Deep neural network model with Bayesian hyperparameter optimization for prediction of NOx at transient conditions in a diesel engine - Shin, Seunghyup; Lee, Youngbok; Kim, Minjae; Park, Jihwan; Lee, Sangyul; Min, Kyoungdoug (Engineering applications of artificial intelligence, 2020)
Owing to increasing interest in the environment, particularly on air quality, regulations in the automobile industry have become stricter. Test cycles have been substituted to simulate real driving conditions, and they offer opportunities for researchers to satisfy regulations and predict emissions using models. The objective of this study is to develop a deep neural network (DNN) model, optimize its hyperparameters using the Bayesian optimization method, and use hidden-node determination logic to predict engine-out NOx emissions by using the worldwide harmonized light vehicles test procedure (WLTP) of diesel engines. A DNN network learns the internal relationships between inputs and target outputs even though they are complicated. However, the hyperparameters of DNNs are typically determined by researchers before training, and they affected the accuracy of the model. In this study, the hyperparameters of the DNN model such as the number of hidden layers, number of nodes in each hidden layer, learning rate, learning rate decay, and batch size are automatically optimized using the Bayesian optimization method. Some logical equations are combined with the number of nodes in the first hidden layer and the number of hidden layers to realize the model’s structure instead of using the number of hidden nodes in each hidden layer. Compared with grid search and random sampling, the Bayesian optimization method is a promising solution to optimize hyperparameters. In addition, a hidden-node determination logic further improved the accuracy of the model. The accuracy of the optimized model is indicated by an R2 value of 0.9675 with 14 input features. The result of cycle prediction shows that the mean absolute errors are approximately 16–17 ppm for four WLTP cycles, which are 1.6% of the maximum NOx value. These results indicate that the accuracy of the model is comparable to that of a physical NOx measurement device whose linearity is 1% of the full scale (5,000 ppm).