Abstract
Battery energy storage systems and multilevel converters are the most essential constituents of modern medium voltage networks. In this regard, the modular multilevel converter offers numerous advantages over other multilevel converters. The key feature of modular multilevel converter is its capability to integrate small battery packs in a split manner, given the opportunity to submodules to operate at considerably low voltages. In this paper, we focus on study of potential SMs for modular multilevel converter based battery energy storage system while, keeping in view the inconsistency of secondary batteries. Although, selecting a submodule for modular multilevel converter based battery energy storage system, the state of charge control complexity is a key concern, which increases as the voltage levels increase. This study suggests that the half-bridge, clamped single, and full-bridge submodules are the most suitable submodules for modular multilevel converter based battery energy storage system since, they provide simplest state of charge control due to integration of one battery pack along with other advantages among all 24 submodule topologies. Depending on submodules analysis, the modular multilevel converter based battery energy storage system based on half-bridge submodules is investigated by splitting it into AC and DC equivalent circuits to acquire the AC and DC side power controls along with an state of charge control. Subsequently, to validate different control modes, a downscaled laboratory prototype has been developed.
Funder
Ministry of Small and Medium-sized Enterprises (SMEs) and Startups (MSS), Korea, under the “Regional Specialized Industry Development Plus Program and Korea Institute for Advancement of Technology
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
Reference58 articles.
1. Energy Storage—A Key Technology for Global Energy Sustainability;Dell;J. Power Sources,2001
2. Analysis and Control of Modular Multilevel Converters With Integrated Battery Energy Storage;Vasiladiotis;IEEE Trans. Power Electron.,2015
3. Overview of Current and Future Energy Storage Technologies for Electric Power Applications;Hadjipaschalis;Renew. Sustain. Energy Rev.,2009
4. Jafari, S., Shahbazi, Z., and Byun, Y.-C. (2022). Lithium-Ion Battery Health Prediction on Hybrid Vehicles Using Machine Learning Approach. Energies, 15.
5. Jafari, S., Shahbazi, Z., Byun, Y., and Lee, S.-J. (2022). Lithium-Ion Battery Estimation in Online Framework Using Extreme Gradient Boosting Machine Learning Approach. Mathematics, 10.
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献