{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T08:56:03Z","timestamp":1777107363290,"version":"3.51.4"},"reference-count":53,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,2,13]],"date-time":"2025-02-13T00:00:00Z","timestamp":1739404800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFB2401300, 2022YFA1004100, 2020YFA0713900;"],"award-info":[{"award-number":["2021YFB2401300, 2022YFA1004100, 2020YFA0713900;"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2172329, U1811461, U21A6005, 11690011."],"award-info":[{"award-number":["2172329, U1811461, U21A6005, 11690011."]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>Recent years have witnessed an unprecedented boom of Electric Vehicles (EVs). However, EVs\u2019 further development confronts critical bottlenecks due to EV Energy (EVE) issues like battery hazards, range anxiety, and charging inefficiency. Emerging data-driven EVE Management (EVEM) is a promising solution but still faces fundamental challenges, especially in terms of reliability and efficiency. This article presents iEVEM, the first big data-empowered intelligent EVEM framework, providing systematic support to the essential driver-, enterprise-, and social-level intelligent EVEM applications. Particularly, a layered data architecture from heterogeneous EVE data management to knowledge-enhanced intelligent solution design is provided, and an edge\u2013cloud collaborative architecture for the networked system is proposed for reliable and efficient EVEM, respectively. We conducted a proof-of-concept case study on a typical EVEM task (i.e., EV energy consumption outlier detection) using real driving data from 4000+ EVs within three months. The experimental results show that iEVEM achieves a significant boost in reliability and efficiency (i.e., up to 47.48% higher in detection accuracy and at least 3.07\u00d7 faster in response speed compared with the state-of-art approaches). As the first intelligent EVEM framework, iEVEM is expected to inspire more intelligent energy management applications exploiting skyrocketing EV big data.<\/jats:p>","DOI":"10.3390\/systems13020118","type":"journal-article","created":{"date-parts":[[2025,2,13]],"date-time":"2025-02-13T04:03:35Z","timestamp":1739419415000},"page":"118","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["iEVEM: Big Data-Empowered Framework for Intelligent Electric Vehicle Energy Management"],"prefix":"10.3390","volume":"13","author":[{"given":"Siyan","family":"Guo","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"},{"name":"National Engineering Laboratory for Big Data Analytics, Xi\u2019an 710049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4317-5535","authenticated-orcid":false,"given":"Cong","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"},{"name":"National Engineering Laboratory for Big Data Analytics, Xi\u2019an 710049, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1038\/s41928-021-00709-3","article-title":"How cities can drive the electric vehicle revolution","volume":"5","author":"Heidrich","year":"2022","journal-title":"Nat. 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