Enhancing Vehicular Charging Systems with a Natural Language HMI Solution

The convergence of wireless communications and transportation technologies has propelled the evolution of Intelligent Transportation Systems (ITS), enhancing connectivity, safety, and efficiency in vehicular networks. As electric vehicle (EV) adoption grows within connected ecosystems, the need for intuitive charging management solutions has surged. This paper presents a task-oriented, bilingual chatbot to optimize EV charging control via an intelligent human-machine interface (HMI). Using Natural Language Processing (NLP) and Artificial Intelligence (AI), the system enables seamless interaction with charging infrastructure through wireless channels, supporting English and Spanish to address linguistic diversity. Integrated with ITS frameworks, it facilitates real-time monitoring and control, aligning with cooperative sensing and user experience goals. Two approaches are assessed: Voiceflow, for fast prototyping, and BERT (Bidirectional Encoder Representations from Transformers)-based models, implemented as a multilingual system and separate monolingual variants. Results show monolingual BERT models outperform the multilingual model in terms of intent classification accuracy, reinforcing reliability in dynamic settings. By linking wireless connectivity with AI-driven HMI, our proposal enhances EV charging scalability and accessibility within ITS, advancing sustainable transportation and efficient vehicular networking.