Real-Time Optimization of Piezoelectric Energy Harvesting Using ANN-Based Maximum Power Point Tracking

Real-Time Optimization of Piezoelectric Energy Harvesting Using ANN-Based Maximum Power Point Tracking | 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 Research Article Real-Time Optimization of Piezoelectric Energy Harvesting Using ANN-Based Maximum Power Point Tracking IsmailAlazhari Abubaker Bashar Omer This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8423366/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 Piezoelectric Energy Harvesting (PEH) offers a promising solution for powering low-power electronic systems exposed to ambient vibration. However, the fluctuating and nonlinear nature of mechanical input leads to continuous variations in electrical output, making Maximum Power Point Tracking (MPPT) essential for efficient energy extraction. Traditional fixed-duty or pulse-based control methods struggle to maintain MPPT under dynamic vibration conditions, resulting in significant power loss. This study proposes an Artificial Neural Network (ANN)-based MPPT controller capable of adaptively determining the optimal duty cycle for a DC-DC buck converter in real time. A comprising a piezoelectric bender, full-wave rectifier, and lithium-ion battery-was modeled and simulated in performed under varying vibration amplitudes and frequencies to generate training data for the ANN. The trained ANN achieved a high correlation coefficient (R = 0.99332), confirming its accuracy and generalization capability. Simulation results show that the ANN controller significantly stabilizes the rectifier voltage, enhances impedance matching, and improves battery charging performance. These findings demonstrate that ANN-based MPPT provides an efficient and robust solution for real-time power optimization in piezoelectric vibration energy harvesting systems. Piezoelectric Energy Harvesting ANN MPPT Buck Converter Matlab Simulink Renewable Energy System Full Text Additional Declarations No competing interests reported. Supplementary Files paperdata.xlsx 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-8423366","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":569395265,"identity":"3a6dc6f4-9d7e-431d-bb20-dd3f5e02a5a7","order_by":0,"name":"IsmailAlazhari Abubaker Bashar 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Tracking","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Piezoelectric Energy Harvesting, ANN, MPPT, Buck Converter, Matlab Simulink, Renewable Energy System","lastPublishedDoi":"10.21203/rs.3.rs-8423366/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8423366/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePiezoelectric Energy Harvesting (PEH) offers a promising solution for powering low-power electronic systems exposed to ambient vibration. However, the fluctuating and nonlinear nature of mechanical input leads to continuous variations in electrical output, making Maximum Power Point Tracking (MPPT) essential for efficient energy extraction. Traditional fixed-duty or pulse-based control methods struggle to maintain MPPT under dynamic vibration conditions, resulting in significant power loss. This study proposes an Artificial Neural Network (ANN)-based MPPT controller capable of adaptively determining the optimal duty cycle for a DC-DC buck converter in real time. A comprising a piezoelectric bender, full-wave rectifier, and lithium-ion battery-was modeled and simulated in performed under varying vibration amplitudes and frequencies to generate training data for the ANN. The trained ANN achieved a high correlation coefficient (R\u0026thinsp;=\u0026thinsp;0.99332), confirming its accuracy and generalization capability. Simulation results show that the ANN controller significantly stabilizes the rectifier voltage, enhances impedance matching, and improves battery charging performance. These findings demonstrate that ANN-based MPPT provides an efficient and robust solution for real-time power optimization in piezoelectric vibration energy harvesting systems.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e","manuscriptTitle":"Real-Time Optimization of Piezoelectric Energy Harvesting Using ANN-Based Maximum Power Point Tracking","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-07 06:19:00","doi":"10.21203/rs.3.rs-8423366/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":"47b7d51b-c89f-4784-84f1-73a96b1db982","owner":[],"postedDate":"January 7th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-16T13:15:07+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-07 06:19:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8423366","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8423366","identity":"rs-8423366","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}