This is an open access article distributed under the CC BY 4.0
Volume 20 article 920 pages: 195-205
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%.
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.
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