Singh, Yash PratapYash PratapSinghSaffari, MohsenMohsenSaffariMokhtari, MohmmadsadeghMohmmadsadeghMokhtariAdhikari, IshikaIshikaAdhikari2026-10-152026-10-1520262516-84012516-8401https://imec-publications.be/handle/20.500.12860/60579</jats:title> <jats:p>Nowadays, the rapid adoption of electric vehicles (EVs) across modern countries has caused significant challenges in EV management in modern transportation systems. This creates a significant demand for efficient and intelligent recommendation systems to decrease EVs' travel time and cost and minimise charging wait time. Recently, many researchers have addressed this issue by designing AI‐based EV charging station (EVCS) recommendation systems. In contrast to traditional routing problems, EVCS recommendations must consider dynamic demand that shifts across time and location and diverse infrastructure constraints. This survey article first introduces a categorisation of EVs and charging stations. Then, we provide a comprehensive review of different EVCS recommendation systems, including algorithmic and optimization frameworks, and recent deep learning (DL), multi‐agent reinforcement learning (MARL), federated learning (FL), and blockchain approaches. Furthermore, this survey provides a unified problem formulation for EVCS recommendation systems and examines the performance of different recommendation categories in terms of reliability, cost‐saving, and availability scores. Moreover, this paper reviews the characteristics of publicly available datasets in this realm and provides future research directions for designing EVCS intelligent recommendation systems. Experimental results on the UrbanEV dataset show the superior performance of MARL methods compared with other baselines in terms of the considered key performance metrics.</jats:p>engJournal article review10.1049/esi2.70055WOS:001868458400001ROUTING PROBLEMTIME WINDOWSSELECTIONBLOCKCHAINEFFICIENT2516-8401