Fuel consumption is one of the key factors of a heavy-duty vehicle when determining the expenses of operating the vehicle. A customer may therefor wish to estimate the fuel consumption of a desired 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, and the data provided by them. The usefulness of different parameters are examined. 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 previous studies in both estimation scenarios.