{"ID":2837526,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.19055","arxiv_id":"2511.19055","title":"Large Language Model-Assisted Planning of Electric Vehicle Charging Infrastructure with Real-World Case Study","abstract":"The growing demand for electric vehicle (EV) charging infrastructure presents significant planning challenges, requiring efficient strategies for investment and operation to deliver cost-effective charging services. However, the potential benefits of EV charging assignment, particularly in response to varying spatial-temporal patterns of charging demand, remain under-explored in infrastructure planning. This paper proposes an integrated approach that jointly optimizes investment decisions and charging assignments while accounting for spatial-temporal demand dynamics and their interdependencies. To support efficient model development, we leverage a large language model (LLM) to assist in generating and refining the mathematical formulation from structured natural-language descriptions, significantly reducing the modeling burden. The resulting optimization model enables optimal joint decision-making for investment and operation. Additionally, we propose a distributed optimization algorithm based on the Alternating Direction Method of Multipliers (ADMM) to address computational complexity in high-dimensional scenarios, which can be executed on standard computing platforms. We validate our approach through a case study using 1.5 million real-world travel records from Chengdu, China, demonstrating a 30% reduction in total cost compared to a baseline without EV assignment.","short_abstract":"The growing demand for electric vehicle (EV) charging infrastructure presents significant planning challenges, requiring efficient strategies for investment and operation to deliver cost-effective charging services. However, the potential benefits of EV charging assignment, particularly in response to varying spatial-t...","url_abs":"https://arxiv.org/abs/2511.19055","url_pdf":"https://arxiv.org/pdf/2511.19055v1","authors":"[\"Xinda Zheng\",\"Canchen Jiang\",\"Hao Wang\"]","published":"2025-11-24T12:45:10Z","proceeding":"eess.SY","tasks":"[\"eess.SY\",\"cs.AI\",\"math.OC\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}