{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T06:06:59Z","timestamp":1789020419330,"version":"build-2803163510"},"reference-count":33,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T00:00:00Z","timestamp":1788998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>Ensuring the safety and dependability of power batteries has become a major concern due to the rapid expansion of electric vehicles (EVs), and fault detection has emerged as an essential approach for guaranteeing system stability. This study develops a Deep Learning (DL)-based defect prediction technique that combines an optimization algorithm and a Context-Aware Recurrent Neural Network with Adaptive Temporal Transformer (CAR-TATNet) to overcome these drawbacks. The aim is to reduce noise and improve the extraction of pertinent operating modes from EV battery signals. The dataset was first pre-processed by data cleaning and with Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN). Key time-frequency features linked to EV battery fault patterns were captured using Short-Time Fourier Transform (STFT). Inspired by the way magpies forage, the Red-Billed Blue Magpie Optimization (RBBMO) algorithm finds the most important traits for effective feature selection. Finally, the CAR-TATNet model was employed to model long-term dependencies in temporal sequences and adaptively respond to time-varying fault patterns. The developed methodology performs better than current methods, according to experimental validation in Python software, with a superior accuracy of 94.94%, providing a potential alternative for precise EV battery fault detection.<\/jats:p>","DOI":"10.3389\/frai.2026.1876996","type":"journal-article","created":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T05:31:25Z","timestamp":1789018285000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep learning-based EV battery fault diagnosis using RBBMO with CAR-TATNET detection approach"],"prefix":"10.3389","volume":"9","author":[{"given":"G.","family":"Tamizharasi","sequence":"first","affiliation":[{"name":"School of Electrical Engineering, VIT University","place":["Vellore, Tamil Nadu, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"G. 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