• 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.

• 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.

• 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.

• 전자공학회논문지 IE
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• v.48 no.2
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• pp.19-25
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• 2011

This paper presented neural network modeling method using vehicle data to predict fuel consumption. To acquire data for training and testing the proposed neural network, medium-class gasoline vehicle drove at downtown and parameters measured include speed, engine rpm, throttle position sensor (TPS), and mass air flow (MAF) as input data, and fuel consumption as target data from OBD-II port. Multi layer perception network was used for nonlinear mapping between the input and the output data. It was observed that the neural network model can predict the vehicle quite well with mean squared error was $1.306{\times}10^{-6}$ for the fuel consumption.

• The Journal of the Korea Contents Association
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• v.19 no.7
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• pp.436-443
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• 2019

The purpose of this paper is to establish a predictive model by analyzing the influence and correlation of factors affecting the fuel consumption of unmanned helicopters in Captive Flight Test. In this study, a four-factor two-level full factorial experiment was designed and tested using the design of experiments, results were analyzed to derive the main effects and interactions of the factors, and the predictive model was established through regression analysis. It is expected that the results from this study contribute to carrying out Captive Flight Test efficiently and the improvement of the test capability of Electronic Testing Range.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2017.11a
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• pp.124-127
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• 2017

선박에서 배출되는 환경오염 물질 및 온실가스에 대한 규제가 강화됨에 따라, 환경오염 물질 및 온실가스의 배출과 직접적으로 관련있는 연료 소모량을 줄이려는 다양한 연구가 진행되고 있다. 연료 소모량을 줄이기 위한 방안 중 하나는 환경 및 기상 예보를 이용하여 연료가 가장 적게 소모되는 항로를 찾는 것이다. 기존 연구에서는 연료 소모량을 주된 목적함수로 최소화 하되, 도착 시간에 대한 조건을 평가하기 위해 도착 시간의 기댓값을 계산하고 추가적인 목적함수로 고려하는 경우가 많았다. 그러나 선박 운항 예측 시 적용되는 환경 외란 정보는 상당한 불확실성을 포함하고, 이로 인해 발생하는 운항 속도 및 도착 시간에 대한 불확실성도 상당히 클 수 있기 때문에, 도착 시간의 기댓값뿐만 아니라 도착 시간에 대한 불확실성을 기반으로 제한 시간 내에 선박이 도착할 확률을 정량적으로 평가하는 것이 필요하다. 본 연구에서는 다목적 최적화 기법을 이용해 도착 시간의 기댓값과 연료 소모량에 대한 Pareto set을 구하되, 환경 외란으로부터 발생하는 도착 시간의 불확실성을 계산하여, 제한 시간 내에 선박이 도착할 확률을 계산하고 이를 항로 최적화 시 적용한다. 제안하는 방법의 유용성을 검증하기 위해 실제 환경에 가까운 맵을 기반으로 부산-도쿄 간의 항로를 최적화하고, 그 결과에 대해 논의한다.

• Journal of Navigation and Port Research
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• v.47 no.4
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• pp.182-190
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• 2023

This study proposes a predictive model using XGBoost and SHapley Additive exPlanation (SHAP) to estimate fuel consumption in bulk carriers. Previous studies have also utilized ship engine data and weather data. However, they lacked reliability in predicted results and explanations of variables used in the fuel consumption prediction model implementation. To address these limitations, this study developed a predictive model using XGBoost and SHAP. It provides research background, scope, relevant regulations, previous studies, and research methodology. Additionally, it explains the data cleaning method for bulk carriers and verifies results of the predictive model.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2003.10a
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• pp.65-70
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• 2003

In this work variations of orbital parameters are first derived from the perturbation equations using difference equation method under Earth oblateness and atmospheric drag. A simple and effective scheme is proposed to compute the required delta v and fuel consumption to compensate for atmospheric drag. The scheme is applied to KOMPSAT example. And by means of numerical simulations we quantitatively analyze influences due to each perturbation source, i.e., nonspherical Earth, atmospheric drag, third body gravities (Sun, Moon), and solar radiation.

• Journal of Korean Society of Transportation
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• v.34 no.3
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• pp.234-247
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• 2016

As the necessity of the evaluation of environmentally-friendly traffic operations strategies becomes obvious, the characteristics of fuel consumption models should be comprehended in advance. This study selected three fuel consumption models developed in Korea and another three models widely used in North America, and compared their applicabilities. Specifically, the national institute of environmental research (NIER) drive modes and the VISSIM software were utilized to model various driving patterns, and their fuel consumptions were estimated using the fuel consumption models. Based on the results, all the models showed the similar results in the analysis of the most fuel efficient cruising speed. On the other hand, caution should be taken when using the KR-1 and KR-2 models in microscopic analyses because they are not sensitive to instantaneous power requirements of vehicles.

• Journal of Advanced Navigation Technology
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• v.3 no.2
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• pp.147-155
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• 1999

In this work variations of orbital parameters are derived from the perturbation equations under Earth oblateness and atmospheric drag. A simple and effective scheme is proposed to compute the required delta v and fuel consumption to compensate for atmospheric drag. The scheme is applied to KOMPSAT example.