[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127042-en":3,"doc-seo-127042-105":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},127042,962084928904,"Asher","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Boosting Predictive Accuracy of Single Particle Models for Lithium-Ion Batteries using Machine Learning - Research Article","Single particle (SP) models for lithium-ion batteries at high C-rates face accuracy limits driven by lithium concentration gradients in the electrolyte, which influence ionic conductivity, overpotential, and reaction rates. This study uses extreme gradient boosting machine learning trained on data from a comprehensive electrochemical (P2D) model and applies sensitivity analysis to key battery parameters. The ML-based SP approach matches P2D predictive accuracy under constant current while improving computational efficiency, and it also improves accuracy under dynamic loading, supporting battery management and safety.","Missouri University of Science and Technology  \nScholars' Mine  \n\n| Mechanical and Aerospace Engineering Faculty Research & Creative Works | Mechanical and Aerospace Engineering |\n| --- | --- |\n| 30 Sep 2024\u003Cbr>Boosting Predictive Accuracy of Single Particle Models for Lithium-Ion Batteries using Machine Learning\u003Cbr>Emmanuel Olugbade\u003Cbr>Jonghyun Park\u003Cbr>Missouri University of Science and Technology, [parkjonghy@mst.edu](parkjonghy@mst.edu)\u003Cbr>Follow this and additional works at: [https://scholarsmine.mst.edu/mec_aereng_facwork](https://scholarsmine.mst.edu/mec_aereng_facwork)\u003Cbr> Part of the Aerospace Engineering Commons, and the Mechanical Engineering Commons |  |\n\nRecommended Citation  \nE. Olugbade and J. Park, \"Boosting Predictive Accuracy of Single Particle Models for Lithium-Ion Batteries using Machine Learning,\" Applied Physics Letters, vol. 125, no. 14, article no. 143903, American Institute of Physics, Sep 2024.  \nThe definitive version is available at [https://doi.org/10.1063/5.0230376](https://doi.org/10.1063/5.0230376)  \nThis Article-Journal is brought to you for free and open access by Scholars' Mine. It has been accepted for inclusion in Mechanical and Aerospace Engineering Faculty Research & Creative Works by an authorized administrator of Scholars' Mine. This work is protected by U. S. Copyright Law. Unauthorized use including reproduction for redistribution requires the permission of the copyright holder. For more information, please contact [scholarsmine@mst.edu](scholarsmine@mst.edu).  \nRESEARCH ARTICLE | OCTOBER 03 2024  \nBoosting predictive accuracy of single particle models for lithium-ion batteries using machine learning 􀀅  \nEmmanuel Olugbade  ; Jonghyun Park 􀀤   \nAppl. Phys. Lett. 125, 143903 (2024)  \n[https://doi.org/10.1063/5.0230376](https://doi.org/10.1063/5.0230376)  \n􀀪  \nView Online  \n􀀮  \nExport Citation  \nArticles You May Be Interested In  \nBuilding a better lithium-ion battery management system  \nScilight (October 2024)  \nNonlinear phase field model for electrodeposition in electrochemical systems  \nAppl. Phys. Lett. (December 2014)  \nFailure Prediction Modeling of Lithium Ion Battery toward Distributed Parameter Estimation Chin. J. Chem. Phys. (October 2017)  \n23 October 2024 14:21:26  \nApplied Physics Letters ARTICLE  \n[pubs.aip.org/aip/apl](pubs.aip.org/aip/apl)  \nBoosting predictive accuracy of single particle models for lithium-ion batteries using machine learning   \nCite as: Appl. Phys. Lett. 125, 143903 (2024); doi: 10.1063/5.0230376  \nSubmitted: 23 July 2024 . Accepted: 25 August 2024 .  \nPublished Online: 3 October 2024  \nEmmanuel Olugbade1  and Jonghyun Park1,2,a)   \n\n| AFFILIATIONS\u003Cbr>1 Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology, Rolla, Missouri 65401, USA 2 Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, Missouri 65401, USA\u003Cbr>a)Author [to whom correspondence should be addressed:](to whom correspondence should be addressed: parkjonghy@mst.edu)[ parkjonghy@mst.edu](to whom correspondence should be addressed: parkjonghy@mst.edu) |\n| --- |\n| ABSTRACT\u003Cbr>The accuracy of single particle (SP) models for lithium-ion batteries at high C-rates is constrained by lithium concentration gradients in the electrolyte, which affect ionic conductivity, overpotential, and reaction rates. This study addresses these limitations using extreme gradient boosting machine learning (ML) . By training our ML model with data from a comprehensive electrochemical (P2D) model and performing sensitivity analysis on key battery parameters, we enhance predictive accuracy. Compared to conventional SP and P2D models under constant current loading, our ML-based SP model achieves similar predictive accuracy to P2D, with significant improvements in computational efficiency. Additionally, the ML-based SP model demonstrates improved predictive accuracy under dynamic loading conditions, providing a practical framewo","cbCaibUvZALMiODx","https://ap.wps.com/l/cbCaibUvZALMiODx","pdf",2128107,1,9,"English","en",105,"# Abstract\n## Motivation and limitations of SP models at high C-rates\n## Machine learning approach with P2D-derived training data\n## Comparative performance under constant and dynamic loading\n## Implications for battery management and safety","[{\"question\":\"Why do single particle (SP) models struggle at high C-rates in lithium-ion batteries?\",\"answer\":\"Accuracy is limited by lithium concentration gradients in the electrolyte, which affect ionic conductivity, overpotential, and reaction rates.\"},{\"question\":\"How does the study improve SP model predictive accuracy?\",\"answer\":\"It trains an extreme gradient boosting machine learning model using data from a comprehensive electrochemical (P2D) model and performs sensitivity analysis on key battery parameters.\"},{\"question\":\"How does the machine-learning-based SP model compare with P2D and conventional SP models?\",\"answer\":\"Under constant current loading, it achieves similar predictive accuracy to P2D while improving computational efficiency, and it also shows improved accuracy under dynamic loading conditions.\"}]","Boosting Predictive Accuracy of Single Particle Models for Lithium-Ion Batteries using Machine Learning - Research Article | PDF",1785936508,23,{"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},"boosting-predictive-accuracy-of-single-particle-models-for-lithium-ion-batteries-using-machine-learning-research-article","",{"@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/boosting-predictive-accuracy-of-single-particle-models-for-lithium-ion-batteries-using-machine-learning-research-article/127042/",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","2026-08-05",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},"Why do single particle (SP) models struggle at high C-rates in lithium-ion batteries?","Question",{"text":75,"@type":76},"Accuracy is limited by lithium concentration gradients in the electrolyte, which affect ionic conductivity, overpotential, and reaction rates.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study improve SP model predictive accuracy?",{"text":80,"@type":76},"It trains an extreme gradient boosting machine learning model using data from a comprehensive electrochemical (P2D) model and performs sensitivity analysis on key battery parameters.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the machine-learning-based SP model compare with P2D and conventional SP models?",{"text":84,"@type":76},"Under constant current loading, it achieves similar predictive accuracy to P2D while improving computational efficiency, and it also shows improved accuracy under dynamic loading conditions.","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},[93,97,101,105,110,115,120,123,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]