New energy vehicle fast charging reservation algorithm based on Internet of Things coordination

New energy vehicle fast charging reservation algorithm based on Internet of Things coordination | 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 New energy vehicle fast charging reservation algorithm based on Internet of Things coordination Weinan Han, Dongzhen Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5916841/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Nov, 2025 Read the published version in Discover Internet of Things → Version 1 posted 9 You are reading this latest preprint version Abstract To meet the rising demand for electric vehicles (EVs), effective and dependable fast-charging reservation systems are required. Conventional charging reservation systems frequently lack coordination between user preferences, real-time station status, and environmental factors, leading to poor user experiences and station ineffectiveness. Existing methods to EV charging reservation systems fail to account for dynamic real-world conditions such as changing traffic patterns, station uptime, and IoT sensor inputs, resulting in suboptimal station allocation and failed reservations. This study fills a gap by proposing an IoT-Coordinated EV Fast Charging Reservation Approach (IoT-CFCRA), which uses real-time data to predict reservation success and suggest the best charging stations under different conditions. The IoT-CFCRA uses the IoT-Enhanced EV Charging Reservation Dataset, which contains user attributes, vehicle data, and IoT-enhanced station data like vehicle type, battery level, distance to station, sensor status, and real-time traffic. Data preprocessing entails normalization, encoding, and feature selection to find important features. A Support Vector Machine (SVM) model is trained to predict reservation success through hyperparameter tuning and 80 − 20 data splitting. The algorithm also includes a station scoring method that considers IoT uptime, distance, traffic conditions, and membership status to provide ranked station suggestions. Users receive real-time notifications to help them adapt to traffic conditions and reservation results. The experimental results show that the proposed IoT-CFCRA approach outperforms other methods. It achieved an accuracy of 87%, outperforming the best baseline (Gradient Boosting) by 4% and improving on Logistic Regression by 10%. The AUC score of 0.92 indicates excellent discriminative capability, a 5-point improvement over Random Forest. The F1-Score of 0.84 demonstrates a strong balance of precision and recall, outperforming SVM by 8%. Furthermore, RMSE was reduced to 0.25, indicating a 19.4% decrease in prediction error when compared to KNN (RMSE = 0.36). The cross-validation score of 88% confirms the model's robustness, outperforming the next-best performing model by 4%. These metrics highlight the resilience of the IoT-CFCRA in creating precise reservations and suggesting optimum charging stations. The IoT-CFCRA seamlessly combines IoT capacities with machine learning to tackle dynamic factors that influence EV charging reservations. The proposed approach encourages user-centered decision-making and effective resource allocation, laying the groundwork for future advances in IoT-driven EV infrastructure. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 05 Nov, 2025 Read the published version in Discover Internet of Things → Version 1 posted Editorial decision: Revision requested 25 Apr, 2025 Editor assigned by journal 24 Apr, 2025 Reviews received at journal 24 Apr, 2025 Reviews received at journal 21 Apr, 2025 Reviewers agreed at journal 20 Apr, 2025 Reviewers agreed at journal 17 Apr, 2025 Reviewers invited by journal 17 Apr, 2025 Submission checks completed at journal 17 Apr, 2025 First submitted to journal 10 Apr, 2025 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. 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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-5916841","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":444422613,"identity":"766001d9-cd51-4707-ae3a-4391bd117e2b","order_by":0,"name":"Weinan Han","email":"","orcid":"","institution":"Cangzhou Jiaotong College","correspondingAuthor":false,"prefix":"","firstName":"Weinan","middleName":"","lastName":"Han","suffix":""},{"id":444422614,"identity":"ef12d0ac-1b94-48bb-ad0d-239ca126bfc9","order_by":1,"name":"Dongzhen Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEElEQVRIiWNgGAWjYBACexDB2MDAYHAAyPhQYQMkmYHcA7i1GDYgaWGccSaNgYGNEb8WsOEwLcy8bYeJ0HK89/DLnzts8gyOnz38gufMeTmD+42NDz6cYZDnF8Ouz+DMuTQLyTNpxWZn8tIsJCpuGxscY2w2nHGDwXDm7ATsWm7kmBkYth1O3HYAyDA4cztxwzHGNmmeDwwJBrfxaEls+5+47fwbEOMcUVqMHxxsO5C4H8aAaLmBW4thzxkzxsa25MSdN96YMTacSTaWPJYI9MsZCZx+sWfvMf74s80uccP5HOPPfyrs5PgOHz744MMxG3l+aexagIBNAp0BAhLYlMIA8wd0xigYBaNgFIwCFAAAJkV0eZIgYxQAAAAASUVORK5CYII=","orcid":"","institution":"Cangzhou Jiaotong College","correspondingAuthor":true,"prefix":"","firstName":"Dongzhen","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-01-28 07:38:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5916841/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5916841/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s43926-025-00212-7","type":"published","date":"2025-11-05T15:58:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":95564210,"identity":"400d4232-e8be-4d20-bede-6b23d0ce2fd8","added_by":"auto","created_at":"2025-11-10 16:08:57","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":601754,"visible":true,"origin":"","legend":"","description":"","filename":"Newenergyvehiclewithinformation121.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5916841/v1_covered_1546aeac-4902-4917-94d0-8922b38fd1f0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"New energy vehicle fast charging reservation algorithm based on Internet of Things coordination","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"discover-internet-of-things","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diot","sideBox":"Learn more about [Discover Internet of Things](https://www.springer.com/journal/43926)","snPcode":"","submissionUrl":"","title":"Discover Internet of Things","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5916841/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5916841/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo meet the rising demand for electric vehicles (EVs), effective and dependable fast-charging reservation systems are required. Conventional charging reservation systems frequently lack coordination between user preferences, real-time station status, and environmental factors, leading to poor user experiences and station ineffectiveness. Existing methods to EV charging reservation systems fail to account for dynamic real-world conditions such as changing traffic patterns, station uptime, and IoT sensor inputs, resulting in suboptimal station allocation and failed reservations. This study fills a gap by proposing an IoT-Coordinated EV Fast Charging Reservation Approach (IoT-CFCRA), which uses real-time data to predict reservation success and suggest the best charging stations under different conditions. The IoT-CFCRA uses the IoT-Enhanced EV Charging Reservation Dataset, which contains user attributes, vehicle data, and IoT-enhanced station data like vehicle type, battery level, distance to station, sensor status, and real-time traffic. Data preprocessing entails normalization, encoding, and feature selection to find important features. A Support Vector Machine (SVM) model is trained to predict reservation success through hyperparameter tuning and 80\u0026thinsp;\u0026minus;\u0026thinsp;20 data splitting. The algorithm also includes a station scoring method that considers IoT uptime, distance, traffic conditions, and membership status to provide ranked station suggestions. Users receive real-time notifications to help them adapt to traffic conditions and reservation results. The experimental results show that the proposed IoT-CFCRA approach outperforms other methods. It achieved an accuracy of 87%, outperforming the best baseline (Gradient Boosting) by 4% and improving on Logistic Regression by 10%. The AUC score of 0.92 indicates excellent discriminative capability, a 5-point improvement over Random Forest. The F1-Score of 0.84 demonstrates a strong balance of precision and recall, outperforming SVM by 8%. Furthermore, RMSE was reduced to 0.25, indicating a 19.4% decrease in prediction error when compared to KNN (RMSE\u0026thinsp;=\u0026thinsp;0.36). The cross-validation score of 88% confirms the model's robustness, outperforming the next-best performing model by 4%. These metrics highlight the resilience of the IoT-CFCRA in creating precise reservations and suggesting optimum charging stations. The IoT-CFCRA seamlessly combines IoT capacities with machine learning to tackle dynamic factors that influence EV charging reservations. The proposed approach encourages user-centered decision-making and effective resource allocation, laying the groundwork for future advances in IoT-driven EV infrastructure.\u003c/p\u003e","manuscriptTitle":"New energy vehicle fast charging reservation algorithm based on Internet of Things coordination","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-18 06:11:33","doi":"10.21203/rs.3.rs-5916841/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-25T12:22:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-24T09:58:48+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-24T05:16:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-21T07:29:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22567578103198667198713816393623239277","date":"2025-04-20T17:09:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62313520836180695864673307726763893925","date":"2025-04-17T13:22:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-17T13:10:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-17T09:50:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Internet of Things","date":"2025-04-10T15:48:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-internet-of-things","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diot","sideBox":"Learn more about [Discover Internet of Things](https://www.springer.com/journal/43926)","snPcode":"","submissionUrl":"","title":"Discover Internet of Things","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4d92d2b3-be66-4ea5-b8ea-7e3752045f89","owner":[],"postedDate":"April 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-10T16:05:30+00:00","versionOfRecord":{"articleIdentity":"rs-5916841","link":"https://doi.org/10.1007/s43926-025-00212-7","journal":{"identity":"discover-internet-of-things","isVorOnly":false,"title":"Discover Internet of Things"},"publishedOn":"2025-11-05 15:58:00","publishedOnDateReadable":"November 5th, 2025"},"versionCreatedAt":"2025-04-18 06:11:33","video":"","vorDoi":"10.1007/s43926-025-00212-7","vorDoiUrl":"https://doi.org/10.1007/s43926-025-00212-7","workflowStages":[]},"version":"v1","identity":"rs-5916841","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5916841","identity":"rs-5916841","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}