 가속하는 경우 $2.598m/s^2$, 의 가속도가 연료 및 $CO_2$ 배출량을 크게 증가시키는 임계 가속도인 것으로 추정되었다.

• International Journal of Highway Engineering
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• v.12 no.1
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• pp.55-59
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• 2010

High VOC(Vehicle Operating Cost) is the main reason for the rehabilitation of paved road and VOC is composed of fuel consumption, lubricant oil consumption, parts consumption, etc. Fuel consumption is one of the largest components of VOC and the roughness of road represents the deterioration level of the road. For these reasons, the fuel consumption is measured for different IRI(International Roughness Index) in this study. The fuel consumption was measured by processing the voltage signal of fuel injector of vehicle and the speed was measured with GPS. The change rate of fuel consumption for different IRI can be calculated with the results of this test. It's concluded that fuel consumption(L/100km) of medium and large passenger car increases 7 times fast of the increase of IRI(m/km) around 3.5m/km in the speed range of 40 ~ 100km/h, and fuel consumption is the best at 60km/h.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.13-20
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• 2022

It's proposed and analyzed ML(Machine Learning) models to predict vehicle FC(Fuel Consumption) in real-time. The test driving was done for a car to measure vehicle speed, acceleration, road gradient and FC for training dataset. The various ML models were trained with feature data of speed, acceleration and road-gradient for target FC. There are two kind of ML models and one is regression type of linear regression and k-nearest neighbors regression and the other is classification type of k-nearest neighbors classifier, logistic regression, decision tree, random forest and gradient boosting in the study. The prediction accuracy is low in range of 0.5 ~ 0.6 for real-time FC and the classification type is more accurate than the regression ones. The prediction error for total FC has very low value of about 0.2 ~ 2.0% and regression models are more accurate than classification ones. It's for the coefficient of determination (R²) of accuracy score distributing predicted values along mean of targets as the coefficient decreases. Therefore regression models are good for total FC and classification ones are proper for real-time FC prediction.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.6D
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• pp.749-754
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• 2011

It is common that basic unit and model of fuel consumption have been used to evaluate effectiveness analysis of transportation infrastructure investment programs. However they could not reflect vehicle characteristics such as loading capacity and types of heavy vehicles. For these reasons, this study reviews convention fuel consumption model which is widely used and conducts a field experiment for 5 classes of heavy vehicles. To develop the fuel consumption quadratic model the field data are used and we develop each model by classes, and then compare with convention fuel consumption model. As a result, between convention and suggested model, there are considerable differences, which have a similar pattern between an 11-ton cargo of convention model and a 25-ton cargo type dump truck of the suggested model. Likewise we identify that there is an approximately 26% gap between convention model result and the result which is calculated a weighted average by registered number of heavy vehicles based on 5 types of fuel consumption model suggested in this study. This result implies that convention fuel assumption model has a realistic limitation.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.2
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• pp.9-14
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• 2011

This Paper proposed the prediction method of fuel consumption from vehicle informations through OBD-II Interface. We assumed RPM, TPS had a relationship with fuel consumption. We got the output as fuel-consumption from a vehicle RPM, TPS as input by using polynomial equation. We had modelling as quadric function with OBD-II data and fuel consumption data supported by automotive company in real. In order to verify the effectiveness of proposed method, 5 km real road-test was performed. The results showed that the proposed method can estimate precisely the fuel consumption from vehicle multi-data.

• Journal of Korean Society of Transportation
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• v.33 no.5
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• pp.449-460
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• 2015

The reliability of air quality evaluation results for green transportation could be improved by applying correct emission factors. Unlike previous studies, which estimated emission factors that focused on vehicles in laboratory experiments, this study investigates emission factors according to road types and time using real driving data. The real driving data was collected using a Portable Activity Monitoring System (PAMS) according to road types and time, which it compared and analyzed fuel consumption from collected data. The result of the study shows that fuel consumption on national highway is 17.33% higher than the fuel consumption on expressway. In addition, the average fuel consumption of peak time is 4.7% higher than that of non-peak time for 22.5km/h. The difference in fuel consumption for road types and time is verified using ANOCOVA and MANOVA. As a result, the hypothesis of this study - that fuel consumption differs according to road types and time, even if the travel speed is the same - has proved valid. It also suggests correction factor of emission factors by using the difference in fuel consumption. It is highly expected that this study can improve the reliability of emissions from mobile pollution sources.

• 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%.