Signal Processing - March 2017 - 79

transform (HHT)-based knock detection method [49], which
allows for the decomposition of a multicomponent nonstationary
signal into individual components. A precise characterization of
the knock phenomenon can be observed from separated knock
resonance components using HHT.
Signal processing approaches for knock detection have
become a very productive approach to supporting engine
control and diagnostics. Due to today's ECU processing capabilities, the most popular methods used in mass production
applications are still those based on filtering. DFT methods
are becoming feasible for emerging ECUs with high-performance processors or with additional dedicated DSP coprocessors or special hardware systems. It is still not practical
for many advanced signal processing methods, such as timefrequency analysis and wavelet transform techniques, to be
performed on an ECU in real time; however, these techniques
can possibly be used in off-board analyses during production
development stages.

Knock Signal
2,000
1,500
Amplitude

1,000
0
-500
-1,000
-1,500
-2,000

0

20

Frequency (kHz)

40
60
Crank Angle (°)
(a)

80

Winger-Ville Spectrum

25

Summary and outlook
This article has provided an overview of how DSP is being
applied in several important areas associated with engine control and OBD. At a high level, the DSP techniques used in
these applications mainly involve frequency filtering, FFTs and
DFTs, time-frequency signal analysis, and wavelet transforms.
Additionally, a wide array of advanced signal processing techniques, including Kalman filtering, neural networks, the HHT,
and other techniques, have been studied and shown to be
promising, and the industry is anticipating that these methods
will become more valuable as their efficiency increases and
their cost of implementation decreases in the future. DSP techniques show great promise for solving many challenging problems in misfire detection, estimating individual cylinder
fuel-air ratios, and performing knock detection. Some of the
DSP algorithms that have been developed demonstrate unique
advantages that are difficult to obtain using other non-DSP
methods, particularly in terms of achieving high accuracy and
robustness when performing event detection and estimation of
underlying parameters.
As one surveys these different DSP applications for automobiles, what is evident is that over the past few decades there has
been an evolution in how DSP techniques have been researched
and applied to engine control and diagnostics. This evolution is
basically a reflection of the challenges facing automobile powertrain applications before they can be successfully integrated
into a production application: a newly developed method is
required to address an important safety and regulatory requirement, while achieving robust performance, being able to carry
out its processing in real time, having low implementation cost,
and requiring minimal to low calibration effort.
These challenges are complex, especially as an engine
needs to run in various ambient environments and under various specified operating conditions. Unfortunately, many methods can work for a special case or in a limited range, but they
are still not useful for real automotive applications because
they are unable to fulfill all of the required tasks faced in a

500

20
15
10
5
0

0

10

20
30
40
Crank Angle (°)
(b)

50

60

Figure 9. (a) High-pass filtered knock pressure signal; (b) Wigner-Ville
spectrum of several knock signals. (Figure reproduced from [48].)

real production vehicle. Thus, a useful DSP method has to
be robust enough to handle all these situations in production
without any exception.
Because of real-time engine control and OBD, DSP methods also should not be too complicated so that they can be
processed within a permitted time slot, while all of the other
processing tasks are being performed. For example, the time
interval between two engine firings is only 2.5 ms for an
eight-cylinder engine at an engine speed of 6,000 rev/min.
Frequency-domain-filtering techniques have a relatively low
computational time compared to most advanced DSP techniques. Since many engine signals have periodic characteristics, automotive engineers have artfully designed DSP
algorithms that take full advantage of these characteristics to
make their system design and sampling strategies more robust
and simpler, without having to rely blindly on heavy-hammer
approaches to solving problems.
Although ECU processing power continues to increase,
the engine control and diagnostic algorithms become more
and more sophisticated and complex thus outpacing available
computational resources. The application of many advanced
algorithms is currently limited by ECU processing capability. More powerful processing capability means more costs

IEEE SIgnal ProcESSIng MagazInE

|

March 2017

|

79



Table of Contents for the Digital Edition of Signal Processing - March 2017

Signal Processing - March 2017 - Cover1
Signal Processing - March 2017 - Cover2
Signal Processing - March 2017 - 1
Signal Processing - March 2017 - 2
Signal Processing - March 2017 - 3
Signal Processing - March 2017 - 4
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Signal Processing - March 2017 - 8
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Signal Processing - March 2017 - Cover3
Signal Processing - March 2017 - Cover4
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