Integrating experimental data and neural computation for emission forecasting in automotive systems

Magdalena Zimakowska-Laskowska, Olga Orynycz, Ewa Kulesza, Jonas Matijošius, Karol Tucki, Antoni Świć · 2025 · 2 citationsRead the paper

This work presents an integrated approach combining experimental testing and mathematical modeling to analyze fuel consumption and pollutant emissions in a spark-ignition engine vehicle.Experimental data were obtained from chassis dynamometer tests under the WLTP driving cycle, including time series of vehicle speed, energy consumption, and CO, CO, THC, and other compounds emissions.Two classes of artificial neural networks were implemented to capture the complex, nonlinear relationships between driving dynamics and emission profiles: Multi-layer perceptrons (MLP) and self-associative neural networks (SANN).These models were trained on real-world time series data to predict vehicle speed and energy consumption as functions of emission parameters and vice versa.The models demonstrated high accuracy, especially in the validation phase, confirming their potential for forecasting and environmental performance assessment.The neural network models underwent training, validation, and testing processes, allowing for the assessment of their effectiveness in predicting energy consumption under various system operating scenarios.The results demonstrated high prediction accuracy, confirming the usefulness of ANN as a tool for analyzing complex relationships between emissions and energy efficiency.The best-performing model achieved a mean absolute error (MAE) of 0.034 MJ/km and a coefficient of determination (R) of 0.91 for energy consumption prediction.The study developed models identifying the relationships between emission parameters and energy consumption characteristics, enabling precise modeling of combustion processes.The input data included key emission indicators such as carbon monoxide (CO), carbon dioxide (CO), and hydrocarbons (HC, NMHC and CH 4 ), as well as operational parameters of energy systems.Additionally, the observed patterns in energy use were interpreted through a physical lens, considering the thermodynamics and chemical kinetics of combustion processes under different driving conditions.This hybrid methodology -combining data-driven AI with domain-specific physical insight -provides a robust framework for predicting the environmental impacts of internal combustion engines and optimizing their operation.The proposed approach applies to broader engineering contexts, including emission control strategy design, digital twin development for powertrains, and intelligent vehicle energy management systems.The proposed approach represents a significant step toward leveraging modern artificial intelligence methods to improve energy efficiency and develop emission reduction strategies through combustion condition optimization.The obtained results can serve as a foundation for further refining industrial processes in the context of sustainable development and environmental protection.

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