Real-Time Dynamic Adhesion Coefficient Estimation and BP Neural Network-Optimized Lateral Stability Control for Distributed-Drive Electric Vehicles

Real-Time Dynamic Adhesion Coefficient Estimation and BP Neural Network-Optimized Lateral Stability Control for Distributed-Drive Electric Vehicles | 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 Article Real-Time Dynamic Adhesion Coefficient Estimation and BP Neural Network-Optimized Lateral Stability Control for Distributed-Drive Electric Vehicles Zhigang Zhou, Ruili Yang, Fang Xu, Wei Shen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6114111/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract To address the instability issues of distributed-drive electric vehicles (DDEV) operating on roads with abrupt changes in adhesion coefficients, a lateral stability control strategy and torque distribution method based on backpropagation (BP) neural network optimization were proposed. First, an Unscented Kalman Filter (UKF) estimation algorithm incorporating real-time variation detection of adhesion coefficients is developed. To ensure rapid response and accurate estimation of current adhesion coefficients during sudden road condition changes, threshold-based real-time detection of adhesion coefficient fluctuations is introduced. Second, a hierarchical stability control strategy specifically designed for varying adhesion coefficient conditions is established. The upper-layer controller employs a Bat Algorithm (BA) optimized BP neural network, which takes the sideslip angle and yaw rate as control targets to calculate the required yaw moment for vehicle stabilization, thereby enhancing real-time computational efficiency and solution accuracy. The lower-layer controller utilizes the estimated road adhesion coefficients to implement a quadratic programming algorithm, optimizing wheel torque distribution with the objective of minimizing tire load rate. Finally, a co-simulation platform is constructed using Carsim/Simulink for validation. The results demonstrate that the proposed estimation algorithm can precisely estimate road adhesion coefficients under extreme conditions of abrupt coefficient changes; The developed stability controller significantly enhances both handling stability and driving stability of DDEV. Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Engineering/Mechanical engineering Distributed driver electric vehicle Torque distribution road adhesion coefficient neural network Active Safety Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 05 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 14 Jul, 2025 Reviews received at journal 10 Jul, 2025 Reviewers agreed at journal 30 Jun, 2025 Reviews received at journal 09 May, 2025 Reviewers agreed at journal 29 Apr, 2025 Reviewers agreed at journal 27 Mar, 2025 Reviewers invited by journal 06 Mar, 2025 Editor assigned by journal 06 Mar, 2025 Editor invited by journal 28 Feb, 2025 Submission checks completed at journal 28 Feb, 2025 First submitted to journal 26 Feb, 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. 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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-6114111","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":422330072,"identity":"d93ef0ed-6a4d-40e9-8c83-e9ff10b3d76d","order_by":0,"name":"Zhigang Zhou","email":"","orcid":"","institution":"Henan University Of Science And Technology","correspondingAuthor":false,"prefix":"","firstName":"Zhigang","middleName":"","lastName":"Zhou","suffix":""},{"id":422330073,"identity":"d85a5ddb-8d4a-46f4-897b-cc5ddb31a46c","order_by":1,"name":"Ruili Yang","email":"","orcid":"","institution":"Henan University Of Science And Technology","correspondingAuthor":false,"prefix":"","firstName":"Ruili","middleName":"","lastName":"Yang","suffix":""},{"id":422330074,"identity":"bee547cb-92d9-488a-a337-7572a7006d4f","order_by":2,"name":"Fang Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYFACxgdAwiYBymMmRguzAZBIA2lhbCBFy2EStBjcSGb8XPDrfB7/jPTnDxgqrBMb2M8ewKtFckYys/TMvtvFEjdyDBsYzqQnNvDkJeDVwi+Rf0Cat+d2YsONHMYGxrbDiQ0SPAZ4tbBJJDP/5u05lzj/RvrDBsZ/RGjhl0hmk+b5cSBxw40EwwbGBiK0SPY8ZrPmbUhO3HjmjeGMhGPpxm08Ofi1GBxPZr7N88cucd7x9AcfPtRYy/azn8GvBQwY26CMBJDvCKsHgT/EKRsFo2AUjIIRCgBeM0faDxqJ9AAAAABJRU5ErkJggg==","orcid":"","institution":"Henan University Of Science And Technology","correspondingAuthor":true,"prefix":"","firstName":"Fang","middleName":"","lastName":"Xu","suffix":""},{"id":422330075,"identity":"c98c1f88-f1b1-447a-910f-3d2025d5f61c","order_by":3,"name":"Wei Shen","email":"","orcid":"","institution":"Henan University Of Science And Technology","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Shen","suffix":""}],"badges":[],"createdAt":"2025-02-26 14:38:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6114111/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6114111/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-31029-7","type":"published","date":"2025-12-05T15:57:59+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":97723916,"identity":"59c393b0-ccaa-4afb-ad23-8e0f195d42a8","added_by":"auto","created_at":"2025-12-08 16:09:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":754249,"visible":true,"origin":"","legend":"","description":"","filename":"RealTimeDynamicAdhesionCoefficientEstimationandBPNeuralNetworkOptimizedLateralStabilityControlforDistributedDriveElectricVehicles.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6114111/v1_covered_b4e25e0f-6f3f-41d5-b50c-63bf5c10a572.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Real-Time Dynamic Adhesion Coefficient Estimation and BP Neural Network-Optimized Lateral Stability Control for Distributed-Drive Electric Vehicles","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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Distributed driver, electric vehicle, Torque distribution, road adhesion coefficient, neural network, Active Safety","lastPublishedDoi":"10.21203/rs.3.rs-6114111/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6114111/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"To address the instability issues of distributed-drive electric vehicles (DDEV) operating on roads with abrupt changes in adhesion coefficients, a lateral stability control strategy and torque distribution method based on backpropagation (BP) neural network optimization were proposed. First, an Unscented Kalman Filter (UKF) estimation algorithm incorporating real-time variation detection of adhesion coefficients is developed. To ensure rapid response and accurate estimation of current adhesion coefficients during sudden road condition changes, threshold-based real-time detection of adhesion coefficient fluctuations is introduced. Second, a hierarchical stability control strategy specifically designed for varying adhesion coefficient conditions is established. The upper-layer controller employs a Bat Algorithm (BA) optimized BP neural network, which takes the sideslip angle and yaw rate as control targets to calculate the required yaw moment for vehicle stabilization, thereby enhancing real-time computational efficiency and solution accuracy. The lower-layer controller utilizes the estimated road adhesion coefficients to implement a quadratic programming algorithm, optimizing wheel torque distribution with the objective of minimizing tire load rate. 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