Utilization of a Neural Network to Improve Fuel Maps of an Air-Cooled Internal
Combustion Engine
A thesis presented to
the faculty of
the Russ College of Engineering and Technology of Ohio University
In partial fulfillment
of the requirements for the degree
Master of Science
Ryan Frank Young
August 2010
© 2010 Ryan Frank Young. All Rights Reserved.
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This thesis titled
Utilization of a Neural Network to Improve Fuel Maps of an Air-Cooled Internal
Combustion Engine
by
RYAN FRANK YOUNG
has been approved for
the Department of Industrial and Systems Engineering
and the Russ College of Engineering and Technology by
Gary R. Weckman
Associate Professor of Industrial and Systems Engineering
Dennis Irwin
Dean, Russ College of Engineering and Technology
3
ABSTRACT
YOUNG, RYAN FRANK, M.S., August 2010, Industrial and Systems Engineering
Utilization of a Neural Network to Improve Fuel Maps of an Air-Cooled Internal
Combustion Engine (75 pp.)
Director of Thesis: Gary R. Weckman
Fuel maps are utilized by the fuel injection system as a guide for accurate delivery
of fuel under a specified load. A fuel map is determined by the manufacturer and usually
not manipulated. This research involves exhaust gas oxygen data collection using an
original equipment engine control module (ECM), artificial neural network (ANN)
modeling, response surface generation that will act as the new fuel map, implementing
the map into the ECM, and testing. ANN modeling is used first to predict volumetric
efficiency (VE) values in the fuel map, then used to optimize the VE values based on the
air to fuel ratio. The results are then compared with an alternative optimization technique
and the original equipment fuel map. Optimization of the fuel map will provide physical
performance, economic, and environmental gains. Applying this methodology would
allow the fuel map to be updated using little expert knowledge.
Approved: _____________________________________________________________
Gary R. Weckman
Associate Professor of Industrial and Systems Engineering
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ACKNOWLEDGMENTS
First of all I would like to recognize Dr. Gary R. Weckman for allowing me to
pursue a topic that was of my personal interest. He was able to see the big picture
throughout the process and push me during the times when I needed it most. His
sustaining presence kept me focused and allowed me to persevere using multiple
techniques while determining the most optimum solutions. Also, thank you to Jan
Weckman for putting up with us throughout this process, whether work related or not.
Next on the list is Dr. William A. Young II for constant encouragement and additional
input that allowed me to gain further insight into the system and interpret the results with
more confidence and accuracy. My committee members Dr. Namkyu Park, Dr. Helmut
Paschold, and Dr. Tao Yuan also deserve recognition for the time, effort, and suggestions
they provided me.
A much deserved thank you to my parents, Eddie and Karen Young. Thanks Dad
for getting me interested in mechanical things such as motorcycles at the young age of
five years old and all the input throughout my learning experiences, without this driven
interest I would have never finished. Mom, thanks for always supporting me in
everything that I do, always proof-reading, and your contribution toward my continuous
personal improvement.
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TABLE OF CONTENTS
Page
Abstract ............................................................................................................................... 3
Acknowledgments............................................................................................................... 4
List of Tables ...................................................................................................................... 8
List of Figures ..................................................................................................................... 9
1
Introduction ............................................................................................................... 10
1.1
Internal Combustion Engine Performance ......................................................... 10
1.2
Fuel Delivery Systems ....................................................................................... 12
1.2.1
The Carburetor ............................................................................................ 12
1.2.2
Fuel Injection .............................................................................................. 14
1.3
2
Intro to ECM ...................................................................................................... 15
1.3.1
Maps ............................................................................................................ 15
1.3.2
Onboard Diagnostics ................................................................................... 17
1.3.3
Lambda Sensor............................................................................................ 17
1.4
The Test Specimen ............................................................................................. 18
1.5
Machine Learning .............................................................................................. 18
1.5.1
Artificial Neural Networks (ANNs)............................................................ 19
1.5.2
Knowledge Extraction ................................................................................ 20
1.6
Thesis Purpose.................................................................................................... 20
1.7
Organization ....................................................................................................... 20
Literature Review...................................................................................................... 22
2.1
System Operations.............................................................................................. 22
2.1.1
Static vs. Dynamic ...................................................................................... 22
2.1.2
Analog vs. Digital ....................................................................................... 23
2.1.3
Open and Closed Loop................................................................................ 23
2.2
Model Creation and Map Improvement Method................................................ 24
2.2.1
MegaLogViewer ......................................................................................... 24
2.2.2
ANNs .......................................................................................................... 25
2.2.3
Surface Generation...................................................................................... 29
2.3
ANNs in Engine Related Field ........................................................................... 29
6
3
2.4
Alternate Optimization Technique ..................................................................... 32
2.5
Summary ............................................................................................................ 32
Methodology ............................................................................................................. 33
3.1
4
The Factory Buell System .................................................................................. 35
3.1.1
Dynamic Digital Fuel Injection .................................................................. 35
3.1.2
O2 Sensor .................................................................................................... 35
3.1.3
System Operation Methods ......................................................................... 37
3.2
Environment ....................................................................................................... 39
3.3
Method for Data Collection................................................................................ 39
3.3.1
Equipment ................................................................................................... 40
3.3.2
Software ...................................................................................................... 41
3.3.3
Data Collection ........................................................................................... 42
3.4
The Data ............................................................................................................. 43
3.5
Building ANNs ................................................................................................... 45
3.5.1
Preprocessing the Data ................................................................................ 45
3.5.2
Artificial Neural Network Architecture ...................................................... 46
3.6
Implementation and 3-Dimensional Input/Output Surface ................................ 47
3.7
Surface Validation .............................................................................................. 48
3.7.1
Original Equipment Setting ........................................................................ 49
3.7.2
Optimization ............................................................................................... 49
3.7.3
MegaLogViewer ......................................................................................... 50
3.8
ECM Flashing .................................................................................................... 51
3.9
Road Testing ...................................................................................................... 52
3.9.1
Definition of Scheme and Data Collection ................................................. 52
3.9.2
Calculation of Mean Squared Error ............................................................ 53
Results ....................................................................................................................... 54
4.1
Prediction Model Performance........................................................................... 54
4.1.1
4.2
Optimization Model Performance ...................................................................... 56
4.2.1
4.3
Predicted Surface ........................................................................................ 55
Optimized Fuel Maps .................................................................................. 57
ANN vs. MegaLogViewer and Factory Settings ............................................... 58
7
4.4
5
6
Discussion ................................................................................................................. 61
5.1
ANN Understanding ........................................................................................... 61
5.2
Testing ................................................................................................................ 67
5.3
Employing the System ....................................................................................... 68
Conclusion ................................................................................................................ 70
6.1
7
Evaluation of O2 sensor ..................................................................................... 59
Future Research .................................................................................................. 70
Bibliography ............................................................................................................. 72
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LIST OF TABLES
Table 3.1: Areas of fuel map [12] ..................................................................................... 43
Table 3.2: Data reported from ECM ................................................................................. 44
Table 4.1: Model Trial Results ......................................................................................... 55
Table 4.2 ANN Optimized Rear Fuel Map ....................................................................... 57
Table 4.3 MegLogViewer Optimized Rear Fuel Map ...................................................... 58
Table 4.4O2 Mean Squared Error ..................................................................................... 59
Table 4.5 EGO corr. Mean Squared Error ........................................................................ 59
Table 5.1 MegaLogViewer-ANN Rear Fuel Map ............................................................ 65
Table 5.2 O2 Mean Squared Error .................................................................................... 66
Table 5.3 EGO corr. Mean Square Error .......................................................................... 66
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LIST OF FIGURES
Figure 1.1: Four Stroke Cycle ........................................................................................... 12
Figure 1.2: Fuel Map [12] ................................................................................................. 16
Figure 2.1: MLP ................................................................................................................ 27
Figure 2.2: GFF ................................................................................................................. 27
Figure 2.3: Modular .......................................................................................................... 28
Figure 2.4: RBF ................................................................................................................ 28
Figure 2.5: Recurrent ........................................................................................................ 29
Figure 3.1: Flow of Proposed Methodology ..................................................................... 34
Figure 3.2: O2 Sensor Output ........................................................................................... 37
Figure 3.3: System Operation Methods [11]..................................................................... 38
Figure 3.4: TTL-232R USB to TTL Serial Converter Cable ............................................ 41
Figure 3.5: EcmSpy Overview Screen [12] ...................................................................... 42
Figure 3.6: MegaLogViewer GUI [23] ............................................................................. 51
Figure 4.1: ANN Structure used in Prediction Model ...................................................... 55
Figure 4.2: a.) ANN predicted fuel map b.) O.E. fuel map .............................................. 56
Figure 5.1 Sensitivity Analysis with OE Collection Map................................................. 64
Figure 5.2 Sensitivity Analysis with MegaLogViewer Collection Map........................... 65
Figure 5.3 Comparison of EGO corr. Mean Squared Error from Defined Scheme......... 67
Figure 5.4: Wideband and Narrowband O2 Sensor Output [47] ...................................... 69
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1
INTRODUCTION
As fuel prices continue to rise, motorcycles can be thought of as an alternative
option to automobiles in regards to fuel economy. Motorcycles are lighter, more fuel
efficient forms of transportation than traditional automobiles. Environmental concerns are
present when dealing with fossil fuel dependant, gasoline internal combustion engines;
therefore, the importance of improving an engine’s performance cannot be understated.
1.1
Internal Combustion Engine Performance
Engine performance can be described in different ways. Physically a person can
“feel” poor performance on a motorcycle as any hesitation of the engine is directly
transferred to the rider. Performance at the environmental level is a completely different
issue. For example, according to EPA (Environmental Protection Agency) standards, a
combustion engine is performing poorly when it emits 5 g/km hydrocarbon and 12g/km
carbon monoxide [1]. Based on these standards, the EPA requires vehicles with low
performing engines to be removed from the road until the engine’s performance is
improved.
Many variables affect the performance of an internal combustion engine. The
main variables to consider include air, fuel, spark, and compression. For example, as
combustion efficiency increases, so does horsepower. Combustion engines are basically
pumps that provide energy through a rotational force. Four variables are needed to create
a powerful combustion force: fuel, air, compression, and spark. The force is created by
filling the combustion chamber to maximum capacity with the air/fuel mixture, or
maximum volumetric efficiency, burning the mixture as long and as hot as possible, then
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releasing the remaining contents to start the process over. As the reliability of the
combustion process increases, the physical performance improves. Economical
performance is also affected by the combustion process and the factors that are going into
it. If too much fuel is introduced into the cylinder, not only will it physically
underperform but the operation cost increases. Finally, as combustion efficiency
increases, the exhaust gas emissions decrease. A balanced chemical equation for
combustion shows that a fuel enriched mixture will heighten levels of hydrocarbons
leaving the engine.
The internal combustion engine of interest utilizes a four-stroke cycle to produce
the energy required to propel the vehicle shown in Figure 1.1. Intake stroke is the first
stroke of the cycle where a piston, connected to a crankshaft, is at the top of a closed
cylinder and moves downward in the cylinder. The intake valve is opened allowing air
and fuel to enter the combustion chamber. In the second stroke, the piston travels back up
the cylinder compressing the air- fuel mixture, preparing them for ignition: compression
stroke. At the top of the compression stroke a spark is produced and the mixture
explodes, forcing the piston back down. This is called the combustion stroke. When the
piston reaches the bottom again, the exhaust valve is opened and the piston once again
returns to the top of the cylinder, forcing the freshly burned air-fuel fumes out the valve:
exhaust stroke [2].
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Figure 1.1: Four Stroke Cycle
1.2
Fuel Delivery Systems
There have been various systems used to ensure that the correct amount of fuel is
transported from the storage tank to the engine for combustion. Simplistic systems
utilized gravity to move fuel from its reservoir through a metering zone to keep an engine
running, while the most complex systems are still being designed that employ multiple
tanks, electric pumps, filters, pressure regulators, injectors, manifolds, and finally a
complex electronic management system to precisely transfer fuel into the engine.
1.2.1
The Carburetor
For a century the carburetor was the dominant gasoline delivery system for the
internal combustion engine in motor vehicles. Carburetors operate on the basis of
Bernoulli’s principle, proving that the accelerator pedal does not actually control the
amount of fuel pulled into the engine but the amount of air pulled through. During
13
operation the carburetor’s tasks include measuring the engines flow of air, fuel delivery,
taking air/fuel mixture in consideration, making adjustments for changes in environment,
then completely mixing and distributing the air and fuel delicately and uniformly [3] [4]
[5]. If this seems even remotely straightforward, consider today’s rigorous environmental
standards on carbon emissions. Carburetors were essentially the brain of the engine and
although they were able to perform well up through the mid 1980’s, they finally were
taken over by fuel-injection systems.
Vehicle emission control systems began to make more sense in the 1960’s when
California’s Los Angeles needed to consider the amount of hydrocarbon, carbon
monoxide, nitrogen oxide, and lead emissions that automobiles were creating. As
emission standards became tighter, a more precisely controlled engine management
system needed to be put into place. By 1970 the Clean Air Act was passed and
automotive manufacturers began introducing electronic components into and around the
mechanical carburetor [6]. Computer controlled carburetors were therefore introduced. A
microcomputer, or electronic control unit, utilized feedback from various sensors
allowing the computer to calculate how lean or rich the air/fuel mixture should be for the
carburetor. Lambda (oxygen), temperature, and manifold pressure sensors were a part of
the computer controlled emission system which also included an electromechanical
carburetor and a mixture control solenoid. Mechanics now needed a way to evaluate or
monitor the engine’s performance and complete fault detection; therefore, diagnostic link
connectors were placed on the vehicles [7].
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1.2.2
Fuel Injection
A Swedish engineer in 1925 developed the first documented direct gasoline
injection engine, even though diesel injection systems had been around for years. From
then until the mid 1980’s many different fuel injection systems were developed. There
were mechanical fuel injection systems that mimicked diesel injection systems with a
direct-injection pump and throttle valve. Other ideas such as injecting the fuel directly
into the port above the intake were used during the developmental era of mechanical fuel
injection. While some were working with mechanical type fuel injection, others were
experimenting into the electronic age. Commercially, electronic fuel injection, or EFI,
was finally introduced by American Motors in 1957, and was called Electrojector [8]. In
the late 1980’s, however, fuel injection finally surpassed carburetion as the most utilized
system for mixing the air and fuel for internal combustion engines.
Fuel injection systems can vary in their functionality and structure, but the
purpose is always the same: deliver fuel to the engine for combustion. The type of system
used depends heavily on the many different possible objectives. These objectives include
efficient fuel consumption, emissions, output of power, reliability, cost of maintenance,
cost of initial setup, etc. Although certain objectives may contradict one another, the goal
for public street use is to maximize power, reliability, and efficiency while minimizing
cost, fuel consumption, and emissions. To control the many variables in a fuel injection
system, a precise engine controller is needed.
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1.3
Intro to ECM
The use of engine management technology in automobiles has grown significantly
throughout the past four decades. Vehicles used to be completely mechanically operated
and now use mostly electronics and computer technology. Fuel injection systems utilize a
computer for engine management that is known as the Engine Control Module (ECM).
Many other features are operated directly or indirectly from the ECM. Initially, ECMs
controlled only the amount of fuel being delivered to a specific cylinder. Presently, the
function of the ECM is to control not only fuel delivery but ignition timing, cooling fan,
air pump, fuel pump, and various other engine operations. By monitoring the many
engine sensor input and output signals, the ECM can adjust the appropriate parameters
for optimal engine performance. The sensors can be thought of as the feedback about
current conditions affecting the engine and operating conditions of the engine. In order to
control the amount of fuel and spark being distributed to the engine, the ECM must have
other inputs that are hard coded into lookup tables: the spark and fuel maps [9].
1.3.1
Maps
As engine operation conditions change, variables are changed inside the ECM to
accommodate for engine speed and load. The spark map is essentially a table of ignition
timing values that are specific to engine speed (Revolutions Per Minute, RPM) and load
(throttle position, TP). As RPM and throttle position change the ECM looks up the
appropriate ignition timing value from the spark map. The purpose of a fuel map shown
in Figure 1.2 is same as the spark map except the ECM looks up the value for fuel input
at a specified RPM and throttle position. The volumetric efficiency values (comparison of
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the density of air available at the intake manifold and the actual density of the air inside
the cylinder [10]) in the fuel map represent the duty cycle of the fuel injector. The fuel in
the system is withheld from the engine by a fuel injector which is under pressure at all
times. Fuel injectors open for a specific amount of time to let fuel into the engine, this
time is called the pulse width. In a simplistic system, the ECM looks up the value in the
fuel map and that value is correlated to the actual pulse width of the injector [11].
Figure 1.2: Fuel Map [12]
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1.3.2
Onboard Diagnostics
The first engine management system, Bosch Motronic, was brought to life by
BMW in the late 1970’s and onboard diagnostics was now customary for automobile
manufacturers. Due to such a large amount of smog in L.A. California, the federal
government required emission control systems on all automobiles across the nation in
1968. Two years later the Environmental Protection Agency (EPA) was established and a
series of emission standards for vehicles were put in place. Eventually in 1985, with the
introduction of OBD-1, the way engine management systems monitored was well-known.
In 1988 the Society of Automotive Engineers (SAE) standardized the plug used and
established a set of diagnostic test signals. Today a 16-pin connector is standard on most
vehicles, OBD-11. The standardization of onboard diagnostic connectors allows one
device to check diagnostic trouble codes on almost any vehicle. Diagnostic trouble codes,
or service codes, allow technicians with the correct equipment to read the various codes
displayed by OBD-11. The service code can be anything from fuel injector pulse width to
exhaust gas oxygen sensor operation [13].
1.3.3
Lambda Sensor
The lambda sensor has interchangeable names: exhaust gas oxygen sensor (EGO)
and O2 sensor are the most common. Although lambda sensors have only one purpose, it
is arguably one of the most important sensors to engine fuel control, since it acts as the
feedback loop from the combustion process. Placed strategically in the exhaust, the
lambda sensor provides numerically detailed feedback to the ECM about the fuel
mixture. The sensor generates a voltage signal that distinguishes how much unburned
18
fuel or excess oxygen is left in the exhaust after combustion; however, the sensor is only
accurate after it is fully heated [14]. An oxygen excessive mixture is reported as lean and
a fuel excessive mixture as rich. The sensor has the ability to update the mixture status
every 50 to 150 milliseconds. Simplistic systems that are working properly change
between lean and rich about once per second.
1.4
The Test Specimen
In order to provide a real world solution, an air-cooled twin cylinder four-stoke
gasoline internal combustion test engine is needed. The test specimen is a 2004 Buell
model XB12R. The test vehicle has not been modified in any way other than typical
routine maintenance. The engine is a 1203cc, two 3.5mm bore by 3.8mm stroke
cylinders, two over head valves per cylinder, 10.0:1 compression ratio engine that is
fuelled by a 49mm down draft dynamic fuel injection system controlled by a Buell engine
control module. The feedback to the ECM is provided by a single Bosch narrowband
exhaust gas oxygen sensor placed in the rear exhaust tube; therefore, evaluation of the
system will occur using data recorded from the rear cylinder combustion gases [15].
1.5
Machine Learning
An expertly designed system that can acquire and integrate knowledge from
experience has the possibility of being more efficient than that of a system which does
not learn from mistakes and has no intelligence. Machine learning refers to systems that
incorporate algorithms that have the capability to improve the system based on data by
identifying patterns in the data and making intelligent decisions based upon them. Many
types of algorithms can be employed throughout this process and their use depends on the
19
preferred result of the algorithm. An Artificial Neural Network using the backpropagation
algorithm is among the most effective machine learning approaches in complex data
modeling.
1.5.1
Artificial Neural Networks (ANNs)
One of the most effective ways to interpret systems is to incorporate data
modeling techniques. ANNs can model complex, non-linear relationships within numeric
data. ANNs have the ability to outperform other linear and non-linear model in many
types of problems by using pattern recognition in order to understand noisy and
incomplete data. To capture all the benefits of ANNs, often modelers must have large
data sets, ample time for training the network, and the patience to try multiple network
learning rules of different sizes and topologies.
ANNs are trained from the given data and require no real expert knowledge about
the data at hand. Input data must be numeric, therefore techniques can be applied to
convert nominal data to numeric. This means that basically any possible input can be
introduced as an attribute. One way of converting nominal to numeric is through the use
of binary labeling [16]. Once converted, using ANNs for research can be very powerful.
Unfortunately, ANNs have previously been labeled as a “black box [17],” because the
estimating relationships that the model uses are not transparent. In order to rectify this
accusation, methods have been proposed to make the ANNs more transparent.
Understanding the way ANNs are predicting is a key factor in validating the modeling
method. This is critical as practitioners are not likely to implement a model unless it can
20
be validated. Knowledge extraction techniques have been proposed as the solution to the
lack of clarity in ANN modeling [18].
1.5.2
Knowledge Extraction
Knowledge extraction can yield governing rules and insights into complex
processes. These techniques provide rules that are human comprehensible; if-then-else
rules, which allows modelers to derive true principles from complex behaviors [18].
ANNs have been claimed to be “black boxes” with little transparency; through the use of
knowledge extraction techniques, the interworking functions can be better explained [19].
For the current research study, sensitivity analysis about the mean will be performed. The
best network will be selected, implemented into Excel, and a three dimensional surface
will be created and optimized.
1.6
Thesis Purpose
This thesis has multiple objectives: first to show that ANNs can provide models
that are capable of predicting real world occurrences, secondly provide insight into the
model in order to produce a 3-dimensional surface that will represent an optimized fuel
map, and finally to demonstrate that the ANN created fuel map can perform as well as
other fuel map optimization techniques established currently.
1.7
Organization
Six sections separate the thesis research: an introduction to engine operation,
engine control techniques, the machine being tested and ANNs is presented in section 1.
A review of related terms, model creation and current fuel map optimization techniques,
21
where ANNs are being applied in current area research and details about ANNs are
discussed in section 2. Section 3 describes the thorough method proposed in this research
along with any equipment or software needed. In section 4 the results for model
performance and validation will be given, including a comparison of proposed method
performance against a current method and the factory settings. Discussion of the results
will follow in section 5, and finally conclusions about the validity of the research and any
future possible research will be brought to attention in section 6.
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2
LITERATURE REVIEW
The following section provides a review of the terms used in the techniques of the
current operating system, and machine learning methods being applied to alternative
systems. The operating system is very complex and must be analyzed. Understanding the
current operating method is a crucial process that will allow accurate prediction of the
outcomes from the input variables. Also, it is imperative to investigate similar areas of
interest in order to determine the techniques that are performing well and whether current
research can be improved.
2.1
System Operations
The current system utilizes a variety of simplistic and advanced technology to
ensure an enjoyable, safe, and economical mode of transportation. An advanced
technique, ANN modeling will be examined, along with other currently used system
operation techniques to determine if the purposed methodology is feasible.
2.1.1
Static vs. Dynamic
Systems can act differently depending on time of observation. The output of a
static model at a specific time depends directly on the value of the input variables at that
specific time. Alternatively, dynamic system outputs not only depend on input variables
at the time of interest, but also on past values of input to the system [20]. The question is,
how far back should the system look to attain the new output, and is it possible to
minimize how much input information is needed?
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2.1.2
Analog vs. Digital
Analog and digital systems have significant differences in their form of operation.
An analog signal is “a signal that is defined for all points in time and can take any real
magnitude value within its range.” Systematically thinking, an analog system is “a system
that represents data using a direct conversion from one form to another; one that is
continuous in both time and magnitude.” “Digital data is represented by discrete number
values and is defined as a signal or system that is both discrete-time and quantized.”
Digital data tends to have some error associated with it that can be positively offset by the
use of powerful computers [21].
2.1.3 Open and Closed Loop
Some systems are unable to compute their input from previous outputs to achieve
the desired goal; these types of systems are called open loop systems. Open loop systems
contain no feedback loop; therefore, are not monitoring the output of the process.
Unfortunately, without observing the output, the open loop system cannot make
corrections from previously made mistakes to improve the process. All inputs to the
system are hard-coded for each operating condition. These control systems have
preprogrammed instructions or codes that allow them to perform all tasks. Closed loop
systems however, contain a means of comparing the output to the input: active feedback.
When the system has deviated from the expected value in the code or model, the
feedback loop provides information about previous errors giving the system the ability to
correct or improve the nonconformance [22].
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2.2
Model Creation and Map Improvement Method
In order to learn more about the system, an appropriate and accurate mathematical
model must be constructed. The observed system is dynamic in nature and has been said
to be nonlinear; therefore, research will investigate the use of ANNs for the construction
of a nonlinear mathematical prediction and optimization model. Improving the fuel map
through ANN modeling will provide a new approach to a real world problem.
2.2.1
MegaLogViewer
Software exists to allow data captured from the dynamic fuel injection system to
be viewed, analyzed, and optimized. MegaLogViewer is the application that allows the
user to view data captured from any data logging performed on Megasquirt fuel injection
systems. The type of information viewed by the user can be manipulated to an almost
unlimited quantity. MegaLogViewer allows the user to tune fuel maps, play back a data
logging session, calculate air to fuel ratios for wideband lambda sensors, and compare
changes made; however, the main focus of the application is placed on the Volumetric
Efficiency (VE) analysis tool [23].
2.2.1.1 Volumetric Efficiency (VE) Analysis
Volumetric efficiency is a comparison of the density of air available at the intake
manifold and the actual density of the air inside the cylinder. Many ways exist to improve
the volumetric efficiency (VE) of an engine ranging from streamlining or smoothing the
intake ports to using larger valves, but the goal of VE analysis is to optimize the amount
of air and fuel in the combustion chamber in comparison to the air to fuel ratio [10].
25
2.2.2
ANNs
Mathematical relations can be applied to models that allow the viewer to explain,
predict, and control the processes that generate the data. Simple modeling techniques
such as linear regression can accurately be used with data that resembles a linear pattern,
while a more complex approach may be needed to understand non-linear, real world data.
ANNs outperform standard and non-linear models and retrieve excellent results for
problems in a variety of fields and categories [24]. The structure and inner-workings of
an ANN are similar to that of a human brain. Synapses, or interlinked weights connect
neurons (processing elements) to allow the weights to be updated during the training
process. This structure allows the ANN to determine underlying patterns and develop
associations between the input and output variables. In order to interpret results of
systems, data modeling techniques can be used to extract knowledge that otherwise may
have been difficult to understand.
There are many attributes that make ANNs so unique. The main contributing
force is that they are nonlinear models, and although there are many other nonlinear
models available, they require mathematics that is highly involved or nonexistent. ANNs
are nonlinear systems with combinations of nonlinear functions in their process. ANNs
are trained from the data so no expert knowledge is required beforehand, and they are
able to learn and adapt to changing conditions online [25].
ANNs have been said to be “universal approximators” that can learn any model
given enough data and processing elements [18]. Since adaptive systems are trained, the
data collection process is critical. The data needs to be sufficient, as noisy-free as
26
possible, and must include enough samples of information to accurately capture the
distribution of the model. These inputs include adequate representations of the operating
conditions without exclusions. Data collection must be performed in a precise method,
otherwise unwanted variances can be introduced [26]. Since processing time for the ANN
is not the concern, more information is never a bad thing; computer programs offer the
ability to easily model highly complex systems. A review of ANNs for use in engine
modeling follows to review what has been accomplished in the field.
2.2.2.1 Topologies
There are many paradigms of ANNs; this means that there are many way for the
synapses to connect to the processing elements. Multi-layered perceptrons (MLP Figure
2.1) are feed-forward networks that are layered and usually are trained with back
propagation. Similar to that is a generalized feed forward (GFF Figure 2.2) network
which is a MLP that allows synapses to skip over one or more layers, making the GFF
network more efficient during the training process. Some other network topologies worth
mentioning include modular neural networks Figure 2.3, radial basis function networks
Figure 2.4, and recurrent neural networks Figure 2.5. Modular NN are special types of
MLP that use a number of MLPs in parallel, creating organization inside the topology,
before recombining the results. Radial basis function networks are hybrid in nature and
employ gaussian transfer functions where MLPs use the sigmoidal transfer function [27].
Finally, recurrent neural networks allow data from previously processed instances to be
kept in the network’s memory for a specific amount of time [28]. Each of the five figures
27
below include an input layer, followed by a series of hidden layers and ending with an
output layer shown from left to right in the diagrams.
Figure 2.1: MLP
Figure 2.2: GFF
28
Figure 2.3: Modular
Figure 2.4: RBF
29
Figure 2.5: Recurrent
2.2.3
Surface Generation
Fuel maps can be represented in a 2-dimensional fashion in the form of a table;
however the 3-dimensional representation allows the viewer to visually comprehend the
changes being made from one cell value to the next. Generating a surface from the ANN
model will be carried out through the use of a macro-enabled Excel spreadsheet that
employs data output from NeuroSolutions [27]. First the data is normalized, then
multiplied by the network weights and finally the bias is added. This is completed for
each node in each hidden layer and the appropriate transfer function is applied. The
inputs are varied throughout a specified range and desired outputs are calculated from the
ANN model.
2.3
ANNs in Engine Related Field
Neural networks are being applied in a variety of engine related control [29] [30]
[31] [32], optimization [33], prediction [34], monitoring [35], and diagnostic [36]
systems as well as many other engine modeling efforts [37] [38] [39] [40]. In order to
30
capitalize on a more influential structure than the general feedforward neural network
structure, engine models have been built using time lagged recurrent neural networks to
provide function approximation. The structure implemented six inputs, one hidden layer
with eight neurons, and two outputs. An adaptive critic design, using only two modules:
critic and action, was used to approximate the dynamic programming cost function.
Engine torque and air/fuel ratio were better controlled in comparison to existing
controllers because the system was able to automatically learn the environment in real
time with actual data, allowing progression of fundamental performance using neural
networks in prototype controllers [41].
Radial basis function neural networks have been used in prediction and control of
internal combustion engines. Internal combustion engines are widely recognized as
complicated nonlinear dynamic systems. Adapting the neural network online, while
utilizing the Hessian method for creation of the control signals, allows the engine’s
quickly changing and uncertain operating speed to be considered using a straightforward
structure along with an algorithm that permits fast optimization. Investigation of fixed
and adaptive parameter techniques showed that the adaptive parameter, or recursive least
squares algorithm, was more effective for the non-linearity of the model [34].
The air/fuel ratio can be monitored to check performance using neural networks.
The high non-linearity of the internal combustion engine allows the use of neural
networks for calibration. Walters et al. discuss three air/fuel ratios as operating points to
collect a spark profile. As the EPA has required manufacturers of motor vehicles to
reduce the engine emissions, it also required owners of those vehicles to maintain them.
31
In order for the ECM to understand what is occurring in the engine, it relies on various
sensors. Combustion is one process that must be evaluated in order to control emissions.
A low cost, robust measurement system was developed using the spark plug as the
combustion sensor. Neural networks were used to investigate the time-varying sparkvoltage vector in order to estimate the air/fuel ratio. Using a multi-layered perceptron
(MLP) with sigmoid activation in the hidden and output layers, two engines were tested.
For training, two of the three air/fuel ratio operating points were used as data, while
testing was completed on all three data points. The neural network provided good
generalization over the three operating points and proved itself useful in engine
monitoring analysis [35].
As alternative fuels continue to present themselves to us, implementation of their
unique structures and requirements may be probable. Gnanam et al. proposed a hardware
add-on control module using a neural network to control the air/fuel ratio of a bi-fueled
automobile. Compressed natural gas was introduced as an alternate source of fuel, for a
simulated engine model in a Dodge pickup truck. The bi-fuel system required two of all
major fuel-injection components, including a second, add-on ECM. The original
equipment ECM controlled the engine while operating under gasoline and the add-on
controlled the engine when operating under natural gas. In order to calibrate to the
existing system, an ANN model was implemented into the controller to manage the pulse
width during natural gas operation, permitting it to refine itself [42].
32
2.4
Alternate Optimization Technique
Currently, riders have few ways to optimize the fuel map. Dealerships have
computer diagnostic systems that evaluate the performance of the fuel map. Purchasing
similar systems, which can be expensive, is another alternative, or a free analysis
software package such as MegaLogViewer v2.958 for Megasquirt fuel injection systems
is available. MegaLogVeiwer is a software package that allows the user to analyze data
from a data logging session. The fuel optimization function of MegaLogVeiwer is called
the VE analyzer. The function allows the user to optimize the values in the fuel map
based on the data collected from riding [23]. Once analysis is completed, the new fuel
map values can be flashed into the ECM. Data collection should take place once more in
order to evaluate the optimized map, and this process can be repeated numerous times for
complete optimization.
2.5
Summary
ANNs have shown great promise in learning non-linear behaviors and applying
that knowledge to predict the imminent future. This machine learning technique has the
potential to work well for optimization of the fuel map of an internal combustion engine
operating in real world conditions. Dynamic operating systems used in the ECM can
benefit from optimization of the fuel map because it is a hard coded table of values that is
not changed during operation; therefore, having improved table values will result in the
whole system becoming more efficient.
33
3
METHODOLOGY
This section describes the proposed method for optimizing a fuel map for an aircooled internal combustion engine through the use of an ANN data modeling technique.
The complete process flow is shown in Figure 3.1. The optimized fuel map aims to
improve combustion efficiency during closed loop operation by keeping the output
exhaust gas air to fuel ratio at the appropriate stoichiometric value of 14.7:1. Data
collected from the operating vehicle was used to create an ANN prediction model of the
values of volumetric efficiency used in the fuel map to produce the targeted air to fuel
ratio. Predicted values from the neural network were shown through the use of an Excelderived spreadsheet implementation method. The implementation method then was used
to generate a 3-dimensional surface which encompasses all RPM and throttle position
entities in the closed-loop area of the map. The ANN optimized map was critically
compared against an alternative optimization technique’s solution and the factory map
prior to installation to determine if the map was safe for use on the machine. Expert
knowledge was also used in order to validate the map.
34
Figure 3.1: Flow of Proposed Methodology
The factory, alternative method, and the ANN method fuel map were each loaded
into the test machine’s ECM. A testing scheme was defined and each map was separately
tested in the scheme so that an equal comparison could be made. Data collected during
35
the road test was analyzed; the mean squared error of O2 and exhaust gas oxygen
correction outputs were calculated for each fuel map, and conclusions were drawn.
3.1
3.1.1
The Factory Buell System
Dynamic Digital Fuel Injection
Modern engine control modules (ECMs) allow fuel injection systems to deliver
exact amounts of fuel under various specific loads. The test specimen’s Dynamic Digital
Fuel Injection (DDFI) system contains a microprocessor in the ECM that allows it to
make hundreds of changes per second to the program. These changes utilize hard coded
values in the fuel and ignition maps to accommodate changes in the surrounding
environment, i.e., temperature, humidity, altitude, etc. The ECM also contains functions
that allow easier, more efficient cold start ups, optimal midrange power, and also onboard
diagnostics [43]. The DDFI uses various sensors to receive feedback about operating
characteristics of the motorcycle. Sensors used include: throttle position (TPS), cam
position (CMP), intake air temperature (IAT), engine temperature (ET), and exhaust gas
oxygen (O2). Each of these sensors produce critical information that allow the ECM to
optimize ignition advance and fuel, in turn meeting rider demands and EPA standards
[44].
3.1.2
O2 Sensor
An oxygen (O2) sensor is used in a fuel injection system as the measurement of
the air/fuel mixture. The O2 sensor is constructed of a zirconium stabilized yttrium oxide
ceramic shell coated with a layer of platinum. The goal of the chemical generator is to
compare the oxygen outside the engine to the oxygen in the exhaust system [45]. When
36
the engine is running, heat and exhaust are produced from the combustion process. Each
travel through the exhaust system and eventually exit through the muffler, and as the
engine warms up, the exhaust temperature rises. This is fortunate for the narrowband O2
sensor as it does not reach operating potential until it reaches approximately threehundred and fifteen degrees Centigrade. As the nose of the sensor approaches this
temperature the platinum reacts with the exhaust gases, thus creating a voltage potential
between the layers.
Output of a narrowband O2 sensor can be between 0.0v and 1.1v, ranging from
0.2v to 0.7v, and centering around 0.45v or 14.7:1 air/fuel ratio as shown in Figure 3.2. A
value of 14.7:1 represents Stoichiometry, which is the optimal mixture of air and fuel for
perfect combustion. An air/fuel ratio can be expressed as lean, an excess of oxygen in the
exhaust (< 0.45v), or rich, excessive amounts of fuel in the exhaust (>0.45v). With an
engine running at optimal operating temperature the O2 sensor, however, spends almost
no time at 0.45v [43]. A good system is updated continuously, allowing the value to
remain close to 0.45v but crossing over it time and time again. The system can be
evaluated by measuring the distance and time spent away from stoichiometry, and by
counting the number of times the value crosses over 0.45v: cross counts. A more
optimum system is one with the least amount of distance and time deviation from desired,
with more cross counts [44]. The system uses cross counts to produce an exhaust gas
oxygen correction (EGO corr.) factor: if a cross count is detected, then no correction is
applied. In a perfect system EGO corr. will remain at 100, an EGO corr. >100 means the
system requires more fuel than provided in the fuel map.
37
Figure 3.2: O2 Sensor Output
3.1.3
System Operation Methods
The observed Buell system makes use of two operating methods: open loop and
closed loop. Inside each operating method there are categories describing the current
employing engine operating control shown in Figure 3.3. During open loop operation the
ECM controls the system’s fuel and spark through the use of the maps hard-coded into
the program along with information learned from closed loop, but no new information
from open loop is used. Open loop operation happens when the bike is at its extreme
conditions including idle and wide open throttle. Closed loop operation allows the ECM
to use only the fuel and spark maps for efficient power delivery; however, during closed
loop operation feedback is received from the O2 sensor [43]. Closed loop operation
occurs when the engine is under light loads and during typical highway cruising speeds.
In all operations fuel and spark maps are used; therefore, the ECMs ability to accurately
control the system is directly correlated to the accuracy of the maps [44].
38
Figure 3.3: System Operation Methods [11]
3.1.3.1 Closed Loop Operation
Like any system, feedback is essential to the process. Feedback from the O2
sensor allows the ECM to learn the behavior of the rider and the environment over time,
and is the primary compensation tool during closed loop operation. As usual, the more
controlled yet robust the riding conditions are, the more information that can be learned
for use in open loop. EGO corr. is applied to the pulse width of the fuel injector in order
to make small increases or decreases in the amount of fuel used. The system also includes
a learning capability called the Adaptive Fuel Value (AFV). This value is learned and
optimized during closed loop operation and then applied in open loop operation. The
AFV compensates for situations where the feedback from closed loop is different from
39
what is in basic programming code. The AFV remains in the range of 85-115 with a goal
of 100 [44].
3.2
Environment
A key factor in data collection was the amount of noise in the data. In order to
eliminate excess amounts of noise in the data, the environment in which the data was
collected had to accurately represent the typical working environment of the test
specimen. In this case, the specimen was a motorcycle being ridden in normal, fairweather, Ohio conditions and roadways including, but not limited to county, state, and
interstate roads. All engine vitals were examined prior to beginning any data collection.
For this motorcycle, one main specification had to be followed: the cold start enrichment
percent was required to be higher than one hundred. It was imperative for the engine
temperature to be at least one hundred and sixty degrees Centigrade, meaning the engine
was normal running temperature without any assisted mechanisms, i.e. choke.
3.3
Method for Data Collection
Collecting data from the Buell’s engine control module required a few things: a
special interface lead, specific software, a Windows PC (preferably a laptop), and a Buell
fuel injection system. During the data collection process approximately four data points
were collected every second, resulting in a substantial amount of data with only an hour
of operation. The process of data collection involved connecting a portable computer to
the motorcycle diagnostic port on the engine control module, operating a software
package for monitoring engine running condition and data collection process, and
eventually operating the motorcycle. As the motorcycle was ridden, real time data was
40
stored in a file so that the data could be observed and analyzed when the riding session
was completed.
3.3.1
Equipment
A portable computer was a necessity when collecting real time onboard data from
a moving motorcycle. For this task a Gateway M-Series laptop, model number w650a,
was used. The connection from the laptop to the motorcycle was completed using a
modified special PC interface lead from Future Technology Devices International Ltd.
(FTDI). To accept the connection from the lead a driver was installed and special
software was downloaded.
The interface lead in Figure 3.4 is a TTL-232R USB to TTL Serial Converter
Cable modified with Deutsch IPD plug housing: DT06-4S-C015, wedge: W4S, and
contact socket: Buell part number 72191-94. The FTDI cable incorporated a FT232RQ
USB – Serial UART interface Integrated Circuit device, allowing an efficient way to
connect TTL level serial interface to USB. Using six feet of six way cable, the tenth of an
inch pitch header socket was removed in order to use only three of the connections:
orange (ECM receive), black (ground), yellow (ECM transmit). With the use of this
modified lead, the laptop could connect physically with the Buell ECM. Finally in order
for the laptop to communicate with the ECM, a driver was installed. A virtual COM port
(VCP) type driver was installed from the FTDI website.
41
Figure 3.4: TTL-232R USB to TTL Serial Converter Cable
3.3.2
Software
Software that allowed data collection from the ECM was downloaded from the
EcmSpy website and after minimal option changes and tests, the laptop and ECM were
communicating with one another. The software website also provided information about
the intended use of the software and recommendations, along with some guidance of how
to operate the software. The EcmSpy program overview screen shown in Figure 3.5
allows the user to monitor what the Buell ECM is reading from the operating system in
real time, collect data from a running motorcycle, and make changes to the ECM.
42
Figure 3.5: EcmSpy Overview Screen [12]
3.3.3
Data Collection
The data collection process was random for initial data collection. The motorcycle
was operated under normal riding conditions with no imposed scheme or architecture.
Data collected during this time represented as many engine loads as possible; including
all nine operating zones shown in Table 3.1. Each operating zone was labeled and
characterized in order to aid in the understanding of the function of the zone and the
process taking place as the motorcycle operated in that zone [12]. The purpose of this
random riding was to collect a robust sampling of data from all engine loads many times,
43
with a focus on the areas where the most general riding occurred, or the time spent while
riding in the closed loop operating system.
Table 3.1: Areas of fuel map [12]
TPS /
RPM
0
800
1000
1350
1900
2400
2900
3400
4000
5000
6000
7000
8000
255
175
125
100
Zone 7: Maximum
Throttle, Low RPM
Zone 8: Full Power Through The
Gears
Zone 9: Full Power
Maximum Throttle
Zone 4: Pulling Away
Zone 5: Cruising Midrange
Zone 6: Accelerating on the
top end
Zone 1: Startup and
Idle
Zone 2: Closed Throttle Overrun
Zone 3: High Speed Closing
Throttle
80
60
50
40
30
20
15
10
3.4
The Data
Once data was collected, interpretation of the report from the ECM during the
data collection session was the next step. Table 3.2 shows the attribute values collected
from the ECM along with the units associated with those values and the shorthand
attribute notation. For the current research, engine speed (RPM) and throttle position
(TPS) were inputs to the prediction model and rear volumetric efficiency fuel table was
the output. Later the O2 output and EGO corr., along with RPM and TPS, were used to
optimize the volumetric efficiency (veCurr2) values in the map. During the testing phase
the O2 value and EGO corr. were used to determine the accuracy of the fuel maps in
44
comparison to one another. The mean square error was calculated from testing data, and
cross counts were evaluated.
Table 3.2: Data reported from ECM
Name
Units
Attribute
10 Millisecond
Time
Seconds
Centisec
Seconds
Seconds
sec
Engine Speed
RPM
RPM
Spark Advance
Front
Degrees
spark1
Spark Advance
Rear
Degrees
Table Fuel,
Front
Name
Units
Attribute
Degrees C
CLT
Degrees C
MAT
Volt
O2
Engine Temp
Correction
%
WUE
spark2
Air Temp
Correction
%
Air Temp
Corr.
Milliseconds
veCurr1
Wide Open
Throttle
Correction
%
WOT
Corr.
Table Fuel, Rear
Milliseconds
veCurr2
Open Loop
Correction
%
OL Corr.
Fuel Pulsewidth
Front
Milliseconds
pw1
Adaptive Fuel
Value
%
AFV
Fuel Pulsewidth
Rear
Milliseconds
pw2
%
EGO Corr.
Throttle
Position
Degrees
Degrees
TPS deg.
Volt
BAS Volt.
Load Rear
8-bit
TP
Volt
TPS Volt
Throttle
Percentage
8-bit
TPS 8Bit
Volt
IAT Volt
Battery Voltage
Volts
Batt.
Volt.
mph
speed mph
Engine
Temperature
Air
Temperature
O2 Sensor
Voltage
Exhaust Gas
Oxygen
Correction
Bank Angle
Sensor
Voltage
Throttle
Position
Sensor
Voltage
Inlet Air
Temperature
Voltage
Speed MPH
45
3.5
Building ANNs
In order to not only create an accurate ANN model, but also an effective one,
many structural attributes were varied in NeuroSolutions. The type of data being
analyzed greatly affected the category of neural network, number of layers and
processing elements, type of transfer functions and learning rules, as well as other inputs
used in the construction of a precise and useful ANN model. Determination of which
setup resulted in the best type of model was made, and this best model was used for
further studies. Before creating a new ANN model, some preprocessing was completed.
3.5.1
Preprocessing the Data
Initially, little preprocessing was applied to the data. Unnecessary attributes and
values were removed from the dataset. To be consistent with other tuning techniques and
reduce the risk of training with bad data, a filter was applied to eliminate data points
where the system was running below the operating cylinder temperature of one hundred
and sixty degrees Centigrade. Preparation for the ANN preprocessing involved
randomizing the rows of the datasheet, then the attributes and corresponding data were
then tagged as input and desired. In this research two models were created: a prediction
model and an optimization model. The inputs were RPM, TPS 8-bit, for prediction; then
included O2, and EGO corr. for optimization of the desired value, veCurr2. After the data
was declared as input and output, the percentage of the total data used for training (60%),
testing (20%), and cross-validation (20%) was established. The next step in the process
was the creation of a custom network.
46
3.5.2
Artificial Neural Network Architecture
Although there have been many tested structures for ANN prediction and
optimization models, this research employed a multilayer perceptron (MLP) with one
input layer, two hidden layers, and one output layer for each model. The ANN prediction
model aimed to predict the veCurr2 from the given RPM and TPS 8-bit values. It can be
shown that an internal combustion engine’s volumetric efficiency is correlated to the
speed of the turning engine (RPM) and the load (TPS) being placed on the engine. The
ANN optimization model used RPM, TPS 8-bit, O2, and EGO corr. as inputs for the
veCurr2 output. Optimization occurred by holding O2 to 0.45 and EGO corr. to 100 as
these are the ideal values in an optimal system.
The number of processing elements in the first hidden layer was varied from five
to fifteen, while the number of processing elements in the second hidden layer was varied
from three to nine. Transfer functions and learning rules were varied as well in order to
retrieve the most optimal model. Initially the model was trained 10,000 epochs to
discover if further training would result in more knowledge; if more could be learned
from the model the number of epochs or training iterations was increased.
Once the network was trained sufficiently, testing began. New “testing” data was
presented to the prediction model. The given input values were applied in the model to
predict the outcome. Examination of the absolute error took place and outliers found to
be bad data points were removed and new models were trained and tested.
47
Once testing was completed, the best model was selected and the corresponding
.bst file [27] was saved for later use. The .bst file contains the amplitude and offset values
that are required in the implementation sheet to normalize the data, and all of the bias and
weight values for the processing elements in the first and second hidden layer as well as
the output layer. Each model has a .bst file associated with it, so it was imperative to label
models accurately. To determine the best overall model for use, the coefficient of
determination (R2) was used to explain the amount of variance understood by the
prediction model.
3.6
Implementation and 3-Dimensional Input/Output Surface
A macro enabled Excel ® spreadsheet was developed to allow more insight into
the inner-workings of the ANN. Values taken from the NeuroSolutions software, located
in the .bst file were used in the spreadsheet to provide mathematical transparency as to
how the model is predicting the outcomes. Implementation into this spreadsheet allowed
the user to vary the input values and observe how it affected the outcome.
The spreadsheet went a step further and allowed the user to generate a 3dimensional input/output surface created from trial input values. Trial ranges were
initially set as minimum input to maximum input for each input attribute allowing the
macro to test all possible operating conditions, and the output for each was recorded in a
table. The resulting 3-dimensional plot of the table represents the ANN predicted or
optimized fuel map [18].
48
3.7
Surface Validation
Validation of the ANN generated surface took place through visual and
comparative inspection. In theory, as RPM and TPS (inputs) rise the value of VeCurr2
(output) should also increase. As this is a complex system, there are other factors such as
O2 and EGO corr. that are crucial to current optimized values of veCurr2. If O2 is above
or below the desired value for more than one collected data point, EGO corr. is utilized.
EGO corr. is one correction applied to the veCurr to establish more optimum combustion.
Expert knowledge used in this research included determining safe characteristics
of an optimized fuel map. The original equipment map has worked in the motorcycle
from the factory until now; therefore, this was a good place to start when tuning. Safe
constraints needed to be established for manipulating values in the optimized map. A
reduction in fuel could lean the combustion process enough to cause failure and damage
to the engine. This translated to applying a safe maximum reduction constraint of five
points of volumetric efficiency from the original equipment cell. The addition of fuel to
the combustion process can richen the mixture to a point where spark plugs can be
fouled, but no serious damage will occur, so a maximum of ten points of volumetric
efficiency can be added to the original equipment cell in the map. No real constraint
exists for the maximum difference between adjacent fuel map cells, as a result, the
factory fuel maps were examined and a rule was developed. During closed loop
operation, the adjacent outputs in the table should not differ more than thirty-five points
from one another. The optimized surface was compared to the original equipment fuel
map surface in order to complete validation.
49
3.7.1
Original Equipment Setting
The original equipment (OE) fuel map was used by the ECM to collect the data in
the beginning of this research. This fuel map was flashed into the ECMs electrically
erasable programmable read-only memory (EEPROM) from the factory and has been
providing fairly dependent values for fuel delivery since the motorcycle was built. The
OE fuel map had another purpose in this research: prove that the prediction of this map
could be completed by an ANN model. As previously stated, the OE fuel map was used
to validate the optimized ANN generated fuel map.
3.7.2
Optimization
Initially, the ANN modeling technique was used to predict volumetric efficiency
of the current instance. The optimization component of this research involved using the
ANN to understand the relationship between RPM, TPS, veCurr2, O2 and EGO corr.
Once an acceptable model was created, it was implemented into the Input/Output surface
generation spreadsheet, and the O2 and EGO corr. inputs were held to their desired
values (0.45, and 100, respectively). The surface was generated by working through the
ANN hidden layer bias’ and weights with the user inputs. By changing RPM and TPS
while holding O2 and EGO corr. at their desired values, the ANN learned the relationship
needed from the x and y inputs in order to obtain the desired value of rear cylinder
volumetric efficiency. This was accomplished by deriving two separate models: one
model holding O2 to its desired value, and the second holding the EGO corr. to its
desired value. These two maps were compared to one another and an average delta per
cell was calculated; therefore, optimization was completed relative to the O2 output,
50
while also evaluating a component correlated to cross counts: EGO corr. As previously
stated, prior to loading the optimized fuel map into the motorcycle, the generated surface
was compared to the OE fuel map and visually inspected to determine if it was safe for
use.
3.7.3
MegaLogViewer
Currently, MegaLogViewer is one application that allows users to optimize their
fuel map (GUI shown in Figure 3.6Figure 3.6: MegaLogViewer GUI). For this research,
the application was initially used as a secondary validation of the ANN generated fuel
map. The values in each map are compared to reassure the generated map is safe for use.
MegaLogViewer allowed the user to import the collected data and current fuel maps to
their application, select the type of O2 sensor used for collection, and analyze volumetric
efficiency of the collected data. After completion, a new fuel map was given with
changes highlighted in red or blue depending on whether fuel was taken away from or
added to the cell. Once satisfied with the outcome, the MegaLogViewer optimized map
was saved for later use.
51
Figure 3.6: MegaLogViewer GUI [23]
3.8
ECM Flashing
Coded into the EEPROM inside the ECM are the values used in the fuel map.
When new fuel maps are created, they can be loaded onto the ECM using the EcmSpy
software. With the software open and the “Maps” tab selected, the map of interest was
loaded onto the software and placed onto the rear fuel map location. At this point, the
map was only loaded to the software and not onto the ECM. The map was then copied to
the front fuel map and two points were added in each cell as recommended from expert
knowledge [46]. When finished, the maps were saved, the motorcycle ECM was
connected to the software, and the maps were flashed onto the ECM. Now the motorcycle
52
contains the fuel maps and was ready for testing. This process was completed for each
fuel map prior to testing: original equipment, MegaLogViewer optimized, and ANN
optimized.
3.9
Road Testing
The purpose of this research was to provide an alternative way to optimize the
fuel map for a real-world operating motorcycle. In order to prove the methodology was
worthwhile, the system had to be tested. Testing the optimization technique was a
strategic process that involved real-world environmental aspects along with a repetitive
testing schematic. Research testing was completed in an environment that reflects the end
user requirements of the motorcycle. Unfortunately, no real-world fuel map optimization
testing scheme had been defined; therefore a specifically designed testing method was
proposed.
3.9.1
Definition of Scheme and Data Collection
Fuel maps are used from the most extreme conditions of RPM and TPS in order to
deliver accurate amounts of fuel to the engine for combustion; therefore, testing a new
fuel map must cover all ranges as well. First, the motorcycle must start, idle, and run until
the engine temperature reaches one hundred and sixty degrees Centigrade. Data
previously collected was examined and the most utilized range of RPM and TPS in the
map was defined. The specimen was then operated in that range for fifteen minutes. Next,
the motorcycle was operated in the range of idle to 2000 RPM representing transition
from open loop to closed loop operation. The motorcycle then made ten minute runs in 5
MPH increments ranging from 35-65 MPH, representing typical riding conditions, with
53
some transition from closed loop to open loop operation. Operation between 3500 and
5000 RPM then took place to incorporate exiting closed loop and entering open loop
operation, and finally multiple runs from idle up through three gears to wide open throttle
then back to idle were recorded in order to cover any possible missed areas of the map.
3.9.2
Calculation of Mean Squared Error
O2 sensor values were one of the attributes collected during testing and were
useful as they provided details about the combustion process. A narrowband sensor value
close to 0.45v or 14.7:1 air/fuel ratio represents the optimal mixture for perfect
combustion and was used to evaluate the three tested fuel maps. Once a map was loaded
into motorcycle, the defined scheme was completed and data was collected. The mean
1
squared error, MSE= 𝑛
n
j=1
2
Xij − Tj , of the actual O2 sensor value represented by Xij,
compared to the goal value Tj, along with actual EGO corr. versus the objective value
were calculated. The calculation was done for each map: original equipment,
MegaLogViewer optimized, and ANN optimized.
54
4
RESULTS
The following section presents the details about the performance of the ANN
model used for prediction of the fuel map values, a comparison of the predicted map to
the original equipment map, and the resulting analysis of VEcurr2 value prediction.
Results are then given from the optimization ANN model, optimized fuel maps, and the
outcome of the testing scheme with respect to the ANN, O.E., and MegaLogViewer fuel
maps. Finally, the O2 sensor is evaluated for use in the research, and the results are given.
4.1
Prediction Model Performance
After completing all iterations of prediction model trials, it was determined that
the number of processing elements in the two hidden layers was insignificant; therefore,
the most simplistic model was accepted. The architecture given in Figure 4.1 gives a
visual representation of the network. Five neurons were used in the first hidden layer and
three neurons were used in the second layer. The sigmoid transfer function was used in
each of the hidden layers and the output layer, while the conjugant gradient approach was
employed as the learning rule for all. The overall coefficient of determination for the best
model was 99%. In comparison to a multiple linear regression model with R2= 83%, the
ANN outperforms the regression approach by 16.2%. Full results from various model
trials are given in Table 4.1. Each trial model type and structure is given, along with the
mean squared error derived from training the model, and finally the testing coefficient of
determination.
55
Figure 4.1: ANN Structure used in Prediction Model
Table 4.1: Model Trial Results
Type of
Model
MLP
MLP
GFF
MLP
MLP
MLP
# of
Layers
2
2
2
2
2
2
4.1.1
# of
Processing
Elements
5,3
5,3
10,10
10,10
5,3
5,3
Training MSE
0.029541411
0.000659187
0.000671494
0.000636673
0.000599024
0.000386974
Testing R2
0.18349033
0.9717854
0.97860117
0.98011994
0.98563073
0.99056233
Predicted Surface
The surface generated by the Microsoft Excel implementation spreadsheet from
the ANN model is compared to the original fuel map values to show the generalization
capabilities of ANN modeling. An illustration of the two surfaces is given in Figure 4.2.
56
The resulting 3-dimensional plot of the table, Figure 4.2a.), represents the ANN predicted
fuel map. Values in the fuel map have been masked to preserve confidentiality. Once
satisfied with the ANN model closed loop portion of the prediction surface, the attention
was turned toward optimization.
Figure 4.2: a.) ANN predicted fuel map b.) O.E. fuel map
4.2
Optimization Model Performance
After multiple ANN models were created, the best performing model was selected
for the implementation into the surface generation spreadsheet. A MLP with two hidden
layers, utilizing fourteen processing elements in the first layer and seven processing
elements in the second layer, was selected as the most accurate model based on the
amount variance explained. Tanh transfer functions were used throughout the network
that was trained 10,000 epochs for 50 runs. The selected model produced an R2= 98%.
57
4.2.1 Optimized Fuel Maps
Two optimization techniques were used to create new fuel maps, one through the
use of an ANN model and another using the MegaLogViewer technique. Inside Table 4.2
and Table 4.3, cells highlighted represent the closed loop region of the map where values
were changed by the ANN model and by MegaLogViewer, respectively. Values in all
optimized fuel maps have also been masked to maintain privacy. Investigation of these
two maps shows that the ANN fuel map is lean on average throughout the closed loop
region.
Table 4.2 ANN Optimized Rear Fuel Map
255
175
125
100
80
60
50
40
30
20
15
10
TPS/
RPM
72
72
72
72
72
72
67
60
54
42
33
31
72
72
72
72
72
72
67
60
51
40
30
27
76
76
76
76
76
76
67
60
49
38
27
24
78
78
78
78
78
78
67
56
40
29
21
20
83
83
83
83
85
72
65
46
34
23
19
18
107
105
101
101
83
65
53
42
30
19
17
16
114
112
98
85
74
59
49
38
25
15
13
11
101
97
89
85
70
55
45
34
22
13
11
9
98
97
87
74
66
51
40
30
20
12
10
9
102
89
85
72
59
43
34
25
19
11
9
9
108
98
74
65
51
39
28
20
16
11
9
9
105
96
72
54
49
37
25
18
16
11
10
10
103
94
72
58
45
34
25
18
16
11
10
10
0
800
1000
1350
1900
2400
2900
3400
4000
5000
6000
7000
8000
58
Table 4.3 MegLogViewer Optimized Rear Fuel Map
255
175
125
100
80
60
50
40
30
20
15
10
TPS/
RPM
72
72
72
72
72
72
67
60
54
42
33
31
72
72
72
72
72
72
67
60
51
40
30
27
76
76
76
76
76
76
67
60
49
38
27
24
78
78
78
78
78
78
67
58
40
28
21
20
83
83
83
83
85
74
67
48
35
24
19
18
107
105
101
101
83
68
53
43
30
21
17
16
114
112
98
85
75
63
52
41
30
15
13
11
101
97
89
85
73
60
50
40
29
13
11
9
98
97
87
74
68
55
45
35
25
12
10
9
102
89
85
72
58
42
32
25
19
11
9
9
108
98
74
65
51
39
28
20
16
11
9
9
105
96
72
54
49
37
25
18
16
11
10
10
103
94
72
58
45
34
25
18
16
11
10
10
0
800
1000
1350
1900
2400
2900
3400
4000
5000
6000
7000
8000
4.3
ANN vs. MegaLogViewer and Factory Settings
Testing the fuel maps at all ranges defined in the scheme involved hours of
continuous riding in similar conditions. Each map was flashed; data was collected, and
analyzed. Table 4.4 shows the results of the analysis based on output from the O2 sensor.
The OE map has the lowest mean squared error of all the maps tested, at every tested
speed. The MegaLogViewer optimized map was second best at all speeds, followed
closely by the ANN optimized map. In Table 4.5, the MegaLogViewer optimized map
shows substantially lower values for EGO corr. at all speeds, followed by the OE map,
and then the ANN optimized map.
59
Table 4.4 O2 Mean Squared Error
Speed
OE
MegaLogViewer
ANN
35
0.079
0.081
0.083
40
0.084
0.086
0.089
45
0.076
0.093
0.111
50
0.072
0.086
0.101
55
0.070
0.092
0.099
60
0.075
0.088
0.097
55
113.577
13.271
183.002
60
144.280
8.940
213.467
Table 4.5 EGO corr. Mean Squared Error
Speed
OE
MegaLogViewer
ANN
35
59.904
25.528
73.866
4.4
40
94.861
11.331
110.971
45
59.035
13.003
218.069
50
45.006
7.183
204.022
Evaluation of O2 sensor
Data retrieved from the ECM during data logging represents the values for the
inputs into the system. For this research, VEcurr2 was predicted from a representative
engine speed and load. This value of VEcurr2 actually is an adjusted value of the fuel
map based on feedback from the previous O2 value; therefore, the ANN predicted fuel
map values symbolize the Buell system’s adjusted value of volumetric efficiency. For the
ANN prediction fuel map to be optimized, the model must understand the goal of the
system. As previously stated the objective is efficient combustion, or an O2 value of
0.45V. Keep in mind that the system itself adjusts in real time, and crosses over 0.45V
many times during operation.
60
The output value of the narrowband O2 sensor is sporadic and difficult to predict.
To include this into the research, an ANN model was created in order to forecast the
current O2 value based on current inputs and past instances. First, a MLP model using
only one previous O2 value instance was created with undesirable results. Next, two
previous instances were added to the inputs, with minimal improvement. Eventually, a
smoothing technique was applied that provided a local averaging or moving average. The
number of sampling elements, or window size, was varied from 2- 6, then 9, and 12
resulting in the desired improvement of the predictive model. Once sensitivity analysis
had been completed, it was determined that windows of 2 and 3 were much more
significant than the alternatives; therefore all other moving average windows were
removed from the model.
Recurrent networks and time-lag recurrent networks were explored at this time to
determine if their temporal structure could increase model performance. Although
networks with the capability to store information learned from the prior training instances
seemed viable in this situation, the results from O2 prediction models showed that a MLP
with moving averages provided a better model prediction performance.
61
5
DISCUSSION
This section allows for the discussion of various barriers and other information
that materialized throughout this research. The results are dissected and further
conversed. Also, other thoughts and ideas that were incorporated into the research are
presented. The majority of the section is dedicated to ANN modeling and the changes
made to achieve acceptable performing models and fuel maps, while actual testing and
the employment of the proposed system is finally discussed.
5.1
ANN Understanding
It was interesting to note that when comparing the ANN prediction model to a
simple multiple linear regression model, not only is the ANN model a better prediction
model for the given data, it is also more robust. As all data points in the set are presented
to the regression model for prediction, the dataset was separated into three sections for
use in the neural network: training, cross-validation, and testing. When new information
was made available to the ANN model, it predicted the outcome with more accuracy. A
comparison of the ANN predicted fuel map and original equipment (O.E.) fuel map given
in Figure 4.2a.) and b.), demonstrates the excellent generalization and predictive
capabilities of the ANN model, especially in the area of consideration: closed loop
operation.
Previously it was noted that while examining the testing output, some data points
were determined to be consistent outliers in each of the models. After closer inspection,
the data points were related to up-shifting or down-shifting point locations in the data.
This data was introduced to the model by free-revving the motorcycle. Essentially, there
62
was no load on the motorcycle while the throttle position changed rapidly and thus
requiring the calculation of volumetric efficiency in the fuel table to be lagged from
actual input from the operator.
In regard to optimization, multiple efforts were made to capture an effective ANN
model. Initially, a simple MLP was created including all of the collected data attributes.
This model was inaccurate in generalizing the output of O2; therefore, several temporal
networks were built, as an argument could be made that the data may have a natural
structure to it. Some success occurred using time lagged recurrent neural networks
employing a tapped-delay. Next, in an effort to further improve model accuracy, a
smoothing technique was used. Moving averages with window size 2, 3, 4, 5, 6, 9, and 12
were explored; sensitivity analysis revealed that windows of 2, 3, and 4 were the most
pertinent to the model. Utilizing moving averages allowed a MLP to achieve acceptable
model results. Unfortunately, after a number of implementation trials, the models did not
produce a fuel map that was feasible for use in the motorcycle. This was due to the
utilization of the moving average as an input in the model. The ANN was learning a
substantial amount of information from the moving averages, as they were the most
sensitive attributes in the model. Little information was being attained from the inputs in
the model that were being varied, such as RPM and TPS 8bit. So little information was
being learned from the these inputs that the fuel map was essentially flat, meaning that no
matter the RPM or TPS 8bit, the same amount of fuel should be introduced into the
engine. Further research at this point could have gone one of two ways: manipulate the
code in the ECM to incorporate a new variable, such as a moving average of O2, or a
63
different, more effective modeling approach must be found. Since this research was
predominately about ANN modeling, a more effective approach was researched and
established.
An ANN was created to optimize veCurr2 by holding EGO corr. to the desired
value of 100. The modeling and implementation were eventually a success; therefore,
testing occurred. One thing to be noted was the fact that the ANN model was more
prominent to remove fuel from the map than the MegaLogViewer technique. This was
addressed by applying a limit to the amount of fuel being removed from the OE value:
five points of fuel taken away by the ANN model resulted in removing one point from the
ANN map. Following testing analysis, it was determined that the ANN model made
changes in the map that were not consistent with the alternative map solution.
After examining the sensitivity about the mean in Figure 5.1, a conclusion was
made that the data presented to the ANN was not broad enough to allow the ANN to
accurately adjust the model, with respect to EGO corr. The dashed line represents a
projection made by the ANN. The mode value in the data was 110, with a maximum data
point at 133, and an average of the data points at 115. With data in that range, the ANN
had no way to make completely accurate decisions about what was occurring when the
value was held at 100. In order to rectify the lack of collectable data from the OE map,
data collected from the MegaLogViewer optimized map was used to create a new
optimized ANN map shown in Table 5.1. Figure 5.2 shows how output was affected
differently from the new information provided with a range of EGO corr. values along
both sides of the target value. Flashing this map into the ECM, then collecting data
64
through the defined testing scheme resulted in a more optimum map than the ANN
created map alone based on the MSE calculations in Table 5.2 and Table 5.3.
Network Output(s) for Varied Input EGO Corr.
80.4
80.3
80.2
Output(s)
80.1
80
79.9
79.8
79.7
veCurr2
79.6
79.5
79.4
Varied Input EGO Corr.
Figure 5.1 Sensitivity Analysis with OE Collection Map
65
Network Output(s) for Varied Input EGO Corr.
68.4
68.3
68.2
Output(s)
68.1
68
67.9
67.8
67.7
veCurr2
67.6
67.5
67.4
Varied Input EGO Corr.
Figure 5.2 Sensitivity Analysis with MegaLogViewer Collection Map
Table 5.1 MegaLogViewer-ANN Rear Fuel Map
255
175
125
100
80
60
50
40
30
20
15
10
72
72
72
72
72
72
67
60
54
42
33
31
0
72
72
72
72
72
72
67
60
51
40
30
27
800
76
76
76
76
76
76
67
60
49
38
27
24
1000
78
78
78
78
78
78
67
56
39
29
21
20
1350
83
83
83
83
85
73
66
47
35
25
19
18
1900
107
105
101
101
83
67
56
44
32
22
17
16
2400
114
112
98
85
75
62
53
42
31
15
13
11
2900
101
97
89
85
72
59
51
41
29
13
11
9
3400
98
97
87
74
68
55
48
39
27
12
10
9
4000
102
89
85
72
61
49
43
35
19
11
9
9
5000
108
98
74
65
51
39
28
20
16
11
9
9
6000
105
96
72
54
49
37
25
18
16
11
10
10
7000
103
94
72
58
45
34
25
18
16
11
10
10
8000
66
Table 5.2 O2 Mean Squared Error
Speed
MegaLogViewer-ANN
35
0.0865
40
0.0977
45
0.1013
50
0.0987
55
0.0844
60
0.0904
55
26.056
60
26.823
Table 5.3 EGO corr. Mean Square Error
Speed
MegaLogViewer-ANN
35
21.218
40
14.720
45
27.719
50
24.741
As EGO corr. is the correction factor applied to the fuel map by the ECM in an
attempt to achieve more efficient combustion, it seemed appropriate to compare the
performance measurement of each map type. Figure 5.3 shows that once the ANN model
had sufficient data, including data below and above the target EGO corr. value of 100, an
improvement of MSE occurred in comparison to having only data above the target.
67
Mean Squared Error
250
200
OE
150
MegaLogViewer
100
ANN
MegaLogViewer
-ANN
50
0
35
40
45
50
55
60
Speed (MPH)
Figure 5.3 Comparison of EGO corr. Mean Squared Error from Defined Scheme
5.2
Testing
One of the first obstacles to overcome prior to testing was the method and
procedure of loading front and rear maps into the ECM. Loading the rear map was
straight-forward, as the data being collected from this process is from the rear cylinder;
therefore, information learned from the ANN can be applied directly to the rear fuel map.
Unfortunately there is only one sensor collecting data about the output of the combustion
engine to create an optimized map for the front and rear cylinders. Little accredited
research is available for the process of altering a rear map for safe use in the front
cylinder; therefore, the net changes implemented into the rear map were applied to the
front map. The OE front map was copied and the net change per cell in the closed loop
area of the map was applied. Once the maps were created and flashed into the ECM,
testing began.
68
Testing took place on various Ohio roadways, so traffic was a concern throughout
the entire testing scheme. Anytime there was a delay or set-back during the scheme due
to traffic or other unexpected impedances, time was added to the scheme to ensure ten
minutes of data collection at each speed range. As previously stated, the motorcycle was
operated from 35mph to 60mph in 5mph increments for ten minutes at each. In order to
increase the amount of closed loop areas of the map being utilized during each range, the
motorcycle was operated in a lower gear for five of the ten minutes, and the next higher
gear for the remaining five minutes.
5.3
Employing the System
There is an alternative O2 sensor that may be more effective for data collection of
the air to fuel ratio. This research incorporated the use of a Bosch narrowband O2 sensor
which the factory system is designed to use. Unfortunately the narrowband sensor output
voltage shown in the bottom of Figure 5.4, has a steep slope from one to zero volts;
therefore, having a large voltage range close to the target value of 14.7:1. The
narrowband O2 sensor is accurate in relaying to the computer whether the system is
running rich or lean, but unable to quantify the extent. The narrowband sensor can
basically be described as an on/off switch. An alternative to the narrowband sensor is a
wideband O2 sensor. As shown in the top graph in Figure 5.4, the output curve of the
wideband sensor is more linear; therefore, having a tighter voltage range close to the
target value. A wideband O2 sensor not only can report whether the operating system is
rich or lean, but it can deliver details about how rich or lean the system is running.
69
Figure 5.4: Wideband and Narrowband O2 Sensor Output [47]
70
6
CONCLUSION
From this research, an ANN model is shown to be a viable technique used for
improving the fuel maps of an air-cooled internal combustion engine, based on the
defined criteria. Once given appropriate and relevant data, the ANN was able to
accurately model the operating system and understand the relationship between the inputs
and outputs of the system. Various obstacles have been overcome throughout this
research: successful reduction of inputs to the model, accurate prediction of the closed
loop portion of the fuel map, selection of the appropriate attribute needed to achieve an
optimized fuel map, utilizing that attribute to improve the fuel map, a set of steps to
ensure safe operation of the engine, the definition of a real-world testing scheme, and a
comparison of this technique with an alternative solution. All of these accomplishments
allow for the success of this research; as the goal was to find a real-world technique for
utilizing an ANN in order to improve the fuel maps of an air-cooled internal combustion
engine.
6.1
Future Research
There are many directions further research in this topic could logically go. With
proper funding and laboratory equipment, the motorcycle engine could be further
constrained, allowing for a more tightly controlled data collection process. Data could be
collected at individual locations on the fuel map, rather than at a random distribution, to
ensure a more accurate record of the output. Next, extended research could be completed
involving the use of one or more wideband O2 sensors. As previously shown, the output
of the wideband sensor is linear; therefore, resulting in a more predictable output, and
71
quantifiable air to fuel ratio output values. A third option could involve re-writing the
ECM code to incorporate a learning ANN algorithm that adjusts the fuel map values in
real time to optimize a specific objective function. Each of these techniques could prove
to be feasible for increasing power, decreasing fuel consumption, or reducing the amount
of pollutants released into the atmosphere. With adequate funding, these three
performance measures could be researched more extensively using the appropriate
equipment.
72
7
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