KIPS Transactions on Software and Data Engineering (정보처리학회논문지:소프트웨어 및 데이터공학)

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

A Modeling of Realtime Fuel Comsumption Prediction Using OBDII Data

OBDII 데이터 기반의 실시간 연료 소비량 예측 모델 연구

  • Received : 2020.07.08
  • Accepted : 2020.08.25
  • Published : 2021.02.28
Download PDF
⟨ Previous Next ⟩

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.

자율주행차 시대가 도래하면서 ECU (Electronic Control Unit)는 점차 고도화되고 있고, 이에 따라 차량에서 정확한 데이터를 추출하고 분석하려는 연구가 다양하게 시도되어 왔다. 그러나 ECU는 차량 제조사별로 상이한 프로토콜을 가지고 있어 상용 단말기로는 정확한 데이터 추출과 분석이 어렵다. 본 연구에서는 정확한 차량 데이터를 추출하기 위하여 전용 펌웨어를 개발하여 차량의 2019년 1월부터 2월의 실제 주행데이터 53,580건의 데이터를 추출하였으며, 20회가 넘는 실제 도로 주행을 통해서 데이터의 정확도를 검증하였다. 이러한 데이터를 바탕으로 실시간 연료 소비량 예측 모델의 정확도를 높이기 위하여 스태킹 앙상블 기법을 이용하였다. 본 연구에서는 베이스 모델로 Ridge, Lasso, XGBoost, LightGBM이 사용되고 메타 모델은 Ridge가 사용되었으며, 예측 성능은 MAE 0.011, RMSE 0.017로 최적의 결과를 보였다.

Keywords

References

  1. T. Lee, J. Jung, J. Kang, H. Choi, and J. Ko, "System for analyzing big data collected while driving a car," The Institute of Electronics and Information Engineers, pp.1367-1370, 2018.
  2. W. J. Lee and D. S. Ko, "Classification of the safe threats using clustering of vehicle OBD2 Data by road section type," Korean Institute of Information Technology, Vol.18, No.4, pp.1-8, 2020.
  3. G. Geraldo, "Differences between on board diagnostic systems (EOBD, OBD-II, OBD-BR1 and OBD-BR2)," SAE Technical Paper 2006-01-2671, 2006.
  4. D. Rimpas, A. Papadakis, and M. Samarakou, "OBD-II sensor diagnostics for monitoring vehicle operation and consumption," Energy Reports, Vol.6, pp.55-63, 2020.
  5. T. H. DeFries, M. Sabisch, S. Kishan, F. Posada, J. German, and A. Bandivadekar, "In-use fuel economy and CO2 emissions measurement using OBD data on US light-duty vehicles," SAE International Journal of Engines, Vol.7, No.3, pp.1382-1396, 2014. https://doi.org/10.4271/2014-01-1623
  6. A. Aliyu and S. Adeshina, "Classifying auto-MPG data set using neural network," 2014 11th International Conference on Electronics, Computer and Computation (ICECCO), Abuja, pp.1-4, 2014.
  7. M. N. Jamala, and S. S. Abu-Naser, "Predicting MPG for automobile using artificial neural network analysis," Information Systems Research, Vol.2, No.10. pp.5-21, 2018.
  8. K. H. Ahn, A. G. Stefanopoulou, and M. Jankovic, "Tolerant ethanol estimation in flex-fuel vehicles during MAF sensor drifts," Proceedings of the ASME Dynamic Systems and Control Conference 2009, pp.581-588, 2009.
  9. S. Boverie, D. Dubois, X. Guerandel, O. de Mouzon, and H. Prade, "Online diagnosis of engine dyno test benches: A possibilistic approach," Proceedings of the 15th European Conference on Artificial Intelligence (ECAI'02), pp.658-662, 2002.
  10. D. Opitz and R. Maclin, "Popular ensemble methods: An empirical study," Journal of Artificial Intelligence Research, Vol.11, No.1, pp.169-198, 1999. https://doi.org/10.1613/jair.614
  11. J. Zaldivar, C. T. Calafate, J. C. Cano, and P. Manzoni, "Providing accident detection in vehicular networks through OBD-II devices and Android-based smartphones," 2011 IEEE 36th Conference on Local Computer Networks, Bonn, pp.813-819, 2011.
  12. FUNK, Tom. System and method for implementing added services for OBD2 smart vehicle connection. U.S. Patent, US10249103B2, 2019.
  13. De Schutter, B. and T. J. J. van den Boom, "Model predictive control for max-min-plus-scaling systems - efficient implementation," Sixth International Workshop on Discrete Event Systems, 2002 Proceedings, pp.343-348, 2002.
  14. W. Cho, "Big Data-Based Fuel Consumption Estimation Model using Actual On-road DTG Data and Spatial Data," Graduate School of Business IT, Kookmin University, 2016.
  15. B. Predic, M. Madic, M. Roganovic, M. Kovacevic, and D. Stojanovic, "Prediction of passenger car fuel consumption using artificial neural network: A case study in the city of Nis," Automatic Control and Robotics, Vol.15, No.2, pp.105-116, 2016.
  16. M. Lee, Y. Park, K. Jung, and J. Yoo, "Estimation of fuel consumption using in-vehicle parameters," International Journal of U- and E- Service, Science and Technology, Vol.4, No.4, pp.37-46, 2011.