ANN-Based Modeling of Engine Performance from Dynamometer Sensor Data

Accurate prediction of the performance of an internal combustion engine is an essential step towards achieving efficiency and complying with emission standards. This study presents an artificial neural network (ANN) model that uses sensor-derived parameters, such as design power, wheel power, torque, and rotational speed, to predict engine load. Data were collected from a dynamometer and a hardware-in-the-loop (HiL) setup to ensure realistic, sensor-based measurements. The proposed ANN architecture achieved high accuracy (99%) in multiclass classification and strong regression performance (R2≈0.98), demonstrating its ability to model complex engine load relationships under normal operating conditions. Performance was validated using 5-fold stratified cross-validation, achieving an average accuracy of 0.988±0.011, macro-F1 of 0.984±0.011, and regression R2 of 0.962±0.052, confirming strong generalization and robustness. The model can be extended to include additional sensor inputs and adapted for use with other powertrain systems, allowing it to be used in a range of automotive and industrial applications.