基于离子电流与LSTM神经网络的汽油机早燃判断  

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

作　　者：丁伟奇 王金秋 胡宗杰[1] 李明龙 董光宇[1] 李理光[1] Ding Weiqi;Wang Jinqiu;Hu Zongjie;Li Minglong;Dong Guangyu;Li Liguang(School of Automotive Studies,Tongji University,Shanghai 201804,China)

机构地区：[1]同济大学汽车学院,上海201804

出　　处：《内燃机学报》2022年第2期171-178,共8页Transactions of Csice

基　　金：国家自然科学基金资助项目(51761135105).

摘　　要：通过一台1.5 L的废气涡轮增压缸内直喷汽油发动机快速建立积碳工况,从而引发早燃,对比了正常工况和早燃工况的离子电流信号波形特征.并利用Python和TensorFlow建立了基于离子电流信号长短期记忆(LSTM)神经网络的早燃检测模型,以转速为1500 r/min、负荷为72%工况下的早燃判断为例,在离线分析中模拟在线采集过程,采用K折交叉验证和粒子群优化算法对超参数进行优化,结果表明:LSTM神经网络模型的判别准确率达到98.50%,同时在CA 10前能够准确判别出早燃的概率为76.09%.与前馈(BP)神经网络和支持向量机(SVM)相比,具有更小的均方根误差(RMSE)以及更好的判别提前性,利用受试者工作特征曲线法对3种深度学习分类模型进行对比,LSTM模型曲线下面积更大,是性能更优的分类模型;与传统的阈值判别法相比,准确性更高,是一种兼顾准确性和提前性的判别模型,符合基于离子电流信号判断早燃的根本目标.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 1500r/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.Com-pared 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.

关 键 词：汽油机 早燃 离子电流 长短期记忆 神经网络 

分 类 号：TK411.2[动力工程及工程热物理—动力机械及工程]