Please use this identifier to cite or link to this item: http://elar.urfu.ru/handle/10995/141535
Title: Hybrid multimodule DC–DC converters accelerated by wide bandgap devices for electric vehicle systems
Authors: Waheed, A.
Rehman, S. U.
Alsaif, F.
Rauf, S.
Hossain, I.
Pushkarna, M.
Gebru, F. M.
Issue Date: 2024
Publisher: Nature Research
Citation: Waheed, A., Rehman, S. U., Alsaif, F., Rauf, S., Hossain, I., Pushkarna, M., & Gebru, F. M. (2024). Hybrid multimodule DC–DC converters accelerated by wide bandgap devices for electric vehicle systems. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-55426-6
Abstract: In response to the growing demand for fast-charging electric vehicles (EVs), this study presents a novel hybrid multimodule DC–DC converter based on the dual-active bridge (DAB) topology. The converter comprises eight modules divided into two groups: four Insulated-Gate Bipolar Transistor (IGBT) modules and four Metal–Semiconductor Field-Effect Transistor (MESFET) modules. The former handles high power with a low switching frequency, while the latter caters to lower power with a high switching frequency. This configuration leverages the strengths of both types of semiconductors, enhancing the converter’s power efficiency and density. To investigate the converter’s performance, a small-signal model is developed, alongside a control strategy to ensure uniform power sharing among the modules. The model is evaluated through simulation using MATLAB, which confirms the uniformity of the charging current provided to EV batteries. The results show an impressive power efficiency of 99.25% and a power density of 10.99 kW/L, achieved through the utilization of fast-switching MESFETs and the DAB topology. This research suggests that the hybrid multimodule DC–DC converter is a promising solution for fast-charging EVs, providing high efficiency, power density, and switching speed. Future studies could explore the incorporation of advanced wide bandgap devices to handle even larger power fractions. © The Author(s) 2024.
Keywords: BINARY GENETIC ALGORITHM
LOAD FORECASTING
MEAN ABSOLUTE PERCENTAGE ERROR
PRINCIPAL COMPONENT ANALYSIS
4 DIMETHYLAMINOAZOBENZENE
ARTICLE
BIPOLAR TRANSISTOR
CONTROLLED STUDY
DATA ANALYSIS SOFTWARE
FIELD EFFECT TRANSISTOR
FORECASTING
GENETIC ALGORITHM
HYBRID
PHARMACEUTICS
PRINCIPAL COMPONENT ANALYSIS
SEMICONDUCTOR
SIMULATION
VELOCITY
URI: http://elar.urfu.ru/handle/10995/141535
Access: info:eu-repo/semantics/openAccess
cc-by
SCOPUS ID: 85186262985
WOS ID: 001177603900059
PURE ID: 53805052
ISSN: 2045-2322
DOI: 10.1038/s41598-024-55426-6
Sponsorship: King Saud University, KSU
This work was supported by the Researchers Supporting Project number (RSPD2024R646), King Saud University, Riyadh, Saudi Arabia.
RSCF project card: King Saud University, KSU
Researchers Supporting Project number (RSPD2024R576), King Saud University, Riyadh, Saudi Arabia.
Appears in Collections:Научные публикации ученых УрФУ, проиндексированные в SCOPUS и WoS CC

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