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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Ariyanto Ariyanto
National Research and Innovation Agency
Computer Science & IT

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Application of artificial intelligence in emission prediction for hybrid electric vehicles: integrating ANN and GPR Heru Priyanto; Rizqon Fajar; Yaaro Telaumbanua; Ariyanto Ariyanto; Mohammad Mukhlas Af; Sigit Tri Atmaja; Muhammad Samsul Maarif; Kurnia Fajar Adhi Sukra; Fauzi Dwi Setiawan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 6: December 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i6.27128

Abstract

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.
Co-Authors Fauzi Dwi Setiawan Heru Priyanto Kurnia Fajar Adhi Sukra Mohammad Mukhlas Af Muhammad Samsul Maarif Rizqon Fajar Sigit Tri Atmaja Yaaro Telaumbanua