[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118058-en":3,"doc-seo-118058-105":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},118058,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Lithium-ion battery digitalization - Combining physics-based models and machine learning","Digitalization of lithium-ion batteries enables smarter operating control strategies and reduces design and development risks and costs. Accurate physics-based models offer deep system understanding, but their high computational demand limits real-time use and parametric design. Machine learning models increasingly support lithium-ion battery research, while hybrid approaches combine physics-based and learning methods to achieve high accuracy with improved computational efficiency. This paper reviews current integration trends, covering explicit modeling and learning methods, hybrid architectures, applications for design/development and real-time monitoring/control, plus challenges and research gaps.","Renewable and Sustainable Energy Reviews 200 (2024) 114577  \nContents lists available at ScienceDirect  \nRenewable and Sustainable Energy Reviews  \njournal [homepage: www.elsevier.com/locate/rser](homepage: www.elsevier.com/locate/rser)  \n| Lithium-ion battery digitalization: Combining physics-based models and machine learning\u003Cbr>Mahshid N. Amiria, Anne Håkansson b, Odne S. Burheima, Jacob J. Lamb a, *\u003Cbr>a Department of Energy and Process Engineering, NTNU, Trondheim, Norway b Department of Computer Science, UiT, Tromsø, Norway |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Lithium-ion battery Digitalization\u003Cbr>Digital twin\u003Cbr>Physics-based models Electrochemical models Machine learning Hybrid models Computational cost Battery design\u003Cbr>Battery control |  | Digitalization of lithium-ion batteries can significantly advance the performance improvement of lithium-ion batteries by enabling smarter controlling strategies during operation and reducing risk and expenses in the design and development phase. Accurate physics-based models play a crucial role in the digitalization of lithiumion batteries by providing an in-depth understanding of the system. Unfortunately, the high accuracy comes atthe cost of increased computational cost preventing the employment of these models in real-time applications and for parametric design. Machine learning models have emerged as powerful tools that are increasingly being used in lithium-ion battery studies. Hybrid models can be developed by integrating physics-based models and machine learning algorithms providing high accuracy as well as computational efficiency. Therefore, this paper presents a comprehensive review of the current trends in integration of physics-based models and machine learning algorithms to accelerate the digitalization of lithium-ion batteries. Firstly, the current direction in explicit modeling methods and machine learning algorithms used in battery research are reviewed. Then a thorough investigation of contemporary hybrid models is presented addressing both battery design and development as well as real-time monitoring and control. The objective of this work is to provide details of hybrid methods including the various applications, type of employed models and machine learning algorithms, the architecture of hybrid models, and the outcome of the proposed models. The challenges and research gaps are discussed aiming to provide inspiration for future works in this field. |\n\nThis work is funded by Norwegian University of Science and Technology (NTNU) and no external funding has been used. The authors have no conflicts of interest to disclose.  \n1. Introduction  \nTo achieve sustainable electrification and decarbonization of the energy sector, reliable energy storage devices are essential. The lithiumion battery (LIB) is the cornerstone of portable and stationary energy storage in the modern industrial age [1]. It is primarily due to their high specific energy (170–250 Wh/kg), high specific power (200–1000 W/kg), high voltage (3.05–4.2 V), low self-discharge rate (less than 10 % per month), long cycle life (up to 3000 cycles), high efficiency (95 %), high rate capability, low toxicity, safety, compatibility with existing infrastructures and low heat release [2–5]. It is pertinent to note that, despite the exponential growth of LIBs in the last decade, large amounts of electrical energy storage are required to meet societal demands such as growing need for long-range hybrid and electric vehicles [6] and also  \nmaintaining reliable electricity supply from renewable energy systems [7]. In addition, more effective controlling strategies, and better battery designs are needed to allow for higher capacity and power, longer lifetime, lower cost, and increased safety [8,9].  \nIt is conventional to develop novel LIB design ideas by testing several prototypes in the lab, which can be costly, unsustainable, and timeconsuming [10,11]. Digitaliza","cbCaimSCjWDzN8Uh","https://ap.wps.com/l/cbCaimSCjWDzN8Uh","pdf",7968590,1,16,"English","en",105,"# Introduction\n# Digitalization of Lithium-Ion Battery Development\n# Physics-Based Models vs Machine Learning\n# Hybrid Models for Design and Real-Time Control\n# Challenges and Research Gaps","[{\"question\":\"Why does lithium-ion battery digitalization matter for performance and cost?\",\"answer\":\"It supports smarter control strategies during operation and can reduce risks and expenses during battery design and development.\"},{\"question\":\"What is the main limitation of physics-based models in digitalization?\",\"answer\":\"High computational cost makes accurate models difficult to deploy in real-time applications and for parametric design.\"},{\"question\":\"How do hybrid models address accuracy and computational efficiency?\",\"answer\":\"They integrate physics-based models with machine learning algorithms to retain high accuracy while improving computational efficiency for practical use.\"}]","Lithium-ion battery digitalization - 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