A Study on Predicting Engine Performance Outputs by Machine Learning Algorithms in a Single Cylinder HCCI Engine

Ahmet Çelik; Mehmet Akif Kunt

doi:10.64808/engineeringperspective.1782349

A Study on Predicting Engine Performance Outputs by Machine Learning Algorithms in a Single Cylinder HCCI Engine

Abstract

Machine learning algorithms are often used to mathematically establish relationships between data sets. Successful results have been achieved in performance, production, consumption, fault, and wear prediction applications using learning algorithms. The high testing costs of Homogeneous Charge Compression Ignition (HCCI) engines, the determination of efficient operating ranges, and the challenges of performance prediction in untested regions have recently made the use of artificial intelligence technologies increasingly popular. In this study, a dataset (805 data) was created by varying the λ value in a single-cylinder HCCI engine (Ricardo Hydra) and conducting performance measurements at different engine speeds. Based on the input values of Compression Ratio, RON (Research Octane Number), Intake Air Temperature (K), Engine Speed (rpm), and Lambda (λ) within the dataset, the output variables IMEP(Bar), Effective Torque, Indicated Thermal Efficiency, and COVimep (%) were predicted. In this study, a prediction model was developed using the AdaBoost and Tree machine learning algorithms. The experimental results demonstrated that the AdaBoost algorithm achieved the highest accuracy in predicting IMEP (Bar) output values and the lowest error rates in predicting Indicated Thermal Efficiency output values. The highest performance was obtained with an R metric a value of 9.57×10-1, while the lowest error rates were calculated as 2.89×10-4 for the MSE error metric, 1.70×10-2 for the RMSE error metric, 1.30×10-2 for the MAE error metric, and 4.10×10-2 for the MAPE error metric. The results indicate that high-accuracy predictions can be made using the proposed model.

Keywords

References

1. Arora, S., Vyas, A., & Johnson, L. R. (2011). Projections of highway vehicle population, energy demand, and CO2 emissions in India to 2040. Natural Resources Forum, Oxford, UK: Blackwell Publishing Ltd. https://doi.org/10.1111/j.1477-8947.2011.01341.x
2. Güler, Ö. F. (2025). Working Fluid Selection and Performance Analysis for the Afyon Geothermal Energy Plant. Engineering Perspective, 5(1), 1-8. https://doi.org/10.29228/eng.pers.80205
3. Kocakulak, T., & Arslan, T. A. (2023). Investigation of the Use of Fuel Cell Hybrid Systems for Different Purposes. Engineering Perspective, 3(1), 1-8. https://doi.org/10.29228/eng.pers.68466
4. Calam, T. T. (2020). Electrochemical oxidative determination and electrochemical behavior of 4-nitrophenol based on an Au electrode modified with electro-polymerized 3,5-diamino-1,2,4-triazole film. Electroanalysis, 32(1), 149–158. https://doi.org/10.1002/elan.201900450
5. Tabanlıgil, C. T. (2020). Investigation of the electrochemical behavior of phenol using 1H–1,2,4-triazole-3-thiol modified gold electrode and its voltammetric determination. Journal of the Faculty of Engineering and Architecture of Gazi University, 35(3), 835–844. https://doi.org/10.17341/gazimmfd.543608
6. Tabanlıgil, C. T. (2019). Analytical application of the poly(1H-1,2,4-triazole-3-thiol) modified gold electrode for high sensitive voltammetric determination of catechol in tap and lake water samples. International Journal of Environmental Analytical Chemistry, 99(13), 1298–1312. https://doi.org/10.1080/03067319.2019.1619716
7. Ellabban, O., Abu-Rub, H., & Blaabjerg, F. (2014). Renewable energy resources: Current status, future prospects and their enabling technology. Renewable and Sustainable Energy Reviews, 39, 748–764. https://doi.org/10.1016/j.rser.2014.07.113
8. Tabanlıgil, C. T., & Yılmaz, E. B. (2020). Electrochemical determination of 8-hydroxyquinoline in a cosmetic product on a glassy carbon electrode modified with 1-amino-2-naphthol-4-sulphonic acid. Instrumentation Science & Technology, 48(6), 653–672. https://doi.org/10.1080/10739149.2020.1765175

9. Karakaş, O., Şeker, U. B., & Solmaz, H. (2021). Modeling of an Electric Bus Using MATLAB/Simulink and Determining Cost Saving for a Realistic City Bus Line Driving Cycle. Engineering Perspective, 1(2), 52-62. https://doi.org/10.29228/eng.pers.51422
10. Yilmaz, E., Solmaz, H., Polat, S., & Altin, M. (2013). Effect of the three-phase diesel emulsion fuels on engine performance and exhaust emissions. Journal of the Faculty of Engineering and Architecture of Gazi University, 28(1), 127–134.
11. Bayrakçeken, H., Yavuz, H., & Arslan, T. A. (2023). Effect of Clutch Pedal Distances on Fuel Consumption Under Actual Operating Conditions. Engineering Perspective, 3(4), 63-67. https://doi.org/10.29228/eng.pers.72460
12. Panting, J., Pullen, K. R., & Martinez-Botas, R. F. (2001). Turbocharger motor-generator for improvement of transient performance in an internal combustion engine. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 215(3), 369–383. https://doi.org/10.1243/0954407011525700
13. Ardebili, S. M. S., Solmaz, H., Calam, A., & İpci, D. (2021). Modelling of performance, emission, and combustion of an HCCI engine fueled with fusel oil-diethylether fuel blends as a renewable fuel. Fuel, 290, 120017. https://doi.org/10.1016/j.fuel.2020.120017
14. Liu, Z., Zuo, Q., Wu, G., & Li, Y. (2018). An artificial neural network developed for predicting performance and emissions of a spark ignition engine fueled with butanol–gasoline blends. Advances in Mechanical Engineering, 10, 1687814017748438. https://doi.org/10.1177/1687814017748438
15. Mohammad, H. J., Dadvand, A., Khalilarya, S., & Solimanpurb, M. (2015). Prediction and reduction of diesel engine emissions using a combined ANN–ACO method. Applied Soft Computing, 34, 139–150. https://doi.org/10.1016/j.asoc.2015.04.059
16. Solmaz, H., & İpci, D. (2020). RCCI bir motorda farklı giriş sıcaklıkları için direct enjeksiyon zamanlaması ile yanma fazının kontrolü. Isı Bilimi ve Tekniği Dergisi, 40(2), 267–279. https://doi.org/10.47480/isibted.817027
17. Uyumaz, A., Aydoğan, B., Calam, A., Aksoy, F., & Yılmaz, E. (2020). The effects of diisopropyl ether on combustion, performance, emissions and operating range in a HCCI engine. Fuel, 265, 116919. https://doi.org/10.1016/j.fuel.2019.116919
18. Aydoğan, B. (2020). An experimental examination of the effects of n-hexane and n-heptane fuel blends on combustion, performance and emissions characteristics in a HCCI engine. Energy, 192, 116600. https://doi.org/10.1016/j.energy.2019.116600
19. Aydoğan, B., & Calam, A. (2020). Combustion, performance and emission characteristics of a HCCI engine fuelled with n-butanol/n-heptane blends. International Journal of Automotive Engineering and Technologies, 9(1), 1–10. https://doi.org/10.18245/ijaet.593811
20. Hwang, W., Dec, J. E., & Sjoberg, M. (2007). Fuel stratification for low-load HCCI combustion: Performance & fuel-PLIF measurements. SAE Transactions, 116, 1437–1460. https://doi.org/10.4271/2007-01-4130
21. Dec, J. E., & Sjoberg, M. (2003). A parametric study of HCCI combustion: The sources of emissions at low loads and the effects of GDI fuel injection. SAE Transactions, 112, 1119–1141. https://doi.org/10.4271/2003-01-3172
22. Walter, B., & Gatellier, B. (2002). Development of the high-power NADI concept using dual mode diesel combustion to achieve zero NOx and particulate emissions. SAE Transactions, 111, 779–787. https://doi.org/10.4271/2002-01-1744
23. Juttu, S., Thipse, S. S., Marathe, N. V., & Babu, M. G. (2007). Homogeneous charge compression ignition (HCCI): A new concept for near zero NOx and particulate matter (PM) from diesel engine combustion. SAE Technical Paper 2007-26-020. https://doi.org/10.4271/2007-26-020
24. Singh, A. P., & Agarwal, A. K. (2012). Combustion characteristics of diesel HCCI engine: An experimental investigation using external mixture formation technique. Applied Energy, 99, 116–125. https://doi.org/10.1016/j.apenergy.2012.03.060
25. Olsson, J. O., Tunestål, P., & Johansson, B. (2001). Closed-loop control of an HCCI engine. SAE Transactions, 110, 1076–1085. https://doi.org/10.4271/2001-01-1031
26. Saxena, S., & Bedoya, I. D. (2013). Fundamental phenomena affecting low temperature combustion and HCCI engines, high load limits and strategies for extending these limits. Progress in Energy and Combustion Science, 39(5), 457–488. https://doi.org/10.1016/j.pecs.2013.05.002
27. Dempsey, A. B., Walker, N. R., Gingrich, E., & Reitz, R. D. (2014). Comparison of low temperature combustion strategies for advanced compression ignition engines with a focus on controllability. Combustion Science and Technology, 186(2), 210–241. https://doi.org/10.1080/00102202.2013.858137
28. Omran, R., Younes, R., Champoussin, J. C., & Outbib, R. (2011). New indicated mean effective pressure (IMEP) model for predicting crankshaft movement. Energy Conversion and Management, 52, 3376–3382. https://doi.org/10.1016/j.enconman.2011.05.014
29. Roy, S., Banerjee, R., & Bose, P. K. (2014). Performance and exhaust emissions prediction of a CRDI assisted single cylinder diesel engine coupled with EGR using artificial neural network. Applied Energy, 119, 330–340. https://doi.org/10.1016/j.apenergy.2014.01.044
30. Rezaei, J., Shahbakhti, M., Bahri, B., & et al. (2015). Performance prediction of HCCI engines with oxygenated fuels using artificial neural networks. Applied Energy, 138, 460–473. https://doi.org/10.1016/j.apenergy.2014.10.088
31. Yaşar, H., Çağıl, G., Torkul, O., & Şişci, M. (2021). Cylinder pressure prediction of an HCCI engine using deep learning. Chinese Journal of Mechanical Engineering, 34(7), 1–12. https://doi.org/10.1186/s10033-020-00525-4
32. Heister, F., & Froehlich, M. (2001). Non-linear time series analysis of combustion pressure data for neural network training with the concept of mutual information. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 215(2), 299–304. https://doi.org/10.1243/0954407011525539
33. Benneth, C., Dunne, J. F., Trimby, S., & et al. (2017). Engine cylinder pressure reconstruction using crank kinematics and recurrently-trained neural networks. Mechanical Systems and Signal Processing, 85, 126–145. https://doi.org/10.1016/j.ymssp.2016.07.015
34. Maass, B., Deng, J., & Stobart, R. (2011). In-cylinder pressure modeling with artificial neural networks. SAE Technical Paper 2011-01-1417. https://doi.org/10.4271/2011-01-1417
35. Norouzi, A., Heidar, H., Shahbakhti, M., Koch, C. R., & Borhan, H. (2021). Model predictive control of internal combustion engines: A review and future directions. Energies, 14(19), 6251. https://doi.org/10.3390/en14196251
36. Goudarzi, K., Moosaei, A., & Gharaati, M. (2015). Applying artificial neural networks (ANN) to the estimation of thermal contact conductance in the exhaust valve of internal combustion engine. Applied Thermal Engineering, 87, 688–697. https://doi.org/10.1016/j.applthermaleng.2015.05.060
37. Şahin, F. (2015). Effects of engine parameters on ionization current and modeling of excess air coefficient by artificial neural network. Applied Thermal Engineering, 90, 94–101. https://doi.org/10.1016/j.applthermaleng.2015.06.100
38. Luján, J. M., Climent, H., García-Cuevas, L. M., & et al. (2017). Volumetric efficiency modelling of internal combustion engines based on a novel adaptive learning algorithm of artificial neural networks. Applied Thermal Engineering, 123, 625–634. https://doi.org/10.1016/j.applthermaleng.2017.05.087
39. Sarae, H. S., Taghavifar, H., & Jafarmadar, S. (2017). Experimental and numerical consideration of the effect of CeO2 nanoparticles on diesel engine performance and exhaust emission with the aid of artificial neural network. Applied Thermal Engineering, 113, 663–672. https://doi.org/10.1016/j.applthermaleng.2016.11.044
40. İpci D., Yılmaz E., Aksoy F., Uyumaz A., Polat S., Solmaz H. (2015). The Effects of iso propanol and n heptane Fuel Blends on HCCI Combustion Characteristics and Engine Performance. Makine Teknolojileri Elektronik Dergisi (elektronik), 12, 49-56.
41. Kunt, M. A., Calam, A., & Gunes, H. (2023). Analysis of the effect of lubricating oil viscosity and engine speed on piston–cylinder liner frictions in a single cylinder HCCI engine by GT-SUITE program. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 237(2), 399–409. https://doi.org/10.1177/09544089221132988
42. Yang, Y., Gao, L., Abbas, M., Elkamchouchi, D. H., Alkhalifah, T., Alturise, F., & Ponnore, J. J. (2023). Innovative composite machine learning approach for biodiesel production in public vehicles. Advances in Engineering Software, 184, 103501. https://doi.org/10.1016/j.advengsoft.2023.103501
43. Xiao, L., Dong, Y., & Dong, Y. (2018). An improved combination approach based on AdaBoost algorithm for wind speed time series forecasting. Energy Conversion and Management, 160, 273–288. https://doi.org/10.1016/j.enconman.2018.01.038
44. Freund, Y., & Schapire, R. (1997). A decision theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55, 119–139. https://doi.org/10.1006/jcss.1997.1504
45. Cao, Y., Miao, Q. G., Liu, J. C., & Gao, L. (2013). Advance and prospects of AdaBoost algorithm. Acta Automatica Sinica, 39(6), 745–758. https://doi.org/10.1016/S1874-1029(13)60052-X
46. Breiman, L., Friedman, J. H., Olshen, R.A., & Stone, C. J. (1984). Classification And Regression Trees. Chapman & Hall. 1-368. https://doi.org/10.1201/9781315139470
47. Kushwah, J.S., Kumar, A., Patel S., Soni R., Gawande, A., Gupta, S. (2022). Comparative study of regressor and classifier with decision tree using modern tools, Materials Proceedings, 56(6), 3571-3576. https://doi.org/10.1016/j.matpr.2021.11.635
48. Pekel, E. (2020). Estimation of soil moisture using decision tree regression. Theor Appl Climatol, 139, 1111–1119. https://doi.org/10.1007/s00704-019-03048-8
49. Şahin, S., & Torun, A. (2024). Comparison of engine performance and emission values of biodiesel obtained from waste pumpkin seeds with machine learning. Agriculture, 14(2), 227. https://doi.org/10.3390/agriculture14020227
50. Yang, R., Xie, T., & Liu, Z. (2022). The application of machine learning methods to predict the power output of internal combustion engines. Energies, 15, 3242. https://doi.org/10.3390/en15093242
51. Liu, J., Ulishney, C., & Dumitrescu, C. E. (2021). Random forest machine learning model for predicting combustion feedback information of a natural gas spark ignition engine. ASME Journal of Energy Resources Technology, 143(1), 012301. https://doi.org/10.1115/1.4047761
52. Yu, Z., Wang, Q., Chen, X., Yan, Y., Yang, R., Liu, Z., & Fu, J. (2022). The prediction of spark-ignition engine performance and emissions based on the SVR algorithm. Processes, 10(2), 312. https://doi.org/10.3390/pr10020312
53. Yang, Y., Gao, L., Abbas, M., Elkamchouchi, D. H., Alkhalifah, T., Alturise, F., & Ponnore, J. J. (2023). Innovative composite machine learning approach for biodiesel production in public vehicles. Advances in Engineering Software, 184, 103501. https://doi.org/10.1016/j.advengsoft.2023.103501
54. Kaleli, M. S., Kaleli, A., & Yalçıner, C. Ç. (2021). Torque prediction-based performance analysis of small-scale wind turbines using data-driven modelling methods. Erzincan University Journal of Science and Technology, 14(1), 260–269. https://doi.org/10.18185/erzifbed.758924
55. Polat, S., Solmaz, H., Calam, A., & Yılmaz, E. (2020). Estimation of the COVIMEP variation in a HCCI engine. Politeknik Dergisi, 23(3), 721–727. https://doi.org/10.2339/politeknik.567865
56. Rajak, U., Ağbulut, Ü., Dasore, A., & Verma, T. N. (2024). Artificial intelligence-based prediction of energy efficiency and tailpipe emissions of soybean methyl ester fuelled CI engine under variable compression ratios. Energy, 294(1), 130861. https://doi.org/10.1016/j.energy.2024.130861
57. Dhande, D. Y., Choudhari, C. S., Gaikwad, D. P., & Dahe, K. B. (2020). Development of artificial neural network to predict the performance of spark ignition engine fuelled with waste pomegranate ethanol blends. Information Processing in Agriculture, 10(4), 459–474. https://doi.org/10.1016/j.inpa.2022.05.001
58. Nagareddy, S., Rajeswaran, S. S., Jaganathan, D., Bala Subramanian, V. P. (2025). Turbocharged Six-Cylinder Direct Injection Hydrogen Fueled Spark Ignition Engine Combustion and NOx Control Model. Engineering Perspective, 5(3), 100-110. https://doi.org/10.29228/eng.pers.82203

Details

Primary Language

English

Subjects

Machine Theory and Dynamics, Automotive Engineering Materials, Automotive Engineering (Other)

Journal Section

Research Article

Authors

Ahmet Çelik ^*
0000-0002-6288-3182
Türkiye

Mehmet Akif Kunt
0000-0001-5710-7253
Türkiye

Publication Date

February 5, 2026

Submission Date

September 11, 2025

Acceptance Date

January 2, 2026

Published in Issue

Year 2026 Volume: 6 Number: 1

DOI

https://doi.org/10.64808/engineeringperspective.1782349

IZ

https://izlik.org/JA35ED22WS

Cite

RIS / Bibtex

APA

Çelik, A., & Kunt, M. A. (2026). A Study on Predicting Engine Performance Outputs by Machine Learning Algorithms in a Single Cylinder HCCI Engine. Engineering Perspective, 6(1), 57-68. https://doi.org/10.64808/engineeringperspective.1782349

AMA

1.Çelik A, Kunt MA. A Study on Predicting Engine Performance Outputs by Machine Learning Algorithms in a Single Cylinder HCCI Engine. engineeringperspective. 2026;6(1):57-68. doi:10.64808/engineeringperspective.1782349

Chicago

Çelik, Ahmet, and Mehmet Akif Kunt. 2026. “A Study on Predicting Engine Performance Outputs by Machine Learning Algorithms in a Single Cylinder HCCI Engine”. Engineering Perspective 6 (1): 57-68. https://doi.org/10.64808/engineeringperspective.1782349.

EndNote

Çelik A, Kunt MA (February 1, 2026) A Study on Predicting Engine Performance Outputs by Machine Learning Algorithms in a Single Cylinder HCCI Engine. Engineering Perspective 6 1 57–68.

IEEE

[1]A. Çelik and M. A. Kunt, “A Study on Predicting Engine Performance Outputs by Machine Learning Algorithms in a Single Cylinder HCCI Engine”, engineeringperspective, vol. 6, no. 1, pp. 57–68, Feb. 2026, doi: 10.64808/engineeringperspective.1782349.

ISNAD

Çelik, Ahmet - Kunt, Mehmet Akif. “A Study on Predicting Engine Performance Outputs by Machine Learning Algorithms in a Single Cylinder HCCI Engine”. Engineering Perspective 6/1 (February 1, 2026): 57-68. https://doi.org/10.64808/engineeringperspective.1782349.

JAMA

1.Çelik A, Kunt MA. A Study on Predicting Engine Performance Outputs by Machine Learning Algorithms in a Single Cylinder HCCI Engine. engineeringperspective. 2026;6:57–68.

MLA

Çelik, Ahmet, and Mehmet Akif Kunt. “A Study on Predicting Engine Performance Outputs by Machine Learning Algorithms in a Single Cylinder HCCI Engine”. Engineering Perspective, vol. 6, no. 1, Feb. 2026, pp. 57-68, doi:10.64808/engineeringperspective.1782349.

Vancouver

1.Ahmet Çelik, Mehmet Akif Kunt. A Study on Predicting Engine Performance Outputs by Machine Learning Algorithms in a Single Cylinder HCCI Engine. engineeringperspective. 2026 Feb. 1;6(1):57-68. doi:10.64808/engineeringperspective.1782349

Cited By

Multi-Dimensional 3D-CFD/Chemical-Kinetics Modeling of Natural Gas–Diesel RCCI Engines: Combustion Phasing, Efficiency and Emissions

Engineering Perspective

https://doi.org/10.64808/engineeringperspective.1802746

Effects of combustion and emission characteristics of a port fuel injection HCCI engine fueled with Isopropanol-Butanol-Ethanol and n-Heptane blends

Fuel

https://doi.org/10.1016/j.fuel.2026.139151

Leveraging Advanced Machine Learning Algorithms for Energy Efficiency and Carbon Footprint Reduction in Diesel Engines

International Journal of Automotive Science And Technology

https://doi.org/10.30939/ijastech..1896122