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1. A Novel Thermal Modeling Analysis for Liquid-Cooled High-Power EV Chargers

   https://doi.org/10.1109/ECCE55643.2024.10861307  

   Bradford, Paul; Zade, Aditya; Gurudiwan, Shubhangi; Wang, Hongjie (October 2024, IEEE)

   As transportation electrification keeps accelerating across a wide range of vehicle classes from light-duty cars to heavy-duty trucks, the need for high-power electric vehicle (EV) charging equipment continues to grow rapidly. Even though the advancements in power electronics are enabling higher efficiency for EV chargers, thermal management continues to be a significant challenge in high-power charger development Liquid cooling with cold plates is commonly used for dissipating the heat generated by semiconductor devices m high-power chargers To design an effective and optimized thermal management system, accurate thermal modeling and analysis are critical, especially m the preliminary design phases. Complex fluid dynamics (CFD) software such as Ansys has been widely used for thermal modeling and analysis in the literature; however, using CFD analysis tools can be expensive, time-consuming, and computationally intense. To address the technical needs for a rapid, accurate preliminary thermal analysis tool, this paper presents a novel and accurate thermal modeling and analysis approach for high- power EV chargers with liquid cooling and Silicon Carbide (SiC) MOSFETs mounted on cold plates. The proposed modeling and analysis approach utilizes a lumped element model for each of the many pieces within the system to mathematically represent the physical system and form thermal networks. The effectiveness, accuracy, and light computational load of the proposed approach have been validated through experimental results conducted on a 21 kW power converter module hardware from a 1 MW EV wireless charge developed by the team for Class 8 semi-trucks. 

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2. Evaluation of a Medium-Voltage Grid-Tied Cascaded H-Bridge for Energy Storage Systems Using SiC Switching Devices

   https://doi.org/10.1109/eGRID.2018.8598683  

   Mhiesan, Haider; Farnell, Chris; McCann, Roy; Mantooth, Alan (November 2018, Evaluation of a Medium-Voltage Grid-Tied Cascaded H-Bridge for Energy Storage Systems Using SiC Switching Devices)

   This paper presents the study and evaluation of a medium-voltage grid-tied cascaded H-bridge (CHB) three-phase inverter for battery energy storage systems using SiC devices as an enabling technology. The high breakdown voltage capability of SiC devices provide the advantage to significantly minimize the complexity of the CHB multilevel converter, with less power loss compared to when Silicon (Si) devices are used. The topology in this study has been selected based on high voltage SiC devices. In order to reach 13.8 kV, a nine-level CHB is needed when using 6.5 kV SiC MOSFETs. However, if 10 kV SiC MOSFETs are used, only five-levels of the CHB are required. The controls were developed, simulated and verified through an experimental prototype. The results from the scaled-down prototype proved the controls and the verification of the performance of five-level CHB three-phase inverter. For the system reliability, both open-loop and short-circuit faults are analyzed. 

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3. Human-centric data-driven optimization and recommendation in EV-interfaced grid at city scale: poster abstract

   https://doi.org/10.1145/3563357.3567752  

   Nie, Jingping; Hu, Lanxiang; Liu, Yian; Fan, Yuang; Preindl, Matthias; Jiang, Xiaofan (November 2022, BuildSys '22: Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation)

   The fast development of electric vehicles (EV) and EV chargers introduces many factors that affect the grid. EV charging and charge scheduling also bring challenges to EV drivers and grid operators. In this work, we propose a human-centric, data-driven, city-scale, multivariate optimization approach for the EV-interfaced grid. This approach takes into account user historical driving and charging habits, user preferences, EV characteristics, city-scale mobility, EV charger availability and price, and grid capacity. The user preferences include the trade-off between cost and time to charge, as well as incentives to participate in different energy-saving programs. We leverage deep reinforcement learning (DRL) to make recommendations to EV drivers and optimize their welfare while enhancing grid performance. 

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4. Multi-level Active Gate Driver for SiC MOSFETs with Paralleling Operation

   https://doi.org/10.1109/COMPEL52922.2021.9645994  

   Wei, Yuqi; Du, Liyang; Du, Xia; Mantooth, Alan (November 2021, IEEE 22nd Workshop on Control and Modelling of Power Electronics (COMPEL)IEEE 22nd Workshop on Control and Modelling of Power Electronics (COMPEL))

   Abstract—Wide band gap (WBG) devices, like silicon carbide (SiC) MOSFET has gradually replaced the traditional silicon counterpart due to their advantages of high operating temperature and fast switching speed. Paralleling operations of SiC MOSFETs are unavoidable in high power applications in order to meet the system current requirement. However, parasitics mismatches among different paralleling devices would cause current unbalance issues, which would reduce the system reliability and maximum current capability. Thus, to achieve current balancing operation, this paper proposes a solution of using multi-level active gate driver, where the dynamic current sharing during turn-on and turn-off processes are achieved by adjusting the delays, intermediate turn-on and turn-off voltages. The static current sharing is maintained by regulating the static turn-on gate voltage, where the on-state resistance mismatch between different devices can be compensated. A double pulse test setup with two different SiC MOSFETs is built to emulate the scenario of worst case application with large differences of threshold voltage and on-state resistance. The experimental results demonstrate that the proposed active gate driver can achieve both dynamic and static current sharing operations for SiC MOSFETs with paralleling operation. Moreover, the system control diagram is discussed. Simulation studies are conducted to achieve closed-loop control of the paralleled SiC MOSFETs with the aid of the active gate driver approach. 

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5. Novel Approach of Determining and Predicting SiC MOSFET’s On Resistance From Device Case Temperature Using Machine Learning

   https://doi.org/10.1109/APEC48143.2025.10977206  

   Bradford, Paul; Deppe, Conner; Wang, Hongjie (March 2025, IEEE)

   Silicon-carbide (SiC) MOSFET devices are increasing in popularity in high-power converter applications. Device on-resistance (Rdson) is an important indicator for SiC MOSFET health status. Increments in Rdson over device lifetime indicate imminent device failure and result in decreased system efficiency. Direct and accurate measurement of SiC MOSFET device Rdson in high-power applications is difficult. Another approach is to estimate/predict the device Rdson from other, more easily measurable quantities, however, little work has been done on this approach in the literature. This leaves a significant technical gap in measuring/predicting device Rdson and slows down the device health status monitoring and power converter reliability research. To address the technical gaps, this work proposes a novel approach to predicting device Rdson from thermal cycle count and instantaneous temperature using machine learning regression models. The actual hardware data collected from accelerated lifetime tests of high-power SiC MOSFETs are used to train, test, and validate the proposed machine-learning regression models. The developed models, coupled with cycle counting algorithms, and device case thermal measurements, provide accurate live estimates of Rdson and can be used to predict changes in Rdson over expected mission profiles during power converter design. 

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