Neural Network Based Models for Virtual NO<sub>x</sub> Sensing of Compression Ignition Engines

Matteo De Cesare; Federico Covassin

doi:10.4271/2011-24-0157

The paper focuses on the experimental identification and validation of different neural networks for virtual sensing of NO_x emissions in combustion compression ignition engines (CI). A comparison of several neural network architectures (NN, TDNN and RNN) has been carried out in order to evaluate precision and generalization in dynamic prediction of NO_x formation. Furthermore the model complexity (number and types of inputs, neuron and layer number, etc.) has been considered to allow a future ECU implementation and on line training. Suited training procedures and experimental tests are proposed to improve the models.

Several measurements of NO_x emissions have been performed through different devices applied to the outlet of a EURO 5 Common Rail diesel engine with EGR. The accuracy of the developed models is assessed by comparing simulated and experimental trajectories for a wide range of operating conditions.

The study highlights that history and proper inputs are significant for the output estimation, and good results can be achieved either through Recursive Neural Networks (RNN) or through Neural Networks (NN) with input history. A virtual NO_x sensor will offer significant opportunities for implementing on-board feed-forward and feedback control strategies in order to improve the performance and the diagnosis of the engine and of the after-treatment devices.

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Neural Network Based Models for Virtual NO _x Sensing of Compression Ignition Engines 2011-24-0157

SAE MOBILUS

Neural Network Based Models for Virtual NO x Sensing of Compression Ignition Engines 2011-24-0157

SAE MOBILUS

Neural Network Based Models for Virtual NO _x Sensing of Compression Ignition Engines 2011-24-0157