This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Comparative Analysis of Performance of Neural Estimators for Diagnostics in Engine Emission System
Journal Article
03-11-03-0018
ISSN: 1946-3936, e-ISSN: 1946-3944
Sector:
Topic:
Citation:
Fravolini, M., Cone, A., Napolitano, M., Pradhan, S. et al., "Comparative Analysis of Performance of Neural Estimators for Diagnostics in Engine Emission System," SAE Int. J. Engines 11(3):277-288, 2018, https://doi.org/10.4271/03-11-03-0018.
Language:
English
Abstract:
This article describes the results of a comparative performance analysis on the
use of neural estimators to accurately estimate the Differential Pressure (DP)
signal from diesel engine systems equipped with a Diesel Particulate Filter
(DPF) aftertreatment system. For most systems, there are known and modeled
relationships between system inputs and outputs; however, in the case of
nonlinear, time-varying systems a detailed modeling of the system might not be
readily available. Therefore, Artificial Neural Networks (ANNs) have been used
for developing critical relationship between system inputs (engine and
aftertreatment parameters) and system output (DP signal). Both batch (offline)
and online learning ANN estimators have been proposed. A control-oriented engine
out DPF-DP model is desirable for on-board applications as a virtual DPF-DP
sensor which could be used in parallel as an alternate analytical
redundancy-based sensor. Furthermore, in order to limit the online computational
effort, a limited set of inputs has been selected after a detailed correlation
analysis. The experimental validation demonstrate that the online learning
estimators provide better overall results in terms of accuracy and overall
robustness to time varying and non-linear conditions.