Optimal engine mapping performances for dual spark-plug ignition internal combustion engine using neural network

Many variables affect the performance and fuel consumption of internal combustion engines. 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 aimed to develop engine maps for dual ignition internal combustion engine using the Artificial Neural Network to predict the fuel consumption, generated torque, and find out the right combination of fire ignition on dual ignition systems to improve performance and reduce fuel consumption. The research was conducted with the initial step of retrieving the data engine map by using an engine scanner to find out the data on the current ECU. Then the data is modified to create a new engine map (modified engine mapping) 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 engine map 3 with an error value (Mean Square Error) of 0.002 and a regression value (R2) of 0.99. Modification map engine 3 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%.