Reinforcement Learning-Based Modulation Strategy for Dual Active Bridge Converter

Reinforcement Learning-Based Modulation Strategy for Dual Active Bridge Converter | 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 Reinforcement Learning-Based Modulation Strategy for Dual Active Bridge Converter Dong-Yi Zhang, Yi-Ying Wang, Jia-Wei Zhao, Jie Wu, Ming-Xian Liu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5375979/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 2 You are reading this latest preprint version Abstract With the continuous growth of global energy demand and the depletion of non-renewable resources, the role of power electronic converters in energy conversion and distribution has become increasingly significant. This paper addresses the modulation strategy for the Dual Active Bridge (DAB) converter under complex operating conditions, proposing an optimization method based on reinforcement learning (RL). By incorporating Deep Q-Network (DQN) and Double Deep Q-Network (DDQN) algorithms, an intelligent agent control system is designed. The agent learns and optimizes the phase-shift modulation strategy through interactions with the DAB circuit environment, aiming to achieve high efficiency and stability in power transmission. Simulations conducted using MATLAB demonstrate that the RL-based DAB modulation strategy significantly enhances stability and self-stability compared to traditional control strategies across various operating conditions. The proposed RL-based control strategy for DAB converters offers a novel approach to overcoming the limitations of conventional control methods in complex scenarios. It not only enhances the performance of DAB converters but also provides new research perspectives and theoretical foundations for the intelligent control of power electronic converters. Dual Active Bridge Converter Reinforcement Learning Modulation Strategy Deep Q-Network Stability Optimization Intelligent Control Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Submission checks completed at journal 03 Nov, 2024 First submitted to journal 01 Nov, 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. 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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-5375979","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":373574037,"identity":"14863f2b-e3b7-4d35-acb9-e850bc3a3099","order_by":0,"name":"Dong-Yi Zhang","email":"","orcid":"","institution":"Hebei University of Engineering","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Dong-Yi","middleName":"","lastName":"Zhang","suffix":""},{"id":373574038,"identity":"b3f8140a-6852-483e-801b-734caf39eeef","order_by":1,"name":"Yi-Ying 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