Rasa-Powered Conversational AI Framework for Intelligent Electric Vehicle Trip Planning and Energy Management

Authors

Phanendra T
Department of Electrical and Electronics Engineering, Koneru Lakshmaiah Education Foundation, India
Swapna G
Koneru Lakshmaiah Education Foundation, India
Vishnuvardhan M
Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, India
K V Govardhan Rao
T Rakesh
M Kiran Kumar
DOI: https://doi.org/10.52549/ijeei.v13i4.6553
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Published: 01-12-2025 • Issue: Vol. 13 No. 4: December 2025 • Section: Regular paper

Abstract

The rapid growth of Electric Vehicles (EVs) calls for intelligent solutions to optimize grid stability and enhance user experience. This paper proposes a geo-optimized, user-centric EV management system integrating open-source geospatial tools with Rasa-based Natural Language Understanding (NLU). Through an interactive conversational interface, EV owners receive real-time trip planning recommendations based on parameters such as State of Charge (SoC), charging point availability, and route efficiency. The system utilizes APIs for real-time geolocation, charging station data, and Battery Management System (BMS) insights to determine optimal charging locations, durations, and trip costs. Extensive testing demonstrates improved energy management and route planning efficiency, highlighting the system’s potential for smart and sustainable EV infrastructure development.
Keywords: Battery Management System; Electric Vehicle (EV); Geo-Optimization; Grid Stability; Rasa NLU; State of Charge (SOC)

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How to Cite

[1]
P. T, S. G, V. M, K. V. G. Rao, T. Rakesh, and M. K. Kumar, “Rasa-Powered Conversational AI Framework for Intelligent Electric Vehicle Trip Planning and Energy Management”, IJEEI, vol. 13, no. 4, pp. 911–922, Dec. 2025, doi: 10.52549/ijeei.v13i4.6553.