Development of Energy Management Systems for Electric Vehicle Charging Stations Associated With Batteries: Application to a Real Case
Date
Authors
Olano, Jon
Camblong, Haritza
López-Ibarra, Jon Ander
Lie, Tek Tjing
Supervisor
Item type
Journal Article
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Publisher
MDPI AG
Abstract
Implementing 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.Description
Keywords
4605 Data Management and Data Science, 46 Information and Computing Sciences, 40 Engineering, 7 Affordable and Clean Energy, energy management systems, electric vehicle charging stations, lithium-ion batteries, rule-based algorithms, fuzzy logic, genetic algorithm
Source
Applied Sciences, ISSN: 2076-3417 (Print); 2076-3417 (Online), MDPI AG, 15(16), 8798-8798. doi: 10.3390/app15168798
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© 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/).