Development of Energy Management Systems for Electric Vehicle Charging Stations Associated with Batteries: Application to a Real Case
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Battery Technologies Research · Electric Vehicles and Infrastructure · Electric and Hybrid Vehicle Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Systems Engineering & Control, Faculty of Engineering of Gipuzkoa, University of the Basque Country (UPV-EHU), Europa Plaza 1, CP 20018 Donostia, Spain
Department of Electrical and Electronic Engineering, Auckland University of Technology, Auckland 1010, New Zealand
Jema Energy, Paseo del Circuito 10, CP 20160 Lasarte-Oria, Spain
References (31)
Show source-provided references
1. World Resources Institute (2024, January 29). World Greenhouse Gas Emissions. Available online: https://www.wri.org/data/world-greenhouse-gas-emissions-2019.
2. Serrano, J.R., Novella, R., and Piqueras, P. (2019). Why the development of internal combustion engines is still necessary to fight against global climate change from the perspective of transportation. Appl. Sci., 9.10.3390/app9214597 ↗
3. Pricing and energy management of EV charging station with distributed renewable energy and storage
4. High-power electric vehicle charging: Low-carbon grid integration pathways with stationary lithium-ion battery systems and renewable generation10.1016/j.apenergy.2022.120541 ↗
5. An energy management strategy with renewable energy and energy storage system for a large electric vehicle charging station10.1016/j.etran.2020.100076 ↗
6. Yan, Y., Wang, H., Jiang, J., Zhang, W., Bao, Y., and Huang, M. (2019). Research on configuration methods of battery energy storage system for pure electric bus fast charging station. Energies, 12.10.3390/en12030558 ↗
7. Grid-connected battery energy storage system: A review on application and integration10.1016/j.rser.2023.113400 ↗
8. Examination on Classification of EVs and Energy Management Strategies of HEV10.35940/ijitee.A1053.1191S19 ↗
9. Demand Side Energy Management of EV Charging Stations by Approximate Dynamic Programming10.1016/j.enconman.2019.06.058 ↗
10. Discrete Stochastic Control for Energy Management with Photovoltaic Electric Vehicle Charging Station10.24295/CPSSTPEA.2022.00020 ↗
11. Fundamentals of Scatter Search and Path Relinking
12. A review of reinforcement learning based energy management systems for electrified powertrains: Progress, challenge, and potential solution10.1016/j.rser.2021.111833 ↗
13. A genetic algorithm approach for smart energy management of microgrids10.1016/j.renene.2022.07.055 ↗
14. Comprehensive optimization of electrical heacy-duty truck battery swap stations with a SOC-dependant charge scheduling method10.1016/j.energy.2024.132773 ↗
15. Jeon, M., Tong, L., and Zhao, Q. (2023). On the Optimality of Procrastination Policy for EV charging under Net Energy Metering. arXiv.10.1109/PESGM52003.2023.10252707 ↗
16. A novel method for co-optiimzing battery sizing and charging strategy of battery electric bus flets: An application to the city of Paris10.1016/j.energy.2023.129459 ↗
17. Hosny, M., and Mumford, C. (2009, January 13–16). Investigating Genetic Algorithms for Solving the Multiple Vehicle Pickup and Delivery Problem with Time Windows. Proceedings of the MIC2009, the VIII Metaheuristics International Conference, Hamburg, Germany.10.1007/s10732-008-9083-1 ↗
18. Optimal scheduling for electric vehicle charging with discrete charging levels in distribution grid10.1109/TSG.2016.2558585 ↗
19. Reinforcement Learning Based EV Charging Management Systems-A Review10.1109/ACCESS.2021.3064354 ↗
20. Rafał, K., Radziszewska, W., Grabowski, O., Biedka, H., and Verstraete, J. (2023). Energy Cost Minimization with Hybrid Energy Storage System Using Optimization Algorithm. Appl. Sci., 13.10.3390/app13010518 ↗
21. Novel battery degradation cost formulation for optimal scheduling of battery energy storage systems10.1016/j.ijepes.2021.107795 ↗
22. Hybrid Optimization for Economic Deployment of ESS in PV-Integrated EV Charging Stations10.1109/TII.2017.2713481 ↗
23. Optimal scheduling of electric-hydrogen integrated charging station for new energy vehicles10.1016/j.renene.2024.120224 ↗
24. How do Depth of Discharge, C-rate and Calendar Age Affect Capacity Retention, Impedance Growth, the Electrodes, and the Electrolyte in Li-Ion Cells?
25. Adaptative energy management strategy and optimal sizing applied on a battery-supercapacitor based tramway10.1016/j.apenergy.2016.02.079 ↗
26. De Santis, E., Rizzi, A., and Sadeghian, A. (2016). A Hierarchical Genetic Optimization of a Fuzzy Logic System for Flow Control in Micro Grids. arXiv.10.1016/j.asoc.2017.05.059 ↗
27. The Optimal Crossover or Mutation Rates in Genetic Algorithm: A Review
28. A Genetic Approach for Simultaneous Design of Membership Functions and Fuzzy Control Rules10.1023/A:1008186427312 ↗
29. Polat, H., Hosseinabadi, F., Hasan, M., Chakraborty, S., Geury, T., El Baghdadi, M., Wilkins, S., and Hegazy, O. (2023). A Review of DC Fast Chargers with BESS for Electric Vehicles: Topology, Battery, Reliability Oriented Control and Cooling Perspectives. Batteries, 9.10.3390/batteries9020121 ↗
30. (2024, January 29). OMIE Webpage. Available online: https://www.omie.es/es.
31. Battery cost forecasting: A review of methods and results with an outlook to 205010.1039/D1EE01530C ↗