Conceptual Structural Optimization and AI-Based Stress Prediction of a Monocoque Chassis for Formula Student Electric Vehicle

Conceptual Structural Optimization and AI-Based Stress Prediction of a Monocoque Chassis for Formula Student Electric Vehicle | 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 Conceptual Structural Optimization and AI-Based Stress Prediction of a Monocoque Chassis for Formula Student Electric Vehicle B.A.PATIL This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9277913/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 study presents the conceptual design, optimization, smart material assessment, and ai based stress prediction of the monocoque chassis of the Formula Student Electric Vehicle (FSEV). The chassis designed according to the 2025 Formula Student guidelines focusing on high structural compliance and safety, stiffness, and minimal weight [1],[2]. A total of 14 engineering materials were compared and analysed structurally for their performance in regards to impacts. The material selection was done based on the finite element analysis (FEA) simulations of front and rear impacts concerning total deformation. Only the most favourable material was selected based on the FEA simulations of the front and rear impacts [3],[4]. A detailed computer-aided design (CAD) model created on SolidWorks, and ANSYS Workbench was used to conduct the FEA for front, rear, and side impacts. Through iterative topology optimization, unnecessary materials were eliminated to the maximum degree while ensuring that the structure remained crashworthy. The result of the optimization process on the CFRP monocoque is a weight reduction of 35–40% while ensuring that high stiffness and safety requirements are met compared to the conventional spaceframe. An AI regression model for stress prediction has been created as a method for increasing efficiency. Using Young’s modulus, yield strength, and Poisson’s ratio as inputs, the model has been shown to reduce the number of simulations needed for stress distribution predicting and accelerate the process of selecting materials. The combination of AI and FEA has created a solid, time-saving approach to the design of chassis. The research suggests that AI modelling and CFRP monocoques provide next generation Formula Student electric vehicles with improved safety, better efficiency, and cost-effective alternatives to competitors with high stiffness-to-weight ratios [2]. Mechanical Engineering FSEV Monocoque Chassis Spaceframe Carbon Fiber Reinforced Polymer (CFRP) Torsional Stiffness Crashworthiness Finite Element Analysis (FEA) Artificial Intelligence (AI) Regression Model Stress Prediction 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-9277913","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":615173398,"identity":"e2a7f66c-c0a4-4747-ab5d-5730df4a429d","order_by":0,"name":"B.A.PATIL","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEElEQVRIiWNgGAWjYBACAwhpASLZGD4gS7Hh03LAQAKshnEG8VoYIFqYeYhxmDn72YOfPxRIMOi2Nz97bPPLTo6f/ezDDww1dgx80g1YtVj25CVLgBxmduaYuXFuX7KxZE+6sQTDsWQGNpkD2B12IMcAouVGDpt0bs+BxA0H0oDuZDvAwCaRgF3L+TfGP8Ba7r9hk7YEatl//hnzD4Z/eLTcyDGD2sLDJs3wA2iLRBqbBGMbbi2WM96lWZwxkOAxO5NmJtnbkGwsceMZm0ViXzIPLi3m/LmHb1T8sZEzO374mcSPP8AQ609jvvHhm52c/AzsWhgYeJBIxjaoYAJMBI8WKPiDW90oGAWjYBSMXAAAZgZXGXZX1EoAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-6859-4464","institution":"Government College of Engineering and Research Avasari Khurd","correspondingAuthor":true,"prefix":"","firstName":"","middleName":"","lastName":"B.A.PATIL","suffix":""}],"badges":[],"createdAt":"2026-03-31 09:43:07","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9277913/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9277913/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105870361,"identity":"0a47a851-7d68-43c0-8a38-68e11bda0475","added_by":"auto","created_at":"2026-04-01 04:25:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":791771,"visible":true,"origin":"","legend":"","description":"","filename":"NEWPAPER.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9277913/v1_covered_25ef9e79-bfa6-42c1-8cb7-f86737163263.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eConceptual Structural Optimization and AI-Based Stress Prediction of a Monocoque Chassis for Formula Student Electric Vehicle\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Government College of Engineering and Research Avasari Khurd","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":"FSEV, Monocoque Chassis, Spaceframe, Carbon Fiber Reinforced Polymer (CFRP), Torsional Stiffness, Crashworthiness, Finite Element Analysis (FEA), Artificial Intelligence (AI), Regression Model, Stress Prediction","lastPublishedDoi":"10.21203/rs.3.rs-9277913/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9277913/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study presents the conceptual design, optimization, smart material assessment, and ai based stress prediction of the monocoque chassis of the Formula Student Electric Vehicle (FSEV). The chassis designed according to the 2025 Formula Student guidelines focusing on high structural compliance and safety, stiffness, and minimal weight [1],[2]. A total of 14 engineering materials were compared and analysed structurally for their performance in regards to impacts. The material selection was done based on the finite element analysis (FEA) simulations of front and rear impacts concerning total deformation. Only the most favourable material was selected based on the FEA simulations of the front and rear impacts [3],[4]. A detailed computer-aided design (CAD) model created on SolidWorks, and ANSYS Workbench was used to conduct the FEA for front, rear, and side impacts. Through iterative topology optimization, unnecessary materials were eliminated to the maximum degree while ensuring that the structure remained crashworthy. The result of the optimization process on the CFRP monocoque is a weight reduction of 35\u0026ndash;40% while ensuring that high stiffness and safety requirements are met compared to the conventional spaceframe. An AI regression model for stress prediction has been created as a method for increasing efficiency. Using Young\u0026rsquo;s modulus, yield strength, and Poisson\u0026rsquo;s ratio as inputs, the model has been shown to reduce the number of simulations needed for stress distribution predicting and accelerate the process of selecting materials. The combination of AI and FEA has created a solid, time-saving approach to the design of chassis. 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