IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i3p1206-d1043880.html

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/3/1206/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/3/1206/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    5. 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.
    6. 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.
    7. Michael Ben-Chaim & Efraim Shmerling & Alon Kuperman, 2013. "Analytic Modeling of Vehicle Fuel Consumption," Energies, MDPI, vol. 6(1), pages 1-11, January.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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).
    14. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chen, Ziqiang & Ju, Peng & Gong, Man & Shi, Xiaodong & Qin, Ciwei & Shi, Lei, 2025. "Study on control-oriented emission predictions of ammonia-diesel dual-fuel engine with combustion identification," Energy, Elsevier, vol. 333(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Iftikhar Ahmad & Adil Sana & Manabu Kano & Izzat Iqbal Cheema & Brenno C. Menezes & Junaid Shahzad & Zahid Ullah & Muzammil Khan & Asad Habib, 2021. "Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions," Energies, MDPI, vol. 14(16), pages 1-27, August.
    2. Abraham Itzhak Weinberg, 2025. "Hybrid Quantum-Classical Ensemble Learning for S\&P 500 Directional Prediction," Papers 2512.15738, arXiv.org.
    3. Vitor Azevedo & Christopher Hoegner, 2023. "Enhancing stock market anomalies with machine learning," Review of Quantitative Finance and Accounting, Springer, vol. 60(1), pages 195-230, January.
    4. Tang, Xuli & Li, Xin & Ding, Ying & Song, Min & Bu, Yi, 2020. "The pace of artificial intelligence innovations: Speed, talent, and trial-and-error," Journal of Informetrics, Elsevier, vol. 14(4).
    5. Moews, Ben & Ibikunle, Gbenga, 2020. "Predictive intraday correlations in stable and volatile market environments: Evidence from deep learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
    6. Doron Avramov & Si Cheng & Lior Metzker, 2023. "Machine Learning vs. Economic Restrictions: Evidence from Stock Return Predictability," Management Science, INFORMS, vol. 69(5), pages 2587-2619, May.
    7. Flori, Andrea & Regoli, Daniele, 2021. "Revealing Pairs-trading opportunities with long short-term memory networks," European Journal of Operational Research, Elsevier, vol. 295(2), pages 772-791.
    8. Jiang, Kangqi & Du, Xinyi & Chen, Zhongfei, 2022. "Firms' digitalization and stock price crash risk," International Review of Financial Analysis, Elsevier, vol. 82(C).
    9. Rubesam, Alexandre, 2022. "Machine learning portfolios with equal risk contributions: Evidence from the Brazilian market," Emerging Markets Review, Elsevier, vol. 51(PB).
    10. Zhengyong Jiang & Jeyan Thiayagalingam & Jionglong Su & Jinjun Liang, 2023. "CAD: Clustering And Deep Reinforcement Learning Based Multi-Period Portfolio Management Strategy," Papers 2310.01319, arXiv.org.
    11. Junjie Guo, 2025. "Integration of Wavelet Transform Convolution and Channel Attention with LSTM for Stock Price Prediction based Portfolio Allocation," Papers 2507.01973, arXiv.org, revised Jul 2025.
    12. Sungsoo Kim & Hyukjae Kwon & Jisang Yoon & Ha Young Kim, 2026. "Dynamic factor-informed reinforcement learning for enhancing portfolio optimization," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 12(1), pages 1-33, December.
    13. Mohammad Rezoanul Hoque & Md Meftahul Ferdaus & M. Kabir Hassan, 2025. "Reinforcement Learning in Financial Decision Making: A Systematic Review of Performance, Challenges, and Implementation Strategies," Papers 2512.10913, arXiv.org.
    14. Charyung Kim & Hyunwoo Lee & Yongsung Park & Cha-Lee Myung & Simsoo Park, 2016. "Study on the Criteria for the Determination of the Road Load Correlation for Automobiles and an Analysis of Key Factors," Energies, MDPI, vol. 9(8), pages 1-17, July.
    15. Cakici, Nusret & Fieberg, Christian & Metko, Daniel & Zaremba, Adam, 2023. "Machine learning goes global: Cross-sectional return predictability in international stock markets," Journal of Economic Dynamics and Control, Elsevier, vol. 155(C).
    16. A. Madhavi & A. Nagesh & A. Govardhan, 2025. "Sentiment-Based Hierarchical Deep Learning Framework Using Hybrid Optimization for Course Recommendation in E-learning," Annals of Data Science, Springer, vol. 12(5), pages 1661-1690, October.
    17. Kim, A. & Yang, Y. & Lessmann, S. & Ma, T. & Sung, M.-C. & Johnson, J.E.V., 2020. "Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting," European Journal of Operational Research, Elsevier, vol. 283(1), pages 217-234.
    18. Arasteh, Abdollah, 2025. "A data-driven prediction method for multi-period portfolio optimization using the real options approach," Finance Research Letters, Elsevier, vol. 80(C).
    19. Uddin, Ajim & Yu, Dantong, 2020. "Latent factor model for asset pricing," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    20. Qiong Wu & Christopher G. Brinton & Zheng Zhang & Andrea Pizzoferrato & Zhenming Liu & Mihai Cucuringu, 2019. "Equity2Vec: End-to-end Deep Learning Framework for Cross-sectional Asset Pricing," Papers 1909.04497, arXiv.org, revised Oct 2021.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1206-:d:1043880. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.