[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123394-en":3,"doc-seo-123394-105":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},123394,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","A standardized comparative framework for machine learning techniques in lithium-ion battery state of health estimation","Accurate estimation of lithium-ion battery State of Health (SOH) is critical for improving performance, safety, and lifecycle management in energy systems. While many studies explore machine learning for SOH prediction, consistent experimental protocols and rigorous cross-validation for fair comparison remain limited. This work provides a standardized comparison of XGBoost, Random Forest, and SVM using NASA battery datasets with unified training-validation splits, systematic hyperparameter tuning, and benchmarks for Battery Management System (BMS) model selection.","Future Batteries 7 (2025) 100099  \nContents lists available at ScienceDirect  \nFuture Batteries  \njournal [homepage: www.sciencedirect.com/journal/future-batteries](homepage: www.sciencedirect.com/journal/future-batteries)  \n| A standardized comparative framework for machine learning techniques in lithium-ion battery state of health estimation\u003Cbr>*\u003Cbr>Ravi Pandit , Nikhil Ahlawat\u003Cbr>Centre for Life-Cycle Engineering and Management (CLEM), Faculty of Applied Science and Engineering, Cranfield University, Bedford MK43 0AL, UK |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords: Lithium battery State of Health Model Prognostics XGBoost Random Forest\u003Cbr>Support Vector Machine |  | The accurate estimation of lithium-ion battery State of Health (SOH) is essential for enhancing performance, safety, and lifecycle management in modern energy systems. While numerous individual studies have explored machine learning approaches for SOH prediction, a systematic comparative analysis using consistent experimental protocols and rigorous cross-validation remains limited. This study addresses this gap by presenting the first comprehensive comparison of three advanced machine learning models—Extreme Gradient Boosting (XGBoost), Random Forest, and Support Vector Machine (SVM)—using a standardized experimental framework with NASA battery datasets. Our novel contribution lies in implementing a unified training-testing protocol using battery \\#5 for training and batteries \\#6, \\#7, and \\#18 for validation, combined with systematic hyperparameter optimization through grid search and k-fold cross-validation. Key improvements include: (1) first standardized one-to-many validation protocol ensuring cross-battery generalization assessment that eliminates the data splitting limitations of previous comparative studies, (2) unified hyperparameter optimization methodology applied identically across all algorithms, eliminating the confounding effects of inconsistent parameter tuning that have biased previous comparisons, and (3) establishment of quantitative performance benchmarks providing evidence-based model selection criteria for practical Battery Management System (BMS) applications. The XGBoost model achieved superior performance with MAE of 0.016 and MSE of 0.000347, establishing empirical benchmarks for model selection in battery health diagnostics through our systematic comparative methodology. This work provides the first standardized comparative framework for SOH estimation, offering evidence-based guidance for BMS implementations and advancing the field toward more rigorous and replicable research practices in battery prognostics. |\n\n1. Introduction  \n1.1. Introduction to energy storage and batteries  \nThe global demand for energy is steadily increasing due to economic development and population growth. While traditional fossil fuel sources like oil, gas, and coal have historically been the primary energy sources, their availability is limited and is not sustainable in the long term. Renewable energy sources such as wind, solar, hydroelectricity, and geothermal energy are becoming increasingly accessible and costeffective. Nonetheless, the continued reliance on fossil fuels contributes to environmental problems such as air pollution, water contamination, and greenhouse gas emissions, which exacerbate global warming and climate change [1,2]. Thus, there is an urgent need for new energy sources to meet the daily requirements of energy across the globe. A  \nremarkable limitation of renewable energy sources is that that they are stochastic (thus not completely programmable) and highly fluctuating in daytime/night or season cycles. These matters of fact motivate the importance of doing research in the field of innovative energy storage technologies.  \nAmong the various types of batteries, lithium-ion batteries stand out due to their high energy density, long lifetime, stable electrochemical properties, ability to store","cbCais2N4HloUikt","https://ap.wps.com/l/cbCais2N4HloUikt","pdf",3536164,1,14,"English","en",105,"# Abstract\n# Introduction\n## Introduction to energy storage and batteries\n## Traditional methods and data-driven approaches","[{\"question\":\"Why is standardized SOH estimation important for lithium-ion batteries?\",\"answer\":\"Accurate SOH estimation supports performance, safety, and lifecycle management, which are essential for modern energy systems. It also enables more reliable diagnostics and prognostics for remaining useful life and fault detection.\"},{\"question\":\"Which machine learning models are compared in the standardized framework?\",\"answer\":\"The study compares three models: XGBoost, Random Forest, and Support Vector Machine (SVM). All are evaluated under the same standardized experimental approach and tuning methodology.\"},{\"question\":\"How does the study ensure fairness and generalization across batteries?\",\"answer\":\"It uses a unified training-testing protocol where battery #5 trains the models, while batteries #6, #7, and #18 validate them. It also applies identical hyperparameter optimization and systematic cross-validation to reduce confounding from inconsistent tuning.\"}]","A standardized comparative framework for machine learning techniques in lithium-ion battery state of health estimation | PDF",1785816269,35,{"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},"a-standardized-comparative-framework-for-machine-learning-techniques-in-lithium-ion-battery-state-of-health-estimation","",{"@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/a-standardized-comparative-framework-for-machine-learning-techniques-in-lithium-ion-battery-state-of-health-estimation/123394/",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 standardized SOH estimation important for lithium-ion batteries?","Question",{"text":75,"@type":76},"Accurate SOH estimation supports performance, safety, and lifecycle management, which are essential for modern energy systems. It also enables more reliable diagnostics and prognostics for remaining useful life and fault detection.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are compared in the standardized framework?",{"text":80,"@type":76},"The study compares three models: XGBoost, Random Forest, and Support Vector Machine (SVM). All are evaluated under the same standardized experimental approach and tuning methodology.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the study ensure fairness and generalization across batteries?",{"text":84,"@type":76},"It uses a unified training-testing protocol where battery #5 trains the models, while batteries #6, #7, and #18 validate them. It also applies identical hyperparameter optimization and systematic cross-validation to reduce confounding from inconsistent tuning.","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"]