Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/143394
Title: A model predictive current controlled bidirectional three-level DC/DC converter for hybrid energy storage system in DC microgrids
Authors: Zhang, Xinan
Wang, Benfei
Manandhar, Ujjal
Gooi, Hoay Beng
Foo, Gilbert
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Zhang, X., Wang, B., Manandhar, U., Gooi, H. B., & Foo, G. (2019). A model predictive current controlled bidirectional three-level DC/DC converter for hybrid energy storage system in DC microgrids. IEEE Transactions on Power Electronics, 34(5), 4025-4030. doi:10.1109/TPEL.2018.2873765
Journal: IEEE Transactions on Power Electronics
Abstract: This letter proposes a new three-level dc/dc converter configuration for a hybrid energy storage system (HESS) in dc microgrids. It effectively integrates different energy storage devices (ESDs), such as battery and ultracapacitor (UC), using one converter with bidirectional power flow. Furthermore, the proposed converter provides the flexibility of independent regulation of different ESDs with significantly reduced inductor current ripple due to the availability of three voltage levels. The voltage ratings of power semiconductors employed in this converter are also reduced. To further enhance the performance of HESS, a constant switching frequency based model predictive current control is employed for HESS regulation. The design guideline and operating principle of the proposed converter are discussed. Experimental results are presented to verify the efficacy of the proposed converter and control.
URI: https://hdl.handle.net/10356/143394
ISSN: 0885-8993
DOI: 10.1109/TPEL.2018.2873765
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TPEL.2018.2873765.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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