Real-time vehicular fuel consumption estimation using machine learning and on-board diagnostics data

Abstract

Instantaneous fuel consumption estimation of fleet vehicles provides essential tools for fleet operation optimization and intelligent fleet management. This study aims to develop practical and accurate models to estimate instantaneous fuel consumption based on on-board diagnostics (OBD) data. Fuel consumption data is measured by a high-precision fuel flow meter. Two machine learning algorithms of Random Forest (RF) and Artificial Neural Networks (ANN) are trained with real-world urban and highway driving data of four fleet vehicles with different types and powertrain systems. In addition, the cold-start period of the vehicle operation is included to cover the fuel consumption penalty in the warm-up period. The validation results show that the RF method is more accurate than the ANN method, and both of the machine learning models have a better accuracy compared to the existing fuel consumption calculation methods based on the engine control unit (ECU) parameters.