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

This thesis titled

Utilization of a Neural Network to Improve Fuel Maps of an Air-Cooled Internal

Combustion Engine

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 4

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.

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 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 2 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

2.4 Alternate Optimization Technique ...... 32 2.5 Summary ...... 32 3 Methodology ...... 33 3.1 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 4 Results ...... 54 4.1 Prediction Model Performance...... 54 4.1.1 Predicted Surface ...... 55 4.2 Optimization Model Performance ...... 56 4.2.1 Optimized Fuel Maps ...... 57 4.3 ANN vs. MegaLogViewer and Factory Settings ...... 58 7

4.4 Evaluation of O2 sensor ...... 59 5 Discussion ...... 61 5.1 ANN Understanding ...... 61 5.2 Testing ...... 67 5.3 Employing the System ...... 68 6 Conclusion ...... 70 6.1 Future Research ...... 70 7 Bibliography ...... 72

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

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

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 11 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].

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]. 14

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. 15

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 16 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]

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.

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? 23

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]. 24

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

Figure 2.3: Modular

Figure 2.4: RBF

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 spark- voltage 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.

3 METHODOLOGY

This section describes the proposed method for optimizing a fuel map for an air- cooled 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 Excel- derived 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.

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 The Factory Buell System

3.1.1 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 three- hundred 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, fair- weather, 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 Zone 7: Maximum Zone 8: Full Power Through The Zone 9: Full Power 125 Throttle, Low RPM Gears Maximum Throttle 100 80 60 Zone 6: Accelerating on the Zone 4: Pulling Away Zone 5: Cruising Midrange 50 top end 40 30 20 Zone 1: Startup and Zone 3: High Speed Closing Zone 2: Closed Throttle Overrun 15 Idle Throttle 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 Name Units Attribute

10 Millisecond Engine Seconds Centisec Degrees C CLT Time Temperature Air Seconds Seconds sec Degrees C MAT Temperature O2 Sensor Engine Speed RPM RPM Volt O2 Voltage Spark Advance Engine Temp Degrees spark1 % WUE Front Correction Spark Advance Air Temp Air Temp Degrees spark2 % Rear Correction Corr. Wide Open Table Fuel, WOT Milliseconds veCurr1 Throttle % Front Corr. Correction Open Loop Table Fuel, Rear Milliseconds veCurr2 % OL Corr. Correction Fuel Pulsewidth Adaptive Fuel Milliseconds pw1 % AFV Front Value Exhaust Gas Fuel Pulsewidth Milliseconds pw2 Oxygen % EGO Corr. Rear Correction Throttle Bank Angle Position Degrees TPS deg. Sensor Volt BAS Volt. Degrees Voltage Throttle Position Load Rear 8-bit TP Volt TPS Volt Sensor Voltage Inlet Air Throttle 8-bit TPS 8Bit Temperature Volt IAT Volt Percentage Voltage Batt. Battery Voltage Volts Speed MPH mph speed mph Volt.

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 3- dimensional 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.

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 n 2 squared error, MSE= X − T , of the actual O2 sensor value represented by Xij, 푛 j=1 ij j 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.

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.

Figure 4.1: ANN Structure used in Prediction Model

Table 4.1: Model Trial Results

# of Type of # of Processing Model Layers Elements Training MSE Testing R2 MLP 2 5,3 0.029541411 0.18349033 MLP 2 5,3 0.000659187 0.9717854 GFF 2 10,10 0.000671494 0.97860117 MLP 2 10,10 0.000636673 0.98011994 MLP 2 5,3 0.000599024 0.98563073 MLP 2 5,3 0.000386974 0.99056233

4.1.1 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 72 72 76 78 83 107 114 101 98 102 108 105 103 175 72 72 76 78 83 105 112 97 97 89 98 96 94 125 72 72 76 78 83 101 98 89 87 85 74 72 72 100 72 72 76 78 83 101 85 85 74 72 65 54 58 80 72 72 76 78 85 83 74 70 66 59 51 49 45 60 72 72 76 78 72 65 59 55 51 43 39 37 34 50 67 67 67 67 65 53 49 45 40 34 28 25 25 40 60 60 60 56 46 42 38 34 30 25 20 18 18 30 54 51 49 40 34 30 25 22 20 19 16 16 16 20 42 40 38 29 23 19 15 13 12 11 11 11 11 15 33 30 27 21 19 17 13 11 10 9 9 10 10 10 31 27 24 20 18 16 11 9 9 9 9 10 10 TPS/ RPM 0 800 1000 1350 1900 2400 2900 3400 4000 5000 6000 7000 8000

Table 4.3 MegLogViewer Optimized Rear Fuel Map

255 72 72 76 78 83 107 114 101 98 102 108 105 103 175 72 72 76 78 83 105 112 97 97 89 98 96 94 125 72 72 76 78 83 101 98 89 87 85 74 72 72 100 72 72 76 78 83 101 85 85 74 72 65 54 58 80 72 72 76 78 85 83 75 73 68 58 51 49 45 60 72 72 76 78 74 68 63 60 55 42 39 37 34 50 67 67 67 67 67 53 52 50 45 32 28 25 25 40 60 60 60 58 48 43 41 40 35 25 20 18 18 30 54 51 49 40 35 30 30 29 25 19 16 16 16 20 42 40 38 28 24 21 15 13 12 11 11 11 11 15 33 30 27 21 19 17 13 11 10 9 9 10 10 10 31 27 24 20 18 16 11 9 9 9 9 10 10 TPS/ RPM 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.

Table 4.4 O2 Mean Squared Error

Speed 35 40 45 50 55 60 OE 0.079 0.084 0.076 0.072 0.070 0.075 MegaLogViewer 0.081 0.086 0.093 0.086 0.092 0.088 ANN 0.083 0.089 0.111 0.101 0.099 0.097

Table 4.5 EGO corr. Mean Squared Error

Speed 35 40 45 50 55 60 OE 59.904 94.861 59.035 45.006 113.577 144.280 MegaLogViewer 25.528 11.331 13.003 7.183 13.271 8.940 ANN 73.866 110.971 218.069 204.022 183.002 213.467

4.4 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.

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 80.1 80 79.9

79.8 Output(s) 79.7 veCurr2 79.6 79.5 79.4

Varied Input EGO Corr.

Figure 5.1 Sensitivity Analysis with OE Collection Map

Network Output(s) for Varied Input EGO Corr.

68.4 68.3 68.2 68.1 68 67.9

67.8 Output(s) 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 72 72 76 78 83 107 114 101 98 102 108 105 103 175 72 72 76 78 83 105 112 97 97 89 98 96 94 125 72 72 76 78 83 101 98 89 87 85 74 72 72 100 72 72 76 78 83 101 85 85 74 72 65 54 58 80 72 72 76 78 85 83 75 72 68 61 51 49 45 60 72 72 76 78 73 67 62 59 55 49 39 37 34 50 67 67 67 67 66 56 53 51 48 43 28 25 25 40 60 60 60 56 47 44 42 41 39 35 20 18 18 30 54 51 49 39 35 32 31 29 27 19 16 16 16 20 42 40 38 29 25 22 15 13 12 11 11 11 11 15 33 30 27 21 19 17 13 11 10 9 9 10 10 10 31 27 24 20 18 16 11 9 9 9 9 10 10 0 800 1000 1350 1900 2400 2900 3400 4000 5000 6000 7000 8000

Table 5.2 O2 Mean Squared Error

Speed 35 40 45 50 55 60 MegaLogViewer-ANN 0.0865 0.0977 0.1013 0.0987 0.0844 0.0904

Table 5.3 EGO corr. Mean Square Error

Speed 35 40 45 50 55 60 MegaLogViewer-ANN 21.218 14.720 27.719 24.741 26.056 26.823

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.

250

200

Error OE 150 MegaLogViewer

Squared 100 ANN

Mean 50 MegaLogViewer -ANN 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.

Figure 5.4: Wideband and Narrowband O2 Sensor Output [47]

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

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