Neural Modelling of CO2 Emissions from a Selected Vehicle

The article addresses the problem of modelling instantaneous CO2 emissions from a specific motor vehicle equipped with an internal combustion engine. The article’s concept is based on a review of current research, which led to the identification of a list of variables essential to constructing the numerical model. Data was collected through recording selected vehicle parameters during test drives using the OBD diagnostic interface. The article describes the model development process and presents the results of CO2 emission modelling using MLP neural networks. The performance of various architectures was examined, considering the number of hidden layers (1, 2, 3), the number of neurons in each layer (from 10 to 267), and different activation functions (such as sigmoid, hyperbolic tangent, and ReLU). Consistency in the values of the MSE, RMSE, and MAE indicators during both validation and testing phases demonstrates the accuracy of the models. High R2 values of around 0.8 confirm that MLP networks can effectively model CO2 emissions from motor vehicles.