A computationally efficient Kalman filter based estimator for updating look-up tables applied to NOx estimation in diesel
engines
NOx estimation in diesel engines is an up-to-date problem but still
some issues need to be solved. Raw sensor signals are not fast enough
for real-time use while control-oriented models suffer from drift and
aging. A control-oriented gray box model based on engine maps and
calibrated off-line is used as benchmark model for NOx
estimation. Calibration effort is important and engine
data-dependent. This motivates the use of adaptive look-up tables. In
addition to, look-up tables are often used in automotive control
systems and there is a need for systematic methods that can estimate
or update them on-line. For that purpose, Kalman filter (KF) based
methods are explored as having the interesting property of tracking
estimation error in a covariance matrix. Nevertheless, when coping
with large systems, the computational burden is high, in terms of time
and memory, compromising its implementation in commercial electronic
control units. However look-up table estimation has a structure, that
is here exploited to develop a memory and computationally efficient
approximation to the KF, named Simplified Kalman filter
(SKF). Convergence and robustness is evaluated in simulation and
compared to both a full KF and a minimal steady-state version, that
neglects the variance information. SKF is used for the online
calibration of an adaptive model for Nox estimation in dynamic engine
cycles. Prediction results are compared with the ones of the benchmark
model and of the other methods. Furthermore, actual online estimation
of Nox is solved by means of the proposed adaptive structure. Results
on dynamic tests with a diesel engine and the computational study
demonstrate the feasibility and capabilities of the method for an
implementation in engine control units.
Carlos Guardiola, Benjamin Pla, David Blanco-Rodriguez and Lars Eriksson
Control Engineering Practice,
2013
Informationsansvarig: webmaster
Senast uppdaterad: 2021-11-10
LiU Homepage
