[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123778-en":3,"doc-seo-123778-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123778,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Machine Learning Image Analysis for Electrochemical Energy Systems - Dissertation","Machine learning image analysis is developed to characterize electrochemical energy systems, with a focus on neutron radiography for PEM fuel cell water management and model-driven assessment for Li-ion battery thermal behavior. The work introduces deep learning architectures including convolutional neural networks, long short-term memory networks, and CNN-LSTM hybrids, trained with dedicated datasets and verified through algorithm validation and model validation. Results include quantitative water content estimation, spatial water distribution mapping, accuracy evaluation, and discussion of limitations for each approach.","UC Irvine  \nUC Irvine Electronic Theses and Dissertations  \nTitle  \nMachine Learning Image Analysis for Electrochemical Energy Systems  \nPermalink  \n[https://escholarship.org/uc/item/2070z7s4](https://escholarship.org/uc/item/2070z7s4)  \nAuthor  \nPang, Yiheng  \nPublication Date  \n2023  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA,  \nIRVINE  \nMachine Learning Image Analysis for Electrochemical Energy Systems  \nDISSERTATION  \nsubmitted in partial satisfaction of the requirements  \nfor the degree of  \nDOCTOR OF PHILOSOPHY  \nin Mechanical and Aerospace Engineering  \nby  \nYiheng Pang  \nDissertation Committee: Professor Yun Wang, Chair Assistant Professor Xian Shi Assistant Professor David Copp  \n© 2023 Yiheng Pang  \nDEDICATION  \nTo  \nmy parents and friends  \nin recognition of their  \nselfless support and unconditional love  \nTABLE OF CONTENTS  \nTABLE OF CONTENTS ............................................................................................................ iii  \n[LIST OF FIGURES ..................................................................................................................... vi](LIST OF FIGURES ..................................................................................................................... vi)  \n[LIST OF TABLES ...............................................................................................](LIST OF TABLES ...............................................................................................)......................... x  \nACKNOWLEDGEMENTS ........................................................................................................ xi  \nVITA............................................................................................................................................. xii  \nABSTRACT OF THE DISSERTATION ................................................................................ xiv  \nCHAPTER 1. INTRODUCTION ................................................................................................ 1  \n1.1 Electrochemical Energy Systems ..................................................................................... 1  \n1.2 Water Management of PEM Fuel Cell ............................................................................. 5  \n1.3 Thermal Management of Li-ion Battery .......................................................................... 6  \n1.4 Machine Learning Approach............................................................................................ 8  \n1.5 Objectives ....................................................................................................................... 13  \nCHAPTER 2. NEUTRON RADIOGRAPHY IMAGES ......................................................... 14  \n2.1 Fuel Cell Testing ............................................................................................................ 14  \n2.2 Neutron Imaging ............................................................................................................ 16  \nCHAPTER 3. PHYSICAL MODEL OF LI-ION BATTERY ................................................ 18  \n3.1 Experiments.................................................................................................................... 18  \n3.2 Physical Method ............................................................................................................. 21  \n3.2.1 Model Assumptions .................................................................................................... 22  \n3.2.2 Governing Equations .................................................................................................. 22  \n3.2.3 Initial and Boundary Conditions................................................................................. 24  \nCHAPTER 4. MACHINE LEARNING APPROACH.............................................","cbCaickOUsCs14Tn","https://ap.wps.com/l/cbCaickOUsCs14Tn","pdf",4037607,1,112,"English","en",105,"# Chapter 1. Introduction\n## Electrochemical Energy Systems\n## Water Management of PEM Fuel Cell\n## Thermal Management of Li-ion Battery\n## Machine Learning Approach\n## Objectives\n# Chapter 2. Neutron Radiography Images\n## Fuel Cell Testing\n## Neutron Imaging\n# Chapter 3. Physical Model of Li-ion Battery\n## Experiments\n## Physical Method\n## Model Assumptions\n## Governing Equations\n## Initial and Boundary Conditions\n# Chapter 4. Machine Learning Approach\n## Convolutional Neural Network\n## CNN Structure\n## CNN Training Dataset\n## Long Short-Term Memory Network\n## CNN-LSTM\n## CNN-LSTM Structure\n## CNN-LSTM Training Dataset\n# Chapter 5. Results and Discussion\n## CNN for PEM Fuel Cell\n## Algorithm Verification\n## Water Content Quantification\n## Water Spatial Distribution\n## CNN Accuracy & Limitation\n## Physical Model Prediction for Machine Learning Training\n## Model Validation\n## Effect of Heat Transfer Coefficient","[{\"question\":\"What electrochemical systems are analyzed in this dissertation?\",\"answer\":\"The dissertation targets electrochemical energy systems including PEM fuel cells and Li-ion batteries. It addresses PEM fuel cell water management and Li-ion battery thermal management through imaging and modeling.\"},{\"question\":\"Which machine learning models are used for the image analysis?\",\"answer\":\"The study uses convolutional neural networks, long short-term memory networks, and a CNN-LSTM hybrid. Each architecture is paired with specific datasets and evaluation steps.\"},{\"question\":\"How are the model predictions evaluated and what results are reported?\",\"answer\":\"The work reports algorithm verification, quantitative water content estimation, and spatial water distribution mapping for the CNN applied to the PEM fuel cell. It also includes model validation for physics-based training and discusses accuracy and limitations.\"}]","Machine Learning Image Analysis for Electrochemical Energy Systems - Dissertation | PDF",1785818523,282,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-image-analysis-for-electrochemical-energy-systems-dissertation","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-image-analysis-for-electrochemical-energy-systems-dissertation/123778/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What electrochemical systems are analyzed in this dissertation?","Question",{"text":75,"@type":76},"The dissertation targets electrochemical energy systems including PEM fuel cells and Li-ion batteries. It addresses PEM fuel cell water management and Li-ion battery thermal management through imaging and modeling.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are used for the image analysis?",{"text":80,"@type":76},"The study uses convolutional neural networks, long short-term memory networks, and a CNN-LSTM hybrid. Each architecture is paired with specific datasets and evaluation steps.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the model predictions evaluated and what results are reported?",{"text":84,"@type":76},"The work reports algorithm verification, quantitative water content estimation, and spatial water distribution mapping for the CNN applied to the PEM fuel cell. It also includes model validation for physics-based training and discusses accuracy and limitations.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]