[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117134-en":3,"doc-seo-117134-105":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},117134,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Machine Learning in Lithium-Ion Battery: Applications, Challenges, and Future Trends","Machine learning is gaining strong momentum in lithium-ion battery research due to its ability to improve multiple technical stages of the field. The review discusses key practical applications across battery design, manufacturing, operational service, and end-of-life, while also outlining major obstacles such as limited data, preprocessing and cleaning difficulties, small sample sizes, computational cost, model generalization limits, black-box behavior, scalability for large datasets, data bias, and interdisciplinary integration. Future directions are analyzed, including transfer learning and N-shot learning for small datasets.","University of Dundee  \nMachine Learning in Lithium-Ion Battery  \nValizadeh, Alireza; Amirhosseini, Mohammad Hossein  \nPublished in:  \nSN Computer Science  \nDOI:  \n10.1007/s42979-024-03046-2  \nPublication date:  \n2024  \nLicence: CC BY  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nLink to publication in Discovery Research Portal  \nCitation for published version (APA):  \nValizadeh, A. , & Amirhosseini, M. H. (2024) . Machine Learning in Lithium-Ion Battery: Applications, Challenges, and Future Trends. SN Computer Science, 5, Article 717. [https://doi.org/10.1007/s42979-024-03046-2](https://doi.org/10.1007/s42979-024-03046-2)  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in Discovery Research Portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nDownload date: 11. Oct. 2024  \nSN Computer Science (2024) 5:717  \n[https://doi.org/10.1007/s42979-024-03046-2](https://doi.org/10.1007/s42979-024-03046-2)  \nREVIEW ARTICLE  \nMachine Learning in Lithium-Ion Battery: Applications, Challenges, and Future Trends  \nAlireza Valizadeh1 · Mohammad Hossein Amirhosseini2  \nReceived: 3 January 2024 / Accepted: 8 June 2024 © The Author(s) 2024  \nAbstract  \nMachine Learning has garnered significant attention in lithium-ion battery research for its potential to revolutionize various aspects of the field. This paper explores the practical applications, challenges, and emerging trends of employing Machine Learning in lithium-ion battery research. Delves into specific Machine Learning techniques and their relevance, offering insights into their transformative potential. The applications of Machine Learning in lithium-ion-battery design, manufacturing, service, and end-of-life are discussed. The challenges including data availability, data preprocessing and cleaning challenges, limited sample size, computational complexity, model generalization, black-box nature of Machine Learning models, scalability of the algorithms for large datasets, data bias, and interdisciplinary nature and their mitigations are also discussed. Accordingly, by discussing the future trends, it provides valuable insights for researchers in this field. For example, a future trend is to address the challenge of small datasets by techniques such as Transfer Learning and N-shot Learning. This paper not only contributes to our understanding of Machine Learning applications but also empowers professionals in this field to harness its capabilities effectively.  \nKeywords Lithium-ion battery · Machine learning · Data-driven approach · Artificial intelligence  \nAbbreviations  \nAI  \nCNN EBSD  \nEIS  \nELM  \nEV  \nGPR  \nHPC  \nLIB  \nLSTM  \nML  \nArtificial Intelligence  \nConvolutional Neural Network Electron Backscatter Diffraction electrochemical impedance spectroscopy  \nExtreme Learning Machine Electric Vehicle  \nGaussian Process Regression High-performance computing Lithium Ion Battery  \nLong Short-Term Memory Machine Learning  \n􀀍 Alireza Valizadeh [1524560@alumni.brunel.ac.uk](1524560@alumni.brunel.ac.uk)  \n􀀍 Mohammad Hossein Amirhosseini  \n[M.H.Amirhosseini@uel.ac.uk](M.H.Amirhosseini@uel.ac.uk)  \n1 NEX Power Ltd, 9 Centurion Court, Kiln Farm, Milton Keynes MK11 3JB, UK  \n2 Department of Computer Science and Digital Technologies, School of Architecture, Computing and Engineering, University of East London, London, UK  \nNASA National Aeronautics and Space  \nAdministration  \nNMC LiNi0 . 8Mn0 . 1Co 0.1 O2  \nPINN Physics-Informed Neural Networks  \nRF Random Forest  \nRL Reinforcement  \nRNN Recurrent Neural Network  \nRUL Remaining Useful Life  \nSOC State of Charge  \nSOH State of Health  \nSVM Support Vector Ma","cbCaioxfTvCw7QMw","https://ap.wps.com/l/cbCaioxfTvCw7QMw","pdf",2124594,1,18,"English","en",105,"# Abstract\n# Keywords and Abbreviations\n# Introduction\n## Background on Lithium-Ion Battery Research\n## Overview of Machine Learning and its Relevance in Scientific Research\n# Machine Learning Applications and Techniques\n# Challenges and Mitigation Strategies\n# Future Trends","[{\"question\":\"这篇综述主要关注锂离子电池研究中的哪些机器学习应用环节？\",\"answer\":\"涵盖机器学习在锂离子电池设计、制造、运行服务以及退役（end-of-life）等阶段的应用。\"},{\"question\":\"综述指出了哪些机器学习在锂离子电池领域面临的关键挑战？\",\"answer\":\"包括数据可用性不足、数据预处理与清洗困难、小样本规模、计算复杂度、模型泛化能力有限、模型黑箱性、算法对大数据集的扩展性不足以及数据偏差等。\"},{\"question\":\"未来趋势部分如何应对“小数据集”问题？\",\"answer\":\"通过迁移学习（Transfer Learning）以及 N-shot learning 等方法来缓解小数据集带来的建模困难。\"}]","Machine Learning in Lithium-Ion Battery: Applications, Challenges, and Future Trends | 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