{"id":1581,"date":"2026-05-12T12:22:27","date_gmt":"2026-05-12T04:22:27","guid":{"rendered":"https:\/\/www.translab.top\/?p=1581"},"modified":"2026-05-12T12:22:28","modified_gmt":"2026-05-12T04:22:28","slug":"%e3%80%90ep44-%c2%b7-research-highlights%e3%80%91-self-supervised-learning-for-electric-vehicle-battery-remaining-useful-life-prediction-using-real-world-unlabeled-data","status":"publish","type":"post","link":"https:\/\/www.translab.top\/index.php\/en\/2026\/05\/12\/%e3%80%90ep44-%c2%b7-research-highlights%e3%80%91-self-supervised-learning-for-electric-vehicle-battery-remaining-useful-life-prediction-using-real-world-unlabeled-data\/","title":{"rendered":"\u3010EP44 \u00b7 Research Highlights\u3011\u2014\u00a0Self-supervised learning for electric vehicle battery remaining useful life prediction using real-world unlabeled data"},"content":{"rendered":"\n<p>In May 2026, the TRANS Research Group published a study on data-efficient remaining useful life (RUL) prediction for electric vehicle batteries using real-world operational data. This work proposes a contrastive-enhanced VAE-LSTM self-supervised learning framework that learns degradation-aware representations from large-scale unlabeled charging data. Validated on three real-world EV datasets covering 340 vehicles, the proposed method achieves accurate and robust RUL prediction across different fleets. Using labels from only 30% of vehicles, the framework maintains strong cross-fleet transferability while reducing RUL labeling costs by about 70%, offering a scalable and economically practical pathway for fleet-level battery health management.<\/p>\n\n\n\n<p>CITATION: Lv, Z., Ou, S., Jing, H., Wu, G., &amp; Shi, D. (2026). Self-supervised learning for electric vehicle battery remaining useful life prediction using real-world unlabeled data. Energy, 141302. <a href=\"https:\/\/doi.org\/10.1016\/j.energy.2026.141302\">https:\/\/doi.org\/10.1016\/j.energy.2026.141302<\/a><\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In May 2026, the TRANS Research Group published a study [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":1571,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[70],"tags":[],"class_list":["post-1581","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-publications-en"],"blocksy_meta":[],"jetpack_sharing_enabled":true,"jetpack_featured_media_url":"https:\/\/www.translab.top\/wp-content\/uploads\/2026\/05\/1.png","_links":{"self":[{"href":"https:\/\/www.translab.top\/index.php\/wp-json\/wp\/v2\/posts\/1581","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.translab.top\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.translab.top\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.translab.top\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.translab.top\/index.php\/wp-json\/wp\/v2\/comments?post=1581"}],"version-history":[{"count":1,"href":"https:\/\/www.translab.top\/index.php\/wp-json\/wp\/v2\/posts\/1581\/revisions"}],"predecessor-version":[{"id":1582,"href":"https:\/\/www.translab.top\/index.php\/wp-json\/wp\/v2\/posts\/1581\/revisions\/1582"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.translab.top\/index.php\/wp-json\/wp\/v2\/media\/1571"}],"wp:attachment":[{"href":"https:\/\/www.translab.top\/index.php\/wp-json\/wp\/v2\/media?parent=1581"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.translab.top\/index.php\/wp-json\/wp\/v2\/categories?post=1581"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.translab.top\/index.php\/wp-json\/wp\/v2\/tags?post=1581"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}