[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119698-en":3,"doc-seo-119698-105":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":20,"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},119698,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Battery impedance spectrum prediction from partial charging voltage curve by machine learning","Electrochemical impedance spectroscopy (EIS) enables lithium-ion battery state-of-health diagnosis, yet impedance spectrum prediction from charging curves is limited by unclear mechanistic links between voltage behavior and impedance features. This study predicts impedance spectrum directly from the battery charging voltage curve using machine learning, while selecting and optimizing the input through electrochemical mechanistic analysis. It explores internal relations among charging curves, incremental capacity curves, and impedance spectrum to improve physical interpretability and define proper partial voltage ranges. Experiments confirm high accuracy and robustness, with predicted impedance errors below 1.9 mΩ for the selected range and reliable performance even when reduced to 3.65–3.75 V.","Aalborg Universitet  \nBattery impedance spectrum prediction from partial charging voltage curve by machine learning  \nGuo, Jia; Che, Yunhong; Pedersen, Kjeld; Stroe, Daniel Ioan  \nPublished in:  \nJournal of Energy Chemistry  \nDOI (link to publication from Publisher):  \n10.1016/j.jechem.2023.01.004  \nCreative Commons License  \nCC BY 4.0  \nPublication date: 2023  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nLink to publication from Aalborg University  \nCitation for published version (APA):  \nGuo, J. , Che, Y. , Pedersen, K. , & Stroe, D. I. (2023) . Battery impedance spectrum prediction from partial charging voltage curve by machine learning. Journal of Energy Chemistry, 79 , 211-221.  \n[https://doi.org/10.1016/j.jechem.2023.01.004](https://doi.org/10.1016/j.jechem.2023.01.004)  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public 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.  \n-Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n-You may not further distribute the material or use it for any profit-making activity or commercial gain  \n-You may freely distribute the URL identifying the publication in the public portal  \nTake down policy  \nIf you believe that this document breaches copyright please contact [us at vbn@aub.aau.dk](us at vbn@aub.aau.dk) providing details, and we will remove access to the work immediately and investigate your claim.  \nContents lists available at ScienceDirect  \nJournal of Energy Chemistry  \njournal [homepage: www. elsevier. com/locate/jechem](homepage: www. elsevier. com/locate/jechem)  \n| Battery impedance spectrum prediction from partial charging voltage curve by machine learning\u003Cbr>Jia Guo a, Yunhong Che a, ⇑, Kjeld Pedersen b, Daniel-Ioan Stroe a\u003Cbr>aAAU Energy, Aalborg University, Aalborg 9220, Denmark\u003Cbr>b Department of Materials and Production, Aalborg University, Aalborg 9220, Denmark |  |  |\n| --- | --- | --- |\n| a r t i c l e i n f o |  | a b s t r a c t |\n| Article history:\u003Cbr>Received 6 November 2022\u003Cbr>Revised 19 December 2022\u003Cbr>Accepted 3 January 2023\u003Cbr>Available online 20 January 2023 |  | Electrochemical impedance spectroscopy (EIS) is an effective technique for Lithium-ion battery state of health diagnosis, and the impedance spectrum prediction by battery charging curve is expected to enable battery impedance testing during vehicle operation. However, the mechanistic relationship between charging curves and impedance spectrum remains unclear, which hinders the development as well as optimization of EIS-based prediction techniques. In this paper, we predicted the impedance spectrum by the battery charging voltage curve and optimized the input based on electrochemical mechanistic analysis and machine learning. The internal electrochemical relationships between the charging curve, incremental capacity curve, and the impedance spectrum are explored, which improves the physical interpretability for this prediction and helps define the proper partial voltage range for the input for machine learning models. Different machine learning algorithms have been adopted for the verification of the proposed framework based on the sequence-to-sequence predictions. In addition, the predictions with different partial voltage ranges, at different state of charge, and with different training data ratio are evaluated to prove the proposed method have high generalization and robustness. The experimental results show that the proper partial voltage range has high accuracy and converges to the findings of the electrochemical analysis. The predicted errors for impedance spectrum are less than 1.9 mO with the proper partial voltage range selected by the corelative analysis of the electrochemical reactions inside the ba","cbCaityuJ0jpDx8V","https://ap.wps.com/l/cbCaityuJ0jpDx8V","pdf",3185135,1,12,"English","en",105,"# Abstract\n# Introduction\n## Lithium-ion battery aging and prognosis needs\n## Role of EIS in battery diagnostics\n## Machine learning approaches for EIS-based estimation\n# Method Overview","[{\"question\":\"How does the proposed method predict the battery impedance spectrum?\",\"answer\":\"It predicts the impedance spectrum from the battery charging voltage curve using machine learning, with input optimized using electrochemical mechanistic analysis.\"},{\"question\":\"Why is electrochemical mechanistic analysis used in the framework?\",\"answer\":\"It clarifies the mechanistic relationship between charging curves, incremental capacity curves, and the impedance spectrum, improving physical interpretability and helping determine suitable partial voltage ranges.\"},{\"question\":\"How is the effect of partial voltage range validated?\",\"answer\":\"Predictions are evaluated under different partial voltage ranges, state of charge values, and training data ratios, and experimental results show high accuracy with errors below 1.9 mΩ for the proper range.\"}]","Battery impedance spectrum prediction from partial charging voltage curve by machine learning | 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does the proposed method predict the battery impedance spectrum?","Question",{"text":75,"@type":76},"It predicts the impedance spectrum from the battery charging voltage curve using machine learning, with input optimized using electrochemical mechanistic analysis.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is electrochemical mechanistic analysis used in the framework?",{"text":80,"@type":76},"It clarifies the mechanistic relationship between charging curves, incremental capacity curves, and the impedance spectrum, improving physical interpretability and helping determine suitable partial voltage ranges.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the effect of partial voltage range validated?",{"text":84,"@type":76},"Predictions are evaluated under different partial voltage ranges, state of charge values, and training data ratios, and experimental results show high accuracy with errors below 1.9 mΩ for the proper 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