Comparing Machine Learning Estimation of Fuel Consumption of Heavy-duty Vehicles
Abstract: Fuel consumption is one of the key factors in determining expenses of operating a heavy-duty vehicle. A customer may therefor request an estimate of the fuel consumption of a given vehicle. Scania uses modular design when constructing heavy-duty vehicles. The modular design allows a customer to specify which building blocks to use when constructing the vehicle, such as gear box, engine and chassis type. The many possible combinations means that the same vehicle is rarely sold twice, which can make fuel consumption measurements unfeasible. This study investigates the accuracy of machine learning algorithms in predicting fuel consumption for heavy-duty vehicles. The study is conducted at Scania. Scania has also provided the data used in the study. This study also examines the prediction power of different parameters. Performance is evaluated by reporting the prediction error on both simulated data and operational measurements. The performance of Linear regression (LR), K-nearest neighbor (KNN) and Artificial neural networks (ANN) is compared using statistical hypothesis testing. It is found that using Country as an input parameter yields a performance increase in all the algorithms. 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 performance of the final models is comparable to models of previous studies in both the simulated and operational estimation scenarios.
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