170388 20260420103354.0 doi 10.1049/itr2.70198 sideral 148904 ART-2026-148904 eng Donate, Pablo ReChat: A Task‐Based Chatbot for EV Charging Management Optimization 2026 The increasing adoption of electric vehicles (EVs) and the evolution of connected vehicle systems have led to a growing need for intelligent charging management solutions. This article introduces ReChat, a task‐based multilingual chatbot designed to optimize EV charging management through reliable task‐oriented intent understanding and safe action dispatch. By leveraging natural language processing (NLP), ReChat enables seamless user interaction with charging systems across six languages (Spanish, English, German, French, Italian and Portuguese). A custom dataset was developed to train and evaluate the chatbot's intent‐classification capabilities, ensuring robust performance in diverse linguistic contexts. A comparative analysis of multilingual Bidirectional Encoder Representations from Transformers (mBERT)‐based intent classifiers shows that a single pooled multilingual mBERT model achieves macro‐1 values between 68.0% and 78.4% across languages, while language‐specific mBERT models yields 64.7% to 78.9%. This work advances the development of robust conversational AI systems for smart transportation, outlining a modular architecture and empirical evidence on multilingual adaptability in real‐world applications. Access copy available to the general public Unrestricted info:eu-repo/grantAgreement/ES/DGA-FSE/T40-23D info:eu-repo/semantics/openAccess by-nc-nd https://creativecommons.org/licenses/by-nc-nd/4.0/deed.es info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Sanguesa, Julio A. Universidad de Zaragoza (orcid)0000-0001-7657-0075 Garrido, Piedad Universidad de Zaragoza (orcid)0000-0002-1750-7225 Torres-Sanz, Vicente Universidad de Zaragoza (orcid)0000-0002-0787-2667 Martinez, Francisco J. Universidad de Zaragoza (orcid)0000-0001-6945-7330 Calafate, Carlos T. 5007 035 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Arquit.Tecnología Comput. 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. 20, 1 (2026), e70198 [18 pp.] IET Intelligent Transport Systems IET Intelligent Transport Systems 1751-956X 2125289 http://zaguan.unizar.es/record/170388/files/texto_completo.pdf Versión publicada 2401862 http://zaguan.unizar.es/record/170388/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:170388 articulos driver 2026-04-18-10:48:34 ARTICLE