A Modeling of Realtime Fuel Comsumption Prediction Using OBDII Data


KIPS Transactions on Software and Data Engineering, Vol. 10, No. 2, pp. 57-64, Feb. 2021
https://doi.org/10.3745/KTSDE.2021.10.2.57,   PDF Download:
Keywords: Fuel Consumption, Prediction Model, Stacking Ensemble, Regression Model, OBDII
Abstract

This study presents a method for realtime fuel consumption prediction using real data collected from OBDII. With the advent of the era of self-driving cars, electronic control units(ECU) are getting more complex, and various studies are being attempted to extract and analyze more accurate data from vehicles. But since ECU is getting more complex, it is getting harder to get the data from ECU. To solve this problem, the firmware was developed for acquiring accurate vehicle data in this study, which extracted 53,580 actual driving data sets from vehicles from January to February 2019. Using these data, the ensemble stacking technique was used to increase the accuracy of the realtime fuel consumption prediction model. In this study, Ridge, Lasso, XGBoost, and LightGBM were used as base models, and Ridge was used for meta model, and the predicted performance was MAE 0.011, RMSE 0.017.


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Cite this article
[IEEE Style]
H. Yang, D. Kim, H. Choe, "A Modeling of Realtime Fuel Comsumption Prediction Using OBDII Data," KIPS Transactions on Software and Data Engineering, vol. 10, no. 2, pp. 57-64, 2021. DOI: https://doi.org/10.3745/KTSDE.2021.10.2.57.

[ACM Style]
Hee-Eun Yang, Do-Hyun Kim, and Hoseop Choe. 2021. A Modeling of Realtime Fuel Comsumption Prediction Using OBDII Data. KIPS Transactions on Software and Data Engineering, 10, 2, (2021), 57-64. DOI: https://doi.org/10.3745/KTSDE.2021.10.2.57.