Application of artificial intelligence in emission prediction for hybrid electric vehicles: integrating ANN and GPR

Application of artificial intelligence in emission prediction for hybrid electric vehicles: integrating ANN and GPR

Artificial neural networks
Emission prediction
Gaussian process regression
Hybrid electric vehicles
Prediction uncertainty
Vehicle emissions

In recent years, hybrid electric vehicles (HEVs) have emerged as a promising solution to mitigate vehicular emissions and improve fuel efficiency. This study focuses on the Toyota Prius HEV, employing advanced artificial neural networks (ANN) and Gaussian process regression (GPR) to develop a predictive model for vehicle emissions. The model considers multiple pollutants, including carbon monoxide (CO), carbon dioxide (CO₂), hydrocarbons (HC), and nitrogen oxides (NOx), measured under diverse driving conditions. The ANN model predicts emission trends, while GPR estimates prediction uncertainty, enhancing the model’s robustness. The GPR models achieved uncertainty levels of ±0.829 ppm for CO, ±9.978 ppm for HC, ±0.144 ppm for NOx, and ±411.256 ppm for CO₂, respectively, underscoring the robustness of the integrated approach for emission prediction. This research aims to support the development of more sustainable vehicle technologies and inform policy making for environmental sustainability (e.g., Euro 6/Euro 7 standards). Overall, the study addresses how artificial intelligence (AI) can be utilized to achieve accurate multi-pollutant emission predictions in HEVs. The findings reveal that an integrated ANN-GPR approach yields superior predictive performance (R² values approaching 1.0) with quantifiable uncertainty, outperforming a stand-alone ANN model and providing a robust solution to the emission prediction challenge.

Heru Priyanto1, Rizqon Fajar1, Yaaro Telaumbanua1, Ariyanto1, Mohammad Mukhlas Af1, Sigit Tri Atmaja1, Muhammad Samsul Maarif1,2, Kurnia Fajar Adhi Sukra1,2, Fauzi Dwi Setiawan1

Journal homepage: http://journal.uad.ac.id/index.php/TELKOMNIKA

Oct 19, 2025

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