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Review of Artificial Intelligent Algorithms for Engine Performance, Control, and Diagnosis

Author

Listed:
  • Landry Frank Ineza Havugimana

    (School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China)

  • Bolan Liu

    (School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China)

  • Fanshuo Liu

    (School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China)

  • Junwei Zhang

    (School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China)

  • Ben Li

    (School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China)

  • Peng Wan

    (School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China)

Abstract

This paper reviews the artificial intelligent algorithms in engine management. This study provides a clear image of the current state of affairs for the past 15 years and provides fresh insights and improvements for future directions in the field of engine management. The scope of this paper comprises three main aspects to be discussed, namely, engine performance, engine control, and engine diagnosis. The first is associated with the need to control the basic characteristics that prove that the engine is working properly, namely, emission control and fuel economy. Engine control refers to the ability to identify and fulfill the requirements derived from performance, emissions, and durability. In this part, hybrid electric vehicle (HEV) application and transient operations are discussed. Lastly, engine diagnosis entails assessment techniques that can be used to identify problems in the engine and solve them accordingly. In this part, misfire detection, knock detection, and intake system leakage will be evaluated. In engine performance, neural network algorithms provide efficient results in terms of emission control and fuel economy as the requirements are easily achievable. However, when it comes to engine control and diagnosis, the fuzzy logic rule with its strong robustness and neural networks algorithms are limited in efficiency due to the complex nature of the processes and the presence of big data, for instance, in HEVs in engine control. That has brought forward the usage of reinforcement learning and novel machine learning algorithms in recent years to maximize efficiency in engine control and engine diagnosis, as highlighted in the following part. The PRISMA methodology was used to justify the reference selection in this review.

Suggested Citation

  • Landry Frank Ineza Havugimana & Bolan Liu & Fanshuo Liu & Junwei Zhang & Ben Li & Peng Wan, 2023. "Review of Artificial Intelligent Algorithms for Engine Performance, Control, and Diagnosis," Energies, MDPI, vol. 16(3), pages 1-25, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1206-:d:1043880
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    References listed on IDEAS

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    1. Wong, Pak Kin & Wong, Ka In & Vong, Chi Man & Cheung, Chun Shun, 2015. "Modeling and optimization of biodiesel engine performance using kernel-based extreme learning machine and cuckoo search," Renewable Energy, Elsevier, vol. 74(C), pages 640-647.
    2. Gennaro Nicola Bifulco & Francesco Galante & Luigi Pariota & Maria Russo Spena, 2015. "A Linear Model for the Estimation of Fuel Consumption and the Impact Evaluation of Advanced Driving Assistance Systems," Sustainability, MDPI, vol. 7(10), pages 1-18, October.
    3. Jakov Topić & Branimir Škugor & Joško Deur, 2022. "Neural Network-Based Prediction of Vehicle Fuel Consumption Based on Driving Cycle Data," Sustainability, MDPI, vol. 14(2), pages 1-12, January.
    4. Bozza, Fabio & De Bellis, Vincenzo & Teodosio, Luigi, 2016. "Potentials of cooled EGR and water injection for knock resistance and fuel consumption improvements of gasoline engines," Applied Energy, Elsevier, vol. 169(C), pages 112-125.
    5. Michael Ben-Chaim & Efraim Shmerling & Alon Kuperman, 2013. "Analytic Modeling of Vehicle Fuel Consumption," Energies, MDPI, vol. 6(1), pages 1-11, January.
    6. Fontaras, Georgios & Samaras, Zissis, 2010. "On the way to 130 g CO2/km--Estimating the future characteristics of the average European passenger car," Energy Policy, Elsevier, vol. 38(4), pages 1826-1833, April.
    7. Du, Guodong & Zou, Yuan & Zhang, Xudong & Guo, Lingxiong & Guo, Ningyuan, 2022. "Energy management for a hybrid electric vehicle based on prioritized deep reinforcement learning framework," Energy, Elsevier, vol. 241(C).
    8. Xinwei Wang & Pan Zhang & Wenzhi Gao & Yong Li & Yanjun Wang & Haoqian Pang, 2022. "Misfire Detection Using Crank Speed and Long Short-Term Memory Recurrent Neural Network," Energies, MDPI, vol. 15(1), pages 1-24, January.
    9. Silitonga, A.S. & Masjuki, H.H. & Ong, Hwai Chyuan & Sebayang, A.H. & Dharma, S. & Kusumo, F. & Siswantoro, J. & Milano, Jassinnee & Daud, Khairil & Mahlia, T.M.I. & Chen, Wei-Hsin & Sugiyanto, Bamban, 2018. "Evaluation of the engine performance and exhaust emissions of biodiesel-bioethanol-diesel blends using kernel-based extreme learning machine," Energy, Elsevier, vol. 159(C), pages 1075-1087.
    10. James B. Heaton & Nicholas Polson & Jan H. Witte, 2017. "Rejoinder to ‘Deep learning for finance: deep portfolios’," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 33(1), pages 19-21, January.
    11. Ghobadian, B. & Rahimi, H. & Nikbakht, A.M. & Najafi, G. & Yusaf, T.F., 2009. "Diesel engine performance and exhaust emission analysis using waste cooking biodiesel fuel with an artificial neural network," Renewable Energy, Elsevier, vol. 34(4), pages 976-982.
    12. Jarosław Ziółkowski & Mateusz Oszczypała & Jerzy Małachowski & Joanna Szkutnik-Rogoż, 2021. "Use of Artificial Neural Networks to Predict Fuel Consumption on the Basis of Technical Parameters of Vehicles," Energies, MDPI, vol. 14(9), pages 1-23, May.
    13. Hocheol Jeon, 2019. "The Impact of Climate Change on Passenger Vehicle Fuel Consumption: Evidence from U.S. Panel Data," Energies, MDPI, vol. 12(23), pages 1-15, November.
    14. Giorgio Mancini & Jonas Asprion & Nicolò Cavina & Christopher Onder & Lino Guzzella, 2014. "Dynamic Feedforward Control of a Diesel Engine Based on Optimal Transient Compensation Maps," Energies, MDPI, vol. 7(8), pages 1-25, August.
    15. J. B. Heaton & N. G. Polson & J. H. Witte, 2017. "Deep learning for finance: deep portfolios," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 33(1), pages 3-12, January.
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