All Outputs (6)

A Fast and Accurate GaN Power Transistor Model and Its Application for Electric Vehicle (2023)
Journal Article

In order to overcome the challenge of balancing accuracy with simulation speed of power electronics converters for system-level simulation, the paper proposes a GaN power transistor model that can accurately and rapidly predict power losses, which is... Read More about A Fast and Accurate GaN Power Transistor Model and Its Application for Electric Vehicle.

1.2 kV SiC Wirebond-Less Integrated Low Inductance Module for Automotive Application (2023)
Conference Proceeding

In this paper, a 1.2 kV wirebond-less Silicon Carbide (SiC) Intelligent Power Module (IPM) designed for automotive applications is presented. This IPM includes integrated decoupling capacitors and gate drivers. By utilizing Printed Circuit Board (PCB... Read More about 1.2 kV SiC Wirebond-Less Integrated Low Inductance Module for Automotive Application.

IEEE VTS Motor Vehicles Challenge 2024 - Energy and Powertrain Losses Management of an e-Racing Vehicle (2023)
Conference Proceeding

Motor Vehicle Challenge, supported by IEEE Vehicular Technology Society, is an annual activity to find an appropriate energy management strategy to improve electric vehicles’ performance. In Challenge 2024, power losses and efficiency of power conver... Read More about IEEE VTS Motor Vehicles Challenge 2024 - Energy and Powertrain Losses Management of an e-Racing Vehicle.

Behavioural SiC IGBT Modelling Using Non-Linear Voltage and Current Dependent Capacitances (2023)
Conference Proceeding

This paper presents a behavioural silicon carbide (SiC) IGBT model that utilizes voltage and current dependent capacitances to simulate its switching characteristics, and a voltage dependent current source to simulate the static characteristics. The... Read More about Behavioural SiC IGBT Modelling Using Non-Linear Voltage and Current Dependent Capacitances.

Deep Contrastive Representation Learning With Self-Distillation (2023)
Journal Article

Recently, contrastive learning (CL) is a promising way of learning discriminative representations from time series data. In the representation hierarchy, semantic information extracted from lower levels is the basis of that captured from higher level... Read More about Deep Contrastive Representation Learning With Self-Distillation.

N-level GaN Transistor Model for Fast Simulation of Electric Vehicle based Power Electronics Systems (2023)
Conference Proceeding

Accurate and fast power loss estimation models based on a good understanding of the occuring loss mechanisms are crucial to achieve high efficiency, high power density and to take full advantage of the superior characteristics of Gallium Nitride (GaN... Read More about N-level GaN Transistor Model for Fast Simulation of Electric Vehicle based Power Electronics Systems.