000170388 001__ 170388 000170388 005__ 20260420103354.0 000170388 0247_ $$2doi$$a10.1049/itr2.70198 000170388 0248_ $$2sideral$$a148904 000170388 037__ $$aART-2026-148904 000170388 041__ $$aeng 000170388 100__ $$aDonate, Pablo 000170388 245__ $$aReChat: A Task‐Based Chatbot for EV Charging Management Optimization 000170388 260__ $$c2026 000170388 5060_ $$aAccess copy available to the general public$$fUnrestricted 000170388 5203_ $$aThe 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. 000170388 536__ $$9info:eu-repo/grantAgreement/ES/DGA-FSE/T40-23D 000170388 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es 000170388 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000170388 700__ $$0(orcid)0000-0001-7657-0075$$aSanguesa, Julio A.$$uUniversidad de Zaragoza 000170388 700__ $$0(orcid)0000-0002-1750-7225$$aGarrido, Piedad$$uUniversidad de Zaragoza 000170388 700__ $$0(orcid)0000-0002-0787-2667$$aTorres-Sanz, Vicente$$uUniversidad de Zaragoza 000170388 700__ $$0(orcid)0000-0001-6945-7330$$aMartinez, Francisco J.$$uUniversidad de Zaragoza 000170388 700__ $$aCalafate, Carlos T. 000170388 7102_ $$15007$$2035$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Arquit.Tecnología Comput. 000170388 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf. 000170388 773__ $$g20, 1 (2026), e70198 [18 pp.]$$pIET Intelligent Transport Systems$$tIET Intelligent Transport Systems$$x1751-956X 000170388 8564_ $$s2125289$$uhttps://zaguan.unizar.es/record/170388/files/texto_completo.pdf$$yVersión publicada 000170388 8564_ $$s2401862$$uhttps://zaguan.unizar.es/record/170388/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000170388 909CO $$ooai:zaguan.unizar.es:170388$$particulos$$pdriver 000170388 951__ $$a2026-04-18-10:48:34 000170388 980__ $$aARTICLE