Advancing Communication Efficiency in Electric Vehicle Systems: A Survey of Generative AI and Distributed Machine Learning Strategies

Abstract

The surging popularity of Electric Vehicles (EVs) necessitates robust communication systems for critical functions like battery management, Vehicle-to-Infrastructure (V2I) interaction, and real-time traffic updates. However, the ever-increasing data deluge generated by EVs overwhelms existing network resources, creating a bottleneck for efficient communication.
This paper explores Generative Artificial Intelligence (GenAI) as a groundbreaking solution. By leveraging multi-modal and semantic communication techniques, GenAI can generate text and image data, significantly enhancing information transmission and reconstruction processes. Additionally, Deep Reinforcement Learning (DRL) algorithms can be harnessed to optimize communication power and resource allocation, particularly for Vehicle-to-Vehicle (V2V) communication scenarios.
Furthermore, distributed learning emerges as a transformative approach, facilitating collaborative learning across devices without the need for centralized data storage. This strategy unlocks exciting possibilities for optimizing communication power and resource allocation, generating synthetic training data for communication models, and fostering seamless Vehicle-to-Everything (V2X) communication.
This study emphasizes the transformative potential of GenAI and distributed learning in revolutionizing communication efficiency within EVs, paving the way for a more sustainable and intelligent transportation ecosystem. However, it acknowledges the need for further research to address implementation challenges and limitations within the EV communication infrastructure.

Keywords: Communication Efficiency, Generative Artificial Intelligence, Electric Vehicle, Distributed Training, ChatGPT, Machine Learning