Feasibility Study of Fuel Consumption Prediction Model by Integrating Vehicle-Specific Power and Controller Area Network Bus Technology

2013 ◽  
Vol 2341 (1) ◽  
pp. 66-75 ◽  
Author(s):  
Yizheng Wu ◽  
Lei Yu ◽  
Guohua Song ◽  
Long Xu
Keyword(s):  
Prediction Model ◽  
Fuel Consumption ◽  
Feasibility Study ◽  
Controller Area Network ◽  
Specific Power ◽  
Area Network ◽  
Consumption Prediction

scholarly journals A Novel Hybrid Fuel Consumption Prediction Model for Ocean-Going Container Ships Based on Sensor Data

2021 ◽  
Vol 9 (4) ◽  
pp. 449
Author(s):  
Zhihui Hu ◽  
Tianrui Zhou ◽  
Mohd Tarmizi Osman ◽  
Xiaohe Li ◽  
Yongxin Jin ◽  
...  
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Fuel Consumption ◽  
Hybrid Model ◽  
Predictive Performance ◽  
Optimization Method ◽  
Bayesian Optimization ◽  
Sensor Data ◽  
Single Model ◽  
Consumption Prediction

Accurate, reliable, and real-time prediction of ship fuel consumption is the basis and premise of the development of fuel optimization; however, ship fuel consumption data mainly come from noon reports, and many current modeling methods have been based on a single model; therefore they have low accuracy and robustness. In this study, we propose a novel hybrid fuel consumption prediction model based on sensor data collected from an ocean-going container ship. First, a data processing method is proposed to clean the collected data. Secondly, the Bayesian optimization method of hyperparameters is used to reasonably set the hyperparameter values of the model. Finally, a hybrid fuel consumption prediction model is established by integrating extremely randomized tree (ET), random forest (RF), Xgboost (XGB) and multiple linear regression (MLR) methods. The experimental results show that data cleaning, the size of the dataset, marine environmental factors, and hyperparameter optimization can all affect the accuracy of the model, and the proposed hybrid model provides better predictive performance (higher accuracy) and greater robustness (smaller standard deviation) as compared with a single model. The proposed hybrid model should play a significant role in ship fuel consumption real-time monitoring, fault diagnosis, energy saving and emission reduction, etc.

Analysis of Vehicles' Daily Fuel Consumption Frontiers with Long-Term Controller Area Network Data

2015 ◽  
Vol 2503 (1) ◽  
pp. 100-109 ◽  
Author(s):  
Dawei Li ◽  
Tomio Miwa ◽  
Takayuki Morikawa
Keyword(s):  
Fuel Consumption ◽  
Stochastic Frontier ◽  
Random Coefficients ◽  
Controller Area Network ◽  
Network Data ◽  
Area Network ◽  
Linear Regression Models ◽  
Explanatory Variables ◽  
Modeling Methodology

The vehicle fuel consumption frontier (VFCF) is the unobserved maximum amount of fuel that an individual private car user is willing to consume for driving. This study incorporated interindividual and intraindividual variations into the modeling of VFCF. Long-term controller area network data collected from private cars during 10 months in Toyota City, Japan, were used. A stochastic frontier model with random parameters was applied as the modeling methodology to deal with the panel data. The data fit of the estimation results demonstrated that models with random coefficients were preferable and had better model fits than the ordinary linear regression models. VFCFs on working days were significantly affected by the departure time of the first trip, temperature, weather, home location, gender, age, and occupation. All explanatory variables, except weather and temperature, also significantly affected VFCFs on holidays. Predictions made with the estimated parameters showed that the expected VFCFs were about double the corresponding actual vehicle fuel consumption expenditures.

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