Commenced in January 2007
Paper Count: 31103
Comparing Machine Learning Estimation of Fuel Consumption of Heavy-Duty Vehicles
Abstract:Fuel consumption (FC) is one of the key factors in determining expenses of operating a heavy-duty vehicle. A customer may therefore request an estimate of the FC of a desired vehicle. The modular design of heavy-duty vehicles allows their construction by specifying the building blocks, such as gear box, engine and chassis type. If the combination of building blocks is unprecedented, it is unfeasible to measure the FC, since this would first r equire the construction of the vehicle. This paper proposes a machine learning approach to predict FC. This study uses around 40,000 vehicles specific and o perational e nvironmental c onditions i nformation, such as road slopes and driver profiles. A ll v ehicles h ave d iesel engines and a mileage of more than 20,000 km. The data is used to investigate the accuracy of machine learning algorithms Linear regression (LR), K-nearest neighbor (KNN) and Artificial n eural n etworks (ANN) in predicting fuel consumption for heavy-duty vehicles. Performance of the algorithms is evaluated by reporting the prediction error on both simulated data and operational measurements. The performance of the algorithms is compared using nested cross-validation and statistical hypothesis testing. The statistical evaluation procedure finds that ANNs have the lowest prediction error compared to LR and KNN in estimating fuel consumption on both simulated and operational data. The models have a mean relative prediction error of 0.3% on simulated data, and 4.2% on operational data. Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 35
 E. Union, Simulation tool for heavy duty vehicles (hdvs), https://ec.europa.eu/clima/policies/transport/vehicles/ vecto en, 2020. (visited on 04/27/2020).
 L. Ekstr¨om, “Estimating fuel consumption using regression and machine learning,” Master’s thesis, KTH, School of Engineering Sciences, 2018.
 H. Alm´er, “Machine learning and statistical analysis in fuel consumption prediction for heavy vehicles,” Master’s thesis, KTH, School of Computer Science and Communication (CSC), 2015.
 F. Perrotta, T. Parry, and L. C. Neves, “Application of machine learning for fuel consumption modelling of trucks,” in 2017 IEEE International Conference on Big Data (Big Data), IEEE, 2017, pp. 3810–3815.
 N. Hong and L. Li, “A data-driven fuel consumption estimation model for airspace redesign analysis,” in 2018 IEEE/AIAA 37th Digital Avionics Systems Conference. DOI: 10.1109/DASC.2018.8569564.
 S. Wickramanayake and H. M. N. Dilum Bandara, “Fuel consumption prediction of fleet vehicles using machine learning: A comparative study,” in 2016 Moratuwa Engineering Research Conference (MERCon), pp. 90–95. DOI: 10.1109/MERCon.2016.7480121.
 G. James, T. Hastie, R. Tibshirani, and D. Witten, An Introduction to Statistical Learning: With Applications in R. Springer, 2017, (Online). Available: https://faculty.marshall.usc.edu/gareth-james/ISL/.
 R. Rojas, Neural Networks A Systematic Introduction. Springer, 1996, (Online). Available: http://page.mi.fu-berlin.de/rojas/neural/index.html.html.
 I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016.
 D. P. Kingma and J. Ba, Adam: A method for stochastic optimization, 2014. arXiv: 1412.6980.
 S. Varma and R. Simon, “Bias in error estimation when using cross-validation for model selection,” BMC bioinformatics, vol. 7, no. 1, p. 91, 2006.
 J. Demˇsar, “Statistical comparisons of classifiers over multiple data sets,” J. Mach. Learn. Res., vol. 7, pp. 1–30, Dec. 2006, ISSN: 1532-4435.
 J. D. Li, “A two-step rejection procedure for testing multiple hypotheses,” Journal of Statistical Planning and Inference, vol. 138, no. 6, pp. 1521–1527, 2008.
 H. W. Lilliefors, “On the kolmogorov-smirnov test for normality with mean and variance unknown,” Journal of the American Statistical Association, vol. 62, no. 318, pp. 399–402, 1967. DOI: 10 . 1080 / 01621459 . 1967 . 10482916.
 P. B. Brazdil and C. Soares, “A comparison of ranking methods for classification algorithm selection,” in European conference on machine learning, Springer, 2000, pp. 63–75.
 D. Shepard, “A two-dimensional interpolation function for irregularly-spaced data,” in Proceedings of the 1968 23rd ACM National Conference, ser. ACM ’68, New York, NY, USA: Association for Computing Machinery, 1968, pp. 517–524, ISBN: 9781450374866.
 T. Mitchell, Machine Learning, ser. McGraw-Hill International Editions. McGraw-Hill, 1997, ISBN: 9780071154673.
 B. Trawinski, M. Smetek, Z. Telec, and T. Lasota, “Nonparametric statistical analysis for multiple comparison of machine learning regression algorithms,” International Journal of Applied Mathematics and Computer Science, vol. 22, Jan. 2012. DOI: 10.2478/v10006-012-0064-z.