Learning Superior Energy Management from Electric Vehicle Data

Learning Superior Energy Management from Electric Vehicle Data | 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 Learning Superior Energy Management from Electric Vehicle Data Hongwen He, Yong Wang, Jingda Wu, Zhongbao Wei, Fengchun Sun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4523312/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Mar, 2025 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Despite the promising potential of energy management technologies in optimizing electric vehicle (EV) performance and fostering global energy sustainability, the extensive research conducted over the past decade has yet to translate into practical applications. This discrepancy arises primarily from the reliance of existing methodologies on simulation-based development paradigms, leading to a significant disparity between simulated results and real-world efficacy. Herein, we present a pioneering real-world data-driven energy management strategies (EMS) approach that utilizes an innovative offline reinforcement learning (ORL) framework. This paradigm enables EMS to learn from diverse real-world data, obviating the need for explicit rule design or high-fidelity simulators, and allowing for seamless application of the proposed method to any existing EMS. Moreover, it continuously enhances performance even after deployment in actual energy management systems. We evaluate the proposed ORL method on fuel cell EVs, training the ORL agent to optimize energy consumption and system degradation. The EV monitoring and management platform in China provides real-world data for validating our methodology. The results demonstrate that ORL consistently learns superior EMS in various conditions. With increasing data availability, its performance improves significantly, from 88% to 98.6% relative to theoretical optimality after two data updates. After training with more than 60 million kilometers of data, the ORL agent can learn a general EMS that adapts to unseen and corner-case conditions. These results highlight the effectiveness of integrating the data-driven method with established EMS techniques to enhance performance and underline its potential to utilize large-scale data to improve vehicle energy efficiency and longevity. Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Energy science and technology/Energy storage/Batteries Energy management Electric vehicle data Reinforcement learning Fuel cell vehicles Data-driven Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Published Journal Publication published 22 Mar, 2025 Read the published version in Nature Communications → 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-4523312","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":321857825,"identity":"abc7c51c-a669-4189-958a-4cc4191fb2e9","order_by":0,"name":"Hongwen He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYBACAwYeBmYgKWfADOKyEa/FwNiAmZkkLQwGiRsYiNViLpF78HFBwZ/07ez8Bxg+lB1m4J/dgF+L5Yy8ZOMZBga5O5uZGRhnnDvMIHHnAAGH3c4xk+YBatlwmJmBmbftMIOBRAJBLea/gVrSDUBa/hKpxYwZqCUBrIWRKC333xhLzzAwNgT6xeBgz7l0HokbhLScOWP4ueCPnLw5/8GHD36UWcvxzyCgBQUcAGIeEtSPglEwCkbBKMAFAABHO9OX5ST8AAAAAElFTkSuQmCC","orcid":"","institution":"Beijing Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Hongwen","middleName":"","lastName":"He","suffix":""},{"id":321857826,"identity":"9c85d8b1-5b91-4a54-acc3-d1b5572240f8","order_by":1,"name":"Yong Wang","email":"","orcid":"","institution":"Beijing Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Wang","suffix":""},{"id":321857827,"identity":"31dc073d-f439-45d3-94c2-4855d9fce4ba","order_by":2,"name":"Jingda Wu","email":"","orcid":"https://orcid.org/0000-0002-7336-4492","institution":"Nanyang Technological University","correspondingAuthor":false,"prefix":"","firstName":"Jingda","middleName":"","lastName":"Wu","suffix":""},{"id":321857828,"identity":"07e91127-b6e4-4513-a0f0-1bd57cdffd29","order_by":3,"name":"Zhongbao Wei","email":"","orcid":"https://orcid.org/0000-0003-0051-5648","institution":"Beijing Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Zhongbao","middleName":"","lastName":"Wei","suffix":""},{"id":321857829,"identity":"3227899c-b6fe-4a8f-a4e3-210c3e92c171","order_by":4,"name":"Fengchun Sun","email":"","orcid":"","institution":"Beijing Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Fengchun","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2024-06-03 17:00:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4523312/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4523312/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-025-58192-9","type":"published","date":"2025-03-22T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79034136,"identity":"3e0bd331-c94f-487b-9d7d-38c5858ded4b","added_by":"auto","created_at":"2025-03-23 07:06:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3255323,"visible":true,"origin":"","legend":"","description":"","filename":"SuperEMS.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4523312/v1_covered_70a2bdd4-1683-481c-a98d-4b7dbd5069c2.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Learning Superior Energy Management from Electric Vehicle Data","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Energy management, Electric vehicle data, Reinforcement learning, Fuel cell vehicles, Data-driven","lastPublishedDoi":"10.21203/rs.3.rs-4523312/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4523312/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Despite the promising potential of energy management technologies in optimizing electric vehicle (EV) performance and fostering global energy sustainability, the extensive research conducted over the past decade has yet to translate into practical applications. This discrepancy arises primarily from the reliance of existing methodologies on simulation-based development paradigms, leading to a significant disparity between simulated results and real-world efficacy. Herein, we present a pioneering real-world data-driven energy management strategies (EMS) approach that utilizes an innovative offline reinforcement learning (ORL) framework. This paradigm enables EMS to learn from diverse real-world data, obviating the need for explicit rule design or high-fidelity simulators, and allowing for seamless application of the proposed method to any existing EMS. Moreover, it continuously enhances performance even after deployment in actual energy management systems. We evaluate the proposed ORL method on fuel cell EVs, training the ORL agent to optimize energy consumption and system degradation. The EV monitoring and management platform in China provides real-world data for validating our methodology. 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