Junction Temperature Estimation Research Articles

Analysis of errors in junction temperature estimated by temperature-sensitive electrical parameter for parallel-connected SBDs

Abstract Multi-chip topology is widely adopted for large rated current power modules. Thermal management of multi-chip power modules has a significant impact on their long-term reliability. The transient thermal network model used for thermal management relies on the time response of junction temperature, which is estimated using temperature-sensitive electrical parameters (TSEPs) derived from the temperature dependency of I-V characteristics in power devices. However, the parallel connection of power devices complicates the junction temperature estimation because the sensing current is shared unevenly due to differences in the I-V characteristics of individual devices and the temperature gradient within the power module package. Consequently, the temperature estimated using a unified TSEP does not accurately represent the temperature difference among parallel-connected power devices. This paper investigates an error in the junction temperature estimated by using unified static TSEP for parallel-connected silicon carbide (SiC) Schottky barrier diodes (SBDs) as the power device. The results reveal that the unified TSEP underestimates the maximum temperature in multi-chip power modules. However, the semi-physical model of power devices enables us to estimate the junction temperature of each SiC SBD when we measure shared currents for each device.

IGBT Junction Temperature Estimation Based on External Parameters of Multiple Drive Devices in Practical Industrial Scenario

ABSTRACTThe insulated gate bipolar transistor (IGBT) is one of the most important power semiconductor devices in power electronics and is also prone to failure. High junction temperature and junction temperature fluctuation of IGBT are the main causes of IGBT module aging failure. The high‐precision monitoring of the junction temperature of the IGBT module is a prerequisite for IGBT life prediction, which is crucial for reducing maintenance costs and improving equipment reliability. Therefore, an IGBT junction temperature estimation method based on long short‐term memory (LSTM) neural network and sliding window estimation model is proposed and applied in practical industrial scenarios. This method uses the operating data of the motor drive device in the actual industrial application scenario as the training and test data set and uses the external operating parameters of the IGBT module to estimate the junction temperature of the IGBT module. Compared with the internal operating parameters of the IGBT module based on switching transient, the external operating parameters are easier to collect and process, and more suitable for practical application scenes. A sliding window estimation model is proposed to estimate the junction temperature of the IGBT module. Compared with the point‐to‐point estimation method, the sliding window estimation method can capture the influence of historical operation data better and has a higher capability of time series data estimation. The IGBT junction temperature estimation of sliding windows is realized by the LSTM neural network, which is more suitable for time series estimation in real industrial scenarios. The experimental results show that the estimation accuracy of the sliding window estimation method is better than that of the point‐to‐point estimation method, and the accuracy of the sliding window estimation method based on LSTM is better than that of the sliding window estimation method based on other machine learning models. It proves that the proposed method can better capture the dynamic process of the system and has higher estimation accuracy.

A Gate Degradation Independent Online Junction Temperature Estimation Method for SiC MOSFETs Based on Residual Resistance

A Gate Degradation Independent Online Junction Temperature Estimation Method for SiC MOSFETs Based on Residual Resistance

Thermal management implementation method for IGBT modules of inverters based on junction temperature estimation

Thermal management implementation method for IGBT modules of inverters based on junction temperature estimation

A Simple Thermal Model for Junction and Hot Spot Temperature Estimation of 650 V GaN HEMT during Short Circuit

Temperature is a critical parameter for the GaN HEMT as it sharply impacts the electrical characteristics of the device more than for SiC or Si MOSFETs. Either when designing a power converter or testing a device for reliability and robustness characterizations, it is essential to estimate the junction temperature of the device. For this aim, manufacturers provide compact models to simulate the device in SPICE-based simulators. These models provide the junction temperature, which is considered uniform along the channel. We demonstrate through two-dimensional numerical simulations that this approach is not suitable when the device undergoes high electrothermal stress, such as during short circuit (SC), when the temperature distribution along the channel is strongly not uniform. Based on numerical simulations and experimental measurements on a 650 V/4 A GaN HEMT, we derived a thermal network suitable for SPICE simulations to correctly compute the junction temperature and the SC current, even if not providing information about the possible failure of the device due to the formation of a local hot spot. For this reason, we used a second thermal network to estimate the maximum temperature reached inside the device, whose results are in good agreement with the experimental observed failures.

Reliability evaluation of IGBT power module on electric vehicle using big data

There are challenges to the reliability evaluation for insulated gate bipolar transistors (IGBT) on electric vehicles, such as junction temperature measurement, computational and storage resources. In this paper, a junction temperature estimation approach based on neural network without additional cost is proposed and the lifetime calculation for IGBT using electric vehicle big data is performed. The direct current (DC) voltage, operation current, switching frequency, negative thermal coefficient thermistor (NTC) temperature and IGBT lifetime are inputs. And the junction temperature (T j) is output. With the rain flow counting method, the classified irregular temperatures are brought into the life model for the failure cycles. The fatigue accumulation method is then used to calculate the IGBT lifetime. To solve the limited computational and storage resources of electric vehicle controllers, the operation of IGBT lifetime calculation is running on a big data platform. The lifetime is then transmitted wirelessly to electric vehicles as input for neural network. Thus the junction temperature of IGBT under long-term operating conditions can be accurately estimated. A test platform of the motor controller combined with the vehicle big data server is built for the IGBT accelerated aging test. Subsequently, the IGBT lifetime predictions are derived from the junction temperature estimation by the neural network method and the thermal network method. The experiment shows that the lifetime prediction based on a neural network with big data demonstrates a higher accuracy than that of the thermal network, which improves the reliability evaluation of system.

A Behavior Model of SiC DMOSFET Considering Thermal-Runaway Failures in Short-Circuit and Avalanche Breakdown Faults

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.

IGBT Junction Temperature Extraction via Voltage Integral over Voltage Rise Period

In this paper, a temperature sensitive electrical parameter (TSEP) method for insulated gate bipolar transistor (IGBT) modules junction temperature estimation based on voltage integral over-voltage rise period (VIVRP) is proposed. Firstly, combined with the principles of thermodynamic physics, the movement characteristics of internal carriers and holes are discussed, and the waveform of voltage and current during the turn-off process is studied. The feasibility of using voltage integral overvoltage period instead of voltage rise loss in junction temperature measurement is discussed and proved. At the beginning of the turn-off process, the holes in the IGBT almost remain in spatial and temporal distribution (the collector current IC remains constant), which leads the VIVRP to a cheaper TSEP method for junction temperature detection, without the high-frequency current detection equipment. Finally, the junction temperature model based on VIVRP and the extraction circuit of VIVRP are proposed. In addition, it is compared with the junction temperature detection method based on the voltage rise time and voltage rise loss. The results show that the junction temperature detection method based on VIVRP proposed in this paper is detection with high precision and low cost.

An Online Junction Temperature Estimation Method of IGBTs based on the improved on-state voltage measuring circuit

An Online Junction Temperature Estimation Method of IGBTs based on the improved on-state voltage measuring circuit

Coordinated Online Junction Temperature Estimation of MOSFETs and Antiparallel Diodes in Three-Phase SiC Inverters

Coordinated Online Junction Temperature Estimation of MOSFETs and Antiparallel Diodes in Three-Phase SiC Inverters

Active Thermal Control of IGBT Modules Based on Finite-Time Boundedness.

One of the most important causes of the failure of power electronic modules is thermal stress. Proper thermal management plays an important role in more reliable and cost-effective energy conversion. In this paper, we present an advanced active thermal control (ATC) strategy to reduce a power device's thermal stress amplitude during operation, with the aim of improving the reliability and lifetime of the conversion system. A state-space model based on a Foster-type thermal model is developed to achieve junction temperature estimation in real time. A feedback controller based on finite-time boundedness (FTB) is proposed to precisely regulate the temperature in order to reduce the thermal stress according to the temperature profile. The designed controller permits the precise control of the temperature and strongly reduces the thermal stress during fast transients in the power demand. Simulation and experimental results are provided to validate the effectiveness of the proposed method.

Thermo-sensitive electrical parameters of high-power IGBTs based on gate-emitter voltage measurement during switching delay intervals

High-power insulated gate bipolar transistors (IGBTs) are widely used for wind power conversion and electric traction. Accurate estimation of IGBT junction temperature and active thermal control improve the reliability of power converters in such applications. Among the various available methods for junction temperature estimation, thermo-sensitive electrical parameter (TSEP) based estimation is simple, fast and amenable to real-time implementation. Evaluation of many TSEPs (e.g. forward voltage, threshold voltage, peak dv/dt, peak di/dt etc.) require measurement of multiple electrical quantities in the power circuit and/or in the gate-drive circuit. The measurement systems need to have high precision and/or high bandwidth as well. Moreover, the quantities in the power circuit require isolated measurement systems. This paper explores possible new TSEPs, that could be obtained through a single measurement of low-voltage quantity, namely, the gate-emitter voltage. Since the gate-emitter voltage varies primarily during turn-on and turn-off delay intervals, these switching intervals and their parameters are studied over a wide range of operating conditions. Since many practical converters are expected to operate at an ambient temperature as low as −35 °C, the experimental study is carried out over a wide temperature range from −35 °C to 125 °C. Further, to ensure wider applicability of the findings of the study, four IGBTs of different makes are considered for the experimental investigations. The experimental results bring out four different parameters pertaining to turn-on delay and another four parameters pertaining to turn-off delay, which are potential TSEPs. Of these eight TSEPs, five of them can be evaluated based on measurement of gate-emitter voltage only, and four of these have not been reported in the literature previously. In addition, the experimental data are useful for development of temperature-sensitive device models for simulation, performance assessment and design of gate-drive circuit for different temperatures, and for temperature-sensitive compensation of inverter dead-time effect.

Precise estimation of dynamic junction temperature of SiC transistors for lifetime prediction of power modules used in three-phase inverters

Precise transient temperature measurement is fundamental to estimate lifetime of power modules, especially those made with SiC transistors having very low thermal capacitance. This paper shows a precise method to estimate dynamic temperature of SiC components composing a power module used in three-phase inverters. This method is based on the use of a fast thermal camera to precisely measure thermal impedance of each die. Results of thermal camera measurements are validated by comparison with classical on-state resistance measurements to estimate junction temperature. Thermal impedance model is then coupled with precise loss calculation in order to predict dynamic die temperature during operation in a three-phase inverter. Measurements of the temperature variation of SiC dies conducting typical currents of a 540 V/7.5 kW three-phase inverter for aeronautical applications show the accuracy of the developed model for dynamic junction temperature estimation.

A Novel Temperature Sensitive Electrical Parameter of IGBT Modules Without Involvement of Operating Parameters

The junction temperature estimation of the insulated gate bipolar transistor (IGBT) module is crucial for reliability assessment and health management of converters. However, most of the existing methods need to measure the collector-emitter voltage and collector current for the IGBT junction temperature estimation, which increases the cost and complexity of the monitoring and calibration process. In this article, a novel junction temperature monitoring method for IGBT modules without involving operating condition calibration based on the pre-overshoot of gate voltage is proposed. The gate pre-overshoot is influenced by the internal gate resistor, which has a linear relationship with the IGBT junction temperature. Besides, the gate pre-overshoot is only temperature dependent, while not affected by the collector-emitter voltage and collector current. Therefore, the gate pre-overshoot is a promising temperature-sensitive electrical parameter (TSEP). Because the collector-emitter voltage and collector current are not involved, the gate pre-overshoot-based method for junction temperature has the advantages of a simple signal monitoring structure, a simple calculation process, and a simple calibration process.

A Novel Junction Temperature Estimation Method Independent of Bond Wire Degradation for IGBT

The insulated-gate bipolar transistor (IGBT) junction temperature is crucial for condition monitoring, reliability assessment, and health management. However, the existing IGBT temperature monitoring methods are affected by the aging of IGBT bond wires. A novel junction temperature estimation method independent of bond wire degradation for IGBT is proposed in this paper. On-state voltage drop and turn-on gate voltage overshoot are analyzed for dependence on the junction temperature and bond wire degradation. These two temperature and bond wire monitoring electrical parameters are combined by the Artificial Neural Network (ANN), to eliminate the effect of bond wire degradation. A double pulse test platform is built to verify the influence of temperature and bond wire degradation on on-state voltage drop and turn-on gate voltage overshoot, which agrees with the theoretical analysis. Lots of experimental data at various collector-emitter voltages, load currents, IGBT junction temperature, and bond wire degradation are tested. A BP neural network is trained for junction temperature monitoring and its optimal number of hidden layers is studied. The experimental results show that the junction temperature estimation method based on the neural network can effectively eliminate the effect of bond wire degradation. The estimation error is around 4.219°C. The proposed method has the advantages of high accuracy and a wide application range for junction temperature monitoring.

Multiple Points Measurement-Based Junction Temperature Estimation of IGBT Module

The junction temperature ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T_j</i> ) estimation of IGBTs is critical for improving their reliability. To obtain <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T_j</i> , the existing reference point temperature (RPT) measurement based methods require the power loss information, which is difficult to obtain accurately in the real-time applications. Also, these methods utilise the thermal model of IGBT which changes with ageing, resulting in an error in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T_j</i> estimation. In this paper, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T_j</i> estimation technique is proposed which utilises the temperature of multiple leads/terminals of an IGBT module. Unlike the existing methods, the proposed technique does not require the information of power losses in the power semiconductor chips (PSCs). Instead, the power losses in the PSCs are additional outcome of the proposed technique. Also, even for a degraded IGBT module, the proposed technique provides an accurate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T_j</i> estimation. The proposed technique also has the capability of estimating <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T_j</i> of multiple PSCs in an IGBT module simultaneously. A mathematical analysis of leads selection for the proposed technique is presented in this paper. Furthermore, a comparative analysis is presented between the proposed and the conventional method for a degraded IGBT module. The simulation studies of the proposed technique are carried out in ANSYS ICEPAK software. The proposed technique is also validated on an experimental setup developed in the laboratory.

A Large-Scale Identification Approach for Thermal Parameters of Multichips IGBT Modules Based on LLSO-SQP Algorithm

This paper proposes a large-scale optimization approach for identifying thermal parameters of multi-chip IGBT modules. State-space equation, in which the coefficient matrix comprises the thermal resistance and capacitance, is provided to represent the compact three-dimensional thermal network model. Then, the level-based learning swarm optimization (LLSO) algorithm is firstly utilized to identify large-scale thermal parameters. Additionally, to solve the inefficient convergence problem, the optimization results obtained from the LLSO is provided as the initial value of the sequential quadratic programming (SQP) algorithm to find the global optimal solution. Hence, the proposed LLSO-SQP algorithm can identify the large-scale thermal parameters efficiently and accurately. The training dataset for the algorithm is derived from the transient temperature response of a finite element model (FEM) of the IGBT module under power step excitation. Since only one-time simulation is in-demand, this approach needs less computational effort than others. The identified thermal network model is utilized to estimate junction temperature profiles taking a two-level inverter as a case study. In comparison to that of the experiment and FEM, the results validate the feasibility and accuracy of the junction temperature estimation method based on the compact three-dimensional thermal network model.

Estimation of Both Junction Temperature and Load Current of IGBTs from Output Voltage of Gate Driver

Estimation of Both Junction Temperature and Load Current of IGBTs from Output Voltage of Gate Driver

Thermal Analysis and Junction Temperature Estimation under Different Ambient Temperatures Considering Convection Thermal Coupling between Power Devices

The convection thermal coupling between adjacent power devices in power converters is dependent on the ambient temperature. When the ambient temperature changes, the convection thermal coupling also changes. This results in an inaccurate thermal model that causes errors in the prediction of the thermal distribution and junction temperature based on a fixed ambient temperature for power devices in converters application. To solve this variable-ambient-temperature-related issue, a thermal coupling experiment for semiconductor power devices (the MOSFET and diode) was performed to discuss the influence of the thermal coupling effect between adjacent devices and the FEM (Finite Element Method) thermal models for the power devices considering the convection thermal coupling are established. Through these simulations, the junction temperatures of devices under different ambient temperatures were obtained, and the relationships between the junction temperature and ambient temperatures were established. Moreover, the junction temperatures of power devices under different ambient temperatures were calculated and temperature distributions are analyzed in this paper. This method shows a strong significance and has potential applications for high-efficiency and high-power density converter designs.

Online Junction Temperature Estimation Method for SiC MOSFETs Based on the DC Bus Voltage Undershoot

Silicon carbide (SiC) power devices are promising in industrial applications. Junction temperature monitoring is greatly significant for improving the reliability of devices and systems. However, state-of-the-art SiC MOSFET junction temperature estimation methods generally have drawbacks of low resolution, difficult measurement, and complicated installation. In this article, a novel junction temperature estimation for SiC MOSFETs based on the dc bus voltage undershoot is proposed. The correlation between junction temperature and current change rate is analyzed and verified by experiments. The current change rate of each SiC MOSFET can be measured via the dc bus voltage undershoot because of the stray inductance of the bus commutation loop. The undershoot of the dc bus voltage during the turn- <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small> transient displays a linear dependence on the junction temperatures of the corresponding SiC MOSFET. Hence, the dc bus voltage undershoot can be utilized for monitoring the junction temperatures of two SiC MOSFETs in the half-bridge. Besides, practical implementation and estimation errors are presented. The method proposed has high accuracy and resolution for junction temperature monitoring. Besides, the method proposed can reduce the complexity, size, and cost of the monitoring circuit. Moreover, the method proposed is noninvasive and easy to install. It is promising for SiC MOSFET junction temperature monitoring in practical applications.