A Novel Approach to Vehicular CO2 Emission Predictive Modelling

S. Sahay, P. M. Pawar, D. N. Sonawane

Abstract


Climate change today is a global crisis requiring grave concern to the extent that governments worldwide have realized that if this problem is left unmitigated, catastrophic events may occur, ultimately jeopardizing humanity’s survival. Climate change is primarily due to too much presence of greenhouse gases, mainly CO2, in the atmosphere. Vehicular exhaust is one of the main contributors to the emissions of CO2. Although specialized sensors exist for CO2 monitoring, they are inefficient and not highly prevalent. This study suggests a workable, pragmatic, and feasible monitoring system for vehicular CO2 emissions that involves an LSTM network trained and tested based on OBD-II data available in the public domain. Also, this work presents a comparison of the proposed model with a latter-day solution. This proposed system could be deployed on the cloud, with the IoT-based dongles put in the vehicles that can collect in-sensor data from vehicles and send them to the cloud for processing the data, where the deployed model can give real-time predictions of CO2 emissions.

Full Text:

PDF

References


IPCC, 2022: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. Pörtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, B. Rama (eds.)]. Cambridge University Press. In Press.

Susan Solomon, Gian-Kasper Plattner, Reto Knutti, and Pierre Friedlingstein, “Irreversible climate change due to carbon dioxide emissions,” Proceedings of the National Academy of Sciences, vol. 106, no. 6, pp. 1704-1709, 2009, doi: 10.1073/pnas.0812721106.

Hannah Ritchie, Pablo Rosado and Max Roser, “Greenhouse gas emissions,” Published online at OurWorldInData.org, 2020, https://ourworldindata.org/greenhouse-gas-emissions.

IEA, Global CO2 emissions by sector, 2019-2022, IEA, Paris https://www.iea.org/data-and-statistics/charts/global-co2-emissions-by-sector-2019-2022, IEA. Licence: CC BY 4.0.

IEA, CO2 emissions from the Indian energy sector, 2019, IEA, Paris https://www.iea.org/data-and-statistics/charts/co2-emissions-from-the-indian-energy-sector-2019.

D. Bandara, M. Amarasinghe, S. Kottegoda, A.L. Arachchi, S. Muramudalige, and A. Azeez, “Cloudbased driver monitoring and vehicle diagnostic with obd2 telematics,” volume 6, 2015.

Vehicular data trace of the city of belo horizonte and surroundings, brazil. 2018. http://www.rettore.com.br/prof/vehicular-trace/.

Paulo H.L. Rettore, Andre B. Campolina, Leandro A. Villas, and Antonio A.F. Loureiro, “Identifying relationships in vehicular sensor data: A case study and characterization,” DIVANet ’16, page 33–40, New York, NY, USA, 2016. Association for Computing Machinery.

Fernando Ortenzi and Maria Costagliola, “A new method to calculate instantaneous vehicle emissions using obd data,” SAE Technical Papers, 04 2010.

Weiliang Zeng, Tomio Miwa, and Takayuki Morikawa, “Prediction of vehicle co2 emission and its application to eco-routing navigation,” Transportation Research Part C: Emerging Technologies, 68:194 – 214, 2016.

Seth Oduro, Santanu Metia, Hiep Duc, and Quang Ha, “CO2 vehicular emission statistical analysis with instantaneous speed and acceleration as predictor variables,” pp. 158–163, 11 2013.

Matt Grote, Ian Williams, John Preston, and Simon Kemp, “A practical model for predicting road traffic carbon dioxide emissions using inductive loop detector data,” Transportation Research Part D: Transport and Environment, vol. 63, pp. 809 – 825, 2018.

P. Kadam and S. Vijayumar, “Prediction Model: CO 2 Emission Using Machine Learning,” 2018, 3rd Int. Conf. Converg. Technol. I2CT 2018, pp. 1–3, 2018, doi: 10.1109/I2CT.2018.8529498.

T. C. Ho, S. C. Keat, M. Z. M. Jafri, and L. H. San, “A prediction model for CO2 emission from manufacturing industry and construction in Malaysia,” Int. Conf. Sp. Sci. Commun. Iconsp., vol. 2015-Septe, pp. 469–472, 2015, doi:10.1109/IconSpace.2015.7283771

B. Liu, J. Hu, F. Yan, R. F. Turkson, and F. Lin, “A novel optimal support vector machine ensemble model for NOX emissions prediction of a diesel engine,” Meas. J. Int. Meas. Confed., vol. 92, no. X, pp. 183–192, 2016, doi: 10.1016/j.measurement.2016.06.015.

S. Kangralkar and R. Khanai, "Machine Learning Application for Automotive Emission Prediction," 2021 6th International Conference for Convergence in Technology (I2CT), 2021, pp. 1-5, doi: 10.1109/I2CT51068.2021.9418152

M. Mądziel, A. Jaworski, H. Kuszewski, P. Woś, T. Campisi, and K. Lew, “The Development of CO2 Instantaneous Emission Model of Full Hybrid Vehicle with the Use of Machine Learning Techniques,” Energies, vol. 15, no. 1, p. 142, Dec. 2021, doi: 10.3390/en15010142

Li Q, Qiao F, Yu L, “A Machine Learning Approach for Light-Duty Vehicle Idling Emission Estimation Based on Real Driving and Environmental Information,” Environ Pollut Climate Change, 2016, 1, p. 106.

N. Subramaniam, and N. Yusof, "Modelling of CO2 Emission Prediction for Dynamic Vehicle Travel Behavior Using Ensemble Machine Learning Technique," 2021 IEEE 19th Student Conference on Research and Development (SCOReD), 2021, pp. 383-387, doi: 10.1109/SCOReD53546.2021.9652757.

Shah Samveg, Thakar Shubham, Jain, Kashish, Shah Bhavya, and Dhage Sudhir, “A Comparative Study of Machine Learning and Deep Learning Techniques for Prediction of Co2 Emission in Cars,” 2022, doi: 10.48550/arXiv.2211.08268.

M. Singh, and R. Dubey, "Deep Learning Model Based CO2 Emissions Prediction using Vehicle Telematics Sensors Data," in IEEE Transactions on Intelligent Vehicles, 2021, doi: 10.1109/TIV.2021.3102400.

S. Sahay, and P. Pawar, "An Optimal Approach to Vehicular CO2 Emissions Prediction using Deep Learning," 2023 International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India, 2023, pp. 1-5, doi: 10.1109/ESCI56872.2023.10099940.

Thomas G. Dietterich, “Machine learning for sequential data: A review,” in Terry Caelli, Adnan Amin, Robert P. W. Duin, Dick de Ridder, and Mohamed Kamel, editors, Structural, Syntactic, and Statistical Pattern Recognition, pp. 15–30, Berlin, Heidelberg, Springer, 2002.

Y. Wang, Y. Liu, M. Wang, and R. Liu, "LSTM Model Optimization on Stock Price Forecasting," 2018, 17th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES), 2018, pp. 173-177, doi: 10.1109/DCABES.2018.00052.

Sepp Hochreiter, and Jürgen Schmidhuber; “Long Short-Term Memory,” Neural Comput, 1997, vol. 9(8), pp. 1735–1780, doi: https://doi.org/10.1162/neco.1997.9.8.1735

J. Xiang, Z. Qiu, Q. Hao, and H. Cao, “Multi-time scale wind speed prediction based on wt-bi-lstm,” in MATEC Web of Conferences, vol. 309. EDP Sciences, 2020, p. 05011.

K. Moharm, M. Eltahan and E. Elsaadany, "Wind Speed Forecast using LSTM and Bi-LSTM Algorithms over Gabal El-Zayt Wind Farm," 2020 International Conference on Smart Grids and Energy Systems (SGES), 2020, pp. 922-927, doi: 10.1109/SGES51519.2020.00169.

United States Environment Protection Agency (EPA). Vehicle emissions on-board diagnostics (obd). 2020. https://www.epa.gov/state-and-local-transportation/vehicle-emissions-board-diagnostics-obd.

ISO. Open diagnostic data exchange (odx). OBD-II Exchange.

P. H. L. Rettore, A. B. Campolina, L. A. Villas, and A. A. F. Loureiro, “A method of eco-driving based on intra-vehicular sensor data,” in 2017, IEEE Symposium on Computers and Communications (ISCC), pp. 1122–1127, 2017.


Refbacks

  • There are currently no refbacks.


Abava  Кибербезопасность MoNeTec 2024

ISSN: 2307-8162