Machine learning-based carbon dioxide concentration prediction for hybrid vehicles

David Tena-Gago, Gelayol Golcarenarenji, Ignacio Martinez-Alpiste*, Qi Wang, Jose M. Alcaraz-Calero

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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The current understanding of CO2 emission concentrations in hybrid vehicles (HVs) is limited, due to the complexity of the constant changes in their power-train sources. This study aims to address this problem by examining the accuracy, speed and size of traditional and advanced machine learning (ML) models for predicting CO2 emissions in HVs. A new long short-term memory (LSTM)-based model called UWS-LSTM has been developed to overcome the deficiencies of existing models. The dataset collected includes more than 20 parameters, and an extensive input feature optimization has been conducted to determine the most effective parameters. The results indicate that the UWS-LSTM model outperforms traditional ML and artificial neural network (ANN)-based models by achieving 97.5% accuracy. Furthermore, to demonstrate the efficiency of the proposed model, the CO2-concentration predictor has been implemented in a low-powered IoT device embedded in a commercial HV, resulting in rapid predictions with an average latency of 21.64 ms per prediction. The proposed algorithm is fast, accurate and computationally efficient, and it is anticipated that it will make a significant contribution to the field of smart vehicle applications.
Original languageEnglish
Article number1350
Number of pages20
Issue number3
Publication statusPublished - 25 Jan 2023


  • hybrid vehicles
  • IoT
  • CO2
  • LSTM


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