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Title	Harnessing The Power Of Large Language Models For Sustainable And Intelligent Transportation Systems In The Electric Vehicle Era
ID_Doc	28780
Authors	Abraham A.; Aldhanhani T.; Hamidouche W.; Shaaban M.
Year	2025
Published	Lecture Notes in Intelligent Transportation and Infrastructure, Part F99
DOI	http://dx.doi.org/10.1007/978-3-031-72959-1_5
Abstract	Urban ecosystems in the midst of digital transformation utilize technologies like Internet of Things (IoT), big data, and artificial intelligence (AI) to elevate citizens’ well-being and champion sustainable development. Notably, LLMs such as GPT-3 or GPT-4, crafted by OpenAI, and Google’s AI tools like Gemini and Bard, stand as pivotal components, holding immense potential to revolutionize EVs in smart city initiatives. Generative AI and LLM integration prove influential in optimizing EV functionalities, enhancing security, and fortifying data privacy. This chapter delves into battery technology, battery capacities, energy storage, charging infrastructure optimization, autonomous driving, educational outreach, energy management, and predictive maintenance. In particular, LLMs address EV challenges by optimizing routes, speeds, and behaviors, reducing energy usage and emissions. They facilitate the smart integration of EVs and renewable energy sources, managing charging schedules based on availability and grid demands. Additionally, LLMs contribute to public education on EV benefits, environmental impact, cost savings, and sustainable transportation promotion. The chapter explores synergies between LLMs and EVs, focusing on potential applications and challenges, propelling progress in ITSs. Similarly, Multimodal Large Language Models (MLLMs) and their potential applications in the era of the EV represent a promising frontier in this landscape, offering enhanced capabilities to interpret and generate multimodal data, further enriching the EV ecosystem. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
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