IGBT (Insulated Gate Bipolar Translator) module service life prediction method and system based on running and non-running states by comprehensively considering electro-thermal-mechanical coupling effect

Abstract

The invention provides an IGBT (Insulated Gate Bipolar Translator) module service life prediction method and system based on running and non-running states by comprehensively considering an electric-thermal-mechanical coupling effect. According to the method, key parameters required by multi-model joint prediction are obtained through parameter identification based on input of a circulating temperature difference delta T, total thermal cycle times, total thermal mechanical stress and the like. The system fuses four types of life models, including an improved Coffin-Manson thermal cycle fatigue model, a physical life model based on thermal expansion stress, an Arrhenius temperature accelerated aging model and a running time damage model. In a device operation state, the system calculates junction temperature by using power loss and extracts temperature cycle characteristics through rain flow counting to realize multi-model weighted life prediction; time dependent losses are estimated based on a time-to-temperature sequence in a non-operating state. And finally evaluating the residual life of the IGBT through accumulated damage. The method can adapt to multiple working conditions, improves the life prediction precision, and is suitable for health monitoring and predictive maintenance of power electronic equipment.

Description

Technical Field

[0001] This invention belongs to the field of power semiconductor device lifetime prediction technology, and in particular, it is a method and system for predicting the lifetime of IGBT modules based on operating and non-operating states, which comprehensively considers the electro-thermal-mechanical coupling effect. It is applicable to health monitoring and predictive maintenance of power electronic equipment. Background Technology

[0002] Insulated-gate bipolar transistor (IGBT) modules are core components in power electronic devices, and their long-term reliability directly affects the overall system lifespan and operational safety. IGBT failures primarily stem from solder fatigue under temperature cycling and the accumulation of thermal stress in the packaging structure. Therefore, accurately predicting their lifespan is crucial for enabling predictive maintenance and reducing operational costs.

[0003] Currently, junction temperature calculation and lifetime assessment methods based on the electro-thermal coupling model have been extensively studied. For example, the existing technical literature "IGBT module junction temperature calculation method based on electro-thermal coupling model" (CN DOI:10.13234 / j.issn.2095-2805.2016.6.23) discloses a junction temperature prediction method based on the electro-thermal coupling model. By calculating the conduction loss and switching loss of IGBT and FWD diode, the electrical model of the device is established; then, the transient junction temperature change process of the device is solved by inputting the power loss into the thermal model. The system structure usually includes: loss calculation module, thermal resistance-capacitance network model, junction temperature simulation module and waveform extraction module. Its overall process is as follows: (1) calculate conduction and switching losses; (2) solve the temperature response through the equivalent thermal resistance-capacitance network (Rth–Cth); (3) obtain the junction temperature change waveform of IGBT and FWD; (4) analyze the influence of temperature fluctuation amplitude on device lifetime. The principle of this paper is to input electrical losses into a thermal model, obtain the temperature response using thermal impedance curves, and then estimate the lifetime based on the temperature cycle amplitude. Its advantages include a clear structure and simple modeling, making it suitable for predicting IGBT junction temperatures under general operating conditions. However, this existing technology still has the following limitations: It does not consider the coupling between thermo-mechanical stress and material fatigue mechanisms. The paper relies solely on the temperature cycle amplitude ΔT to estimate lifetime, failing to calculate the cumulative mechanical stress and strain of the solder layer, chip, and bonding wires, thus failing to accurately reflect the actual fatigue damage inside the package. It does not consider the changes in device operating parameters over time (such as current aging, duty cycle drift, and cooling performance degradation). In real-world applications, the current, duty cycle, and cooling effect of IGBTs degrade year by year, but the paper does not provide any age-related dynamic degradation model, resulting in an overly optimistic lifetime prediction. It does not consider the Arrhenius temperature acceleration effect (chemical aging). The paper does not include "temperature-activated aging mechanisms" such as chemical diffusion and bonding wire fatigue, leading to results that cannot reflect the shortened lifetime under long-term high-temperature conditions.

[0004] In addition, most existing studies only focus on damage accumulation during operation, ignoring the static aging of IGBTs caused by temperature and time during non-operational (such as storage or standby) states, resulting in systematic biases in the full life cycle assessment.

[0005] Therefore, there is an urgent need for a high-precision IGBT lifetime prediction method that can integrate multiple physical fields and multiple failure mechanisms and distinguish between operating and non-operating states, so as to improve the accuracy and engineering practicality of lifetime assessment in high-reliability applications such as new energy vehicles, photovoltaic inverters, and industrial frequency converters. Summary of the Invention

[0006] This invention addresses the shortcomings of existing IGBT lifetime prediction techniques based on electro-thermal coupling, which typically estimate fatigue life solely through junction temperature fluctuations ΔT, failing to accurately describe the actual failure mechanisms experienced by IGBTs during long-term operation. Specific problems include: neglecting thermomechanical stress and material strain caused by temperature cycling, leading to significant errors in lifetime calculations; failing to consider parameter degradation phenomena that occur after many years of IGBT operation, such as current drift, increased thermal resistance, and decreased heat dissipation capacity; inability to handle irregular temperature waveforms under complex operating conditions, making accurate identification of cycle counts difficult; lack of a temperature-activated aging model (Arrhenius model), failing to describe mechanisms such as interface diffusion and solder aging; and the prevalence of single-model approaches resulting in unstable predictions and poor applicability. Therefore, a comprehensive lifetime prediction technique is needed that simultaneously considers electro-thermal coupling, thermomechanical coupling, material fatigue, temperature aging, and long-term degradation to improve the accuracy and reliability of IGBT module lifetime prediction. Furthermore, existing research only considers damage during IGBT operation, neglecting the aging phenomena of IGBT devices over time.

[0007] This invention proposes a method and system for predicting the lifetime of IGBT modules based on multi-lifetime model coupling and comprehensively considering both the operating and non-operating states of the device.

[0008] The technical solution of the present invention is as follows: A method for predicting the lifetime of IGBT modules based on operating and non-operating states, comprehensively considering the electro-thermal-mechanical coupling effect, is characterized by adaptively switching and fusing multi-physics failure mechanism models to assess lifetime damage based on the identification of the IGBT module's operating state, including the following steps: S1. Real-time acquisition of electrical and environmental parameters of the IGBT module in the target system, wherein the electrical parameters include at least collector current and collector-emitter voltage, and the environmental parameters include at least ambient temperature; S2. Based on the electrical parameters, determine whether the IGBT module is in an operating state or a non-operating state; S3. If in operation, execute the operation state damage assessment process: S3.1: Calculate the instantaneous power loss of the IGBT module based on the electrical parameters; S3.2: Based on the instantaneous power loss and the ambient temperature, calculate the junction temperature time series curve of the IGBT module using a thermal network model; S3.3: Perform rainflow counting analysis on the junction temperature time series curve to extract the characteristic parameters of the effective thermal cycle that meet the amplitude threshold. The characteristic parameters include at least the cycle amplitude ΔT and the number of cycles; S3.4: Based on the characteristic parameters of the effective thermal cycle, perform damage calculations for the first physical model and the second physical model in parallel. First physical model: The Coffin-Manson fatigue model based on material fatigue mechanism, used to calculate the cyclic amplitude. Thermal fatigue damage caused by T D C Second physical model: A thermomechanical stress model based on the thermo-mechanical coupling mechanism, used to calculate the cyclic amplitude. Encapsulation thermal stress damage caused by T D s ; S3.5: The aforementioned thermal fatigue damage D C With the aforementioned thermal stress damage D s Based on the first preset weighting coefficient and Weighted fusion is performed to obtain the comprehensive damage of the operating state. D op ,in + =1; Step S4: If the system is in a non-operating state, execute the non-operating state damage assessment process: S4.1: Obtain the resting time of the IGBT module in the non-operation state and the corresponding ambient temperature sequence; S4.2: Based on the ambient temperature sequence and the settling time, perform damage calculations using the third and fourth physical models in parallel: The third physical model: The Arrhenius temperature-accelerated aging model based on chemical reaction kinetics calculates the chemical aging damage caused by the combined effects of temperature and time. D A ; Fourth physical model: Time-based damage model based on time-cumulative effect, calculating pure time-cumulative damage. D T ; S4.3: The chemical aging damageD A With the aforementioned time-accumulated damage D T Based on the second preset weighting coefficient and Weighted fusion is performed to obtain the comprehensive damage in the non-operational state. D non ,in =1; Step S5: Based on the linear cumulative damage criterion, synthesize the damage of the operating state. D op Combined damage with the non-operational state D non Accumulated to the total historical damage D total middle; Step S6: Determine the updated total historical cumulative damage. D total Has the preset failure threshold been reached? D fail If not reached, then based on the total historical cumulative damage... D total Calculate and output the predicted remaining lifespan of the IGBT module; if the lifespan is reached or exceeded, output a lifespan termination warning.

[0009] Furthermore, the thermal network model in S3.2 calculates the IGBT junction temperature using the following formula: in, Heat capacity; Thermal resistance to the junction shell; ; For power loss; The ambient temperature; This is the junction temperature.

[0010] Furthermore, the rainflow is used to extract the temperature cycle amplitude from the junction temperature sequence. Only the cycle amplitude greater than the preset threshold is retained as the effective thermal cycle.

[0011] Furthermore, in step S3.4, the Coffin-Manson fatigue model is used to calculate thermal fatigue damage. D C The formula is as follows: = in, The baseline lifetime cycle number, The fatigue index. This is a temperature-related correction factor. The device lifespan damage caused by one cycle is counted as 1, and the device fails.

[0012] Furthermore, in step S3.4, the thermomechanical stress model calculates transient thermal stress damage. D s The formula is as follows: in, The coefficient of thermal expansion is... Young's modulus, For thermal stress, To withstand the total thermal stress, The thermal stress damage caused by one cycle is considered as one unit, and the device will fail if the cumulative thermal stress damage reaches 1 unit.

[0013] Furthermore, in step 4.2, the Arrhenius temperature-accelerated aging model is used to calculate chemical aging damage. D A The formula is as follows: = in, To activate energy, Boltzmann's constant, For reference temperature, The recommended usage time is given by the manufacturer, and t is the idle time of the IGBT in non-operating state. To account for IGBT lifespan degradation during non-operational periods due to accelerated temperature changes, the Arrhenius accelerated aging model uses AF to reflect the impact of temperature variations (from the reference temperature T). ref The AF value represents the factor by which the aging rate is accelerated by increasing the temperature to the actual operating temperature (T). A larger AF value indicates a more significant acceleration of material aging due to temperature increase, resulting in greater lifespan damage (D) within the same timeframe. A The larger ) is; D A = • This represents the proportion of lifespan damage resulting from the temperature acceleration effect converted into the actual non-operational downtime t.

[0014] Furthermore, in step 4.2, the time-based damage model calculates the pure time-cumulative damage. D T The formula is as follows: in, To disregard the lifespan degradation of IGBTs in non-operating states due to accelerated temperature, This indicates the expected total lifespan of the IGBT module in a non-operating state (usually in hours).

[0015] Furthermore, the comprehensive lifetime damage is obtained by proportionally weighting the Coffin-Manson damage and stress damage under operating conditions, and the temperature-accelerated damage and time damage under non-operating conditions, and the comprehensive damage is limited to no more than a preset upper limit to avoid numerical divergence.

[0016] Furthermore, the first preset weighting coefficient , With the second preset weighting coefficient , It can be a fixed value, or a variable parameter that is dynamically adjusted by an adaptive algorithm based on at least one of the historical degradation data of the IGBT module, the current working stress level, and the batch characteristics of the material.

[0017] Furthermore, it also includes step S7: periodically repeating steps S1 to S6, and establishing the historical cumulative damage total of the IGBT module. D total The degradation trajectory changes over time; based on the degradation trajectory, a time series prediction model or a machine learning model is used to extrapolate and predict future lifespan decline trends.

[0018] Second, the present invention also provides an IGBT module lifetime prediction system, characterized in that it is used to implement the above-mentioned lifetime prediction method, the system comprising: The data acquisition module is used to acquire the electrical and environmental parameters of the IGBT module in the target system in real time. The electrical parameters include at least the collector current and the collector-emitter voltage, and the environmental parameters include at least the ambient temperature. The state recognition and scheduling module is connected to the data acquisition module and is used to determine whether the IGBT module is in a running state or a non-running state based on the electrical parameters, and generate corresponding model scheduling instructions. A runtime state damage calculation module, connected to the state identification and scheduling module, is configured to start in response to a runtime state scheduling command, and includes: - A power loss and junction temperature calculation unit is used to calculate power loss based on the electrical parameters and to calculate junction temperature time-series curves based on the thermal network model and the ambient temperature. - Rainflow counting and feature extraction unit, used to analyze the junction temperature time series curve and extract characteristic parameters of the effective thermal cycle; - The first parallel model computation unit includes a Coffin-Manson model computation subunit for calculating thermal fatigue damage and a thermomechanical stress model computation subunit for calculating thermal stress damage; - The first weighted fusion unit is used to fuse the thermal fatigue damage and thermal stress damage according to the first preset weight, and output the comprehensive damage of the operating state. A non-operational state damage calculation module, connected to the state identification and scheduling module, is configured to start in response to a non-operational state scheduling command, and includes: - Environmental data recording unit, used to acquire and record the resting time and ambient temperature sequence of the IGBT module in non-operation state; - The second parallel model computation unit includes an Arrhenius model computation subunit for calculating chemical aging damage and a time damage model computation subunit for calculating pure time-cumulative damage. - The second weighted fusion unit is used to fuse the chemical aging damage and the pure time-accumulated damage according to the second preset weight, and output the comprehensive damage in the non-operational state. The damage fusion and accumulation module is connected to the operating state damage calculation module and the non-operating state damage calculation module, respectively, and is used to: receive and accumulate the comprehensive damage in the operating state and the comprehensive damage in the non-operating state, and update the total historical accumulated damage; compare the total historical accumulated damage with a preset failure threshold; and calculate and output the remaining life prediction value or life termination warning signal of the IGBT module based on the comparison result.

[0019] Compared with the prior art, the beneficial effects of the present invention are as follows: By constructing a life modeling system that separates operating and non-operating states, and introducing the Coffin-Manson model, thermomechanical stress model, Arrhenius temperature acceleration model, and time-related damage model in each state, the accurate characterization of IGBT's real-world operating characteristics throughout its entire lifecycle is achieved. Through parameter identification and weight fusion of multiple life mechanisms, this invention can simultaneously reflect multi-dimensional degradation factors such as temperature cycling fatigue, thermal stress accumulation, long-term thermal aging, and static degradation, resulting in a significant improvement in life prediction accuracy compared to traditional single-model methods.

[0020] The model selection mechanism based on operating state identification effectively avoids the problem of ignoring damage during non-operational stages, and solves the defect of large lifetime estimation deviation caused by relying solely on operating temperature cycling in existing technologies. Meanwhile, this invention employs the Miner method to uniformly accumulate operating and non-operational damage, making the prediction results more consistent with the degradation patterns of devices in practical applications.

[0021] The method of this invention can significantly reduce the life prediction error under long-term operating conditions, improve the stability of life assessment by approximately 15% to 30%, and identify aging trends in advance, supporting the optimization of maintenance strategies. This invention features high reliability, high adaptability, and engineering feasibility, and is suitable for health monitoring and predictive maintenance needs in high-reliability power electronic equipment such as electric drives for new energy vehicles, photovoltaic inverters, and industrial frequency converters. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the multi-model parameter identification process in the IGBT lifetime prediction method of the present invention. Figure 2 This is a schematic diagram of the overall architecture and working status judgment process of the IGBT lifetime prediction system of the present invention. Figure 3 This is a schematic diagram of the lifespan trend prediction module based on historical data of the present invention. Figure 4 This is a prediction curve showing the change in the remaining lifespan of the IGBT under different load conditions as a function of operating time.

[0023] Figure 5 This is a flowchart of the closed-loop lifetime prediction method based on dynamic state discrimination of the present invention. Detailed Implementation

[0024] The implementation of the IGBT module lifetime prediction method of the present invention will be described in detail below with reference to the accompanying drawings and embodiments.

[0025] This embodiment uses an IGBT module used in a certain energy storage converter (PCS) system as an example to demonstrate the application process of this method in actual engineering.

[0026] In this energy storage system, the PCS consists of multiple cascaded H-bridges, each containing multiple IGBT power devices. During long-term operation, the system experiences frequent charge-discharge switching, power level jumps, and ambient temperature fluctuations, leading to significant thermal cycling fatigue, thermal stress accumulation, and long-term aging of the IGBTs. The data provided in the actual competition included PCS output current, bus voltage, operating temperature, switching frequency, duty cycle, and historical measurements from multiple operating cycles.

[0027] As shown in Figure 2, this embodiment specifically includes the following steps: (1) Working status identification Based on the real-time current and power change trends of the PCS, determine whether the IGBT is in operation. In operation, the current changes frequently and the power density is high; in non-operation, the current is approximately zero or remains static.

[0028] (2) Temperature calculation and heat cycle extraction Using the provided current, duty cycle, and switching frequency, conduction and switching losses are calculated through an IGBT loss model, and the IGBT junction temperature curve is obtained using an RC thermal model. Subsequently, the effective temperature cycling parameters, including cycle amplitude ΔT, average temperature Tm, and number of cycles N, are extracted from the junction temperature sequence using the rainflow counting method.

[0029] (3) Calculation of life damage under operating conditions The system calls the improved Coffin–Manson model and the thermomechanical stress model respectively, and calculates fatigue damage according to ΔT and the thermal stress formula; then it applies weights w1 and w2 to fuse the two types of damage to obtain the comprehensive cyclic damage during the operation phase.

[0030] (4) Calculation of life damage under non-operational conditions When the PCS is idle, the Arrhenius temperature-accelerated aging model and time-based damage model are invoked to estimate the aging loss during long periods of dormancy based on ambient temperature and static time. This damage is then fused using weights w3 and w4 to output the non-operational phase damage.

[0031] (5) Life accumulation and remaining life prediction The system transmits the aforementioned damage in both operating and non-operating states to the cumulative damage module, which accumulates the multi-stage operating data of the PCS. When the cumulative damage reaches the device threshold, the system outputs the remaining lifetime and degradation trend of the IGBT.

[0032] As shown in Figure 1, this invention first obtains the IGBT's cycle temperature difference ΔT, the number of cycles, and the total stress information calculated from material properties and the coefficient of thermal expansion. Based on these input parameters, the system performs parameter identification, obtaining parameters for the Coffin-Manson thermal cycling fatigue model and the Thermo-Mechanical Stress model based on the material's elastic modulus and thermal expansion. This step enables the lifetime model to adapt to actual changes in different operating environments and the aging characteristics of different devices, improving the model's universality and computational accuracy.

[0033] As shown in Figure 3, the system records the cumulative damage and operating time of the IGBT during multiple operating stages, including junction temperature changes during stable operation, cyclic fatigue damage, and long-term degradation trends. The system stores this historical data as a lifetime estimation sample set and uses machine learning algorithms (such as regression models, time series models, or deep learning models) to predict future lifetimes. This module can utilize long-term operating data to mine aging patterns, making the prediction results closer to the actual degradation patterns. It is particularly suitable for applications with complex operating scenarios such as fluctuating operating conditions, increasing thermal resistance, and current drift.

[0034] Figure 4 shows the predicted trend curves of IGBT remaining lifetime as a function of operating time under different load conditions. The horizontal axis represents the number of years of operation, and the vertical axis represents the percentage of remaining device lifetime. The figure shows the lifetime degradation under three typical operating conditions: light load, medium load, and heavy load. As can be seen from Figure 4, under light load conditions, the temperature cycling amplitude of the IGBT is small, the thermal stress is low, and the lifetime degradation rate is the slowest, maintaining about 95% of the remaining lifetime after 10 years of operation. Under medium load conditions, with the increase of operating time, the temperature cycling and stress levels increase significantly, resulting in a moderate rate of lifetime degradation, with about 80% of the remaining lifetime after 10 years. Under heavy load conditions, the large thermal cycling and high junction temperature cause fatigue damage to accumulate rapidly, resulting in the most significant lifetime reduction, with only about 40% of the lifetime remaining after 10 years. Figure 4 visually demonstrates that the lifetime prediction method proposed in this invention can distinguish the impact of different operating conditions on the device degradation rate, accurately reflect the contribution of factors such as working stress, temperature cycling, and long-term aging to lifetime, and provide a scientific basis for the reliability assessment of IGBTs in scenarios such as energy storage systems and converters.

[0035] like Figure 5 As shown, a closed-loop lifetime prediction method based on dynamic state discrimination is proposed. Starting from parameter initialization, the system adaptively switches lifetime assessment paths by real-time monitoring of the IGBT module's operating status: in the operating state, the system integrates a multiphysics model to calculate electro-thermal-mechanical coupling damage; in the non-operating state, it quantifies material degradation under environmental stress based on a time-based damage model. The damage values ​​output from the two paths are integrated in a damage accumulation module to form a complete lifetime consumption profile. Based on the accumulated damage and real-time status, the system further conducts adaptive future lifetime prediction and achieves closed-loop iterative updates through a "whether to stop monitoring" decision point. This process fully considers the complex scenario of alternating operating and non-operating states of IGBTs in practical applications, combining offline models with online monitoring to achieve dynamic lifetime assessment covering all operating conditions and the entire life cycle. It is particularly suitable for industrial applications such as new energy equipment and rail transit, which involve frequent start-stop cycles, load fluctuations, and long standby times.

[0036] The method of this invention can significantly improve the accuracy of lifetime prediction. Using this method, potential IGBT degradation risks can be identified in advance in PCS energy storage scenarios with large variations in cyclic operating conditions and frequent temperature shocks, providing a reliable basis for predictive maintenance in practical engineering.

[0037] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various modifications or variations within the scope of the claims, which do not affect the essence of the present invention.

Claims

1. A method for predicting the lifetime of an IGBT module based on operating and non-operating states, comprehensively considering the electro-thermal-mechanical coupling effect, characterized in that, The method, based on the identification of the IGBT module's operating state, adaptively switches and fuses multi-physics failure mechanism models for life damage assessment, and includes the following steps: S1. Real-time acquisition of electrical and environmental parameters of the IGBT module in the target system, wherein the electrical parameters include at least collector current and collector-emitter voltage, and the environmental parameters include at least ambient temperature; S2. Based on the electrical parameters, determine whether the IGBT module is in an operating state or a non-operating state; S3. If in operation, execute the operation state damage assessment process: S3.1: Calculate the instantaneous power loss of the IGBT module based on the electrical parameters; S3.2: Based on the instantaneous power loss and the ambient temperature, calculate the junction temperature time series curve of the IGBT module using a thermal network model; S3.3: Perform rainflow counting analysis on the junction temperature time series curve to extract the characteristic parameters of the effective thermal cycle that meet the amplitude threshold. The characteristic parameters include at least the cycle amplitude ΔT and the number of cycles; S3.4: Based on the characteristic parameters of the effective thermal cycle, perform damage calculations for the first physical model and the second physical model in parallel. First physical model: The Coffin-Manson fatigue model based on material fatigue mechanism, used to calculate the cyclic amplitude. Thermal fatigue damage caused by T D C Second physical model: A thermomechanical stress model based on the thermo-mechanical coupling mechanism, used to calculate the cyclic amplitude. Encapsulation thermal stress damage caused by T D s ; S3.5: The aforementioned thermal fatigue damage D C With the aforementioned thermal stress damage D s Based on the first preset weighting coefficient and Weighted fusion is performed to obtain the comprehensive damage of the operating state. D op ,in + =1; Step S4: If the system is in a non-operating state, execute the non-operating state damage assessment process: S4.1: Obtain the resting time of the IGBT module in non-operation state and the corresponding ambient temperature sequence; S4.2: Based on the ambient temperature sequence and the settling time, perform damage calculations for the third and fourth physical models in parallel: The third physical model: The Arrhenius temperature-accelerated aging model based on chemical reaction kinetics calculates the chemical aging damage caused by the combined effects of temperature and time. D A ; Fourth physical model: Time-based damage model based on time-cumulative effect, calculating pure time-cumulative damage. D T ; S4.3: The chemical aging damage D A With the aforementioned time-accumulated damage D T Based on the second preset weighting coefficient and Weighted fusion is performed to obtain the comprehensive damage in the non-operational state. D non ,in =1; Step S5: Based on the linear cumulative damage criterion, synthesize the damage of the operating state. D op Combined damage with the non-operational state D non Accumulated to the total historical damage D total middle; Step S6: Determine the updated total historical cumulative damage. D total Has the preset failure threshold been reached? D fail If this is not achieved, then the total historical cumulative damage will be used as the basis. D total Calculate and output the predicted remaining lifespan of the IGBT module; if the lifespan is reached or exceeded, output a lifespan termination warning.

2. The IGBT module lifetime prediction method based on operating and non-operating states, comprehensively considering the electro-thermal-mechanical coupling effect according to claim 1, is characterized in that... The thermal network model in S3.2 uses the following formula to calculate the IGBT junction temperature: in, Heat capacity; Thermal resistance to the junction shell; ; For power loss; The ambient temperature; This is the junction temperature.

3. The IGBT module lifetime prediction method based on operating and non-operating states, comprehensively considering the electro-thermal-mechanical coupling effect according to claim 1 or 2, is characterized in that... The rainflow is used to extract temperature cycle amplitude from the junction temperature sequence. Only the cycle amplitude greater than the preset threshold is retained as the effective thermal cycle.

4. The IGBT module lifetime prediction method based on operating and non-operating states, comprehensively considering the electro-thermal-mechanical coupling effect according to claim 1, is characterized in that... In step S3.4, the Coffin-Manson fatigue model is used to calculate thermal fatigue damage. D C The formula is as follows: = in, The baseline lifetime cycle number, The fatigue index. This is a temperature-related correction factor. The device lifespan damage caused by one cycle is counted as 1, and the device fails.

5. The IGBT module lifetime prediction method based on operating and non-operating states, comprehensively considering the electro-thermal-mechanical coupling effect according to claim 1, is characterized in that... In step S3.4, the thermomechanical stress model is used to calculate transient thermal stress damage. D s The formula is as follows: in, The coefficient of thermal expansion is... Young's modulus, For thermal stress, To withstand the total thermal stress, The thermal stress damage caused by one cycle is considered as one unit, and the device will fail if the cumulative thermal stress damage reaches 1 unit.

6. The IGBT module lifetime prediction method based on operating and non-operating states, comprehensively considering the electro-thermal-mechanical coupling effect according to claim 1, is characterized in that... In step 4.2, the Arrhenius temperature-accelerated aging model is used to calculate chemical aging damage. D A The formula is as follows: = in, To activate energy, Boltzmann's constant, For reference temperature, The recommended usage time is given by the manufacturer, and t is the idle time of the IGBT in non-operating state. To account for IGBT lifespan degradation during non-operational periods due to accelerated temperature changes, the Arrhenius accelerated aging model uses AF to reflect the impact of temperature variations (from the reference temperature T). ref The AF value represents the factor by which the aging rate is accelerated by increasing the temperature to the actual operating temperature (T). A larger AF value indicates a more significant acceleration of material aging due to temperature increase, resulting in greater lifespan damage (D) within the same timeframe. A The larger ) is; D A = • This represents the proportion of lifespan damage resulting from the temperature acceleration effect converted into the actual non-operational downtime t.

7. The IGBT module lifetime prediction method based on operating and non-operating states, comprehensively considering the electro-thermal-mechanical coupling effect according to claim 1, is characterized in that... In step 4.2, the time-based damage model calculates the pure time-cumulative damage. D T The formula is as follows: in, To disregard the lifespan degradation of IGBTs in non-operating states due to accelerated temperature, This indicates the expected total lifespan of the IGBT module in a non-operating state (usually in hours).

8. The IGBT module lifetime prediction method based on operating and non-operating states, comprehensively considering the electro-thermal-mechanical coupling effect according to any one of claims 1-7, is characterized in that... The comprehensive lifetime damage is obtained by proportionally weighting the Coffin-Manson damage and stress damage under operating conditions, and the temperature-accelerated damage and time damage under non-operating conditions, and the comprehensive damage is limited to no more than a preset upper limit to avoid numerical divergence.

9. The IGBT module lifetime prediction method based on operating and non-operating states, comprehensively considering the electro-thermal-mechanical coupling effect according to any one of claims 1-7, is characterized in that, The first preset weighting coefficient , With the second preset weighting coefficient , It can be a fixed value, or a variable parameter that is dynamically adjusted by an adaptive algorithm based on at least one of the historical degradation data of the IGBT module, the current working stress level, and the batch characteristics of the material.

10. The IGBT module lifetime prediction method based on operating and non-operating states, comprehensively considering the electro-thermal-mechanical coupling effect according to any one of claims 1, is characterized in that, The process also includes step S7: periodically repeating steps S1 to S6, and establishing the historical cumulative damage total of the IGBT module. D total The degradation trajectory changes over time; based on the degradation trajectory, a time series prediction model or a machine learning model is used to extrapolate and predict future lifespan decline trends.

11. An IGBT module lifespan prediction system, characterized in that, The system for implementing the lifetime prediction method according to any one of claims 1 to 10, the system comprising: The data acquisition module is used to acquire the electrical and environmental parameters of the IGBT module in the target system in real time. The electrical parameters include at least the collector current and the collector-emitter voltage, and the environmental parameters include at least the ambient temperature. The state recognition and scheduling module, connected to the data acquisition module, is used to determine whether the IGBT module is in a running state or a non-running state based on the electrical parameters, and to generate corresponding model scheduling instructions. A runtime state damage calculation module, connected to the state identification and scheduling module, is configured to start in response to a runtime state scheduling command, and includes: - A power loss and junction temperature calculation unit is used to calculate power loss based on the electrical parameters and to calculate junction temperature time-series curves based on the thermal network model and the ambient temperature. - Rainflow counting and feature extraction unit, used to analyze the junction temperature time series curve and extract characteristic parameters of the effective thermal cycle; - The first parallel model computation unit includes a Coffin-Manson model computation subunit for calculating thermal fatigue damage and a thermomechanical stress model computation subunit for calculating thermal stress damage; - The first weighted fusion unit is used to fuse the thermal fatigue damage and thermal stress damage according to the first preset weight, and output the comprehensive damage of the operating state. A non-operational state damage calculation module, connected to the state identification and scheduling module, is configured to start in response to a non-operational state scheduling command, and includes: - Environmental data recording unit, used to acquire and record the resting time and ambient temperature sequence of the IGBT module in non-operation state; - The second parallel model computation unit includes an Arrhenius model computation subunit for calculating chemical aging damage and a time damage model computation subunit for calculating pure time-cumulative damage. - The second weighted fusion unit is used to fuse the chemical aging damage and the pure time-accumulated damage according to the second preset weight, and output the comprehensive damage in the non-operational state. The damage fusion and accumulation module is connected to the operating state damage calculation module and the non-operating state damage calculation module, respectively, and is used to: receive and accumulate the comprehensive damage in the operating state and the comprehensive damage in the non-operating state, and update the total historical accumulated damage; compare the total historical accumulated damage with a preset failure threshold; and calculate and output the remaining life prediction value or life termination warning signal of the IGBT module based on the comparison result.

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