XGBoost-Driven State-of-Power Estimation for Lithium-Ion Batteries in Battery Management System

XGBoost-Driven State-of-Power Estimation for Lithium-Ion Batteries in Battery Management System | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article XGBoost-Driven State-of-Power Estimation for Lithium-Ion Batteries in Battery Management System Surasi Sharan Kumar, Vinod Kumar D.M., Ram Deshmukh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7108239/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Predicting the State of Power (SoP) accurately in lithium-ion batteries is a key to safety and optimal performance in battery-powered systems such as electric vehicles (EVs) and renewable energy storage. Although deep learning models show promise in SoP prediction, they often need large datasets, extensive tuning, and high computational resources, which restrict their practical application in real-time Battery Management Systems (BMS). In this paper, we introduce a new machine learning architecture that combines physically-inspired synthetic data generation with an XGBoost regressor to estimate SoP efficiently and accurately with low computational cost. The XGBoost model is trained on a synthetically generated dataset and assess its performance using standard regression metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R² score. In the proposed method, results are compared with CNN-LSTM-PSO with SHAP based XAI. It yields a Mean Squared Error (MSE) of 6.062 × 10⁻⁷, Root Mean Squared Error of (RMSE) 8.14 × 10⁻⁴, Mean Absolute Error (MAE) of 5.53 × 10⁻⁴, and an R² value of 99.99%. Feature importance is further examined using TreeSHAP to understand how input variables influence SoP. This research offers a scalable, data-efficient method for early-stage development and real-time deployment of SoP estimation models. Physical sciences/Energy science and technology Physical sciences/Engineering Physical sciences/Mathematics and computing Long Short-Term Memory (LSTM) Particle Swarm Optimization (PSO) SHapley Additive exPlanations (SHAP) eXtreme Gradient Boosting (XGBoost) Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7108239","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":498873199,"identity":"4f5c6c0a-f1e2-4828-9fcb-0b4a91cbca1a","order_by":0,"name":"Surasi Sharan Kumar","email":"","orcid":"","institution":"SR University","correspondingAuthor":false,"prefix":"","firstName":"Surasi","middleName":"Sharan","lastName":"Kumar","suffix":""},{"id":498873200,"identity":"024b7060-e41e-46c5-8a35-c5e1fe25fefc","order_by":1,"name":"Vinod Kumar D.M.","email":"","orcid":"","institution":"SR University","correspondingAuthor":false,"prefix":"","firstName":"Vinod","middleName":"Kumar","lastName":"D.M.","suffix":""},{"id":498873201,"identity":"14db5294-8287-4e88-812c-a956b67f97e6","order_by":2,"name":"Ram Deshmukh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYJACCQaGAwwMPDyMDyD8BDAiSguzAcla2CTgWvAB8/bTiTc+/LkTbXDm7LHKn3sOM/Cz5xgwPNyBW4vMmdzNljPbnuVuONuXdpvn2WEGyZ43BgyJZ/A5KnebNG/D4dwN53nMbjMcOMxgcANoS2IbHi38b7dJ//kD0VL4A6jFnqAWCaAtDGxALWd7zBh4QLZIENTydrNlb9vh3JlnzhhL8xxI55E486zgAH6H5W688QPosL4zOYYffxywluNvT9748CceLRiAB0QcIEHDKBgFo2AUjAIsAACn8ll+jxZ7GAAAAABJRU5ErkJggg==","orcid":"","institution":"SR University","correspondingAuthor":true,"prefix":"","firstName":"Ram","middleName":"","lastName":"Deshmukh","suffix":""}],"badges":[],"createdAt":"2025-07-12 12:53:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7108239/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7108239/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89452161,"identity":"2638bfa0-f45f-437d-b122-0787b9d229d8","added_by":"auto","created_at":"2025-08-20 06:24:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":994816,"visible":true,"origin":"","legend":"","description":"","filename":"XGBoostDrivenSoPEstimationforLiIonBatteriesinBMS.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7108239/v1_covered_37dc6f4b-86a5-47f5-a8a5-338971f3b95b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"XGBoost-Driven State-of-Power Estimation for Lithium-Ion Batteries in Battery Management System","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Long Short-Term Memory (LSTM), Particle Swarm Optimization (PSO), SHapley Additive exPlanations (SHAP), eXtreme Gradient Boosting (XGBoost)","lastPublishedDoi":"10.21203/rs.3.rs-7108239/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7108239/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePredicting the State of Power (SoP) accurately in lithium-ion batteries is a key to safety and optimal performance in battery-powered systems such as electric vehicles (EVs) and renewable energy storage. Although deep learning models show promise in SoP prediction, they often need large datasets, extensive tuning, and high computational resources, which restrict their practical application in real-time Battery Management Systems (BMS). In this paper, we introduce a new machine learning architecture that combines physically-inspired synthetic data generation with an XGBoost regressor to estimate SoP efficiently and accurately with low computational cost. The XGBoost model is trained on a synthetically generated dataset and assess its performance using standard regression metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R\u0026sup2; score. In the proposed method, results are compared with CNN-LSTM-PSO with SHAP based XAI. It yields a Mean Squared Error (MSE) of 6.062 \u0026times; 10⁻⁷, Root Mean Squared Error of (RMSE) 8.14 \u0026times; 10⁻⁴, Mean Absolute Error (MAE) of 5.53 \u0026times; 10⁻⁴, and an R\u0026sup2; value of 99.99%. Feature importance is further examined using TreeSHAP to understand how input variables influence SoP. This research offers a scalable, data-efficient method for early-stage development and real-time deployment of SoP estimation models.\u003c/p\u003e","manuscriptTitle":"XGBoost-Driven State-of-Power Estimation for Lithium-Ion Batteries in Battery Management System","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-14 08:22:45","doi":"10.21203/rs.3.rs-7108239/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"085eb20d-ea23-4833-aaef-6f62a483e6e9","owner":[],"postedDate":"August 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52987392,"name":"Physical sciences/Energy science and technology"},{"id":52987393,"name":"Physical sciences/Engineering"},{"id":52987394,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2025-08-20T06:23:50+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-14 08:22:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7108239","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7108239","identity":"rs-7108239","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}