TY - GEN
T1 - Toward Intelligent Communication and Optimization in EVs:: A tutorial on the Transformative Impact of Large Language Models
AU - Massaoudi, Mohamed
AU - Abu-Rub, Haithem
AU - Ghrayeb, Ali
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - The integration of the large language model (LLM) technology in electric vehicles (EVs) has sparked a significant leap forward in the evolution of intelligent transportation. LLM technology enables real-time, context-aware communication, thereby elevating the safety and convenience of driving experiences. LLMs play a pivotal role in refining human-vehicle interactions, offering an intuitive and responsive interface for vehicle controls and navigation systems. In addition, LLMs contribute to the sustainable development of EV technology by optimizing energy consumption patterns and supporting the integration of EVs into smart grid systems. To this end, this paper aims to review the essential elements of LLM-based EVs to emphasize their current capabilities toward smart transportation and infrastructure services. This paper explores the multifaceted contributions of LLMs in enhancing functionality, user experience, and technological development of EVs. This tutorial also addresses the challenges and future prospects of LLM applications in EVs, emphasizing their potential to transform EVs into intelligent companions on the road and pave the way for a more sustainable and user-centered future for personal transportation.
AB - The integration of the large language model (LLM) technology in electric vehicles (EVs) has sparked a significant leap forward in the evolution of intelligent transportation. LLM technology enables real-time, context-aware communication, thereby elevating the safety and convenience of driving experiences. LLMs play a pivotal role in refining human-vehicle interactions, offering an intuitive and responsive interface for vehicle controls and navigation systems. In addition, LLMs contribute to the sustainable development of EV technology by optimizing energy consumption patterns and supporting the integration of EVs into smart grid systems. To this end, this paper aims to review the essential elements of LLM-based EVs to emphasize their current capabilities toward smart transportation and infrastructure services. This paper explores the multifaceted contributions of LLMs in enhancing functionality, user experience, and technological development of EVs. This tutorial also addresses the challenges and future prospects of LLM applications in EVs, emphasizing their potential to transform EVs into intelligent companions on the road and pave the way for a more sustainable and user-centered future for personal transportation.
KW - Electric vehicles
KW - human-vehicle interaction
KW - language models
KW - natural language processing
KW - sustainable mobility
UR - https://www.scopus.com/pages/publications/105001014791
U2 - 10.1109/IECON55916.2024.10905200
DO - 10.1109/IECON55916.2024.10905200
M3 - Conference contribution
AN - SCOPUS:105001014791
T3 - IECON Proceedings (Industrial Electronics Conference)
BT - IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society, Proceedings
PB - IEEE Computer Society
T2 - 50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024
Y2 - 3 November 2024 through 6 November 2024
ER -