Deep Learning for EV Charging Infrastructure: Optimization Approaches and Challenges

The optimization of charging station (CS) locations remains a major obstacle in the deployment of electric vehicles (EVs). Numerous methods have been proposed, ranging from traditional mathematical models to heuristic and metaheuristic approaches. In recent years, deep learning (DL) methods, with their high capacity to learn from large-scale data, offer innovative solutions to complex allocation problems. Therefore, this review paper proposes a state-of-art of recent DL approaches to optimize the placement of CSs. By analyzing 17 published studies, we examine the key factors affecting strategic placement, including geographical, demographic, and urban planning aspects, along with emerging trends and existing research gaps.