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http://dx.doi.org/10.3745/KTSDE.2021.10.2.57

A Modeling of Realtime Fuel Comsumption Prediction Using OBDII Data  

Yang, Hee-Eun (단국대학교 EduAI센터)
Kim, Do-Hyun ((주)한국축산데이터)
Choe, Hoseop (단국대학교 EduAI센터)
Publication Information
KIPS Transactions on Software and Data Engineering / v.10, no.2, 2021 , pp. 57-64 More about this Journal
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.
Keywords
Fuel Consumption; Prediction Model; Stacking Ensemble; Regression Model; OBDII;
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