Research on the dynamic pricing strategy of charging piles based on data-driven supply and demand balance and data-driven

This study explores a dynamic pricing strategy for electric vehicle (EV) charging stations, leveraging supply-demand balance and data-driven approaches to optimize resource allocation, enhance grid stability, and improve user satisfaction. With the rapid growth of EVs globally, charging infrastructure faces challenges such as imbalanced utilization, peak-hour congestion, and inefficient energy use. Traditional static pricing models fail to address these issues effectively. The research integrates time series models (e.g., ARIMA) to predict short-term charging demand, combined with reinforcement learning to dynamically adjust prices based on real-time data, including grid load, user behavior, and external factors like weather and traffic. Empirical results demonstrate that dynamic pricing reduces peak-hour congestion by 30%, increases renewable energy utilization by 10%, and boosts operator revenue by 12%. However, limitations arise in rural areas due to sporadic demand and extreme weather conditions, highlighting the need for region-specific strategies.The study proposes practical solutions, such as machine learning-enhanced demand forecasting, differentiated pricing for user segments (e.g., private vs. ride-hailing EVs), and policy frameworks to support flexible pricing. Future work should incorporate real-time GPS data, multi-region policy comparisons, and vehicle-to-grid (V2G) integration to further refine the model. This research provides a scalable framework for stakeholders to balance efficiency, equity, and sustainability in EV charging ecosystems.