Discrimination of Pre-Ignition on Gasoline Engine Based on LSTM Neural Network and Ionic Current

After producing pre-ignition by rapidly establishing carbon deposition condition in a 1.5 L turbocharged gasoline direct injection engine, ionic current characteristics of pre-ignition were analyzed. A pre-ignition detection model of ionic current signal based on long short-term memory(LSTM) neural network was established using Python and TensorFlow. Under the condition of 1 500 r/min rotational speed and 72% load rate, K fold crossvalidation and particle swarm optimization were used to optimize hyper parameters. The results show that, the accuracy of LSTM neural network model established is 98.50%, and the accuracy of detecting pre-ignition before CA 10 is 76.09%. Compared with back propagation(BP) neural network and support vector machine(SVM), LSTM has a less root mean square error(RMSE) and is more advanced in discrimination. By receiver operating characteristic curve, it is proved that LSTM is a better discrimination model with a larger area under curve. Compared with the limit-based discrimination method, the accuracy of LSTM is higher. LSTM takes both accuracy and advance into account, and accords with the fundamental goal of judging pre-ignition based on ionic current signal. © 2022, Editorial Office of the Transaction of CSICE. All right reserved.