[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122309-en":3,"doc-seo-122309-105":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},122309,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine Learning-Based Optimization for Battery Pack Cooling - Automotive Engineering Project 2024","The thermal management of electric vehicle (EV) batteries is central to safety, performance, and long-term durability. Conventional cooling-channel design relies on manual adjustments, trial-and-error iterations, and computationally heavy topology optimizations, which are slow and limited in efficiency. This project presents an ML and CFD-driven optimization framework that accelerates design while improving trade-offs between pressure drop and thermal regulation. A surrogate model learns from CFD data to provide fast predictions, while an NSGA-II genetic algorithm searches for Pareto-optimal geometries. Results indicate reduced computational time and stronger overall performance, supporting future development of battery thermal management methods.","Machine Learning-Based Optimization for Battery Pack Cooling  \nTME180 Automotive Engineering Project 2024  \nAbubakar Abukar Pedro John Francisco Boudagh Salvatore Verde Gabriel Wendel  \nDepartment of Mechanics and Maritime Sciences  \nC H A L M E R S U N IV E R S I TY O F T E C H N O L O GY G¨oteborg, Sweden, 2024  \nMachine Learning-Based Optimization for Battery Pack Cooling  \nTME180 Automotive Engineering Project 2024  \n© ABUBAKAR ABUKAR, PEDRO JOHN, FRANCISCO BOUDAGH, SALVATORE VERDE, GABRIEL WENDEL, 2024  \nSupervisor: Alexey Vdovin, Department of Mechanics and Maritime Sciences  \nSupervisor: Anthony Vivek, AFRY  \nSupervisor: Dimitrios Koutsimanis, AFRY  \nSupervisor: Viktor Alatalo, AFRY  \nExaminer: Alexey Vdovin, Department of Mechanics and Maritime Sciences  \nStudentarbeten – Mekanik och maritima vetenskaper (M2)– Projektarbete Department of Mechanics and Maritime Sciences  \nChalmers University of Technology SE-412 96 G¨oteborg  \nSweden  \nTelephone +46 (0)31 772 1000  \nAbstract  \nThe thermal management of electric vehicle (EV) batteries is a critical factor in safety, performance and durability. Traditional design methods for cooling channels rely on manual adjustments, trial and error processes and intensive topology optimizations leading to time consuming approaches and significant limitations in efficiency. This study introduces an innovative framework that combines machine learning (ML) and computational fluid dynamics (CFD) to optimize cooling channel designs that circumvent the challenges faced by traditional methods. These improvements are achieved by using two different components:  \nThe first component is a surrogate model. It is a machine learning model trained on a large dataset produced through CFD simulations using Star-CCM+ . This model significantly reduces computational costs and time by predicting pressure drops and temperature distributions based on input geometries and system parameters.  \nThe second component is a genetic algorithm for geometry optimization. This component generates an optimal geometry by balancing pressure drop and an effective thermal regulation by using a Non-Dominated Sorting Genetic Algorithm (NSGA-II) . This algorithm creates a number of random solutions and iteratively improves on them to find a Pareto front, which represents the optimal trade-offs between competing objectives: minimizing pressure drop and maximizing thermal regulation. The role of the surrogate model is to provide instant feedback on potential solutions throughout the algorithm, which is vital as the algorithm itself is inherently time consuming.  \nBy combining these two components, the framework accelerates the design process ensuring the creation of advanced cooling solutions. The results demonstrate the potential to reduce computational time while achieving superior performance. This work provides a framework for future advancements in the thermal management of EV batteries, highlighting the importance of combining CFD with ML in modern engineering solutions.  \nContents  \nAcronyms 4  \n1 Introduction 5  \n1.1 Aim .......................................... 5  \n2 Literature Review 6  \n2.1 Simulations ...................................... 6  \n2.2 Machine Learning ................................... 6  \n2.3 Geometry Optimization ............................... 7  \n3 Methodology 8  \n3.1 Geometry Generation ................................. 8  \n3.1.1 CAD Parametric Modeling .......................... 8  \n3.1.2 Handmade Channel Geometries ....................... 9  \n3.2 Simulation Process .................................. 10  \n3.2.1 CFD-Validation ................................ 10  \n3.2.2 Heat Configuration .............................. 14  \n3.2.3 Data Export .................................. 15  \n3.3 Surrogate Model ................................... 15  \n3.3.1 Data Preprocessing .............................. 16  \n3.3.2 Model Architecture .............................. 16  \n3.3.3 Loss Function ...............","cbCaioB5JtHKvNPl","https://ap.wps.com/l/cbCaioB5JtHKvNPl","pdf",3973982,1,27,"English","en",105,"# Acronyms\n# Introduction\n## Aim\n# Literature Review\n## Simulations\n## Machine Learning\n## Geometry Optimization\n# Methodology\n## Geometry Generation\n### CAD Parametric Modeling\n### Handmade Channel Geometries\n## Simulation Process\n### CFD-Validation\n### Heat Configuration\n### Data Export\n## Surrogate Model\n### Data Preprocessing\n### Model Architecture\n### Loss Function\n### Training & Hyperparameter Tuning\n## Geometry Optimization\n### Evolutionary Algorithm\n# Results\n## Surrogate Model\n### Performance\n### Sample Prediction\n## Geometry Optimization\n# Discussion\n## Surrogate Model\n## Evolutionary Algorithm\n# Potential Future Improvements\n## Geometry Generation\n## Surrogate Model\n## Evolutionary Algorithm","[{\"question\":\"Why is cooling-channel optimization critical for EV battery packs?\",\"answer\":\"Efficient thermal management directly affects EV battery safety, performance, and durability. Cooling-channel geometry strongly influences whether temperatures remain stable under operating conditions.\"},{\"question\":\"How does the framework reduce computational cost compared with traditional CFD-only optimization?\",\"answer\":\"It trains a surrogate model on CFD simulation data so the optimization algorithm can rapidly predict pressure drops and temperature distributions without running expensive CFD for every candidate geometry.\"},{\"question\":\"How are optimal cooling-channel geometries selected in the study?\",\"answer\":\"A genetic algorithm based on NSGA-II generates and evolves candidate geometries, producing a Pareto front that balances minimizing pressure drop against maximizing effective thermal regulation.\"}]","Machine Learning-Based Optimization for Battery Pack Cooling - Automotive Engineering Project 2024 | PDF",1785809941,68,{"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},"machine-learning-based-optimization-for-battery-pack-cooling-automotive-engineering-project-2024","",{"@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/machine-learning-based-optimization-for-battery-pack-cooling-automotive-engineering-project-2024/122309/",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","2026-08-04",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 is cooling-channel optimization critical for EV battery packs?","Question",{"text":75,"@type":76},"Efficient thermal management directly affects EV battery safety, performance, and durability. Cooling-channel geometry strongly influences whether temperatures remain stable under operating conditions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the framework reduce computational cost compared with traditional CFD-only optimization?",{"text":80,"@type":76},"It trains a surrogate model on CFD simulation data so the optimization algorithm can rapidly predict pressure drops and temperature distributions without running expensive CFD for every candidate geometry.",{"name":82,"@type":73,"acceptedAnswer":83},"How are optimal cooling-channel geometries selected in the study?",{"text":84,"@type":76},"A genetic algorithm based on NSGA-II generates and evolves candidate geometries, producing a Pareto front that balances minimizing pressure drop against maximizing effective thermal regulation.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]