Recurrent Neural Network to Estimate Intake Manifold O2 Concentration in a Diesel Engine

• Published in: 2020 20th International Conference on Control, Automation and Systems (ICCAS) pp. 715 - 720
• Main Authors: Ventura, Loris, Malan, Stefano A.
• Format: Conference Proceeding
• Language: English
• Published: Institute of Control, Robotics, and Systems - ICROS 13.10.2020
• Subjects: Data models; Diesel engine; Engine modelling and control; Engines; Load modeling; Mathematical model; Neural Networks; Nonlinear system identification; Steady-state; Training; Valves
• ISSN: 2642-3901
• DOI: 10.23919/ICCAS50221.2020.9268307

Emission regulations are becoming more and more stringent, especially on NO x pollutants, making diesel engines with their embedded control systems more and more complex. To ensure a correct and clean engine functioning, all the control strategies related to aftertreatment, fuel injection and air-path have to exploit or target the intake manifold O 2 concentration. The O 2 concentration is strictly related to engine-out NO x emissions and an accurate model, to be implemented in emission control systems, is essential. The paper addresses the modeling of the intake O 2 concentration in a turbocharged diesel engine by means of a Recurrent Neural Network with simulation focus and fed with four inputs. The inputs are engine load, engine speed and the position of Exhaust Gas Recirculation and Variable Geometry Turbochargers valves. Training and validation data are generated using the engine simulation tool GT-Power implementing a detailed model of the engine while the training procedure is performed in MATLAB environment through NNSYSID toolbox. The performances of the obtained model are satisfactory in different tests and the model is able to account for the engine nonlinearities during transients.