A Machine-learning based approach for Hybrid Electric Vehicle Redesign and Processor-in-Loop Based Power Management Validation

A Machine-learning based approach for Hybrid Electric Vehicle Redesign and Processor-in-Loop Based Power Management Validation | 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 A Machine-learning based approach for Hybrid Electric Vehicle Redesign and Processor-in-Loop Based Power Management Validation Debraj Bhattacharjee, Tamal Ghosh, Pranab Dan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7551926/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 This article presents a design planning method for a power-split hybrid electric vehicle, which optimizes the powertrain components and the power management strategy (PMS) for better fuel efficiency and lower emission, following the principles of ecodesign. The road gradient is taken into account in the optimization process, which uses an offline constrained method. The powertrain components are tuned for real-time driving data using a Surrogate assisted evolutionary algorithm, which generates multiple design alternatives and selects the best one using a Modified Technique for Order of Preference by Similarity to Ideal Solution with vehicle weight reduction of 4%. The PMS is based on a model predictive control equipped with two stage optimization approach, which determines the optimal values of engine torque, engine speed, motor torque, motor speed and brake signal to ensure the desired power supply, with minimum fossil fuel consumption and emission. The proposed method achieves more than 5% improvement in fuel efficiency and 10% improvement in emission reduction compared to the existing methods that use Dynamic programming or Fuzzy logic. Artificial Intelligence and Machine Learning Explicit Model Predictive Control Green House Gas Emission High Performance Computing Plug-in Hybrid Electric Vehicle Power Source Optimization Power Management Strategy Full Text Additional Declarations The authors declare no competing interests. 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-7551926","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":511218570,"identity":"a0e73457-fec1-41f5-abd1-841101c3dec7","order_by":0,"name":"Debraj Bhattacharjee","email":"data:image/png;base64,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","orcid":"","institution":"FLAME University","correspondingAuthor":true,"prefix":"","firstName":"Debraj","middleName":"","lastName":"Bhattacharjee","suffix":""},{"id":511218571,"identity":"f45104f8-77e8-45e2-a1cb-9eaa0fae649c","order_by":1,"name":"Tamal Ghosh","email":"","orcid":"","institution":"Adamas University","correspondingAuthor":false,"prefix":"","firstName":"Tamal","middleName":"","lastName":"Ghosh","suffix":""},{"id":511218572,"identity":"563353a9-ee4a-47dd-bff1-772528328a1e","order_by":2,"name":"Pranab Dan","email":"","orcid":"","institution":"SEACOM SKILLS University","correspondingAuthor":false,"prefix":"","firstName":"Pranab","middleName":"","lastName":"Dan","suffix":""}],"badges":[],"createdAt":"2025-09-06 15:43:17","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-7551926/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7551926/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91148846,"identity":"d5433cba-f0f2-4d82-a829-bcb32eb76b37","added_by":"auto","created_at":"2025-09-12 06:45:55","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1842782,"visible":true,"origin":"","legend":"","description":"","filename":"PMSManuscriptV2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7551926/v1_covered_0fd4b286-ffe7-4055-bd3c-69dd47d23979.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eA Machine-learning based approach for Hybrid Electric Vehicle Redesign and Processor-in-Loop Based Power Management Validation\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Flame University","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":"Explicit Model Predictive Control, Green House Gas Emission, High Performance Computing, Plug-in Hybrid Electric Vehicle, Power Source Optimization, Power Management Strategy","lastPublishedDoi":"10.21203/rs.3.rs-7551926/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7551926/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis article presents a design planning method for a power-split hybrid electric vehicle, which optimizes the powertrain components and the power management strategy (PMS) for better fuel efficiency and lower emission, following the principles of ecodesign. 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