Forecasting Fuel Consumption Based-On OBD II Data

  • Satrio Nurcahya Telkom University
  • Bayu Erfianto Telkom University
  • Setyorini Setyorini Telkom University
Abstract views: 224 , 659 downloads: 199
Keywords: RPM, TPS, fuel consumption, OBD-II, forecast

Abstract

Cyber Physical System consists of computing devices that communicate with each other by interacting with the physical world assisted by sensors and actuators with an iterative response. Intelligent Transportation System which aims to apply information and communication technology in every transportation area. Applying ITS to vehicles, especially in the aspect of fuel consumption, vehicles must begin to be able to analyze the use of fuel that is being used to provide users so that users can be more effective. Regarding the analysis of fuel consumption, several researchers have done this with several existing methods such as ANN, SVM and the like. The use of the Multivariate time series method is used as a solution to the forecast analysis of vehicle fuel consumption. In this study, data from vehicles obtained from OBD-II will be processed using the Multivariate time series method with output in the form of analysis and visual data from the forecast with parameters related to RPM, TPS and fuel consumption. So the expected result is the relationship between RPM, TPS and fuel consumption as well as the formation of a system model to obtain sample data related to RPM, TPS and fuel consumption.

Downloads

Download data is not yet available.

References

[1] A. Perallos, U. Hernandez-Jayo, E. Onieva, and I. Garcia-Zuazola, Intelligent Transport Systems Technologies and Applications. WILEY, 2016.
[2] R. I. Meneguette, R. E. De Grande, and A. A. F. Loureiro, Intelligent Transport System in Smart Cities. 2018.
[3] A. Sobota, M. J. Klos, and G. Karo, Intelligent Transport Systems and Travel Behaviour, vol. 505. 2017.
[4] L. Paoli, “Fuel Consumption of Cars and Vans – Analysis,” Iea, 2020. https://www.iea.org/reports/fuel-consumption-of-cars-and-vans (accessed Nov. 25, 2021).
[5] T. Abukhalil, H. Almahafzah, M. Alksasbeh, and B. A. Y. Alqaralleh, “Fuel Consumption Using OBD-II and Support Vector Machine Model,” J. Robot., vol. 2020, 2020, doi: 10.1155/2020/9450178.
[6] K. WITASZEK, “Modeling of fuel consumption using artificial neural networks,” Diagnostyka, vol. 21, no. 4, pp. 103–113, 2020, doi: 10.29354/diag/130610.
[7] X. Ji, H. Zhang, J. Li, X. Zhao, S. Li, and R. Chen, “Multivariate time series prediction of high dimensional data based on deep reinforcement learning,” E3S Web Conf., vol. 256, pp. 0–3, 2021, doi: 10.1051/e3sconf/202125602038.
[8] R. Alur, Principles of Cyber-Physical Systems. 2015.
[9] S. A. Nugroho, E. Ariyanto, and A. Rakhmatsyah, “Utilization of Onboard Diagnostic II (OBD-II) on four wheel vehicles for car data recorder prototype,” 2018 6th Int. Conf. Inf. Commun. Technol. ICoICT 2018, vol. 0, no. c, pp. 7–11, 2018, doi: 10.1109/ICoICT.2018.8528741.
[10] E. Spiliotis, V. Assimakopoulos, and K. Nikolopoulos, “Forecasting with a hybrid method utilizing data smoothing, a variation of the Theta method and shrinkage of seasonal factors,” Int. J. Prod. Econ., vol. 209, pp. 92–102, 2019, doi: 10.1016/j.ijpe.2018.01.020.
[11] H. Guimarães, V. Silva, L. C. Sales, A. Maia, and B. Murta, “An OBD-II based vehicular data tracking system for fuel consumption and emissions improvement,” no. June 2021, 2018, doi: 10.26678/abcm.cobem2017.cob17-1451.
[12] Y. J. Pan, T. C. Yu, and R. S. Cheng, “Using OBD-II data to explore driving behavior model,” Proc. 2017 IEEE Int. Conf. Appl. Syst. Innov. Appl. Syst. Innov. Mod. Technol. ICASI 2017, pp. 1816–1818, 2017, doi: 10.1109/ICASI.2017.7988297.
[13] Elm Electronics Inc., “ELM327 OBD to RS232 Interpreter,” pp. 1–5, 2014, [Online]. Available: https://www.elmelectronics.com/wp-content/uploads/2016/07/ELM327DS.pdf.
[14] K. Meißner and J. Rieck, “Multivariate Forecasting of Road Accidents Based on Geographically Separated Data,” Vietnam J. Comput. Sci., vol. 8, no. 3, pp. 433–454, 2021, doi: 10.1142/S2196888821500196.
[15] G. E. P. BOX, G. M. JENKINS, G. C. REINSEL, and G. M. LJUNG, TIME SERIES ANALYSIS Forecasting and Control Fifth Edition. 2016.
[16] C. Huang, "Research on the linkage relationship between different levels of money supply and economic growth based on VAR model," in 2020 2nd International Conference on Economic Management and Model Engineering (ICEMME), Chongqing, 2020.
[17] R. Wang, "Research on the Relationship between China’s Import and Export Trade and Confidence Index–Dynamic Analysis based on VAR Model," in 2020 2nd International Conference on Economic Management and Model Engineering (ICEMME), Chongqing, 2020.
[18] G. Qingying, Z. Yanjie and C. Zheang, "Application of VAR Model based on Distributed Least Squares Estimation Algorithm," in 2020 International Conference on Information Science, Parallel and Distributed Systems (ISPDS), Xi'an, 2020.
Published
2022-08-01
How to Cite
Nurcahya, S., Erfianto, B., & Setyorini, S. (2022). Forecasting Fuel Consumption Based-On OBD II Data. Indonesia Journal on Computing (Indo-JC), 7(2), 93-102. https://doi.org/10.34818/INDOJC.2022.7.2.659
Section
Information Technology