Istrazivanja i projektovanja za privreduJournal of Applied Engineering Science

OPTIMAL ENGINE MAPPing PERFORMANCES FOR DUAL SPARK-PLUG IGNITION INTERNAL COMBUSTION ENGINE USING NEURAL NETWORK


DOI: 10.5937/jaes0-28542 
This is an open access article distributed under the CC BY 4.0
Creative Commons License

Volume 20 article 920 pages: 195-205

M Munadi*
Diponegoro University, Tembalang, Indonesia

Mochammad Ariyanto
Diponegoro University, Tembalang, Indonesia

M Muchammad
Diponegoro University, Tembalang, Indonesia

Joga D. Setiawan
Diponegoro University, Tembalang, Indonesia

The performance and the fuel consumption of internal combustion engines are affected by many variables. Some of the most influential main variables include air, fuel, ignition, and compression. Spark plugs that play role in the ignition of fire have limitations in the propagation of fire due to their position because of the dual ignition technology. This study aims to develop engine maps for dual ignition internal combustion engine using the Artificial Neural Network to predict the fuel consumption, generated torque, and the right combination of fire ignition on dual ignition systems to improve performance and reduce fuel consumption. This research was conducted with the initial step of retrieving the engine map data using an engine scanner to obtain the data on the standard ECU. Then, the data is modified to create a new engine map (modified engine map) that combines ignition timing 2 with a range of 0.5o - 2o. The test results show different torque and fuel consumption values in four modified engine maps. The optimum engine mapping is obtained on the third engine map modification with an error value (Mean Square Error) of 0.002 and a regression value (R2) of 0.99. The third engine map modification with a combination of ignition timing 2 of 1.5o on ignition timing 1 shows the highest torque result, with an increase in torque of 14.1% and a decrease in fuel consumption of 17.5%.

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This research was funded by the Ministry of Research and Technology/National Research and Innovation Agency of the Republic of Indonesia, Basic Research Grant (Hibah Penelitian Dasar) under contract No. 257-27/UN7.P4/PP/2019 and 257-27/UN7.6.1/PP/2020.


1. Ryan, F, B., (2010), Utilization of a Neural Network to Improve Fuel Maps of an Air-Cooled Internal Combustion Engine, Master Thesis, The Russ College of Engineering and Technology of Ohio University: Ohio.

2. Zambre, D., Shintre, G., Patil, B., (2014), Digital Twin Spark Ignition Using Mechatronics, IOSR Journal of Mechanical and Civil Engineering, 73-78.

3. Khair, M., Ishak A, (2010), The Effect of The Ignition Dwell Time at Constant Speed for CNGDI Engine, Australian Journal of Basic and Applied Sciences, vol. 4 no. 10, 4691-5694

4. Nanlohy, H, Y., (2012), "Perbandingan Variasi Derajat Pengapian Terhadap Efisiensi Termal dan Konsumsi Bahan Bakar Otto Engine BE50", Dinamika Jurnal Ilmiah Teknik Mesin, vol. 3. no 2., 211-215.

5. Han, W. Q., & Yao, C. D., (2015). Research on high cetane and high-octane number fuels and the mechanism for their common oxidation and auto-ignition. Fuel, vol. 150, 29-40. DOI: 10.1016/j.fuel.2015.01.090

6. Bilgin, At, Altin, I, Sezer, I., (2009). Investigation of the Effect of Dual Ignition on the Exhaust Emissions of an SI Engine Operating on Different Conditions by Using Quasi-dimensional Thermodynamic Cycle Model, Strojarstvo, vol. 51, no. 5, 459-464.

7. Nirala, D., Hathile, A., Raj, J., Sen, P. K., & Bohidar, S. K. (2014), Twin‟ s Spark Plug Performance Study on Single Cylinder SI Engine with Gasoline Fuel a Technical Review. International Journal of Research, vol. 1, no. 1, 77 – 801.

8. Garg, A. B., Diwan, P., & Saxena, M., (2012). Artificial neural networks-based methodologies for optimization of engine operations. International Journal of Scientific & Engineering Research, vol 3, no. 5, 1-5.

9. Paridawati, Sinaga, N., (2014). Optimasi Efisiensi Motor Bakar Sistem Injeksi Menggunakan Metode Simulasi Artificial Neural Network. Prosiding SNATIF, 161-164.

10. Müller, R., Hemberger, H. H., & Baier, K. (1997). Engine control using neural networks: a new method in engine management systems. Meccanica, vol. 32, no. 5, DOI: 423-430. 10.1023/A:1004203832719

11. Carbot-Rojas, D. A., Escobar-Jiménez, R. F., Gómez-Aguilar, J. F., García-Morales, J., & Téllez-Anguiano, A. C. (2020). Modelling and control of the spark timing of an internal combustion engine based on an ANN. Combustion Theory and Modelling, vol. 24, no. 3, 510-529. DOI: 10.1080/13647830.2019.1704888

12. Kekez, M., Radziszewski, L., & Sapietova, A. (2017). Application of artificial intelligence methods to modeling of injector needle movement in diesel engine. Procedia Engineering, 177, 303-306. DOI: 10.1016/j.proeng.2017.02.229

13. Serikov, S. A. (2010). Neural network model of internal combustion engine. Cybernetics and Systems Analysis, vol. 46, no. 6, 998-1007. DOI: 10.1007/s10559-010-9281-3

14. Kannan, G. R., Balasubramanian, K. R., & Anand, R. (2013). Artificial neural network approach to study the effect of injection pressure and timing on diesel engine performance fueled with biodiesel. International Journal of Automotive Technology, vol 14, no. 4, 507-519. DOI:10.1007/s12239-013-0055-6

15. Cay, Y. (2013). Prediction of a gasoline engine performance with artificial neural network. Fuel, vol. 111, 324-331. DOI: 10.1016/j.fuel.2012.12.040

16. Çay, Y., Korkmaz, I., Çiçek, A., & Kara, F. (2013). Prediction of engine performance and exhaust emissions for gasoline and methanol using artificial neural network. Energy, vol. 50, 177-186. DOI: 10.1016/j.energy.2012.10.052

17. Liu, Z., Zuo, Q., Wu, G., & Li, Y. (2018). An artificial neural network developed for predicting of performance and emissions of a spark ignition engine fueled with butanol–gasoline blends. Advances in Mechanical Engineering, 10(1), 1687814017748438.

18. Mansor, W. N. W., Abdullah, S., Razali, N. A., Albani, A., Ramli, A., & Olsen, D. (2019, November). Prediction of emissions of a dual fuel engine with Artificial Neural Network (ANN). In IOP Conference Series: Earth and Environmental Science (Vol. 373, No. 1, p. 012007). IOP Publishing. DOI: 10.1088/1755-1315/373/1/012007

19. Turnbull, S., (2008), Safe Tuning Guide for Buell’s using ECM Spy. Software Manual Guide.