数据驱动下农用车辆柴油机NOX排放预测模型

针对农用车辆柴油机NOX排放与实际运行工况之间的复杂非线性关系，提出一种数据驱动下的NOX排放预测模型。基于车辆OBD采集实际运行数据，通过小波阈值降噪降低原始数据的非平稳性，采用集成特征选择算法完成模型输入特征的选择，同时融合BiGRU和注意力机制构成BiGRU-Attention模型，同时利用贝叶斯优化进行模型超参数选择。基于实车道路测试数据集分析，提出的模型相对于LSTM、GRU和BiLSTM-Attention模型NOX瞬时排放预测校正系数分别提高7.65%、3.26%和4.09%，模型平均绝对误差维持在0.001 4 g/s，在不同车辆数据集上预测校正系数均保持在85%以上，可以有效进行实际场景下NOX排放的高精度预测，为农用车辆柴油机NOX排放预测控制提供数据支撑。

Data driven NOX emission prediction model for diesel engines in agricultural vehicles

In view of the complex nonlinear relationship between NOX emissions and actual operating conditions of diesel engines in agricultural vehicles, a data driven NOX emission prediction model is proposed. The actual operating data was collected based on vehicle OBD, and the non smoothness of the original data was reduced by wavelet threshold denoising. The integrated feature selection algorithm was used to complete the selection of model input features. The BiGRU-Attention model was constructed by integrating BiGRU and the attention mechanism, and bayesian optimization was also used to select the model hyperparameters. Based on the experimental analysis of the actual vehicle road test dataset, compared with the LSTM, GRU, and BiLSTM-Attention models, the prediction R square of the model in this paper was increased by 7.65%, 3.26%, and 4.09%, respectively. The mean absolute error of the model was maintained at 0.001 4 g/s. The prediction R square was maintained at 85% on different vehicle datasets. This model can effectively predict NOX emissions with high accuracy in real scenarios and provides data support for NOX emission prediction control of diesel engines in agricultural vehicles.