An Algorithmic Framework for Full-order Physics‑based Simulations of Electrochemical Impedance Spectroscopy

An Algorithmic Framework for Full-order Physics‑based Simulations of Electrochemical Impedance Spectroscopy | 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 An Algorithmic Framework for Full-order Physics‑based Simulations of Electrochemical Impedance Spectroscopy Toshan Wickramanayake, Kamyar Mehran This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7436158/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Simulated Electrochemical Impedance Spectroscopy (sEIS) is a powerful technique for non-invasive analysis of lithium-ion batteries (LiBs). It virtually replicates experimental EIS by applying small-signal current perturbations to a physics-based LiB model to observe the resulting impedance response, enabling applications like model parameterisation and degradation characterisation. To advance this area of sEIS research, this work proposes a novel solver to simulate a full-order physics-based model called the Electrochemical-Ageing-Capacitance (EAC) model. The contributions of this work are threefold. First, the equations of the EAC model are transformed from a set of coupled partial-differential-equations (PDEs) and ordinary-differential-equations (ODEs) into a coupled ODE-only system. Second, a novel ‘ODE+iterative’ solver framework is proposed to accurately and efficiently compute the EAC model equations. To benchmark performance, the solver is compared with state-of-the-art solvers in both MATLAB and PyBaMM. It demonstrates <1% prediction error for most EAC model variables. When computing sEIS impedance spectra, the solver also achieves a 4x improvement in solving performance compared to MATLAB, and competitive performance compared to PyBaMM. Finally, we present a novel demonstration of using sEIS to quantitatively characterise degradation in the EAC model. The solver is provided open-source, offering researchers a validated and efficient tool for high-fidelity sEIS simulations. Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Energy science and technology/Energy modelling Physical sciences/Energy science and technology/Energy storage Lithium-ion Battery Partial-Two Dimensional Model Electrochemical Impedance Spectroscopy Degradation Characterisation Solvers Full Text Additional Declarations There is NO Competing Interest. Supplementary Files FinalSupplementaryInformation.pdf Supplementary Information Cite Share Download PDF Status: Under Review 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-7436158","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":504799805,"identity":"d19b8a5a-7e1f-4a73-b862-45a00dd17d2c","order_by":0,"name":"Toshan Wickramanayake","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0009-6891-6238","institution":"Queen Mary University of London","correspondingAuthor":true,"prefix":"","firstName":"Toshan","middleName":"","lastName":"Wickramanayake","suffix":""},{"id":504799806,"identity":"03a74a8b-bde4-4793-ab49-8f89634e6ed8","order_by":1,"name":"Kamyar Mehran","email":"","orcid":"","institution":"Queen Mary University of London","correspondingAuthor":false,"prefix":"","firstName":"Kamyar","middleName":"","lastName":"Mehran","suffix":""}],"badges":[],"createdAt":"2025-08-22 16:15:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7436158/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7436158/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90850349,"identity":"99453ccd-9588-4c94-bddd-07dcf7cbf9e3","added_by":"auto","created_at":"2025-09-09 02:57:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6855917,"visible":true,"origin":"","legend":"Article File","description":"","filename":"FinalManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7436158/v1_covered_96914e31-8b50-4c77-baca-22ac0487cb7b.pdf"},{"id":90849790,"identity":"cf52a1c5-1936-4ee9-a879-058e3206d1f3","added_by":"auto","created_at":"2025-09-09 02:49:45","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3349412,"visible":true,"origin":"","legend":"Supplementary Information","description":"","filename":"FinalSupplementaryInformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7436158/v1/1f29d452cbd984a7c99d8345.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"An Algorithmic Framework for Full-order Physics‑based Simulations of Electrochemical Impedance Spectroscopy","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Lithium-ion Battery, Partial-Two Dimensional Model, Electrochemical Impedance Spectroscopy, Degradation Characterisation, Solvers","lastPublishedDoi":"10.21203/rs.3.rs-7436158/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7436158/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Simulated Electrochemical Impedance Spectroscopy (sEIS) is a powerful technique for non-invasive analysis of lithium-ion batteries (LiBs). It virtually replicates experimental EIS by applying small-signal current perturbations to a physics-based LiB model to observe the resulting impedance response, enabling applications like model parameterisation and degradation characterisation. To advance this area of sEIS research, this work proposes a novel solver to simulate a full-order physics-based model called the Electrochemical-Ageing-Capacitance (EAC) model. The contributions of this work are threefold. First, the equations of the EAC model are transformed from a set of coupled partial-differential-equations (PDEs) and ordinary-differential-equations (ODEs) into a coupled ODE-only system. Second, a novel ‘ODE+iterative’ solver framework is proposed to accurately and efficiently compute the EAC model equations. To benchmark performance, the solver is compared with state-of-the-art solvers in both MATLAB and PyBaMM. It demonstrates \u003c1% prediction error for most EAC model variables. 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