The global shift towards Electric Vehicles (EVs) is driven by their energy efficiency, lower emissions, and reduced dependence on fossil fuels. As the demand for EVs continues to rise, the need for EV ultra-fast chargers becomes paramount to enable faster charging times and facilitate long-distance travel without compromising convenience. In this context, solid-state transformers (SSTs) have emerged as a promising technology to replace traditional line-frequency transformers (LFTs) in various applications, including EV charging stations. SSTs offer improved system controllability, power factor correction capabilities, and reduced size and weight through the utilization of medium-frequency transformers (MFTs). This thesis focuses on enhancing the efficiency and power density of the MFT used in SSTs.
A 1.2 MVA SST for EV ultra-fast charging stations is designed and simulated. The SST incorporates average controllers responsible for regulating the output voltage and the input power factor, as well as, voltage and power balancing controllers to ensure stable operation among its cells. Furthermore, a design methodology for optimizing the MFT used in DC-to-DC converters for SST-based ultra-fast chargers is introduced. The methodology is optimizing the efficiency and power density of the transformer based on the transformer parameters input by the designer. A software tool is developed to streamline the design process and enable the optimization of various parameters, such as core material, size, and winding configurations. The tool facilitates the development of high-performance MFTs for SST applications.
The developed MFT optimization methodology is utilized to design a 100 kW, 20 kHz MFT, resulting in a remarkable 22.7% improvement in power density compared to conventional design methods. The transformer showed superior efficiency and power density compared to MFT designs in literature. Additionally, two scaled-down transformers are designed and tested at 5 kW, employing both conventional and optimization methods. The results demonstrate a significant 57.8% improvement in specific power. / Thesis / Master of Applied Science (MASc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/29004 |
| Date | January 2023 |
| Creators | Abdelhamid Younis, Eslam |
| Contributors | Narimani, Mehdi, Electrical and Computer Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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