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T-type primary bridge-based resonant converters employed in unfolding-based single-stage ac–dc conversion systems commonly adopt a leading-edge aligned modulation strategy, as it facilitates zero-voltage switching (ZVS) throughout the grid cycle. However, the application of this modulation strategy can result in partial ZVS of the common-source mosfets within the T-type bridge. In this letter, we investigate the underlying reasoning of such partial ZVS, quantify the severity of the problem, and propose a mitigation solution. Specifically, an optimized leading-edge aligned modulation strategy is introduced, incorporating an intentional staggered time delay for the turn-off of the common-source mosfets during the leading edge. The proposed modulation strategy is validated through hardware testing on a 20-kW unfolding-based ac–dc conversion system. 

Unfolder-based quasi-single-stage ac-dc power converter has been widely used for high-power electric vehicle (EV) charging systems for its high efficiency and power density. However, the resonance between the grid inductance (impedance) and the capacitors on the soft-dc-link of the converter impacts the system stability and significantly limits the system control bandwidth and dynamic response performance. A quasi-single-stage ac-dc converter with unfolder plus T-bridge series resonant converter (T-SRC) is studied in this work. The small-signal modeling and plant transfer function derivation of the T-SRC is presented in this paper. A damping filter design using the extra element theorem (EET) is then proposed to achieve high- bandwidth and stable operation of the quasi-single-stage ac-dc converter. Simulation and hardware results from an 18 kW module for high-power EV charging are provided to validate the proposed modeling and damping filter design. 

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. 

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. 

To optimize the utilization of a T-type bridge structure in resonant converters, one must thoroughly examine the soft-switching criteria specific to the T-type configuration. This work proposes an energy-based method to determine the softs-witching requirements of a T-type bridge during its various switching transitions. The study estimates the minimum required zero voltage switching (ZVS) current while considering the nonlinearity and voltage dependence associated with the output capacitance of MOSFETs. Moreover, this paper demonstrates that existing studies on ZVS analysis for T-type bridge-based resonant dc-dc converters, which rely only on capacitive energy considerations, significantly underestimate the necessary ZVS current values, with errors as high as 50%. Simulation and hardware results on a T-type primary bridge circuit validate the accuracy of the proposed minimum ZVS current calculation. Hardware tests are conducted on a T-type bridge in a 20 kW electric vehicle charger. 

Due to environmental concerns, electric vehicles (EVs) have become increasingly popular in recent decades. While EV s offer several benefits, they also present challenges such as prolonged charging times and range anxiety. To address these issues and enhance EV market participation, dynamic wireless power transfer (DWPT) is gaining a great attention in electrified-transportation sector, leading to an emergence of DWPT for EVs. DWPT offers advantages like charging while in-motion. However, DWPT roadways also impose additional demands on the power system, potentially increasing operational costs. The main objective of this paper is to manage effectively the additional load caused by DWPT roadways, and this paper presents the utilization of distributed energy resources (DERs), such as photovoltaic (PV) systems and battery storage system (BSS), to minimize the system costs. The importance of our proposed load management strategy becomes even more critical during extreme events. Therefore, this paper further examines two scenarios, i.e., normal operations and under extreme conditions considering line outages, to compare the costs associated with DWPT systems. The efficiency of the proposed method is validated using IEEE 33-bus distribution systems through a mixed integer linear programming (MILP) optimization problem. Test results demonstrate that integrating DWPT system increases the system costs under both normal and extreme conditions, however, the DER-based mechanism is capable of mitigating these costs optimally. 

This paper examines an application of a two-lane microscopic Traffic Flow (TF) simulation to comprehend the impact of the complex behavior of Dynamic Wireless Power Transfer (DWPT) charging systems onto electric power distribution grids. The proposed approach utilizes real-world data to determine a more accurate TF density at each time interval. The simulation is carried out considering all vehicles, whether electric vehicles (EVs) or non-electric, and they have a randomized lane changing behavior and fluctuating velocities following a leading car model. Three different scenarios are conducted for 5 mile, 10 mile, and 15 mile DWPT networks that are proportionally connected to an IEEE 33-bus distribution grid. Our findings indicate that EVs' average State-of-Charge (SOC) increases proportionally and significantly at each DWPT network length. Furthermore, the load demand generated from the DWPT network also increases proportionally with its length; and this increment in load demand causes adverse impacts on distribution grid voltage magnitudes exceeding operational standards that leads to equipment failure or blackout events. 

This paper presents a novel direct duty-to-current control strategy to mitigate the dc bias and eliminate the need for a dc blocking capacitor in Dual Active Bridge (DAB) converters. The proposed control mechanism directly controls the duty cycle of each leg in the primary H-bridge to regulate the average (over one switching period) volt-seconds applied to the transformer primary winding to be zero without a dc blocking capacitor, under both steady-state and transient operations. This strategy is particularly relevant for electric vehicle (EV) applications, where variations in power demand and charging protocols can introduce dc bias, and the proposed control strategy work seamlessly with the output voltage control loop, during both steady-state operation and transients. The analysis, presented in detail, includes simulation results validating the effectiveness of the proposed control strategy under various steady-state and transient conditions, demonstrating its robustness and applicability in EV systems. 

As the widespread adoption of electric vehicles (EVs) keeps increasing, EV charger reliability is becoming critical to provide a satisfactory charging experience for EV users. Wide-bandgap semiconductors such as silicon Carbide (SiC) MOSFETs have been widely deployed in EV chargers for high efficiency, high power density, and thermal capabilities. However, the aging of SiC MOSFETs has not been fully studied with available aging data lacking significantly in the literature. This paper addresses the EV charger reliability problem by developing a new aging test platform for SiC MOSFETs that are commonly used in various EV chargers, collecting aging data with analysis to provide a new understanding of SiC MOSFET aging, and providing new insights into online EV charger health monitoring system design and development. 

Large-scale in-motion inductive wireless charging infrastructure could be a key enabler for widespread adoption of electric vehicles (EVs) leading to net-zero carbon emissions for the transportation sector. However, the challenge of distributing power to the numerous transmitters in such large-scale systems has not been adequately investigated. This paper presents further development of a patented novel power distribution architecture that provides improved system efficiency, reliability, and cost in large-scale EV in-motion wireless charging systems. This paper provides details on operation and analysis of the proposed current-fed wireless charging transmitter. The proposed transmitter achieves load-independent transmitter coil current and high tolerance to mistuning. Simulation results from a 1 kW current-fed transmitter design validate the proposed design and analysis.