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Estimating instant fuel consumption by machine learning and improving fuel consumption

Year 2021, Volume: 4 Issue: 3, 54 - 60, 30.12.2021

Abstract

Modern cars are very technologically advanced and rely on sensors and actuators which communicate with control units, therefore it becomes possible to obtain data by using the vehicle sensor data from the controller area network (CAN) bus. Due to its bus structure, it is possible to reach real-time detailed data from sensors inside the vehicle such as O2 sensor voltage, fuel pressure, catalyst temperature etc. This study aims to predict the instantaneous fuel consumption by collecting a large-scale vehicle sensors' data and create a model with machine learning algorithms with the goal of better understand how the multiple variables influence the instantaneous fuel consumption.With this predictive model, it is better understood how the variables obtained from the sensors affect the instantaneous fuel consumption and it is proposed to reduce the fuel consumption between 1% and 2% by interfering with the intake air temperature information. This approach and the experiments can also support original equipment manufacturers in developing and marketing this technology in the future. This work may lead the way to a cleaner environment due to more economical and less polluting vehicles.

References

  • Pheanis, David & Tenney, Jeffrey. (2003). Vehicle-Bus Interface with GMLAN for Data Collection.. 88-92.
  • Huybrechts, Thomas , Vanommeslaeghe, Yon , Blontrock, Dries , Van Barel, Gregory , Hellinckx, Peter. (2018). Automatic Reverse Engineering of CAN Bus Data Using Machine Learning Techniques. 751-761. 10.1007/978-3-319-69835-971
  • U. Fugiglando et al., Driving Behavior Analysis through CAN Bus Data in an Uncontrolled Environment in IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 2, pp. 737-748, Feb. 2019.
  • Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz,R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020). From local explanations toglobal understanding with explainable ai for trees.Nature Machine Intelligence,2(1), 2522–5839.
  • Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting modelpredictions. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus,S. Vishwanathan, & R. Garnett (Eds.)Advances in Neural Information ProcessingSystems 30, (pp. 4765–4774). Curran Associates, Inc.
  • Lundberg, S. M., Nair, B., Vavilala, M. S., Horibe, M., Eisses, M. J., Adams, T.,Liston, D. E., Low, D. K.-W., Newman, S.-F., Kim, J., et al. (2018). Explainablemachine-learning predictions for the prevention of hypoxaemia during surgery.Nature Biomedical Engineering,2(10), 749.
  • Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). ”why should I trust you?”:Explaining the predictions of any classifier.CoRR,abs/1602.04938.

Anlık yakıt tüketiminin makine öğrenmesi ile tahmin edilerek iyileştirilmesi

Year 2021, Volume: 4 Issue: 3, 54 - 60, 30.12.2021

Abstract

Günümüz araçları bir çok sensör ile donatılmış olup bu sensörlerin birbiriyle haberleşebildiği kontrol ünitelerine sahiptir. Bu sebeple merkezi haberleşme veriyolundaki (Can) araç sensörü verilerine ulaşmak mümkün hale gelmiştir. Bu protokol sayesinde, aracın içinde bulunan sensörler vasıtası ile hava yakıt karışımı oranı, yakıt basıncı, katalizatör sıcaklığı gibi ayrıntılı verilere ulaşılabilmektedir. Bu çalışma, makine öğrenme algoritmaları ile büyük ölçekli araç sensör verilerinin toplanması sonrasında anlık yakıt tüketimini tahmin etmeyi amaçlamaktadır. Oluşturulan model sayesinde sensörlerden elde edilen değişkenlerin anlık yakıt tüketimini nasıl etkilediği daha iyi anlaşılarak emiş hava sıcaklığı bilgisinin yakıt tasarrufuna olan etkisi analiz edilmiştir. Oluşturulan model sayesinde emiş hava sıcaklığı bilgilerne müdahale ederek %1 ile %2 arasında yakıt tüketimi azaltması sağlanması ön görülmüştür. Bu yaklaşım gelecekte araç üreticilerinin yakıt tüketimini azaltma amaçlı yapmış oldukları çalışmaları destekleyerek markete yeni yeknolojiler kazandırabilir. Bu sayede daha ekonomik ve daha az karbon salınımı yapılarak daha temiz bir çevreye sahip olabiliriz.

References

  • Pheanis, David & Tenney, Jeffrey. (2003). Vehicle-Bus Interface with GMLAN for Data Collection.. 88-92.
  • Huybrechts, Thomas , Vanommeslaeghe, Yon , Blontrock, Dries , Van Barel, Gregory , Hellinckx, Peter. (2018). Automatic Reverse Engineering of CAN Bus Data Using Machine Learning Techniques. 751-761. 10.1007/978-3-319-69835-971
  • U. Fugiglando et al., Driving Behavior Analysis through CAN Bus Data in an Uncontrolled Environment in IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 2, pp. 737-748, Feb. 2019.
  • Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz,R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020). From local explanations toglobal understanding with explainable ai for trees.Nature Machine Intelligence,2(1), 2522–5839.
  • Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting modelpredictions. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus,S. Vishwanathan, & R. Garnett (Eds.)Advances in Neural Information ProcessingSystems 30, (pp. 4765–4774). Curran Associates, Inc.
  • Lundberg, S. M., Nair, B., Vavilala, M. S., Horibe, M., Eisses, M. J., Adams, T.,Liston, D. E., Low, D. K.-W., Newman, S.-F., Kim, J., et al. (2018). Explainablemachine-learning predictions for the prevention of hypoxaemia during surgery.Nature Biomedical Engineering,2(10), 749.
  • Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). ”why should I trust you?”:Explaining the predictions of any classifier.CoRR,abs/1602.04938.
There are 7 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Ahmet Teoman Naskali

Buğra Şen

Publication Date December 30, 2021
Published in Issue Year 2021 Volume: 4 Issue: 3

Cite

APA Naskali, A. T., & Şen, B. (2021). Estimating instant fuel consumption by machine learning and improving fuel consumption. Veri Bilimi, 4(3), 54-60.



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