J. M. DENG ET AL.
54
DPF regeneration safely is highly critical [4]. The timing
for safely and efficiently regenerating the DPF has be-
come a major industrial challenge.
It can be seen that a control unit to trigger and control
the regeneration process is crucial for both DPF life and
fuel economy. The measurement of PM is crucial for the
trap system to enable the fine judgment as to when to
initiate the process. The frequency of the regeneration
and the life of the filter are compromised.
Diesel engine PM Prediction has always been a major
challenge to the industry [6, 7, 8]. The conventional
method to estimate the PM loading is pressure drop
measurements. But it is affected by exhaust flow varia-
tions and exhibits a low degree of sensitivity to DPF PM
loading and has bad dynamic response over transient
operating conditions. There is study investigating the use
of radio frequency (RF) to directly monitor measure DPF
PM accumulation levels. However, this technique is not
mature enough to be applied in commercial applications
as it is not easy to be calibrated. Hence, more reliable,
stable and accurate PM loading estimation or sensing
method should be studied. Computational fluid dynamics
(CFD) based PM models are computationally intensive
and are not suitable for control purpose and real time
measurement. Recently, neural networks have been used
in a wide variety of automotive applications. The advan-
tage of neural networks is their ability to be used as an
arbitrary function approximation mechanism which has
no requirement to represent the complex underlying
process and is an economic way to obtain the measure-
ment. Model based PM emission prediction method
could be a good alternative way [9]. Neural networks
have been successfully used for emissions prediction [10].
He et al. [11] built a model that considers several engine
parameters such as boost pressure and exhaust gas recir-
culation (EGR) and it generates several outputs including
PM emissions. Maass et al. presented a smoke prediction
neural network model using a three-layer autoregressive
model with exogenous inputs (NLARX) model to predict
PM [12]. Bose and Kumar [13] use fuzzy logic to predict
the engine emissions.
The simple way to handle the PM estimation is to use
black-box modeling as described in [14].This method is
used to estimate the PM successfully in both steady and
transient engine operation condition. The disadvantage is
the robustness and accuracy of the PM prediction based
on neural networks while different fuels are used in the
engines. The neural network model is built on training
data, which has no information on fuel types. Therefore,
the test of robustness and accuracy is important for the
PM estimation based on neural networks. In order to
carry out the robust and accurate tests, different compo-
sitions of Bio-diesel with standard diesel EN590 are
used.
In order to improve the engine performance and emis-
sions and ensure the sustainability of the fuel supplies,
bio-diesel is a promising alternative for diesels. Bio-
diesel leads to significant reductions in PM emissions,
although the percentage reduction varied with fuel com-
position and engine technology. The average PM reduc-
tions are 26% compared to conventional diesel fuel [15].
In this paper, standard diesel fuel (EN590) will be
blended with up to 20% bio-diesel fuel to test the effect
of different fuel resources on PM. The aim with these
tests is to ensure that with a realistic change in the fuel
composition the estimation of PM based on neural net-
works is still accurate enough to trig a regeneration cycle.
The virtual sensor of PM measurement based on neural
networks will be used to estimate the PM. With the
simulated sensor we will evaluate its performance, ro-
bustness and accuracy in predicting the PM.
In this paper a virtual sensor is proposed to measure
the PM. The purpose of the proposed virtual sensor is to
estimate the PM and to trigger a regeneration cycle for
diesel particulate filter.
In Section 1 - introduction, a brief background of the
research is introduced. Section 2 reviews the non-linear
autoregressive model with exogenous inputs (NLARX)
neural networks that can be used to predict PM. Section
3 described details of the test facility. Section 4 describes
the data collection and neural network training and ro-
bustness test. Section 5 provides conclusions on this
work.
2. Neural Networks
The field of virtual sensing has become more and more
popular with growing systems complexity such as in
combustion engine control. Its origin lies in the field of
estimators which are specified through physical and nu-
merical relations whereas virtual sensors are character-
ized through black-box approaches such as neural net-
works.
Neural networks can be split into the following three
categories:
1) single-layer feed forward networks (SLFN),
2) multi-layer feed forward networks (MLFN),
3) recurrent neural network (RNN).
The chosen network structure or architecture is crucial
for the output performance. Depending on the systems
characteristic: linear or non-linear, static or dynamic, the
network needs to be designed accordingly. Here, the pre-
diction of PM is recognised as highly dynamic and
non-linear that implies a recurrent network structure has
to be chosen to offer sufficient predictive capability. The
NLARX structure can accommodate the dynamics of the
system by feeding previous network outputs back into the
input layer. It also enables the user to define how many
Copyright © 2013 SciRes. CN