Dynamic Pricing Model for Techno Economic Analysis of Battery Swapping Options as a Service

Dynamic Pricing Model for Techno Economic Analysis of Battery Swapping Options as a Service | 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 Dynamic Pricing Model for Techno Economic Analysis of Battery Swapping Options as a Service Namrata Mohanty, Neeraj Kumar Goyal, V N Achutha Naikan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7652428/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Battery as a Service (BaaS) offers a scalable alternative to plug-in charging, yet its adoption is hindered by economic uncertainties, unfair pricing structures, and limited integration of life cycle cost (LCC) principles. Existing models often ignore key aspects such as degradation rates, state of health (SOH), state of charge (SOC), residual resale value, and demand fluctuations, leading to inaccurate cost recovery and non-compliance with standards. Therefore, this work develops first dynamic phase-wise cost recovery model (DCRM) comprising pre-swap (procurement, logistics and testing), swap (age and usage-based User Behaviour Incentive Pricing (UBIP)), and post-swap (residual and second-life recovery) phases. Furthermore, Monte Carlo simulation (MCS) is applied to capture uncertainty in procurement cost, demand elasticity, degradation, and resale value. Case study results show that subsidies significantly enhance financial performance. At 60% subsidy, 2W and 3W fleets achieve ROI > 50% with payback of 1.6–1.8 years, while 4W fleets exceed 80% ROI with payback near 1.3 years. Customer–operator analysis reveals that pay per swap (PPS) pricing model is optimal for low-usage fleets (e.g., 36 swaps/month for 4W), while ensuring operator profitability above their own break-even points. The proposed model provides an overall techno-economic framework aligning LCC recovery with equitable BaaS pricing, bridging a critical gap in sustainable EV fleet adoption. Battery Swapping Battery as a Service (BaaS) Electric Vehicles Lithium-ion Batteries (LIBs) Life Cycle Cost (LCC) Dynamic Cost Recovery Model (DCRM) Monte Carlo Simulation (MCS) Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 19 Oct, 2025 Reviewers agreed at journal 15 Oct, 2025 Reviewers agreed at journal 12 Oct, 2025 Reviewers invited by journal 12 Oct, 2025 Editor assigned by journal 07 Oct, 2025 Submission checks completed at journal 19 Sep, 2025 First submitted to journal 18 Sep, 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. 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-7652428","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":533651994,"identity":"2a0b79c6-b6a5-4ecd-9b32-dc9e2151eb05","order_by":0,"name":"Namrata 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Service","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"clean-technologies-and-environmental-policy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ctep","sideBox":"Learn more about [Clean Technologies and Environmental Policy](https://www.springer.com/journal/10098)","snPcode":"10098","submissionUrl":"https://submission.nature.com/new-submission/10098/3","title":"Clean Technologies and Environmental Policy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Battery Swapping, Battery as a Service (BaaS), Electric Vehicles, Lithium-ion Batteries (LIBs), Life Cycle Cost (LCC), Dynamic Cost Recovery Model (DCRM), Monte Carlo Simulation (MCS)","lastPublishedDoi":"10.21203/rs.3.rs-7652428/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7652428/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBattery as a Service (BaaS) offers a scalable alternative to plug-in charging, yet its adoption is hindered by economic uncertainties, unfair pricing structures, and limited integration of life cycle cost (LCC) principles. Existing models often ignore key aspects such as degradation rates, state of health (SOH), state of charge (SOC), residual resale value, and demand fluctuations, leading to inaccurate cost recovery and non-compliance with standards. Therefore, this work develops first dynamic phase-wise cost recovery model (DCRM) comprising pre-swap (procurement, logistics and testing), swap (age and usage-based User Behaviour Incentive Pricing (UBIP)), and post-swap (residual and second-life recovery) phases. Furthermore, Monte Carlo simulation (MCS) is applied to capture uncertainty in procurement cost, demand elasticity, degradation, and resale value. Case study results show that subsidies significantly enhance financial performance. At 60% subsidy, 2W and 3W fleets achieve ROI\u0026thinsp;\u0026gt;\u0026thinsp;50% with payback of 1.6\u0026ndash;1.8 years, while 4W fleets exceed 80% ROI with payback near 1.3 years. Customer\u0026ndash;operator analysis reveals that pay per swap (PPS) pricing model is optimal for low-usage fleets (e.g., \u0026lt;\u0026thinsp;29 swaps/month for 2W), whereas subscription (SUB) benefits high-mileage fleets (e.g., \u0026gt;\u0026thinsp;36 swaps/month for 4W), while ensuring operator profitability above their own break-even points. The proposed model provides an overall techno-economic framework aligning LCC recovery with equitable BaaS pricing, bridging a critical gap in sustainable EV fleet adoption.\u003c/p\u003e","manuscriptTitle":"Dynamic Pricing Model for Techno Economic Analysis of Battery Swapping Options as a Service","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-27 11:31:05","doi":"10.21203/rs.3.rs-7652428/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-10-19T13:48:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"9457424355845327304785528259431481895","date":"2025-10-15T14:40:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"328947879344003092526814239738179036513","date":"2025-10-13T01:39:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-13T01:13:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-07T11:40:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-20T03:14:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Clean Technologies and Environmental Policy","date":"2025-09-18T19:02:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"clean-technologies-and-environmental-policy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ctep","sideBox":"Learn more about [Clean Technologies and Environmental Policy](https://www.springer.com/journal/10098)","snPcode":"10098","submissionUrl":"https://submission.nature.com/new-submission/10098/3","title":"Clean Technologies and Environmental Policy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"00e03e33-61f4-49d6-b4c6-485fcdb6da24","owner":[],"postedDate":"October 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-27T11:31:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-27 11:31:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7652428","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7652428","identity":"rs-7652428","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}