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CO 2 Emission and Energy Consumption Estimates in the COPERT Model—Conclusions from Chassis Dynamometer Tests and SANN Artificial Neural Network Models and Their Meaning for Transport Management

Author

Listed:
  • Olga Orynycz

    (Faculty of Engineering Management, Department of Production Management, Bialystok University of Technology, Wiejska Street 45A, 15-351 Bialystok, Poland)

  • Magdalena Zimakowska-Laskowska

    (Environment Protection Centre, Motor Transport Institute, 03-301 Warsaw, Poland)

  • Ewa Kulesza

    (Department of Mechanics and Applied Computer Science, Faculty of Mechanical Engineering, Bialystok University of Technology, Wiejska Street 45A, 15-351 Bialystok, Poland)

Abstract

This article aimed to assess the accuracy of the COPERT model in predicting CO 2 emissions and energy consumption in real operating conditions, represented by the WLTP homologation tests. Experimental data obtained for a Euro 6 vehicle were compared with the values estimated by the COPERT model, assuming identical speed conditions. MLP and SANN artificial neural networks were also used to create a model describing the complex relationships between emissions, speed, and energy consumption. The results indicate an apparent overestimation of CO 2 and energy consumption values by the COPERT model, especially in the low-speed range typical of urban traffic. The minimum energy consumption values were observed at speeds of 50–70 km/h, indicating the existence of an optimal drive system operation zone. The neural models showed high efficiency in predicting the tested parameters—the best results were obtained for the MLP 6-10-1 architecture, whose correlation coefficient exceeded 0.98 in the validation set. The paper highlights the need to calibrate the COPERT model using local experimental data and integrate artificial intelligence methods in modern emission inventories.

Suggested Citation

  • Olga Orynycz & Magdalena Zimakowska-Laskowska & Ewa Kulesza, 2025. "CO 2 Emission and Energy Consumption Estimates in the COPERT Model—Conclusions from Chassis Dynamometer Tests and SANN Artificial Neural Network Models and Their Meaning for Transport Management," Energies, MDPI, vol. 18(13), pages 1-20, July.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3457-:d:1692014
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    References listed on IDEAS

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