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

This paper investigates efficiency gains achieved using an 800 V DC bus and wideband gap silicon carbide (SiC) semiconductors for a light-duty electric vehicle (EV), rather than an insulated-gate bipolar transistor (IGBT) inverter with a 400 V bus as is commonly used for EVs. Analytical inverter loss models with 600 V and 1200 V IGBTs, and 1200 V hybrid SiC and 1200 V All-SiC semiconductors are incorporated into a Chevrolet Bolt EV model and simulated over standard drive cycles. Battery pack voltage variations throughout the drive cycles, as well as variations in junction temperature, resulted in 16 to 27 % increased loss compared to fixed voltage and temperature assumptions. To validate the models, experimental testing was performed on a 1200 V IGBT inverter and a 1200 V SiC inverter both powering 160+ kW rated traction machines. Experimentally measured loss was typically within 100 W of the model, demonstrating its accuracy. Going from a 400 V to an 800 V DC bus with IGBTs, EV range was modeled to increase 1.2 %, while an 800 V bus and all SiC inverter results in a range increase of 5.0%. An empirical loss model fitted to measured inverter data shows the analytical model estimates range within 6 km.

Power modules including IGBTs (Insulated Gate Bipolar Transistor) are widely used in motor drivers. For the design optimization of the IGBT modules fitted with motor driving conditions, the IGBT junction temperature and the cooling conditions has become a major issue with the increase of the current density and the demand for downsized modules. Especially, estimation of worst-case junction temperature caused by a motor locked condition is necessary prior to the module design. In this paper, we propose a simple approach for dynamic junction temperature behavior of IGBTs PWM (Pulse Width Modulation) inverter modules. The dynamic electrothermal calculations are performed using familiar spreadsheet software. In this approach, the mathematical models of the temperature dependencies of the device losses and quasi-one- dimensional difference equations of thermal conduction were used to estimate the junction temperature of the module. To narrow down many device and module parameters in the initial stage of thermal design of the inverter modules, this proposed approach will be an effective tool.

The design of a power converter must guarantee that the operating junction temperatures thetavj of all devices do not exceed their limits under all specified operating conditions. Usually, this is ensured by a simulative or analytical junction temperature estimation based on simple electrical and thermal models and semiconductor datasheet values. This paper discusses the difficulties and quantifies the limitations of this approach on the example of a three-level neutral point clamped voltage source converter (NPC VSC) with insulated gate bipolar transistors. The calculations are compared to the results of direct junction temperature measurements with an infrared camera. The paper also provides the experimental proof for the unequal loss and junction temperature distribution in the three-level NPC VSC

Accurate junction temperature prediction in multichip insulated gate bipolar transistor (IGBT) modules is paramount for ensuring the reliability and efficiency of power electronic systems. Given the high integration density and intricate thermal coupling among multiple chips, developing an efficient method for junction temperature estimation is essential for IGBT power modules. This article presents a novel digital twin-based method for accurately monitoring junction temperature in multichip power modules. A digital twin modeling framework of IGBT testing is first presented. Subsequently, the current-sharing behavior in multichip power modules is analyzed and systematically modeled. Power loss calculations based on the current balancing are then performed, complemented by finite element simulations to derive the temperature distribution and heat-dissipation characteristics of the module. Leveraging this foundation, a convolutional neural network (CNN)-enhanced digital twin approach is proposed to achieve real-time temperature prediction. The effectiveness and accuracy of the proposed method are rigorously validated through a double-pulse test (DPT) platform and reactive aging experiments. The results demonstrate that this framework offers a highly efficient solution for real-time junction temperature monitoring and thermal management in multichip IGBT modules.

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.

Research on IGBT junction temperature model based on united-parameters

Insulated gate bipolar transistor (IGBT) module is the most widely used power electronic device in converters. Condition monitoring of IGBT is critical for avoiding sudden failures. Health management of converters based on accurate IGBT junction temperature is of great importance to reliable operation. However, the existing monitoring methods have some drawbacks, including low feasibility of online implementation, intrusiveness, and slow response. IGBT junction temperature has a strong influence on IGBT on-state voltage drop. A novel online IGBT junction temperature measurement method based on the on-state voltage drop is proposed in this article. This technique can monitor the on-state voltage drop of IGBT and extract IGBT junction temperature online. Besides, the influences of the measurement circuit temperature variations and IGBT load current variations are considered and compensated based on the off-state stage of the IGBT. Simulation and experimental results validate the feasibility of the proposed technique. The proposed method has advantages of fast response, low cost, and simple circuit structure for noninvasive online IGBT junction temperature monitoring.

On-state voltage is an important thermal parameter for insulated gate bipolar transistor (IGBT) modules. It is employed widely to predict failure in IGBT module bond wires. However, due to restrictions in work environments and measurement methods, it is difficult to ensure the measurement accuracy for the on-state voltage under practical working conditions. To address this problem, an on-state voltage separation strategy is proposed for the IGBT modules with respect to the influence of collector current (Ic) and junction temperature (Tj). This method involves the separation of the on-state voltage into a dependent part and two independent parts during the IGBT module bond wire prediction. Based on the proposed separation strategy, the independent parts in the failure prediction can be removed, making it possible to directly monitor the voltage variations caused by bond wire failure. The experimental results demonstrate that the proposed diagnosis strategy can accurately predict the bond wire failure stage in an IGBT module under different conditions.

In this paper, a technology computer-aided design (TCAD) model of a silicon carbide (SiC) insulated-gate bipolar transistor (IGBT) has been calibrated against previously reported experimental data. The calibrated TCAD model has been used to predict the static performance of theoretical SiC IGBTs with ultra-high blocking voltage capabilities in the range of 20-50 kV. The simulation results of transfer characteristics, IC-VGE, forward characteristics, IC-VCE, and blocking voltage characteristics are studied. The threshold voltage is approximately 5 V, and the forward voltage drop is ranging from VF = 4.2-10.0 V at IC = 20 A, using a charge carrier lifetime of τA = 20 μs. Furthermore, the forward voltage drop impact for different process dependent parameters (i.e., carrier lifetimes, mobility/scattering and trap related defects) and junction temperature are investigated in a parametric sensitivity analysis. The wide-range simulation results may be used as an input to facilitate high power converter design and evaluation. In this case, the TCAD simulated static characteristics of SiC IGBTs is compared to silicon (Si) IGBTs in a modular multilevel converter in a general high-power application. The results indicate several benefits and lower conduction energy losses using ultra-high voltage SiC IGBTs compared to Si IGBTs.

Capacitance and Insulated gate bipolar transistor (IGBT) junction temperature are two critical healthy parameters for modular multilevel converters (MMC) sub‐modules (SMs) condition monitoring. This paper proposes an integrated monitoring method for capacitance degradation and IGBT junction temperature variation. By measuring multiple characteristics of the same port voltage, the capacitance and the junction temperature can be obtained simultaneously, which greatly simplifies the measurement unit required for condition evaluation and realizes the submodule‐level condition monitoring at a lower cost. The capacitance is characterized by voltage changes during capacitor discharging and the junction temperature is monitored by capacitor voltage overshoot (peak value) during IGBT turn‐off. Theoretical analysis and experimental results verify the effectiveness of the proposed method and articulated the influence of load current, SM capacitor pre‐discharge voltage, and junction temperature. Moreover, an online data‐acquisition circuit for time‐specific capacitor voltage characterization is devised.

Chapter 14 - IGBT Applications: Defense

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.

In this paper the individual component lifetime of an insulated-gate bipolar transistor (IGBT) traction inverter driving an Interior Permanent Magnet Synchronous Motor (IPMSM) is assessed. The performance is compared for two different control strategies during an 1800s WLTC driving cycle. The first strategy is Indirect Field Oriented Control (IFOC) with Maximum-Torque-Per-Ampere (MTPA) and Maximum-Torque-Per-Voltage (MTPV) with sinusoidal pulse width modulation (SPWM). The second control strategy is a Direct Torque Model Predictive Control (DTMPC), specifically the Finite Control Set Optimal Switching Vector (FCS-OSV) MPC. A high-fidelity electro-thermal model of the inverter is used to calculate losses and junction temperatures of IGBTs and diodes. The proprietary physics-of-failure based reliability assessment tool (RELAT) is used to evaluate individual component's reliability percentile based on the junction temperature variation during a WLTC driving cycle. The MPC-controlled traction inverter shows 25.6% less losses, 1.2–1.77% lower junction temperatures, and 21% faster reliability degradation compared to the inverter with the IFOC.

This paper proposes a junction temperature estimation method for power devices such as Insulated Gate Bipolar Transistor (IGBT) using the characteristics of on-state voltage, on-current and junction temperature. In the proposed method, the junction temperature is estimated during the inverter operation without the loss calculation. In addition, the IGBT module with deterioration is estimated by the equivalent resistance among the wire bonds and the emitter metallization in order to reduce the estimation error of the junction temperature. Moreover, the experimental results show that the estimation of the junction temperature is confirmed during the inverter operation. Moreover, the estimation error of the junction temperature is reduced by the proposed estimation method.

The junction temperature of the insulated gate bipolar transistor (IGBT) modules is a vital parameter for the reliability of the power electronic system. For effective thermal management, it is necessary to estimate junction temperature in real time. However, existing thermal models have limitations to achieve fast temperature prediction for multichip IGBT modules. In this study, we investigate the real-time junction temperature prediction for multichip IGBT modules with low computational cost. According to the power module' structure, an average 2D thermal model with physical significance is proposed to predict the virtual junction temperature for multichip IGBT modules, which can be extracted from finite element model based on the least square method. To further improve the computational efficiency, a temperature predictor based on the staggered cycle-by-cycle calculation method is proposed and its prediction error under various extreme operation conditions is analyzed by enumeration method. Finally, the comprehensive experimental results verify the effectiveness of the proposed average thermal model and junction temperature prediction method.