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Artificial Intelligence in Automotives: ANNs’ Impact on Biodiesel Engine Performance and Emissions

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  • Ramozon Khujamberdiev

    (Department of Mechanical Engineering, Kongju National University, Cheonan 31080, Republic of Korea)

  • Haeng Muk Cho

    (Department of Mechanical Engineering, Kongju National University, Cheonan 31080, Republic of Korea)

Abstract

This paper explores the integration and advancements of artificial neural networks (ANNs) in modeling diesel engine performance, particularly focusing on biodiesel-fueled engines. ANNs have emerged as a vital tool in predicting and optimizing engine parameters, contributing to the enhancement of fuel efficiency and a reduction in emissions. The novelty of this review lies in its critical analysis of the existing literature on ANN applications in biodiesel engines, identifying gaps in optimization and emission control. While ANNs have shown promise in predicting engine parameters, fuel efficiency, and emission reduction, this paper highlights their limitations and areas for improvement, especially in the context of biodiesel-fueled engines. The integration of ANNs with big data and sophisticated algorithms paves the way for more accurate and reliable engine modeling, essential for advancing sustainable and eco-friendly automotive technologies. This research underscores the growing importance of ANNs in optimizing biodiesel-fueled diesel engines, aligning with global efforts towards cleaner and more sustainable energy solutions.

Suggested Citation

  • Ramozon Khujamberdiev & Haeng Muk Cho, 2025. "Artificial Intelligence in Automotives: ANNs’ Impact on Biodiesel Engine Performance and Emissions," Energies, MDPI, vol. 18(2), pages 1-21, January.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:2:p:438-:d:1571253
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    References listed on IDEAS

    as
    1. Ramozon Khujamberdiev & Haeng Muk Cho & Md. Iqbal Mahmud, 2023. "Experimental Investigation of Single-Cylinder Engine Performance Using Biodiesel Made from Waste Swine Oil," Energies, MDPI, vol. 16(23), pages 1-15, December.
    2. Fangyuan Zheng & Haengmuk Cho, 2023. "Combustion and Emission of Castor Biofuel Blends in a Single-Cylinder Diesel Engine," Energies, MDPI, vol. 16(14), pages 1-13, July.
    3. Fangyuan Zheng & Haeng Muk Cho, 2024. "The Effect of Different Mixing Proportions and Different Operating Conditions of Biodiesel Blended Fuel on Emissions and Performance of Compression Ignition Engines," Energies, MDPI, vol. 17(2), pages 1-15, January.
    4. Yusri, I.M. & Abdul Majeed, A.P.P. & Mamat, R. & Ghazali, M.F. & Awad, Omar I. & Azmi, W.H., 2018. "A review on the application of response surface method and artificial neural network in engine performance and exhaust emissions characteristics in alternative fuel," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 665-686.
    5. Channapattana, S.V. & Pawar, Abhay A. & Kamble, Prashant G., 2017. "Optimisation of operating parameters of DI-CI engine fueled with second generation Bio-fuel and development of ANN based prediction model," Applied Energy, Elsevier, vol. 187(C), pages 84-95.
    6. Ramozon Khujamberdiev & Haeng Muk Cho, 2024. "Exploring the Environmental and Performance Implications of Utilizing Waste Swine Oil Biodiesel in CI Engines," Energies, MDPI, vol. 17(3), pages 1-14, January.
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