Olano, JonCamblong, HaritzaLópez-Ibarra, Jon AnderLie, Tek Tjing2025-08-242025-08-242025-08-08Applied Sciences, ISSN: 2076-3417 (Print); 2076-3417 (Online), MDPI AG, 15(16), 8798-8798. doi: 10.3390/app151687982076-34172076-3417http://hdl.handle.net/10292/19718Implementing an effective energy management system (EMS) is essential for optimizing electric vehicle (EV) charging stations (EVCSs), especially when combined with battery energy storage systems (BESSs). This study analyzes a real-world EVCS scenario and compares several EMS approaches, aiming to reduce operating costs while accounting for BESS degradation. Initially, significant savings were achieved by optimizing the EV charging schedule using genetic algorithms (GAs), even without storage. Next, different BESS-based EMSs, including rule-based and fuzzy logic systems, were optimized via GAs. Finally, in a dynamic scenario with variable electricity prices and demand, the adaptive GA-optimized fuzzy logic EMS was found to achieve the best performance, reducing annual operating costs by 15.6% compared to the baseline strategy derived from real fleet data.© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).4605 Data Management and Data Science46 Information and Computing Sciences40 Engineering7 Affordable and Clean Energyenergy management systemselectric vehicle charging stationslithium-ion batteriesrule-based algorithmsfuzzy logicgenetic algorithmJournal ArticleOpenAccess10.3390/app15168798