Dynamic Adaptive Optimization of Electric Vehicle Charging Stations Layout Using LSTM-Enhanced Heuristic Approach

This paper presents a study on the optimization of electric vehicle (EV) charging station layouts, with a focus on designing a complete system model that integrates demand forecasting, adaptive heuristics, and optimization functions. The proposed model employs a Long Short-Term Memory (LSTM) network for demand forecasting, effectively capturing complex temporal patterns in EV charging behavior. Adaptive heuristics are then utilized to manage the dynamic nature of EV infrastruc-ture, while the optimization framework is designed to minimize wait times, maximize station utilization rates, and achieve a combined optimization function. The system model is rigorously tested using extensive real-world data, demonstrating significant improvements in key performance indicators compared to traditional methods. The results validate the system's ability to dynamically adapt to varying conditions, offering a robust and scalable solution for urban and regional deployment.