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

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Utilization of a Neural Network to Improve Fuel Maps of an Air-Cooled Internal 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. 2 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 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. 5 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
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