Neural network models for virtual sensing of NOx emissions in automotive diesel engines with least square-based adaptation

作者：Arsie Ivan^*; Cricchio Andrea; De Cesare Matteo; Lazzarini Francesco; Pianese Cesare; Sorrentino Marco

来源：Control Engineering Practice, 2017, 61: 11-20.

DOI：10.1016/j.conengprac.2017.01.005

• Modeling, supervision, control and diagnosis of
• automotive systems
• Engine modeling and control
• Automotive system identification and modeling
• NOx emissions
• Neural networks
• Least square adaptation

To meet current Diesel engine pollutant legislation, it is important to manage after-treatment devices. The paper describes the development of Neural Network based virtual sensors used to estimate NOx emissions at the exhaust of automotive Diesel engines. Suitable identification methodologies and experimental tests were developed with the aim of meeting the conflicting needs of feasible on-board implementation and satisfactory prediction accuracy. In addition, since the prediction of control-oriented models is typically affected by engine aging and production spread as well as components drift, least square technique features were exploited in order to overcome these issues by adapting the virtual sensor output. The NOx adaptive virtual sensor was tested via comparison with experimental data, measured at the engine test bench on a turbocharged common-rail automotive Diesel engine. Furthermore, besides model validation, the experimental measurements were modified to simulate a sensor drift in order to enable full assessment of the proposed LS-based algorithm adaptation capabilities.

• 出版日期2017-4