[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128467-en":3,"doc-seo-128467-105":31,"detail-sidebar-cat-0-en-105":92},{"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":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128467,962085662650,"Jiven","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Bayesian methods for battery state of health estimation","Estimating the state of health of battery energy storage systems is central to operational safety and reliability, which directly influence lifetime cost. Accurate state-of-health estimation is difficult because laboratory measurement techniques are not available during real-world operation. This dissertation develops a Bayesian framework that learns battery-model parameters from input-output data, using operating conditions and lifetime information. It is validated on fleet-level lead-acid internal-resistance estimation and on laboratory Li-ion joint parameter and state estimation, with results supporting early end-of-life failure indication.","Bayesian methods for battery state of  \nhealth estimation  \nAntti Aitio  \nDepartment of Engineering  \nUniversity of Oxford  \nThis dissertation is submitted for the degree of Doctor of Philosophy  \nHertford College January 2023  \nTo Sally, Suvi and Henri.  \nDeclaration  \nI hereby declare that except where specific reference is made to the work of others, the contents of this dissertation are original and have not been submitted in whole or in part for consideration for any other degree or qualification in this, or any other university. This dissertation is my own work and contains nothing which is the outcome of work done in collaboration with others, except as specified in the text and the acknowledgements and publications pages. Parts of the work have been published in journal papers and presented at conferences and seminars. These are specified in the text and referenced as appropriate.  \nAntti Aitio January 2023  \nAcknowledgements  \nSpending the last four years doing my DPhil has been tremendously rewarding. It was a decision long in the making to leave behind my previous career and take the chance to immerse myself again in science. I owe thanks to my wife Sally, whose understanding and support over the years, especially during the COVID-19 pandemic, were invaluable. Without her, this work would undoubtedly not have been completed.  \nI was lucky to do my research with excellent people in the Battery Intelligence Lab. First and foremost, I would like to thank Professor David Howey for his support, guidance and enthusiasm for my research project throughout. I would also like to give special thanks to the post-doctoral researchers in our group and beyond, Pedro Ascencio, Luis Couto, Volkan Kumtepeli, Nicola Courtier, Ross Drummond and Valentin Sulzer, whose technical knowledge and willingness to answer my endless questions is greatly appreciated. Last but not least, these four years would not have been the same without sharing the ups and downs with my fantastic fellow students and colleagues, Samuel Greenbank, Malgorzata Wojtala, Jorn Reniers, Trishna Raj, Adam Lewis-Douglas, Taeho Jung, Zihao Zhou, Masaki Adachi, Rebecca Perriment and Ralph Lane. With them, the hours spent at the office were an absolute delight.  \nAbstract  \nEstimating the state of health of battery energy storage systems is key to their operational safety and reliability, both of which affect lifetime cost. However, accurate estimation of state of health remains challenging, as measurement techniques used in laboratory environments are not available in real-world operating environments. In this work, a framework is developed that combines the relative strengths of commonly applied model-and data-driven approaches to state of health estimation. Gaussian process regression, a flexible Bayesian method of learning arbitrary functions from input-output data, is applied to estimate the parameters of low-order battery models as functions of internal states, operating conditions and lifetime. The approach is first motivated by the difficulty of parameter identification for physics-based battery models from real-world data. Then it is shown how electrical equivalent circuit models can be extended to include parameter dependencies on operating conditions and lifetime in a data-driven manner. The framework is then applied to two different usage scenarios. First, internal resistance is estimated for a fleet of solar-connected lead-acid batteries located in sub-Saharan Africa, where the resulting health metric is shown to provide an early indication of end-of-life failure. Second, a first-order RC circuit, coupled with a one-state thermal model, is parameterised in a joint process that also simultaneously estimates battery states, using data from a Li-ion cell under laboratory conditions. The only prerequisite was the cell-level open-circuit voltage versus charge curve and, in the case of the Li-ion cell model, a single thermal parameter. Given this, the method is agno","cbCaieKugEr11CNm","https://ap.wps.com/l/cbCaieKugEr11CNm","pdf",24678583,3,1,204,"English","en",105,"# Abstract\n# Acknowledgements\n## Research motivation and framework overview\n## Applications to lead-acid and Li-ion scenarios","[{\"question\":\"Why is state of health estimation important for batteries?\",\"answer\":\"State of health estimation underpins operational safety and reliability, which affect lifetime cost. Better health metrics also support improved battery management and fault diagnosis.\"},{\"question\":\"What Bayesian approach is used in this dissertation?\",\"answer\":\"Gaussian process regression is used as a flexible Bayesian method to learn low-order battery model parameters as functions of internal states, operating conditions, and lifetime.\"},{\"question\":\"How is the method validated across different battery scenarios?\",\"answer\":\"The framework is applied to two usage scenarios: estimating internal resistance for a solar-connected lead-acid fleet to signal end-of-life failure, and jointly parameterising a first-order RC plus thermal model for a Li-ion cell under laboratory conditions.\"}]","Bayesian methods for battery state of health estimation | PDF",1786001230,514,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"bayesian-methods-for-battery-state-of-health-estimation","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/bayesian-methods-for-battery-state-of-health-estimation/128467/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-25","2026-08-06",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is state of health estimation important for batteries?","Question",{"text":76,"@type":77},"State of health estimation underpins operational safety and reliability, which affect lifetime cost. Better health metrics also support improved battery management and fault diagnosis.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What Bayesian approach is used in this dissertation?",{"text":81,"@type":77},"Gaussian process regression is used as a flexible Bayesian method to learn low-order battery model parameters as functions of internal states, operating conditions, and lifetime.",{"name":83,"@type":74,"acceptedAnswer":84},"How is the method validated across different battery scenarios?",{"text":85,"@type":77},"The framework is applied to two usage scenarios: estimating internal resistance for a solar-connected lead-acid fleet to signal end-of-life failure, and jointly parameterising a first-order RC plus thermal model for a Li-ion cell under laboratory conditions.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]