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Related Experiment Video

Updated: Mar 10, 2026

Implementation of Portable Emissions Measurement Systems PEMS for the Real-driving Emissions RDE Regulation in Europe
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Implementation of Portable Emissions Measurement Systems PEMS for the Real-driving Emissions RDE Regulation in Europe

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An automated decision making framework for modern vehicles CO2 emissions using multi modal engine telemetry and

Shelesh Krishna Saraswat1, Mustafa Abdullah2, Mohammed Ihsan Habelalmateen3,4

  • 1Department of ECE, GLA University, Mathura, 281406, India. shelesh.saraswat@gla.ac.in.

Scientific Reports
|March 9, 2026
PubMed
Summary

Predicting vehicle CO₂ emissions is complex. This study introduces a machine learning framework using multi-layer perceptron (MLP) and metaheuristic optimization for more accurate CO₂ emission modeling.

Keywords:
CO2 predictionExplainable machine learningIntelligent transportation systemsMetaheuristic optimizationMulti-modal engine profilingSmart mobility analyticsVehicle emission modeling

Related Experiment Videos

Last Updated: Mar 10, 2026

Implementation of Portable Emissions Measurement Systems PEMS for the Real-driving Emissions RDE Regulation in Europe
09:34

Implementation of Portable Emissions Measurement Systems PEMS for the Real-driving Emissions RDE Regulation in Europe

Published on: December 4, 2016

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Area of Science:

  • Environmental Science
  • Automotive Engineering
  • Artificial Intelligence

Background:

  • Vehicle CO₂ emissions prediction is hindered by complex engine dynamics and driving conditions.
  • Existing models struggle with the nonlinear interactions of fuel, mechanical, and operational parameters.

Purpose of the Study:

  • To develop a high-performance machine learning framework for precise vehicle CO₂ emission modeling.
  • To enhance prediction accuracy and convergence stability using advanced AI techniques.

Main Methods:

  • Utilized multi-layer perceptron (MLP) architectures combined with metaheuristic optimization (Horned Lizard Optimization Algorithm and Giant Armadillo Optimization).
  • Employed multi-modal engine telemetry data (fuel type, transmission, displacement, etc.).
  • Applied feature selection (Recursive Feature Elimination) and interpretability techniques (SHAP, Class Activation Mapping) to identify key emission drivers.

Main Results:

  • The Giant Armadillo Optimization-enhanced MLP achieved superior predictive performance with R² = 0.9881 and RMSE = 6.478.
  • Identified dominant drivers influencing vehicle CO₂ emissions through advanced interpretability methods.
  • Demonstrated improved precision and convergence stability in emission modeling.

Conclusions:

  • Integrated interpretable AI models offer a powerful approach to vehicle emission prediction.
  • The framework can inform low-carbon vehicle design and urban mobility planning.
  • Findings support environmentally conscious policy-making for transportation sector emissions.