[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-86266-en":3,"doc-seo-86266-105":29,"detail-sidebar-cat-0-en-105":90},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":11,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},86266,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Geometric Scaling of Battery Cells and Its Effect on Key Performance Indicators","Computationally lightweight geometric scaling model for cylindrical lithium-ion battery cells, enabling early-stage design-space exploration. The model links selected cell-geometry and electrode-level variables—such as cell height, cell diameter, cathode active loading, and cathode porosity—to cell-level key performance indicators including capacity, DC internal resistance, mass, volume, and winding length. Validation against available cylindrical cell data compares predicted capacity, internal resistance, and winding length, then supports single-cell design-space exploration and global sensitivity analysis to reveal dominant variables, favorable directions, and geometry–electrode–resistance–energy density trade-offs.","T. de Krijger, J. van Kampen, M. Boulghalgh, and T. Hofman,  \n“Geometric Scaling of Battery Cells and Its Effect on Key Performance Indicators,”  \naccepted for the 2026 IEEE Vehicle Power and Propulsion Conference, Lyon, France,  \nuploaded to arXiv on July 14, 2026  \nGeometric Scaling of Battery Cells and Its Effect on Key Performance Indicators  \nTim de Krijger, Jorn van Kampen, Mouhriz Boulghalgh, and Theo Hofman  \narXiv :2607 . 11566v1 [ ee ss . SY] 13 Jul 2026  \nAbstract—This paper presents a computationally lightweight scaling model for cylindrical lithium-ion battery cells, intended for early-stage battery design-space exploration. The model maps selected geometric and electrode-level design variables, including cell height, cell diameter, cathode active loading, and cathode porosity, to cell-level performance indicators such as capacity, DC internal resistance, mass, volume, and winding length. The scaling model is validated against available cylindrical cell data by comparing predicted capacity, internal resistance, and winding length. The validated model is subsequently used in a single-cell design-space exploration and global sensitivity analysis to evaluate capacity, internal resistance, gravimetric energy density, and volumetric energy density. The results identify the dominant design variables, favourable parameter directions, and key trade-offs between cell geometry, electrode loading, resistance, and energy density. The proposed model provides a basis for future integration into higher-level battery system and vehicle optimization frameworks.  \nIndex Terms—battery modelling, battery scaling, design-space exploration, sensitivity analysis  \nI. INTRODUCTION  \nThe increasing demand for electrified mobility and renewable energy storage has intensified the need for improved battery system design. Lithium-ion battery cells are used in a wide range of applications, including electric vehicles, grid storage, uninterruptible power supplies, and portable devices [1] . In these applications, battery cell design choices influence system performance through their effect on capacity, internal resistance, mass, volume, and thermal behaviour.  \nLithium-ion battery cells are commonly manufactured in three main form factors: cylindrical, pouch, and prismatic cells. Each form factor has specific manufacturing characteristics and can be combined with different cell chemistries. As a result, cell format and geometry influence not only packaging and manufacturability, but also electrical and thermal performance. This paper focuses on cylindrical cells, for which changes in height, diameter, and electrode-level properties affect the internal jelly-roll geometry and therefore the resulting cell performance.  \nTo support early-stage battery design optimization, this paper develops a simplified scaling model for cylindrical lithium-ion cells. The model maps selected geometric and electrode-level design variables to cell-level performance indicators such as capacity, DC internal resistance, mass, and volume. The proposed model is intended to provide scalable cell data for future integration into higher-level battery system and vehicle optimization frameworks, as illustrated in Figure 1 .  \nA. Related literature  \nBattery cell design has been studied from several perspectives, including electrochemical modelling, thermal behaviour, manufacturing, and cell format selection. For lithium-ion batteries, the selected cell format influences packaging, manufacturability, thermal behaviour, and electrical performance. Existing studies have therefore investigated how cell dimensions and form factor affect the performance of commercial battery cells.  \nSeveral studies compare existing cell formats and cylindrical cell designs. Comparisons between 18650 and 21700 cells, teardown  \nThe authors are with the Department of Mechanical Engineering, Control Systems Technology, Eindhoven University of Technology, Eindhoven, The Netherlands. Corresponding aut","cbCaioybYxHo6lLU","https://ap.wps.com/l/cbCaioybYxHo6lLU","pdf",979416,2,1,"English","en",105,"# Introduction\n## Related literature\n## Statement of contributions","[{\"question\":\"What is the goal of the proposed battery-cell scaling model?\",\"answer\":\"It provides a computationally lightweight way to explore cylindrical lithium-ion battery design spaces early in the design process by mapping geometry and electrode variables to key performance indicators.\"},{\"question\":\"Which design variables and performance indicators does the model connect?\",\"answer\":\"It maps cell height, cell diameter, cathode active loading, and cathode porosity to indicators such as capacity, DC internal resistance, mass, volume, and winding length.\"},{\"question\":\"How is the model validated and what is done after validation?\",\"answer\":\"The model is validated by comparing predicted capacity, internal resistance, and winding length against available cylindrical cell data, then it is used for single-cell exploration and global sensitivity analysis to identify dominant variables and 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is the goal of the proposed battery-cell scaling model?","Question",{"text":74,"@type":75},"It provides a computationally lightweight way to explore cylindrical lithium-ion battery design spaces early in the design process by mapping geometry and electrode variables to key performance indicators.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which design variables and performance indicators does the model connect?",{"text":79,"@type":75},"It maps cell height, cell diameter, cathode active loading, and cathode porosity to indicators such as capacity, DC internal resistance, mass, volume, and winding length.",{"name":81,"@type":72,"acceptedAnswer":82},"How is the model validated and what is done after validation?",{"text":83,"@type":75},"The model is validated by comparing predicted capacity, internal resistance, and winding length against available cylindrical cell data, then it is used for single-cell exploration and global sensitivity analysis to identify dominant variables 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