Adaptive Distributed Explicit Model Predictive Controller with Road Surface Identification for HM-AS

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Abstract The rapid advancement of hub-motor electric vehicle (HMEV) is propelled by its capacity to significantly improve energy efficiency, handling dynamics, and space utilization while minimizing mechanical losses and maintenance costs. A significant challenge in HMEV is mitigating the performance degradation caused by unbalanced electromagnetic force (UMEF), which result from the interaction between the hub motor and road-induced vibrations. This study introduces an Adaptive Distributed Explicit Model Predictive Control (ADEMPC) strategy for hub-motor electric vehicles equipped with air suspension (HM-AS), aiming to enhance ride comfort, handling stability, and reduce eccentricity between the stator and rotor. A full-vehicle dynamic model considering vertical-longitudinal coupling is established and validated. A road surface identification system based on a BP neural network is designed. The Whale Optimization Algorithm (WOA) is used to optimize weight coefficients on 16 conditions, which are then saved as tables for ADEMPC. An ADEMPC controller is designed based on distributed prediction model, which decompose the entire vehicle into four subsystems and consider the coupling of roll and pitch. Simulation results demonstrated that ADEMPC achieves improvements of 25% in body acceleration, 16% in eccentricity, 5% in tire dynamic load, 25% in roll, and 15% in pitch. It showcases its effectiveness in enhancing ride comfort and vehicle stability.
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Adaptive Distributed Explicit Model Predictive Controller with Road Surface Identification for HM-AS | 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 Adaptive Distributed Explicit Model Predictive Controller with Road Surface Identification for HM-AS Ying zhou, Xin Chen, Zhongxing Li, Xue Wang, Yi Yu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4747092/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Dec, 2024 Read the published version in Nonlinear Dynamics → Version 1 posted 13 You are reading this latest preprint version Abstract The rapid advancement of hub-motor electric vehicle (HMEV) is propelled by its capacity to significantly improve energy efficiency, handling dynamics, and space utilization while minimizing mechanical losses and maintenance costs. A significant challenge in HMEV is mitigating the performance degradation caused by unbalanced electromagnetic force (UMEF), which result from the interaction between the hub motor and road-induced vibrations. This study introduces an Adaptive Distributed Explicit Model Predictive Control (ADEMPC) strategy for hub-motor electric vehicles equipped with air suspension (HM-AS), aiming to enhance ride comfort, handling stability, and reduce eccentricity between the stator and rotor. A full-vehicle dynamic model considering vertical-longitudinal coupling is established and validated. A road surface identification system based on a BP neural network is designed. The Whale Optimization Algorithm (WOA) is used to optimize weight coefficients on 16 conditions, which are then saved as tables for ADEMPC. An ADEMPC controller is designed based on distributed prediction model, which decompose the entire vehicle into four subsystems and consider the coupling of roll and pitch. Simulation results demonstrated that ADEMPC achieves improvements of 25% in body acceleration, 16% in eccentricity, 5% in tire dynamic load, 25% in roll, and 15% in pitch. It showcases its effectiveness in enhancing ride comfort and vehicle stability. Electric vehicle Distributed Explicit Model Predictive control Road surface identification BP neural network WOA Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 27 Dec, 2024 Read the published version in Nonlinear Dynamics → Version 1 posted Editorial decision: Revision requested 17 Sep, 2024 Reviews received at journal 05 Sep, 2024 Reviews received at journal 03 Sep, 2024 Reviews received at journal 30 Aug, 2024 Reviews received at journal 25 Aug, 2024 Reviewers agreed at journal 17 Aug, 2024 Reviewers agreed at journal 16 Aug, 2024 Reviewers agreed at journal 16 Aug, 2024 Reviewers agreed at journal 13 Aug, 2024 Reviewers invited by journal 05 Aug, 2024 Editor assigned by journal 25 Jul, 2024 Submission checks completed at journal 17 Jul, 2024 First submitted to journal 16 Jul, 2024 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. 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