Machine learning based prediction of automotive engine fuel consumption

Abstract

Accurate prediction of automotive fuel consumption is important for improving vehicle design, reducing operating costs, and supporting energy-efficient transportation systems. Conventional empirical and statistical approaches often fail to capture the complex nonlinear relationships among vehicle technical characteristics. This study presents a comparative evaluation of four state-of-the-art ensemble machine learning algorithms, namely Random Forest (RF), Light Gradient Boosting Machine (LGB), XGBoost (XGB), and CatBoost (CGB), for predicting vehicle fuel economy using the publicly available UCI Auto MPG dataset, comprising 398 vehicle samples characterized by seven technical attributes. The models were optimized using Grid search optimization (GSO) and evaluated through an 80:20 training-testing split combined with five-fold cross-validation. Model performance was assessed using the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Model interpretability was investigated using SHapley Additive exPlanations (SHAP) to quantify the influence of individual vehicle attributes. Among the evaluated algorithms, CGB achieved the best predictive performance with a testing R² of 0.918, RMSE of 0.048, MAE of 0.035, and MAPE of 9.1%, demonstrating excellent generalization capability. SHAP analysis revealed that vehicle weight, engine displacement, horsepower, and model year were the most influential variables affecting fuel economy predictions. The study demonstrates that optimized ensemble learning methods can accurately model complex relationships between vehicle specifications and fuel economy while providing interpretable predictions. Although the findings are based on a benchmark dataset representing vehicles manufactured between 1970 and 1982, the proposed comparative framework provides a transparent and reproducible methodology that can be extended to larger and more contemporary automotive datasets.