Next-Generation Ultra-Compact/Efficient Data-Center Power Supply Modules A thesis submitted to attain the degree of DOCTOR OF SCIENCES of ETH ZURICH (Dr. sc. ETH Zurich) presented by GUSTAVO CARLOS KNABBEN

 

Next-Generation Ultra-Compact/Efficient Data-Center Power Supply Modules 

A thesis submitted to attain the degree of DOCTOR OF SCIENCES of ETH ZURICH (Dr. sc. ETH Zurich) presented by GUSTAVO CARLOS KNABBEN MSc EE, UFSC born on 23.05.1992 citizen of Joinville, Brazil accepted on the recommendation of Prof. Dr. Johann W. Kolar, examiner Prof. Dr. Marcelo Lobo Heldwein, co-examiner

Abstract

 The increasingly-electric future requires next-generation power supplies that are compact, efficient, low-cost, and ultra-reliable, even across mains failures, to power mission-critical electrified processes. Hold-up time requirements and the demand for ultra-high power density and minimum production costs, in particular, drive the need for DC/DC power converters with (i) a wide input voltage range, to reduce the size of the hold-up capacitor, (ii) soft-switching over the full input-voltage and load ranges, to achieve low losses that facilitate a compact realization, and (iii) complete PCB-integration for low-cost manufacturing. Wide-bandgap power semiconductors, with excellent on-resistance properties and low switching and reverse-recovery losses, come along these requirements toward the conceptualization of nextgeneration power-supply modules, but cannot alone catapult state-of-theart converter technology to the performance baseline of future automotive, automated manufacturing and hyperscale data-center applications. Instead, the combination of wide-bandgap devices with proper converter topology, control and magnetics design has proven to be the real enabler of power supplies for the increasingly-electric future. This thesis makes a case for the combination of these three features (widebandgap devices, proper topology/control and advanced magnetics) as the keys for paving the way toward next-generation power-supply modules. Therefore, a suitable low-complexity circuit topology with improved control scheme that operates across a wide-input-voltage range with complete softswitching is identified, which switches efficiently at higher frequencies and high output currents with PCB-integrated magnetics, improving significantly power density compared to state-of-the-art designs. This topology embeds a sophisticated PCB-integrated matrix transformer that has a single path for the magnetic flux, ensuring equal flux linkage of parallel-connected secondary windings despite possible geometric PCB-layout asymmetries or reluctance imbalances. The so-called snake-core transformer avoids the emergence of circulating currents between parallel-connected secondary windings and guarantees proper operation of parallel-connected, magnetically-coupled converter modules. The benefits of the proposed topology, control scheme and transformer design are validated by three fabricated 300 V-430 V-input, 12 V-output DC/DC hardware demonstrators. The converters utilize an LLC-based control scheme for complete soft-switching and the snake-core transformer to divide the output current with a balanced flux among multiple secondary windings. First, a 3 kW DC/DC series-resonant converter achieves 350Win3 (21”4 kWdm3) vii Abstract power density and 94 % peak efficiency, validating control and transformer operation. Then, a second hardware prototype with 1”5 kW showcases a peak efficiency close to 96 % and a power density of 337Win3 (20”6 kWdm3), with full PCB-integration and zero-voltage switching even down to zero load. Finally, the third demonstrator—a magnetically-coupled, input-parallel/outputparallel, two-1”5 kW-module DC/DC converter—achieves a peak efficiency of nearly 97 % and a power density of 345Win3 (21”1 kWdm3) with ideal current sharing among modules and stable operation, important characteristics enabled by the novel snake-core transformer. Detailed loss models are derived for every converter’s component and the measurement results are in excellent agreement with the calculated values. These loss models are used to identify improvements to further boost efficiency, the most important of which is the minimization of delay times in synchronous rectification with either synchronous rectifier ICs embedded into the power-device’s package or, at a minimum, Kelvin-source connections on high-current MOSFETs. The results accomplished in this thesis indicate the necessity of careful topology/control selection and advanced-magnetics design for enabling WBGbased industrial power supplies that will outperform state-of-the-art solutions and catapult them to the next-generation performance standards. None of these features—be it WBG devices, wide-gain-range resonant converters, or advanced PCB-integrated magnetics—will alone enable next-generation power-supply modules, but the thoughtful combination of these technologies and their careful application to the particular application, with emphasis to high-frequency PCB magnetics and soft-switching topologies, which enable compact and cost-effective converters with competitive efficiencies.

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Optimal Design Methodology for A High-Frequency Transformer Using Finite Element Analysis and Machine Learning by Eunchong Noh School of Electrical and Computer Engineering University of Seoul February 2022

 Optimal Design Methodology for A High-Frequency Transformer Using Finite Element Analysis and Machine Learning

A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science (Electrical and Computer Engineering) December 2021 Thesis committee: Gyu-Sik Kim, Professor, ECE, University of Seoul Seung-Hwan Lee, Associate Professor, ECE. University of Seoul Moon-Que Lee, Professor, ECE, University of Seoul 

 Abstract

 The demand for isolated DC-DC converters is increasing due to the spread of electric vehicles (EV) and the expansion of renewable energy use. Accordingly, the need for a high-frequency transformer, a key component of an isolated DC-DC converter, is also increasing. This trend is also taking place in the field of railway locomotive systems. Solid state transformer (SST) technology to improve the performance and efficiency of railway locomotive propulsion systems is being actively researched, and high-frequency transformer is the core of SST. Highfrequency transformer design for railway locomotive systems has more complex design elements that must be considered for volume-loss optimization and insulation and thermal design. This thesis investigates an optimization design methodology using machine learning and NSGA-II for optimized high-frequency transformer design. For machine learning, Finite-element analysis (FEA) simulation was used to obtain high-frequency transformer parameter data. Conventional high-frequency transformer optimization design methods used analytical models for parameter calculation. However, this analytical model has a significant error when the shape of the high-frequency transformer becomes complicated. In particular, the leakage inductance of the high-frequency transformer is difficult to calculate with an analytical model. So, it is difficult and cumbersome to apply it in the design. This thesis obtained magnetizing inductance, leakage inductance, and copper loss of shell-type transformer models in various shapes using FEA simulation. Then, using the data obtained from the FEA simulation, a machine learning regression model was created to minimize the parameter calculation error in complex shapes. In addition, the NSGA-II algorithm, which is widely used in multi-variable optimization design, is used to find the optimal transformer shape to perform optimization that can satisfy multiple design elements at the same time. Each parameter inferred by the machine learning regression model showed a high correlation and sufficiently low inference error rate, used for parameter calculation in the NSGA-II algorithm. The inferred parameters are used to calculate transformer loss for optimization design or check whether constraints are satisfied. Through the optimization design using NSGA-II, a Pareto front curve for volume and loss that satisfies all design conditions was obtained. The designer can select and use the designs according to the situation. The methodology can be designed for more complex shapes to achieve higherperformance high-frequency transformer design. In addition, the complexity of the design is reduced because numerous consideration factors can be easily considered through constraint setting in the NSGA-II. Finally, unlike the conventional design methodology, which has a significant influence on the skill and intuition of the designer, once the environment is set up, the design proceeds only by inputting target parameters and executing the code so that the design time can be reduced. Therefore, it is possible to design a high-frequency transformer with constantly high performance regardless of the designer's skill level.

VIEW FULL THESIS: https://www.mediafire.com/file/w56it1xqyowh2h8/Optimal+Design+Methodology+for+A+High-FrequencyTransformer+Using+Finite+Element+Analysis+and+Machine+Learning.pdf/file