Search Results (13)

Reliable multilevel inverter IGBT modules require precise loss and heat management, particularly in severe traction applications. This paper presents a comprehensive modeling framework for three-level T-type neutral-point clamped (TNPC) inverters using a high-power Insulated Gate Bipolar Transistor (IGBT) module that combines model predictive control (MPC) with space vector pulse width modulation (SVPWM). The particle swarm optimization (PSO) algorithm is used to methodically tune the MPC cost function weights for minimization, while achieving a balance between output current tracking, stabilization of the neutral-point voltage, and, consequently, a uniform distribution of thermal stress. The proposed SVPWM-MPC algorithm selects optimal switching states, which are then utilized in a chip-level loss model coupled with a Cauer RC thermal network to predict transient chip-level junction temperatures dynamically. The proposed framework is executed in MATLAB R2024b and validated with experiments, and the SemiSel industrial thermal simulation tool, demonstrating both control effectiveness and accuracy of the electro-thermal model. The results demonstrate that the proposed control method can sustain neutral-point voltage imbalance of less than 0.45% when operating at 25% load and approximately 1% under full load working conditions, while accomplishing a uniform junction temperature profile in all inverter legs across different working conditions. Moreover, the results indicate that the proposed control and modeling structure is an effective and common-sense way to perform coordinated electrical and thermal management, effectively allowing for predesign and reliability testing of high-power TNPC inverters. Full article

High-voltage fuses are critical safety components in electric vehicle (EV) battery systems, yet their thermal behavior under charging currents remains insufficiently characterized. This study develops and validates a physics-based thermal resistor-capacitor (RC) network model of a high-voltage melting fuse, accounting for copper elements, quartz sand filling, and polyester casing. Experimental accelerated life tests and current step load profiles were performed in a climate chamber at 70 °C, with temperature measurements at the fuse terminals. The RC model was constructed using material properties and geometry-derived parameters, including three copper element sections, one quartz sand node, and one case node. A discretized state–space formulation was implemented to simulate the transient thermal behavior. The results reveal distinct dynamic and stationary characteristics, with thermal time constants varying strongly between fuse sections. Comparisons with experimental data demonstrate that the proposed model captures both rise time and steady-state behavior, with deviations attributable to contact resistances and parasitic effects. The findings highlight that charging currents in practical profiles typically remain below 50% of fuse current ratings, leaving optimization potential for higher permissible currents, faster charging, and reduced downtime while maintaining safety. The outcome of this model is highly relevant for lifetime prediction models. Full article

Accurately simulating fuses is challenging because the fuse behavior is affected by a variety of thermal and electrical factors. This paper presents a SPICE fuse model and its parameterization procedure. The model mimics the physical behavior of the time–current characteristic including the transition region. For the parameterization only, the time–current characteristic of the fuse, its resistance at room temperature and the melting temperature of the conducting material are needed. The novelty of this SPICE fuse model is the mathematical derivation of a physically based correction factor that considers the temperature dependence of the electrical fuse conductivity. The correction factor is applied to the inverted time–current characteristic. A third-order Foster thermal equivalent network is fitted to the adapted fuse characteristic using a least square algorithm. After a Foster–Cauer transformation, the thermal equivalent network is integrated into the SPICE model. Exemplary LTSpice is used to show and validate the model’s wiring diagram. Comparisons show a very good agreement with data sheet characteristics for a variety of fuse types and current ratings. In the adiabatic and transition region—i.e., at low tripping times—the maximum relative error between the data sheet characteristic and the simulated characteristic was consistently below 15% and thus within the production parameter spread. Full article

Due to the rise of e-mobility applications, there is an increased demand to create more accurate control methods, which can reduce the loss in an e-drive system. The accurate modeling of the rotating machines needs to resolve a partial differential equation system that describes the thermal and mechanical behavior of the different parts in addition to the electromagnetic design. Due to these models’ limited resources and high computation demand, they cannot be used directly for real-time control. Model order reduction methods have been of growing interest in the past decades and offer solutions for this problem. According to the processed literature, many model order reduction-based methods are used for a wide range of problems. However, a paper has not been published that discusses a model order reduction-based real-time control model that is actually used in the industry. This paper aims to summarize and systematically review the model order reduction methods developed for rotating electrical machines in the last two decades and examine the possible usage of these methods for a real-time control problem. Full article

Accurate fault simulation and failure prediction have long been challenges for SiC MOSFETs users. This paper presents a behavior model of Silicon Carbide (SiC) double-implanted MOSFET (DMOSFET), considering thermal-runaway failures in short-circuit and avalanche breakdown faults on the basis of cell-level physical processes. The proposed model can simulate the faults with extremely high accuracy and precisely predict SiC DMOSFET’s short-circuit withstand time and critical avalanche energy. By finite-element simulations, cell-level physical processes of short-circuit and avalanche breakdown faults are clarified. The mechanisms of thermal-runaway failures are deeply discussed with references to existing studies. Based on semiconductor and device physics mechanisms, the proposed model is constructed upon a traditional behavior model of SiC MOSFET with several parallel branches that are proposed to describe the thermal-runaway failures during both faults. The Cauer thermal network model is used for estimating junction temperature within it. The proposed model is constructed in Simulink, and it is validated using short-circuit and unclamped inductive switching (UIS) tests. Full article

Under the operating conditions of high power and high switching frequency, an insulated gate bipolar transistor (IGBT) chip can produce relatively large power loss, causing the junction temperature to rise rapidly; consequently, the reliability of the IGBT module can be seriously affected. Therefore, it is necessary to accurately predict the junction temperature of the IGBT chip. The resistance capacitance (RC) thermal network model is a commonly used method for IGBT junction temperature prediction. In this paper, the model parameters are obtained by two methods to establish the Cauer thermal network models of the IGBT module. The first method is to experimentally obtain the transient thermal impedance curve of the IGBT module and the structure function and then extract the individual thermal parameters of the Cauer thermal network model; the second method is to obtain the thermal parameters of the thermal network model directly by using theoretical formulas that consider the influence of the heat spreading angle. The predicted junction temperatures of the Cauer thermal network models established by the two methods are compared with the junction temperatures obtained from infrared (IR) measurements during the power cycling test, the junction temperatures measured by the temperature-sensitive electrical parameter (TSEP) method, and the junction temperatures calculated by finite element (FE) analysis. Additionally, the Cauer thermal network models established by the two methods are compared and verified. The results indicate that the Cauer thermal network model established based on theoretical formulas can accurately predict the maximum junction temperature of the IGBT chip, and the calculated temperature for each layer, from the IGBT chip layer to the ceramic layer, also accords well with the FE results. The Cauer thermal network model established based on the experimental test and the structure function can accurately predict the average junction temperature of the IGBT chip. Full article

A generalized structure for implementing fractional-order controllers is introduced in this paper. This is achieved thanks to the consideration of the controller transfer function as a ratio of integer and non-integer impedances. The non-integer order impedance is implemented using RC networks, such as the Foster and Cauer networks. The main offered benefit, with regards to the corresponding convectional implementations, is the reduced active and, also, passive component count. To demonstrate the versatility of the proposed concept, a controller suitable for implementing a cardiac pacemaker control system is designed. The evaluation of the performance of the system is performed through circuit simulation results, using a second-generation voltage conveyor as the active element. Full article

Network identification by deconvolution is a proven method for determining the thermal structure function of a given device. The method allows to derive the thermal capacitances as well as the resistances of a one-dimensional thermal path from the thermal step response of the device. However, the results of this method are significantly affected by noise in the measured data, which is unavoidable to a certain extent. In this paper, a post-processing procedure for network identification from thermal transient measurements is presented. This so-called optimization-based network identification provides a much more accurate and robust result compared to approaches using Fourier or Bayesian deconvolution in combination with Foster-to-Cauer transformation. The thermal structure function obtained from network identification by deconvolution is improved by repeatedly solving the inverse problem in a multi-dimensional optimization process. The result is a non-diverging thermal structure function, which agrees well with the measured thermal impedance. In addition, the associated time constant spectrum can be calculated very accurately. This work shows the potential of inverse optimization approaches for network identification. Full article

The Cole–Davidson function is an efficient tool for describing the tissue behavior, but the conventional methods of approximation are not applicable due the form of this function. In order to overcome this problem, a novel scheme for approximating the Cole–Davidson function, based on the utilization of a curve fitting procedure offered by the MATLAB software, is introduced in this work. The derived rational transfer function is implemented using the conventional Cauer and Foster RC networks. As an application example, the impedance model of the membrane of mesophyll cells is realized, with simulation results verifying the validity of the introduced procedure. Full article

State-of-the-art methods for determining thermal impedance networks for IGBT (Insulated Gate Bipolar Transistor) modules usually involves the establishment of the relationship between the measured transistor or diode voltage and temperature under homogenous temperature distribution across the IGBT module. The junction temperature is recomputed from the established voltage–temperature relationship and used in determining the thermal impedance network. This method requires accurate measurement of voltage drop across the transistors and diodes under specific designed heating and cooling profiles. Validation of the junction temperature is usually done using infrared camera or sensors placed close to the transistors or diodes (in some cases and open IGBT module) so that the measured temperature is as close to the junction as possible. In this paper, we propose an alternative method for determining the IGBT thermal impedance network using the principles of least squares. This method uses measured temperatures for defined heating and cooling cycles under different cooling conditions to determine the thermal impedance network. The results from the proposed method are compared with those obtained using state-of-the-art methods. Full article

At present, optical fiber microducts are joined together by mechanical type joints. Mechanical joints are bulky, require more space in multiple duct installations, and have poor water sealing capability. Optical fiber microducts are made of high-density polyethylene which is considered best for welding by remelting. Mechanical joints can be replaced with welded joints if the outer surface layer of the optical fiber microduct is remelted within one second and without thermal damage to the inner surface of the optical fiber duct. To fulfill these requirements, an electro-thermal model of Joule heat generation using a copper coil and heat propagation inside different layers of optical fiber microducts was developed and validated. The electro-thermal model is based on electro-thermal analogy that uses the electrical equivalent to thermal parameters. Depending upon the geometric shape and material properties of the high-density polyethylene, low-density polyethylene, and copper coil, the thermal resistance and thermal capacitance values were calculated and connected to the Cauer RC-ladder configuration. The power input to Joule heating coil and thermal convection resistance to surrounding air were also calculated and modelled. The calculated thermal model was then simulated in LTspice, and real measurements with 50 µm K-type thermocouples were conducted to check the validity of the model. Due to the non-linear transient thermal behavior of polyethylene and variations in the convection resistance values, the calculated thermal model was then optimized for best curve fitting. Optimizations were conducted for convection resistance and the power input model only. The calculated thermal parameters of the polyethylene layers were kept intact to preserve the thermal model to physical structure relationship. Simulation of the optimized electro-thermal model and actual measurements showed to be in good agreement. Full article

There is a growing demand in the semiconductor industry to integrate many functionalities on a single portable device. The integration of sensor fabrication with the mature CMOS technology has made this level of integration a reality. However, sensors still require calibration and optimization before full integration. For this, modeling and simulation is essential, since attempting new, innovative designs in a laboratory requires a long time and expensive tests. In this manuscript we address aspects for the modeling and simulation of semiconductor metal oxide gas sensors, devices which have the highest potential for integration because of their CMOS-friendly fabrication capability and low operating power. We analyze recent advancements using FEM models to simulate the thermo-electro-mechanical behavior of the sensors. These simulations are essentials to calibrate the design choices and ensure low operating power and improve reliability. The primary consumer of power is a microheater which is essential to heat the sensing film to appropriately high temperatures in order to initiate the sensing mechanism. Electro-thermal models to simulate its operation are presented here, using FEM and the Cauer network model. We show that the simpler Cauer model, which uses an electrical circuit to model the thermo-electrical behavior, can efficiently reproduce experimental observations. Full article

This paper proposes a novel method for optimizing the Cauer-type thermal network model considering both the temperature influence on the extraction of parameters and the errors caused by the physical structure. In terms of prediction of the transient junction temperature and the steady-state junction temperature, the conventional Cauer-type parameters are modified, and the general method for estimating junction temperature is studied by using the adaptive thermal network model. The results show that junction temperature estimated by our adaptive Cauer-type thermal network model is more accurate than that of the conventional model. Full article