[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122810-en":3,"doc-seo-122810-105":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},122810,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","A Comprehensive Exploration on Different Machine Learning Techniques for State of Charge Estimation of EV Battery - Conference Paper","State of Charge (SoC) represents the available energy in a battery as a percentage over time and strongly influences Battery Management Systems. SoC estimation relies on proportional relationships among battery electromotive force, current, terminal voltage, and temperature, yet sharp terminal-voltage drops near discharge end can introduce substantial calculation errors. The study applies and compares machine learning methods—including Random Forest, Decision Tree, Gradient Boosting, Support Vector Regression, Polynomial Regression, and Multilayer Perceptron—using two real-world lithium-ion datasets (LG Battery and Unibo Powertools Battery). K-fold cross validation and grid search are used to evaluate model skill.","A comprehensive exploration on different machine learning techniques for state of charge estimation of EV battery  \nMithul Raaj, A. T. ; Ratnam, Justin; Niranjan Kumar, S. ; Gupta, Tanya; Balaji, Keerthi; Rani,  \nC. ; Rajesh Kumar, M. ; Farrag, Mohamed  \nPublished in:  \n2023 58th International Universities Power Engineering Conference (UPEC)  \nDOI:  \n10.1109/UPEC57427.2023.10294352  \nPublication date:  \n2023  \nDocument Version  \nAuthor accepted manuscript  \nLink to publication in ResearchOnline  \nCitation for published version (Harvard):  \nMithul Raaj, AT, Ratnam, J, Niranjan Kumar, S, Gupta, T, Balaji, K, Rani, C, Rajesh Kumar, M & Farrag, M 2023, A comprehensive exploration on different machine learning techniques for state of charge estimation of [EV battery. in](EV battery. in) 2023 58th International Universities Power Engineering Conference (UPEC). 58th International Universities Power Engineering Conference, UPEC 2023, Institute of Electrical and Electronics Engineers Inc. , 58th International Universities Power Engineering Conference, Dublin, Ireland, 29/08/23 .  \n[https://doi.org/10.1109/UPEC57427.2023.10294352](https://doi.org/10.1109/UPEC57427.2023.10294352)  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy  \nIf you believe that this document breaches copyright please view our takedown policy at [https://edshare.gcu.ac.uk/id/eprint/5179 for details](https://edshare.gcu.ac.uk/id/eprint/5179 for details)[ ](https://edshare.gcu.ac.uk/id/eprint/5179 for details)[of how to contact us.](of how to contact us.)  \nDownload date: 12. Jan. 2024  \nA Comprehensive Exploration on Different Machine Learning Techniques for State of Charge Estimation  \nof EV Battery  \nMithul Raaj A T  \nDepartment of Electrical Engineering Vellore Institute of Technology, Vellore, India [mithulraaj24@gmail.com](mithulraaj24@gmail.com)  \nTanya Gupta  \nDepartment of Electrical Engineering Vellore Institute of Technology, Vellore, India [tanyagupta822101@gmail.com](tanyagupta822101@gmail.com)  \nRajesh Kumar M  \nDepartment of Electrical Engineering Vellore Institute of Technology, Vellore, India [mrajeshkumar@vit.ac.in](mrajeshkumar@vit.ac.in)  \nJustin Ratnam  \nDepartment of Electrical Engineering Vellore Institute of Technology, Vellore, India [justinratnam@gmail.com](justinratnam@gmail.com)  \nKeerthi Balaji  \nDepartment of Electrical Engineering Vellore Institute of Technology, Vellore, India keerthi0kiran@gmail  \nMohamed Farrag School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow, [UK mohamed.farrag@gcu.ac.uk](UK mohamed.farrag@gcu.ac.uk)  \nNiranjan Kumar S  \nDepartment of Electrical Engineering Vellore Institute of Technology, Vellore, India[niranjanniru572@gmail.com](niranjanniru572@gmail.com)  \nRani C  \nDepartment of Electrical Engineering Vellore Institute of Technology, Vellore, India [crani@vit.ac](crani@vit.ac)  \nAbstract—The State of Charge (SoC) is a measurement of the amount of energy available in a battery at a specific interval of time, mostly expressed as percentage. Proportional relationships between the electromotive force of a battery, current, terminal voltage and temperature determine the SoC. There can be a considerable error in the calculations due to a sharp drop of the terminal voltage at the end of discharge. This research has explored how important SoC is, as a factor in Battery Management Systems. The work focuses on using machine learning techniques to obtain an accurate and reliable status of battery charge, this includes Random Forest, Decision Tree, Gradient Boosting, Support Vector Regression, Polynomial Regression and Multilayer Perceptron. In this paper, these techniques are tested and compared with two real world cap","cbCaiexALUcfbcNY","https://ap.wps.com/l/cbCaiexALUcfbcNY","pdf",901390,1,7,"English","en",105,"# Introduction\n# State of Charge (SoC) and Error Sources\n# Machine Learning Techniques for SoC Estimation\n## Random Forest\n## Decision Tree\n## Gradient Boosting\n## Support Vector Regression\n## Polynomial Regression\n## Multilayer Perceptron\n# Datasets Used for Evaluation\n## LG Battery\n## Unibo Powertools Battery\n# Model Validation and Hyperparameter Search\n## K-fold Cross Validation\n## Grid Search Cross Validation\n# Results and Comparative Findings","[{\"question\":\"What does SoC mean in battery systems, and why is it important?\",\"answer\":\"SoC measures the energy available in a battery at a given time, usually as a percentage. It is important as a key factor in Battery Management Systems.\"},{\"question\":\"Why can SoC calculations have significant errors?\",\"answer\":\"Errors can occur due to a sharp drop in terminal voltage at the end of discharge, which disturbs the relationships used for estimation.\"},{\"question\":\"Which machine learning models were compared for SoC estimation?\",\"answer\":\"The study compares Random Forest, Decision Tree, Gradient Boosting, Support Vector Regression, Polynomial Regression, and Multilayer Perceptron, evaluated on lithium-ion battery datasets.\"}]","A Comprehensive Exploration on Different Machine Learning Techniques for State of Charge Estimation of EV Battery - Conference Paper | PDF",1785813028,18,{"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-comprehensive-exploration-on-different-machine-learning-techniques-for-state-of-charge-estimation-of-ev-battery-conference-paper","",{"@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-comprehensive-exploration-on-different-machine-learning-techniques-for-state-of-charge-estimation-of-ev-battery-conference-paper/122810/",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},"What does SoC mean in battery systems, and why is it important?","Question",{"text":75,"@type":76},"SoC measures the energy available in a battery at a given time, usually as a percentage. It is important as a key factor in Battery Management Systems.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why can SoC calculations have significant errors?",{"text":80,"@type":76},"Errors can occur due to a sharp drop in terminal voltage at the end of discharge, which disturbs the relationships used for estimation.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning models were compared for SoC estimation?",{"text":84,"@type":76},"The study compares Random Forest, Decision Tree, Gradient Boosting, Support Vector Regression, Polynomial Regression, and Multilayer Perceptron, evaluated on lithium-ion battery datasets.","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},[93,97,101,105,110,115,119,122,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"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"]