柴油机NO_x是机动车主要的有害排放物质，精确测量NO_x排放有利于SCR尿素喷射的控制从而减少排放，而现有的氮氧传感器和通过标定获得的排放MAP均难以实现瞬态条件下NO_x的实时测量。使用主成分分析法（PCA）对全球统一瞬态试验循环（WHTC）的柴油机工况参数进行降维处理，基于长短期记忆神经网络（LSTM）搭建柴油机NO_x实时预测模型，并利用灰狼优化算法（GWO）对LSTM模型进行参数优化。结果显示：GWO-LSTM预测模型在未训练的数据集上的平均相对误差（MAPE）为3.23%，证明该模型能够精准实现柴油机NO_x排放的实时预测，并具有良好的泛化能力和可靠性，为以软件替代硬件实现柴油排放控制提供了参考。

NO_x emission of Diesel engine is the main harmful emission substance of motor vehicles; accurate measurement of NO_x emission is conducive to the control of urea injection to reduce emissions. However, the existing NO_x sensors and emission MAP obtained by calibration are both difficult to achieve real-time measurement of NO_x under transient conditions. Principal component analysis (PCA) was used to reduce the dimension of diesel engine operating parameters for world harmonized transient cycle (WHTC). A real-time diesel NO_x prediction model was built based on long and short-term memory (LSTM) neural network, and the parameters of LSTM were optimized by grey wolf optimization (GWO) algorithm. The results show that the mean absolute percentage error (MAPE) of GMOLSTM prediction model on the untrained data set is 3.23%, which proves that the model can accurately achieve real-time prediction of NO_x emissions of diesel engines. In addition, the model has good generalization ability and reliability, which provides a reference for the realization of diesel emission control with software instead of hardware.