Digital twin for machine-learning-based vehicle CO2 emissions concentration prediction in embedded system

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In this paper, we describe the design, implementation, and installation of a digital twin version of a physical CO 2 monitoring system with the aim of democratizing access to affordable CO 2 emission measuring and enabling the creation of effective pollutant reduction strategies. The presented digital twin acts as a replacement that enables the measuring of CO 2 emissions without the use of a physical sensor. The exhibited work is specifically designed to be installed on a low-powered Micro Controller Unit (MCU), enabling its accessibility to a broader base of users. To this end, an optimized Artificial Neural Network (ANN) model was trained to be capable of predicting CO 2 emission concentrations with 87.15% accuracy when performing on the MCU. The ANN model is the result of a compound optimization technique that enhances the speed and accuracy of the model while reducing its computational complexity. The results outline that the implementation of the digital twin is 86.4% less expensive than its physical CO 2 counterpart, whilst still providing highly accurate and reliable data.
Original languageEnglish
Title of host publicationIEEE 28th International Conference on Applied Electronics 
EditorsJiri Pinker
Number of pages6
ISBN (Electronic)9798350335545
ISBN (Print)9798350335552
Publication statusPublished - 12 Oct 2023

Publication series

NameISBN Conference Proceedings
ISSN (Print)1803-7232
ISSN (Electronic)1805-9597


  • ANN
  • CO2
  • embedded systems
  • micro controllers


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