Deep Learning Based EV’s Charging Network Management

Efficient management of charging networks is crucial as EVs become more popular in transportation systems. This study focuses on two main aspects: deploying EV charging stations using a binary Quadratic model and using reinforcement learning for optimal path selection. Additionally, the paper explores the accurate estimation of SoC in EVs using deep neural networks. The binary Quadratic model considers factors like location, population density, transportation patterns, and infrastructure availability to strategically place charging stations. Reinforcement learning helps EV users find the best path to charging stations based on traffic, station availability, and preferences. Deep neural networks use historical data and real-time factors to estimate SoC precisely, optimizing charging strategies and improving EV performance and reliability. The findings contribute to the advancement of charging network management, promoting accessibility, cost-effectiveness, user experience, and sustainable transportation.