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Research on Misfire Detection Algorithms for Motorcycle Engine Firing at Uneven IntervalsTechnical Paper
Key Words: misfire detection, OBD II, internal combustion engine, motorcycle, signal processing
Katsunori TASAKI*1
1. Introduction
Exhaust gas from internal combustion engines
contains CO, HC, NOx and CO2 and has been one
cause of recent serious environment problems such
as global warming, air pollution and so on. On
the other hand, the market for internal combustion
engine automobiles and motorcycles keeps growing
because of their convenience. Therefore, i t is
necessary to take measures for environmental
issues. Many countries and regions have adopted
regulations on the amount of emissions. These
regulations have become stricter in recent years.
Many emissions reduction technologies such as
fuel injection systems with oxygen feed-back
control and a 3-way catalytic converter have been
developed also for motorcycles.
Regulations for environment protection do not
involve only restrictions on emissions but also the
introduction of OBD systems which automatically
diagnose malfunctions in the vehicle and inform
the driver of the fact. The detection targets of the
OBD II system include an emission control system
malfunction that causes deterioration in tailpipe
emissions. With this system, it is possible to detect
cases where emissions continuously exceed the
regulation limits. The OBD II system has already
been implemented in automobiles. For motorcycles,
implementation will become mandatory when Euro-
5 emission regulations are introduced in Europe and
Bharat Stage VI (BS-VI) emission regulations, in
India.
One item for which detection is mandatory in
OBD II regulations is misfire. Misfire occurs when
the engine does not fire correctly due to ignition
failure or poor combustion of the air fuel mixture,
resulting in serious deterioration of tailpipe emissions
due to discharge of unburned gas. It may also cause
deterioration of the catalytic converter. For these
reasons, the OBD II regulations require detection
and warning for misfiring occurrences which cause
worse tailpipe emissions than the specified levels
and/or pose a risk of catalytic converter erosion by
overheating.
Misfire detection technology for automobiles has
been well developed and there are many strategies
that are currently employed, using variation in
characteristics, such as crank angular velocity,
cylinder pressure or ionic current. These technologies
have proven to be effective in the detection of
misfiring in automobile engines.
However, in the case of motorcycle engines,
have the following characteristics compared with
automobile engines:
- there are unique engine variations such as
engines with uneven firing intervals; and
Research on Misfire Detection Algorithms for
Motorcycle Engine Firing at Uneven Intervals※
*1 System Development Department, R&D Operations
※ Received 14 May 2018, Content reprinted from SAE Technical Paper 2017-32-0052, © SAE International. Reprinted with permission. Further distribution of this material is not permitted without prior permission from SAE International.
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Keihin Technical Review Vol.7 (2018)
TechnicalPapers
sensors or other devices. Firstly, performance
of these algori thms was evaluated efficiently
and quantitatively, by control simulation using
various measurement data as input. The algorithm
which showed good potential in simulation was
subsequently integrated in the ECU in order to
confirm its effectiveness through vehicle testing.
Finally, this paper summarizes the prospect of
practical utilization suggests directions for future
research.
2. Testing Environment
2.1. Test Engine
In this research, a motorcycle with a water-
cooled uneven firing twin-spark V-twin engine was
used as a test vehicle. The engine was installed with
a twin-spark firing system in which two spark plugs
were equipped for each cylinder. Table 1 shows
the specifications of the engine. The engine was
developed for a high performance sports motorcycle.
2.2. Trigger Wheel / Pick-Up Sensor / Timing
Teeth
The test vehicle had a trigger wheel and a pick-
up sensor used for fuel injection and ignition control.
The trigger wheel with 22 teeth (placed at each 15
degrees of rotation with 2 continuous missing teeth)
on its periphery was mounted on an extension of the
crankshaft. A pick-up sensor was installed on the
outside of the trigger wheel. It detected the edge of
the teeth and generated a sinusoidal wave.
The positions of these devices are shown in
Figure 2 and Figure 3. The crank angular velocity
can be calculated using these devices.
In addition, the missing tooth sections correspond
to the following 2 stroke timing points;
- Cyl. #1: Suction stroke, Cyl. #2: Expansion
stroke
- there is a lower combustion stability on no-load
or low-load driving points because the engines
are designed for high-power and high-speed
performance.
As an example, Figure 1 shows the difference
in the Indicated Mean Effective Pressure (IMEP)
between a motorcycle engine and an automobile
engine when running at a steady speed of 50km/
h. In this case, the Coefficient of Variation (COV)
calculated from the IMEP of the motorcycle engine
is 13.77 and that of the automobile engine is 1.27.
This reveals that the combustion stability of the
motorcycle engine is considerably lower than that of
the automobile engine.
Moreover, as a complete vehicle, a less expensive
system is required and there is only limited space
for the installation of hardware.
Furthermore, the system needs to detect misfiring
at a higher engine speed than that for automobiles.
Therefore, it is considerably difficult to adapt misfire
detection methods for automobiles to motorcycles.
This paper presents the results of our study into
misfire detection algorithms focusing on the uneven
firing of a motorcycle V-twin engine. Some of the
proposed algorithms use variation characteristics in
crank angular velocity for detection. Utilization of
these algorithms would not lead to any increase in
cost, but only limited installation space is necessary
since there are no requirements for any additional
Fig. 1 Difference in IMEP between motorcycle engine and automobile engine
IMEP
[bar
]
21.81.61.41.2
10.80.60.40.2
00 100
Cycle [-]
Motorcycle Engineat NE=3,400 rpm, PM=37.0 kPa
Automobile Engineat NE=1,300 rpm, PM=37.3 kPa
200 300
IMEP
[bar
]
87.87.67.47.2
76.86.66.46.2
60 100
Cycle [-]200 300
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Research on Misfire Detection Algorithms for Motorcycle Engine Firing at Uneven Intervals
- Cyl. #1 : Expansion stroke, Cyl. #2 : Suction
stroke
Figure 4 shows the relationship between the
missing tooth sections and the stroke timing of each
cylinder.
2.3. Misfiring Method
Misfi re generat ion sof tware was designed
and integrated in the ECU in order to generate
intentional misfire. This software generates an
ignition cut-off at equal intervals by presetting the
target cylinder, the total number of ignition events
and the number of times for misfire generation. For
example, when cylinder #1 is set to generate one
misfire for each ten combustion cycles, nine ignitions
and one ignition cut-off are repeated alternately.
The test engine adopted a twin-spark firing
system. Therefore, when misfire was generated,
ignition cut-off was executed for both plugs on the
cylinder.
Table 1 Specification of Test Engine
Fig. 2 Mounted position of trigger wheel
Displaced Volume 1,301 ccNumber of Cylinders 2 cylindersEngine Architecture V-type (Angle of the V : 75 deg)Phase Difference Cyl. #1 to Cyl. #2 : 435 degStroke 71 mmBore 108 mmCompression Ratio 13 : 1Valve Train Layout DOHC 4 valvesCooling Type Water cooledMaximum Output 118 kw / 8,750 rpmMaximum Torque 140 Nm / 6,750 rpm
Fig. 3 Timing teeth
Pick-Up Sensor
Timing Teeth(Periphery of
Trigger Wheel)
Crank ShaftAxis
Pick-Up Sensor
Timing Teeth(Periphery of
Trigger Wheel)
Fig. 4 Relationship between missing tooth sections and stroke timing
TDC / BDC
Engine stroke
Output ofPick-Up sensor
Exhaust Compression Exhaust
#2 Cyl.
#1 Cyl.
#1 Cyl.Compression
TDC
ExhaustCompressionSuction Compression
Missing toothsection
(45 deg)
Missing toothsection
(45 deg)
#2 Cyl.Compression
TDC
ExpansionSuction
SuctionExpansion
3. Pre-testing Results
In this research, misfire detection algorithms
using crank angular velocity focused on the fact
that when misfire occurs, no combustion energy
is generated, and thus the crank angular velocity
decelerates. Incidentally, the teeth intervals and
missing teeth of the trigger wheel influenced the
linearity of the crank angular velocity calculation
and affected misfire detection accuracy. The aim
of this research was to create an algorithm misfire
detection with a trigger wheel missing 2 teeth at 15
degree intervals as installed in the test vehicle.
In addition, in conventional misfire detection
algorithms using crank angular velocity, digital
filtering calculations have been often used to extract
characteristic frequency components at misfiring or
to remove components unnecessary for detection.
In order to design such filtering calculations, it is
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Keihin Technical Review Vol.7 (2018)
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Fig. 5 Difference of IMEP value and crank angular velocity variation
necessary to check the frequency components of
angular velocity by normal combustion or misfiring.
Prior to the construction and evaluation of misfire
detection algorithms, the following two items were
pretested.
1. Confirmation of lack of combustion and crank
angular velocity variation
2. Frequency analysis of crank angular velocity
3.1. Confirmation of Lack of Combustion and
Crank Angular Velocity Variation
Purpose:
The purpose of this first stage of testing is to
confirm lack of combustion by misfire generation
and to also confirm that decrease of crank angular
velocity due to lack of combustion can be observed
at 15-degree intervals using the trigger wheel
equipped with missing teeth and the pick-up sensor.
Method:
With the test vehicle secured to a chassis
dynamometer, misfire was generated by software.
The lack of combustion was confirmed from analysis
of the IMEP and angular velocity variation was
calculated from the output of the pick-up sensor. The
same test process was carried out for each cylinder.
Result:
Due to misfiring, IMEP decreased greatly. From
this result, it was evident that lack of combustion
was occurring by misfiring. Additionally, at the
same time with IMEP decrease, a decrease in crank
angular velocity was also observed. Figure 5 shows
the difference of IMEP value and crank angular
velocity variation at NE=1,400rpm and 9,500rpm.
Conclusion/Decision:
A lack of combustion occurred due to misfiring.
At that time, decrease of crank angular velocity
was confirmed by calculations using the trigger
wheel with two missing teeth at 15-degree intervals.
Although this tendency was found to be weaker
1,400 rpm 9,500 rpm
Cyl
. #1
Mis
firin
gC
yl. #
2 M
isfir
ing
-40
-30
-20
-10
0
10
20
30
40
0 90 180 270
Cra
nk A
ngul
ar V
eloc
ity (r
ad/s
)
Crank Angle (deg)
-40
-30
-20
-10
0
10
20
30
40
0 90 180 270
Rel
ativ
e C
rank
Ang
ular
Vel
ocity
(r
ad/s
)
Crank Angle (deg)
Cyl. #2 Comp.TDC Cyl. #2 Comp.TDC
-40
-30
-20
-10
0
10
20
30
40
0 90 180 270 360
Rel
ativ
e C
rank
Ang
ular
Vel
ocity
(r
ad/s
)
Crank Angle (deg)
Cyl. #1 Comp.TDC-40
-30
-20
-10
0
10
20
30
40
0 90 180 270 360
Rel
ativ
e C
rank
Ang
ular
Vel
ocity
(r
ad/s
)
Crank Angle (deg)
Cyl. #1 Comp.TDC
Normal combustionIMEP = 1.03
MisfiringIMEP= -0.56
Normal combustionIMEP = 3.94
MisfiringIMEP = -0.71
Normal combustionIMEP = 1.06
MisfiringIMEP = -0.55
Normal combustionIMEP = 3.83
MisfiringIMEP = -1.05
when the NE was higher, as a result of these tests,
misfire detection using the trigger wheel and pick-up
sensor was judged possible for this test engine.
3.2. Frequency Analysis of Crank Angular
Velocity
Purpose:
The purpose of this analysis was to check
frequency components of angular veloci ty in
test engine for reference in the design of misfire
detect ion algori thms and also to confirm the
characterist ics of an uneven fir ing engine by
comparing with frequency analysis results from an
even firing engine.
Method:
With the test vehicle secured to a chassis
dynamo, angular velocity calculated by the output of
the pick-up sensor was analyzed using a Fast Fourier
Transform (FFT) algorithm in post-processing. Also,
the analysis results were compared with results from
a comparison engine (even firing) which had been
preliminarily tested. Table 2 shows the specifications
of the comparison engine.
Results:
As a result of the FFT analysis of angular
velocity of the test engine, i t was found that
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Research on Misfire Detection Algorithms for Motorcycle Engine Firing at Uneven Intervals
occurred when misfiring. Results of the FFT analysis
is shown in Figure 7.
Conclusion/ Decision;
In the test engine, the 0.5th order of crankshaft
rotation was the basic frequency component in both
normal combustion and misfiring. In addition, a
distinct frequency component occurred when there
was misfiring in the comparison engine (even firing),
but this did not occur in the test engine (uneven
firing). The reason is assumed to be that the crank
angular velocity variation patterns from combustion
for each cylinder are different.
Table 2 Specification of comparison engine
Fig. 6 Result of FFT analysis (test engine)
Fig. 7 Result of FFT (Comparison engine)
Normal combustion
Frequency (1/deg)
#1 Cyl. Misfiring
Pow
er S
pect
rum
0 0.005
120
100
80
60
40
20
0
Pow
er S
pect
rum
120
100
80
60
40
20
0
0.01 0.015 0.02 0.025 0.03
Frequency (1/deg)0 0.005 0.01 0.015 0.02 0.025 0.03
0.5th order
0.5th order
1st order
1st order
#2 Cyl. Misfiring
Pow
er S
pect
rum
120
100
80
60
40
20
0
Frequency (1/deg)0 0.005 0.01 0.015 0.02 0.025 0.03
0.5th order
1st order
Normal combustion
Frequency (1/deg)
50% Misfiring
Pow
er S
pect
rum
0 0.005
60
50
40
30
20
10
0
Pow
er S
pect
rum
60
50
40
30
20
10
0
0.01 0.015 0.02 0.025 0.03
Frequency (1/deg)0 0.005 0.01 0.015 0.02 0.025 0.03
0.5th order
0.5th order
0.25th order
Displaced Volume 109.1 ccNumber of Cylinders 1 cylinderStroke 55.6 mmBore 50.0 mmCompression Ratio 9 : 1Valve Train Layout OHC 4 valvesCooling Type Air cooledMaximum Output 6.4 kw / 7,500 rpm Maximum Torque 9.36 Nm / 5,500 rpm
the 0.5th order of crankshaft rotation is the basic
frequency component. Additionally, even if either
of the cylinders was misfiring, there was no distinct
frequency component. The result of FFT analysis is
shown in Figure 6.
O n t h e o t h e r h a n d , t h e r e s u l t s f r o m t h e
comparison single cylinder engine show that the 0.5th
order of crank-shaft rotation is the basic frequency
component. Additionally, a distinct frequency
component at the 0.25th order of crankshaft rotation
4. Detection Algorithms
In this research, four algorithms were evaluated
in parallel and compared for accuracy. These
algorithms were composed of four parts, and the
input used is the time between the edges of pulses
calculated from the output of the pick-up sensor.
Misfire detection was performed by comparing
the detection index parameter value calculated from
each algorithm with a preset threshold value. Figure
8 shows the construction of each algorithm. The
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a decrease in misfire detection accuracy. In order
to prevent such a decrease, a moving average
calculation, a kind of an LPF calculation, was
adopted. Since moving average calculation can be
applied to the number of data, crank angle, and not
to time, it is possible to remove an intended high
frequency component regardless of the speed of the
crank angular velocity. Incidentally, each detection
index parameter calculation of Part 4 was premised
by monitoring the variation pattern of crank angular
velocity due to combustion. Therefore, it is not
preferable to remove components due to combustion
by moving average calculation. As a result of the
pre-testing, the component due to combustion was a
component at the 0.5th order of crankshaft rotation.
Therefore, in this research, by adopting a moving
average calculation of six data, which constitutes a
90-degree section, only values below that of the 4th
order of the component of rotation were taken into
account, and thus high frequency components were
removed.
The moving average value of crank angular
velocity (ωMAn) was calculated by equation 3.
61
MAn Σ=ω CRKnω0
i = –5 (3)
This calculation was applied to all algorithms.
As an example, Figure 9 shows the calculation
results of ωCRKn and ωMAn for misfiring of the cylinder
#1 at NE=5,000rpm. The high frequency components
were removed while leaving the characteristic
frequency components due to combustion.
4.3. Part 3: Identifying Calculation
In an uneven firing engine, the variation patterns
of crank angular velocity show large differences
for each cyl inder. This ca lcula t ion a imed a t
identifying the characterizing portion of combustion
corresponding to the crank angular velocity variation
detail specification of each algorithm is described
later in this paper.
4.1. Part1: Crank Angular Velocity Calculation
Normally, the pick-up sensor would generate a
sinusoidal wave rising every 15 degrees. When the
time taken to rotate 15 degrees is t, the average
c rank angula r ve loc i ty ω 15 ( rad / s ) would be
calculated by equation 1.
12t15 =ωπ
(1)
In the missing tooth section of the trigger wheel,
a sinusoidal wave was generated at 45-degree
intervals. The crank angular velocity ω 45 (rad/s) at
that time was calculated by equation 2, and this
value was generated three times continuously.
4t45 =ωπ
(2)
Calculated crank angular velocity was generated
in the next part as continuous data (ω CRKn). This
means that continuous 24 data per crank rotation
was the output for the next part.
This calculation was applied to all algorithms.
4.2. Part 2: Low Pass Filter (LPF) Calculation
Unnecessary high frequency components were
included in the crank angular velocity as calculated
in Part 1, and these components may have caused
Fig. 8 Construction of algorithms
DifferenceCalculation
AccumulatingCalculation
Crank Angular Velocity Calculation
Time between crank pulse edge (ms)
AccumulatingCalculation
AccumulatingCalculation
#1 Calibrated #2 Calibrated
Input
Misfire Detection Algorithms
Algorithm 1 Algorithm 4Algorithm 3Algorithm 2
LPF Calculation
RelativeAngular Velocity
Calculation
RelativeAngular Velocity
Calculation
RelativeAngular Velocity
Calculation
Identifying Calculation
Part 1
Part 2
Part 3
Part 4
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Research on Misfire Detection Algorithms for Motorcycle Engine Firing at Uneven Intervals
for each cylinder and formulating it so that the
combustion by each cylinder could be regarded as
equivalent. With this calculation, we were able to
bring the variation pattern closer to that of an even
firing engine.
In this calculation, the following six points, P1 to
P6, were extracted as combustion characteristics for
one cycle (720-degree crank angle). The compression
TDC points were extracted as the reference points
for the start of combustion, and the points during
the expansion stroke and the exhaust stroke were
extracted as points where a large difference occurs
between normal combustion and misfiring.
P1: Cyl. #1 Compression TDC
P2: Cyl. #1 During expansion stroke
P3: Cyl. #1 During exhaust stroke
P4: Cyl. #2 Compression TDC
P5: Cyl. #2 During expansion stroke
P6: Cyl. #2 During exhaust stroke
The ωMAn value at these six points was defined as
a continuous signal of ω idn.
Figure 10 shows ω MAn and extraction points
with cylinder #1 misfiring at NE=5,000rpm as an
example.
This calculation was applied to algorithm 1 and
algorithm 2.
4.4. P a r t 4: D e t e c t i o n I n d e x P a r a m e t e r
Calculation
Algorithm 1
In Part 3, in order to regard the combustion
of each cylinder as equivalent, the ω idn value was
calculated by formulating the crank angular velocity.
In this algorithm, the value is compared with the
angular velocity at the same stroke with the previous
ignit ion cylinder to calculate detection index
parameters. That is, since the previous combustion
and the current combustion are compared, the
detection index parameter value in the case of
misfiring is greatly reduced.
The detect ion index parameters using this
algorithm (DIP1) were calculated by equation 4.
DIP1 = idn –ω idn–3ω (4)
Furthermore, the ω idn values calculated at the
timing of P1 to P3 were used for cylinder #1 and
in the same at the timing of P4 to P6 are used for
misfire detection in cylinder #2.
Algorithm 2
Firs t ly, the re la t ive angula r ve loc i ty was
calculated based on the compression TDC of each
cylinder for the ω idn value in Part 3. For cylinder #1,
the extracted angular velocity at P1 (ω idP1) is used
as a reference, the relative angular velocity at P2
(ω RidP2) is calculated by equation 5 and the same at
P3 (ω RidP3) is calculated by equation 6.
Fig. 9 Calculation results of ωCRKn and ωMAn
Cra
nk A
ngul
ar V
eloc
ity (r
ad/s
)
545
550
540
535
530
525
520
515
510
505
500
Number of Data200 250 300 350 400 450 500
Misfiring on #1 Cyl.
MAnωCRKnω
Fig. 10 ωMAn and extraction points
MA
n (ra
d/s)
530
535
525
520
515
510
505
Number of Data230 250240 270260 280 300 310290 320 340330
#1 Cyl. MisfiringP5 P5
P4P4
P1P1P6
P6
P2
P2
P3
P3ω
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Keihin Technical Review Vol.7 (2018)
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= –idP2ωRidP2ω idP1ω (5)
= –idP3ωRidP3ω idP1ω (6)
In the same manner, for cyl inder # 2, the
extracted angular velocity at P4 (ω idP4) was used as
a reference, the relative angular velocity at P5 (ω RidP5)
was calculated by equation 7 and the same at P6
(ω RidP6) was calculated by equation 8.
= –idP5ωRidP5ω idP4ω (7)
= –idP6ωRidP6ω idP4ω (8)
Then, relative angular velocities based on the
compression TDC timing of each cylinder were
integrated so as to be used as misfire detection index
parameters. The misfire detection index parameter
(DIP21) for cylinder #1 was calculated by equation 9
and the same for cylinder #2 (DIP22) was calculated
by equation 10.
+RidP2 RidP3ω ωDIP21 = (9)
+RidP5 RidP6ω ωDIP22 = (10)
Algorithm 3
For this algorithm, the ωMAn value calculated in
Part 2 was used as is. At first, the relative angular
velocity was calculated based on the compression
TDC of each cylinder for the ω MAn value. The
extracted angular velocities at the cylinder #1
compression TDC (ω MA1CT) and at the cylinder #2
compression TDC (ωMA2CT) were used as references,
and the relative angular velocities (ω RMAn) were
calculated by equations 11 and 12.
If 1CT + 1 ≤ n < 2CT: –RMAn MA1CTω MAnω ω=
(11)
If 2CT + 1 ≤ n < 1CT: –RMAn MA2CTω MAnω ω=
(12)
The ω RMAn value was integrated in the section
up to the point before the compression TDC of the
cylinder firing next, which was used as the detection
index parameter. The misfire detect ion index
parameter for cylinder #1 (DIP31) was calculated by
equation 13, and the same for cylinder #2 (DIP32)
was calculated by equation 14.
RMAnΣ ω2CT–1
i = 1CT+1
DIP31 = (13)
RMAnΣ ω1CT–1
i = 2CT+1
DIP32 = (14)
The schema of these calculations is shown in
Figure 11.
Algorithm 4
The basic logic of this algorithm was the same
as algorithm 3. Only the integration section of the
ω RMAn value was changed so as to be 180 degrees
from compression TDC of each cylinder. The reason
for that was that the ω RMAn value at the section
before the compression TDC of the next firing
cylinder included many disturbance components
such as friction that cannot be attributed to the
combustion state, and large deviations in the angular
velocities.
The misfire detect ion index parameter for
Fig. 11 Calculation schema of algorithm 3
MA
n (ra
d/s)
530
535
525
520
515
510
Number of Data280 300290 320310 330 340 350 370360
#1 Cyl. Misfiring
Negativevalue
Negativevalue
Positivevalue
Positivevalue
Positivevalueω
DIP31
DIP31
DIP32
DIP32
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Research on Misfire Detection Algorithms for Motorcycle Engine Firing at Uneven Intervals
cylinder #1 (DIP41) was calculated by equation 15
and the same for cylinder #2 (DIP42) was calculated
by equation 16.
RMAnΣ ω1CT+12
i = 1CT+1
DIP41 = (15)
RMAnΣ ω2CT+12
i = 2CT+1
DIP42 = (16)
The schema of these calculations is shown in
Figure 12.
Fig. 12 Calculation schema of algorithm 4
MAn
(rad
/s)
530
535
525
520
515
510
Number of Data280 300290 320310 330 340 350 370360
#1 Cyl. Misfiring
Negativevalue
Positivevalue
Positivevalue
Positivevalueω
DIP41
DIP41
DIP42
DIP42
5. Results of Simulations
5.1. Evaluation Method
5.1.1. Evaluation Flow
The signal output from the pick-up sensor was
entered into an external measurement instrument and
measured. After that, the measured value was entered
into a data analysis simulation model on a computer.
There were two parts of the simulation model. The
first part calculated the crank angular velocity from
the pick-up sensor output signal, and the second part
applied the four misfire detection algorithms at the
same time in parallel. For the next step, detection
performance was compared and evaluated based on
the behavior of the parameters calculated by each
detection algorithm. An outline of the evaluation
flow is shown in Figure 13.
One th ing that must be ment ioned is that
sampling rate for the output of pick-up sensor is
substantially influenced by the linearity of crank
angular velocity calculation as well as the tooth
intervals and missing teeth of the trigger wheel.
Subsequently, these factors affect the accuracy of
the misfire detection.
In this evaluation, in order to acquire analysis
results closer to a real system, the recording rate
of the measurement instruments for pick-up sensor
output was set to 1 MHz rate which conforms to the
read interval of the ECU.
5.1.2. Index for Evaluation
The Signal-to-Noise ratio (SNR) was used as an
index for comparing misfire detection performance
for each algorithm. SNR was defined as the power
ratio of the signal and background noise. In this
evaluation, the detection index parameters calculated
by the detection algorithm at misfiring were set
as signal, and the parameters calculated at normal
combustion were set as noise. In addition, assuming
that the detection parameter value exhibits a normal
distribution, the SNR was calculated by equation 17.
Fig. 13 Outline of evaluation flow
TimingRotor
Sensor
Data AcquisitionSystem
AlgorithmSimulation Model
: Crank signal
Angular velocityCalculation Model
: Angular velocity
: Detection parameter
Vehicle
Measurement Instruments
Computer
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Keihin Technical Review Vol.7 (2018)
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+nσ mσSNR = AVEn – AVEm
(17)
AVEn : Average of detection parameter with
combustion
AVEm : Average of detection parameter with
misfiring
σ n : Deviation of detection parameter with
combustion
σ m : Deviation of detection parameter with
misfiring
The higher the SNR value, the easier it is to
distinguish between the distribution of normal
combustion and that of misfiring. This facilitates
the setting of a misfire detection threshold for
satisfactory misfire detection performance. For
example, when the SNR=3.0, the detection accuracy
is calculated at approximately 99.8%.
5.2. Evaluation Targets
With the test vehicle secured to a chassis
dynamometer, data from running tests were recorded
for the following two cases and the data generated
by the simulation were evaluated:
- Steady state: By fixed throttle angle; and
- Acceleration state: By wide throttle opening.
During the simulation, misfires were generated
at even intervals at the rate of once every ten
combustion cycles in one cylinder. The same tests
were conducted for both cylinders on the test engine
in a warmed-up state (TW ≥ 80 degC). Detection
index parameters at a non-intentional misfiring cycles
were classified as noise, and those at intentional
misfiring cycles were classified as valid signals for
analysis.
5.2.1. Measurement Points for Steady State Tests
Data was measured a t several load points
inside the detection area as defined by the OBD II
regulations of the European Union.
Testing points for cylinder #1 are shown in
Figure 14 as an example. The mark indicates the
gear for each driving cycle.
5.3. Evaluation Results
5.3.1. Steady State Test Results
The misfire detection SNR values for cylinder #1
at each load point are shown in Table 3 and those
for cylinder #2 are shown in Table 4. The algorithms
which achieved the highest SNR at each load point
are highlighted in green.
From these results, it was found that algorithm 4
Fig. 14 Cyl. #1 testing points for steady state tests
PM (k
Pa)
70
65
80
75
60
55
50
45
40
35
30
NE (rpm)
Detection Area3rd Gear6th Gear
Neutral4th Gear
1st Gear5th Gear
0 20001000 40003000 5000 6000 7000 100008000 9000
Fig. 15 ωMAn and DIP41 with Cyl. #1 misfiring in steady state
#1 Cyl. Misfiring points
DIP
41 (–
)
100
150
50
0
-50
Number of Cycle0 10 50403020 60 70 9080 100
530
540
520
510
500
Number of Data0 200015001000500 2500 3000 40003500 4500
MA
n (ra
d/s)
ω
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Research on Misfire Detection Algorithms for Motorcycle Engine Firing at Uneven Intervals
5.3.2. Acceleration State Test Results
In this test, the 3rd gear was used to accelerate
with a wide-open throttle. For this acceleration, NE
was increased from 3,000rpm to 10,000rpm in 4.0
seconds.
The misfi re de tec t ion SNR va lues for the
acceleration state are shown in Table 5.
From these results, it was found that the three
algorithms excluding algorithm 1 have a high
detection performance for both cylinders, and this
performance was judged to be sufficient for practical
use.
Figure 16 shows the behavior of ωMAn and DIP41
with intentional misfiring of cylinder #1 for the
acceleration state. The DIP41 value drops sharply
according to the misfiring points in the same manner
as the results for the steady state tests.
Table 3 SNR values for Cyl. #1
Table 4 SNR values for Cyl. #2
Algorithm 1 Algorithm 2 Algorithm 3 Algorithm 4
1400 N 4.51 4.52 4.60 4.202000 N 5.80 6.07 6.10 5.382000 5 2.16 2.78 2.94 4.682000 6 1.00 1.52 1.61 2.663000 N 9.50 10.81 11.15 9.813000 5 1.52 2.25 2.27 4.393000 6 3.10 5.09 5.14 7.844000 1 4.50 5.84 5.56 6.454000 5 2.37 3.72 3.64 6.694000 6 1.69 2.84 2.84 5.315000 1 5.58 7.24 6.87 7.905000 5 2.77 4.16 4.13 6.565000 6 3.15 4.81 4.82 6.176000 1 6.92 8.29 8.79 9.516000 5 3.28 4.76 4.78 6.956000 6 3.58 4.86 5.23 6.047000 1 8.01 8.51 9.45 9.647000 5 3.77 5.27 5.51 8.527000 6 2.80 3.71 4.36 5.998000 1 8.35 10.04 9.52 11.118000 4 4.50 5.60 6.12 8.848000 5 3.95 5.37 5.75 8.899000 1 8.22 8.74 8.56 8.889000 4 4.83 6.25 6.85 9.259000 5 4.45 6.14 6.66 10.479500 1 8.74 9.15 8.32 8.779500 3 5.73 7.45 8.16 8.379500
40.9538.0540.7544.3933.9538.3544.0437.0140.3048.4340.2145.1162.6543.4155.6673.6346.6165.4779.8549.8161.7074.4753.0169.7278.9555.0060.0474.84 4 5.50 7.09 7.59 9.60
GearS/N [ratio] by each algorithm
NE [rpm] PM [kPa]
Algorithm 1 Algorithm 4Algorithm 3Algorithm 2
1400 N 4.62 4.48 4.63 4.242000 N 5.54 5.47 5.68 5.142000 5 2.82 4.32 4.26 4.712000 6 2.55 3.82 3.89 4.453000 N 9.35 9.38 9.70 8.723000 5 4.26 9.21 9.41 9.833000 6 3.55 4.58 4.31 6.394000 1 5.86 7.88 8.14 7.634000 5 4.09 6.36 6.39 6.994000 6 3.37 7.51 7.54 8.185000 1 7.65 11.00 11.55 11.135000 5 3.82 6.78 6.72 7.355000 6 3.42 6.69 6.56 7.326000 1 6.16 7.70 8.20 7.636000 5 3.47 6.30 6.00 7.116000 6 3.23 7.29 7.29 7.697000 1 7.32 10.30 10.51 11.217000 5 3.85 7.46 7.16 8.567000 6 2.93 5.54 5.46 5.918000 1 7.39 9.37 9.29 9.048000 4 4.51 7.52 7.12 8.308000 5 4.18 7.10 7.03 7.999000 1 9.18 10.22 9.33 9.589000 4 5.50 9.56 9.03 10.239000 5 4.44 7.43 7.14 8.559500 1 7.73 8.53 7.89 8.829500 3 6.74 9.95 10.42 9.529500
39.6936.9940.6042.9932.6237.6243.6936.9040.5248.3939.6246.4063.2042.1354.9774.6345.0164.8579.9949.2960.8974.5852.9969.7979.4954.1860.4574.88 4 5.14 6.92 7.53 7.04
NE [rpm] PM [kPa] GearS/N [ratio] by each algorithm
had a high detection performance for both cylinders,
and i t was judged that this performance was
sufficient for practical use.
Figure 15 shows behavior of ωMAn and DIP41 with
intentional misfiring on cylinder #1 at NE = 5,000
rpm with PM = 45.11 kPa. The DIP41 value drops
sharply according to the misfiring points.
Table 5 SNR values for Cyl. #1 and #2
Algorithm 1 Algorithm 2 Algorithm 3 Algorithm 4
#1 2.66 3.41 3.48 3.56
#2 4.03 4.18 4.19 3.62
3000to
10000
NE [rpm] Gear MisfiringCyl.
S/N [ratio] by each algorithm
3
Fig. 16 ωMAn and DIP41 with Cyl. #1 misfiring in acceleration state
#1 Cyl. Misfiring points
DIP
41 (–
)
400
800
600
200
0
-200
Number of Cycle0 15010050 200
800
1200
1000
600
400
200
0
Number of Data0 8000600040002000 10000
MA
n (ra
d/s)
ω
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Keihin Technical Review Vol.7 (2018)
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6. Result of Vehicle Tests
6.1. Evaluation Method
Based on the results of the simulations, algorithm
4 was judged to have the highest potential, and
thus it was entered into the ECU. During these
evaluations, misfire was detected when the detection
index parameter value was lower than the detection
threshold, a value which, for prototype purposes, is
a constant value for the all load points.
With the test vehicle secured to a chassis
dynamometer, data from running tests of the vehicle
was recorded from the ECU and the detection
accuracy of the a lgor i thm was subsequent ly
evaluated.
6.2. Evaluation Targets
The test data for each of the three phases in the
World Motorcycle Test Cycle (WMTC) Class 3-2
test cycle (Figure 17) with the engine in a warmed-
up state (TW ≥ 80 degC) were measured separately.
In each phase, the detected misfiring rate by
our algorithm was compared with the intentional
misfiring rate in the detection area. In addition, the
value calculated by dividing the sum of the number
of undetectable, misdetection or natural misfires
by the number of cycles in the detection area was
defined as the error rate, which was also evaluated.
The schema of the evaluation targets is shown in
Figure 18.
Fig. 17 WMTC Class3-2 test cycle Fig. 18 Schema of evaluation targets
Veh
icle
Spe
ed (k
m/h
)
40
140
120
100
80
60
20
0
Time (s)0 800600200 400 1200 1400 1600 18001000
Phase 3Phase 2Phase 1Cycles in detection area
I-D: UndetectableI: Intentional misfire
I ∩ D: Correct detection
for intentional misfire
D-I: Misdetection or natural misfire
D: Detected misfire
During the vehicle testing, misfire was generated
at even intervals a t a ra te of once every ten
combustion cycles in one cylinder in the same
manner as the earlier simulations. The same testing
was conducted for each cylinder.
6.3. Evaluation Results
The evaluation results for cylinder #1 misfiring at
each phase are shown in Table 6, and results for the
same for cylinder #2 are shown in Table 7.
In the case o f cy l inder #1, a lmos t a l l o f
intentional misfire could be detected. Although there
were a noticeable number of misdetection or natural
misfires, the number of undetectable misfires was
small, allowing for an error rate of less than 1% in
all phases.
In the case of cylinder #2, although the results
were inferior to those of cylinder #1, almost all of
the intentional misfires could be detected. Although
there were a noticeable number of undetectable
misfires, the number of misdetection or natural
misfires was small, allowing for an error rate of less
than 0.5% in all phases.
As mentioned above, the detection thresholds
for each cylinder were set at a constant value
for all load points for prototype purposes. From
these results, it was evident that by optimizing the
thresholds based on the number of undetectable and
of misdetection misfires, detection accuracy can still
be improved further.
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Research on Misfire Detection Algorithms for Motorcycle Engine Firing at Uneven Intervals
Table 6 Detection accuracy of misfiring in Cyl. #1
Number of Cyclesin Detection Area(times)
Number ofIntentional Misfiring(times)
IntentionalMisfiringRate(%)
Numberof Correct Detection(times)
Numberof Undetectable(times)
Numberof Misdetectionor Natural Misfiring(times)
System DetectedRate(%)
Error Rate(%)
10.72 10.19
479373
0 0 2
0.94 0.85 0.29
7,790 10,969 17,084
9.63 9.87 9.93
10.56
750 1,083 1,696
750 1,083 1,694
Phase 1 Phase 2 Phase 3
Number of Cyclesin Detection Area(times)
Number ofIntentional Misfiring(times)
IntentionalMisfiringRate(%)
Numberof Correct Detection(times)
Numberof Undetectable(times)
Numberof Misdetectionor Natural Misfiring(times)
System DetectedRate(%)
Error Rate(%)
9.63 9.70
61212
0 33 55
0.20 0.43 0.36
6,124 10,461 17,026
9.16 9.83 9.99
9.36
561 1,028 1,701
561 995 1,646
Phase 1 Phase 2 Phase 3
Table 7 Detection accuracy of misfiring in Cyl. #2
7. Summary
Misfire detection algorithms using crank angular
velocity fluctuation which can be applied to uneven
firing V-twin engines was proposed. For calculation
of the crank angular velocity, output from pick-up
sensor with a trigger wheel with two missing teeth
at 15-degree intervals was used.
For proposed algorithms, the misfire detection
accuracy was evaluated and compared by control
simulation using various measurement data as input.
Subsequently, the algorithm which showed higher
potential in simulation was integrated in an ECU
and it was confirmed by vehicle testing that misfire
detection was almost possible.
In the future, further studies especially focused
on the following issues are needed in order to
establish a viable misfire detection system for
motorcycles:
- measures to set the optimum detection threshold;
- detection performance for different misfire
occurrence patterns;
- measures for abnormalities in one plug in a twin
spark engine;
- de t ec t i on pe r fo rmance when d i s t u rbance
influences such as rider operation and travel on
rough surfaces; and
- application to different engine types.
References
(1) O f f i c i a l J o u r n a l o f E u r o p e a n U n i o n ,
“REGULATION (EU) No 168/2013 OF THE
EUROPEAN PARLIAMENT AND OF THE
COUNCIL of 15 January 2013”
(2) O f f i c i a l J o u r n a l o f E u r o p e a n U n i o n ,
“COMMISSION DELEGATED REGULATION
(EU) No 44/2014 of 21 November 2013”
(3) H i royasu , H . , “Easy unde r s t and In t e rna l
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Keihin Technical Review Vol.7 (2018)
TechnicalPapers
combus t ion eng ines ,” Nis s in pub l i sh Co .
1996.4.30 6th Edition. (In Japanese), ISBN
4-8173-0075-0/0053-0
(4) K a n e k o , Y. , “ F u n d a m e n t a l s o f I n t e r n a l
Combustion Engine,” Sankaido Co. 1997.1.10
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(5) Ko b a t a k e , H . , H a m a d a , N . , Ta m u r a , Y. ,
“Introduction to Signal Processing,” Corona
publ ishing Co. 2009.5.25 2nd Edi t ion. ( In
Japanese), ISBN4-339-03365-6.
(6) Hsien-Chi, T., Bo-Yu, G., Ming-Hao, C., Bo-
Chiuan, C. et al., “Misfire Diagnostic Strategy
for Motorcycles,” SAE Technical Paper 2013-32-
9058, 2013, doi:10.4271/2013-32-9058.
(7) Katsunori, T., “Misfire detection system for
internal combustion engine,” Japan Patent 2017-
139025.
(8) Katsunori, T., Yuki, M., “Misfire detection system
for internal combustion engine,” Japan Patent
2017-139027.
Acknowledgments
The author would like to express his gratitude to
KTM A.G. R&D EMS team who supported with the
provision of a test engine and a vehicle.
All testing data acquisition in this research are
from the work package of the misfire detection
development project in Keihin Corporation, and the
author’s colleague, Yuki Morita contributed greatly
in his support of this project.
Remarks
It is a great honor that this paper was chosen
for the Best Presentation Award in the 23rd Small
Engine Technology Conference (SETC) in November
15-17, 2017, Jakarta.
Abbreviations
OBD on board diagnosis
ECU engine control unit
CO carbon monoxide
HC hydrocarbon
NOx nitrogen oxides
CO2 carbon dioxide
IMEP indicated mean effective pressure
COV coefficient of variation
DOHC double overhead camshaft
OHC overhead camshaft
TDC top dead center
FFT fast fourier transform
LPF low-pass filter
SNR signal-to-noise ratio
NE number of engine revolution [rpm]
PM pressure of manifold [kPa]
TW engine coolant temperature of water cooled
engine [degC]
Author
K. TASAKI
Do emission regulations and on board diagnosis
requirements make motorcycles fascinating? The
answer seems to be NO. Then, will motorcycles remain
as fascinating without them? The answer is NO.
To make it possible for motorcycles to meet present
day demand, and to sustain motorcycles as fascinating,
motorcycle enthusiasts have to fulfill their own
missions. If I can carry out a part of their missions, it
would be a great pleasure for me. (TASAKI)
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