Inverter IGBT module life estimation methods, devices, equipment and media

Abstract

This invention relates to the field of component monitoring technology and discloses a method, device, equipment, and medium for estimating the lifetime of an inverter IGBT module. The method first utilizes a constructed equivalent thermal network model and a thermal coupling correction method for numerical simulation, fully considering time-varying power losses and thermal coupling effects during actual inverter operation, to accurately obtain the junction temperature fluctuation difference and average junction temperature under substeady-state conditions. Then, combining the junction temperature fluctuation frequency under substeady-state conditions, a pre-trained lifetime analytical model is used for prediction to obtain the maximum lifetime cycle count. Next, combining the current steady-state operating time and junction temperature fluctuation frequency, a lifetime decay ratio model is used to quantify the lifetime decay corresponding to a single steady-state operation. Finally, based on the current lifetime decay and combined with historical lifetime decay, a lifetime cumulative decay superposition model is used for evaluation to obtain the current remaining lifetime percentage, ensuring accurate estimation of the remaining lifetime of the IGBT module.

Description

Technical Field

[0001] This invention relates to the field of component monitoring technology, specifically to a method, apparatus, equipment, and medium for estimating the lifespan of an inverter IGBT module. Background Technology

[0002] Inverters serve as the core interface between renewable energy generation systems (such as photovoltaic and wind power) and the power grid, and their reliability directly impacts the safe and stable operation of the entire power system. Insulated-gate bipolar transistors (IGBTs) are the most critical power switching devices in inverters, undertaking the core function of power conversion. However, IGBT modules operate under harsh conditions of high-frequency switching, high current, and high voltage for extended periods. The internal IGBT chips and packaging structures are subjected to repeated thermal stress shocks, leading to failure. Therefore, accurately predicting the remaining lifespan of IGBTs can effectively ensure the reliable operation of the power system.

[0003] Disclosed methods for estimating IGBT module lifetime in related technologies include analytical lifetime prediction models based on junction temperature parameters, such as the Coffin-Manson model, the Norris-Landzberg model, the LESIT model, and the Bayer model. Among these, the Norris-Landzberg model estimates the device's power cycle lifetime by incorporating three key parameters: junction temperature fluctuation difference, mean junction temperature, and fluctuation frequency. It is one of the most widely used analytical lifetime models, obtaining parameters from power cycle experiments and then using an optimized model for lifetime estimation. However, the disclosed IGBT module lifetime estimation methods in related technologies are insufficient to meet current accuracy requirements for IGBT lifetime prediction. Summary of the Invention

[0004] This invention provides a method, apparatus, device, and medium for estimating the lifespan of an inverter IGBT module, in order to solve the problem that the IGBT module lifespan prediction methods disclosed in related technologies are difficult to accurately estimate the remaining lifespan of IGBT modules.

[0005] In a first aspect, the present invention provides a method for estimating the lifetime of an inverter IGBT module, the method comprising: Based on the target IGBT module to be evaluated, numerical simulation is performed using the constructed equivalent thermal network model and thermal coupling correction method to obtain the junction temperature fluctuation difference and the average junction temperature of the target IGBT module under substeady state. Based on the junction temperature fluctuation difference and mean junction temperature of the target IGBT module in substeady state, combined with the junction temperature fluctuation frequency of the corresponding IGBT module in substeady state, the maximum lifetime cycle number of the target IGBT module is predicted using a pre-trained lifetime analytical model. Based on the maximum lifetime cycle count of the target IGBT module, combined with the operating time and junction temperature fluctuation frequency of the corresponding IGBT module in the current steady state, the ratio of the actual cycle count to the maximum cycle count under the current steady-state operating conditions is obtained using the lifetime decay ratio model, thus obtaining the current lifetime decay of the target IGBT module. Based on the current lifetime decay of the target IGBT module, combined with the historical lifetime decay of the corresponding IGBT module, an evaluation is performed using a lifetime cumulative decay superposition model to obtain the current remaining lifetime percentage of the target IGBT module.

[0006] Through the above implementation method, firstly, numerical simulation is performed using the constructed equivalent thermal network model and thermal coupling correction method to fully consider the time-varying power loss and thermal coupling effect in the actual operation of the inverter, accurately obtaining the junction temperature fluctuation difference and the average junction temperature under substeady state. Then, combined with the junction temperature fluctuation frequency under substeady state, a pre-trained lifetime analytical model is used for prediction to obtain the maximum lifetime cycle count. Next, combined with the current steady-state operating time and junction temperature fluctuation frequency, the lifetime decay ratio model is used to obtain the ratio of the actual cycle count to the maximum lifetime cycle count, quantifying the lifetime decay caused by a single steady-state operation. Finally, based on the current lifetime decay and combined with the historical lifetime decay, the lifetime cumulative decay superposition model is used for evaluation to obtain the current remaining lifetime percentage, thereby achieving an accurate estimation of the remaining lifetime of the IGBT module.

[0007] In one optional implementation, the numerical simulation based on the target IGBT module to be evaluated, using a constructed equivalent thermal network model and a thermal coupling correction method, yields the junction temperature fluctuation difference and average junction temperature of the target IGBT module under substeady-state conditions, including: Based on the target IGBT module, a fourth-order Foster equivalent thermal network model is constructed for equivalence, and the parameters of the corresponding equivalent thermal network model are determined. Based on the parameters and on-state voltage drop model of the inverter corresponding to the target IGBT module in substeady state, the average power loss is obtained by using the integral formula of switching loss and on-state loss. Based on the fourth-order Foster equivalent thermal network model and the average power loss, the junction temperature time series is obtained by using boundary conditions and the fourth-order Runge-Kutta method, and the junction temperature fluctuation difference and the mean junction temperature to be corrected are determined. Based on the junction temperature fluctuation difference and the average junction temperature to be corrected, combined with the equivalent series thermal resistance from the FWD to the IGBT of the target IGBT module and the average power loss of the FWD, the thermal coupling correction method is used to correct the junction temperature fluctuation difference and the average junction temperature, which are then used as the junction temperature fluctuation difference and the average junction temperature of the target IGBT module in the substeady state.

[0008] Through the above implementation method, the target IGBT module is constructed as a fourth-order Foster equivalent thermal network model and its parameters are determined, which facilitates the characterization of the internal heat conduction path of the device with high computational efficiency and easily identifiable parameter forms. Then, based on the inverter's system parameters and on-state voltage drop model under substeady state, the average power loss is calculated using the integral formula of switching loss and conduction loss, fully considering actual operating conditions to obtain a heat source input value that more closely matches real operation and reduces errors caused by the constant loss assumption. Finally, based on the fourth-order Foster model and average power loss, boundary conditions and fourth-order Runge... The Kutta method is used to solve the junction temperature time series and determine the junction temperature fluctuation difference and mean junction temperature to be corrected. This can accurately capture the transient junction temperature response under time-varying power loss. Finally, the equivalent series thermal resistance from the freewheeling diode to the IGBT and the average power loss of the FWD are combined with the thermal coupling correction method to make corrections. This can quantify the additional temperature rise transferred from the freewheeling diode to the IGBT chip through the substrate and filler material within the same IGBT module. This compensates for the underestimation of junction temperature caused by ignoring the coupling heat flow in related lifetime estimation methods, thus obtaining a more accurate corrected junction temperature fluctuation difference and mean junction temperature, significantly improving the accuracy of IGBT module lifetime estimation.

[0009] In one optional implementation, the step of solving the junction temperature time series based on the fourth-order Foster equivalent thermal network model and the average power loss using boundary conditions and the fourth-order Runge-Kutta method, and determining the junction temperature fluctuation difference and the mean junction temperature to be corrected, includes: Based on the time constant of the fourth-order Foster equivalent thermal network model and the average power loss, the initial values ​​of the temperature differences at each order are obtained by solving the boundary conditions of the heating and cooling stages, and a set of thermal network differential equations is constructed. Based on the aforementioned thermal network differential equations, the modulation function is equivalent to a sine function, and numerical integration is performed using the fourth-order Runge-Kutta method to obtain the junction temperature values ​​at each time point, thus constructing a junction temperature time series. Based on the junction temperature time series, the maximum and minimum junction temperatures are obtained using the maximum and minimum value extraction method, and the junction temperature fluctuation difference and the mean junction temperature to be corrected are determined.

[0010] Through the above implementation method, based on the time constant and average power loss of the fourth-order Foster equivalent thermal network model, the boundary conditions of the heating and cooling stages are used to solve the problem, obtaining the initial values ​​of the temperature differences at each order and constructing a set of thermal network differential equations. This accurately describes the start and end states of the periodic fluctuations of the junction temperature under substeady state, laying a reasonable initial value foundation for subsequent numerical solutions. Furthermore, based on this set of differential equations, the modulation function is equivalent to a sine function and numerical integration is performed using the fourth-order Runge-Kutta method. This ensures computational accuracy while efficiently handling the nonlinear characteristics of time-varying power loss, obtaining the junction temperature values ​​at each time node, and constructing a junction temperature time series. This truly reflects the continuous fluctuation process of the junction temperature under the sinusoidal modulation of the inverter, overcoming the deficiency of traditional constant loss assumption methods in capturing instantaneous fluctuation details. Finally, based on this junction temperature time series, the extreme values ​​of the junction temperature are obtained using the maximum and minimum value extraction method, and the junction temperature fluctuation difference and mean value to be corrected are determined accordingly, providing accurate parameters to be corrected for subsequent thermal coupling correction.

[0011] In one optional implementation, the step of obtaining the current lifetime degradation of the target IGBT module by combining the maximum lifetime cycle count of the target IGBT module with the corresponding IGBT module's current steady-state operating time and junction temperature fluctuation frequency, and using a lifetime degradation ratio model to obtain the ratio of the actual cycle count to the maximum cycle count under the current steady-state operating conditions, includes: Based on the maximum lifetime cycle count of the target IGBT module, combined with the duration of the corresponding IGBT module's current steady-state operation and the junction temperature fluctuation frequency, the actual number of thermal cycles that have occurred under the current steady state is obtained using the actual cycle count calculation method. Based on the actual number of thermal cycles that have occurred under the current steady state, and combined with the maximum lifetime cycle count of the target IGBT module, the ratio of the actual number of cycles to the maximum number of cycles is calculated using the lifetime decay ratio model to obtain the lifetime decay percentage corresponding to the current sub-steady state operation. Based on the percentage of lifetime decay corresponding to the current substeady-state operation, the current lifetime decay of the target IGBT module is obtained by substituting it into the Norris Landzberg lifetime decay ratio model.

[0012] Through the above implementation method, based on the maximum lifetime cycle count of the target IGBT module, combined with the duration of the current steady-state operation and the junction temperature fluctuation frequency, the actual number of thermal cycles that have occurred under the current steady state is obtained using the actual cycle count calculation method. This allows the actual operating time to be converted into thermal cycle counts that match the lifetime model. Then, based on the actual thermal cycle count and the maximum lifetime cycle count, the ratio of the two is calculated using the lifetime decay ratio model to obtain the lifetime decay percentage corresponding to the current sub-steady-state operation. This can accurately quantify the relative damage caused to the IGBT module by a single steady-state operation. Finally, based on this lifetime decay percentage, it is substituted into the Norris Landzberg lifetime decay ratio model to obtain the current lifetime decay of the target IGBT module. By directly embedding key parameters such as junction temperature fluctuation difference, average junction temperature, and fluctuation frequency into the decay calculation process, a quantitative conversion from physical junction temperature to engineering lifetime loss is achieved.

[0013] In one optional implementation, the step of evaluating the current remaining lifetime percentage of the target IGBT module based on its current lifetime degradation, combined with the historical lifetime degradation of the corresponding IGBT module, using a lifetime cumulative degradation superposition model, includes: Based on the current lifetime decay of the target IGBT module, combined with the historical lifetime decay of the corresponding IGBT module, the total lifetime decay percentage of the target IGBT module at the current moment is obtained using the lifetime cumulative decay superposition model. Based on the total lifetime decay percentage of the target IGBT module at the current moment, the remaining lifetime percentage is calculated to obtain the current remaining lifetime percentage of the target IGBT module.

[0014] Through the above implementation method, based on the current lifetime decay of the target IGBT module and combined with the historical lifetime decay of the corresponding IGBT module, the total lifetime decay percentage at the current moment is obtained using the lifetime cumulative decay superposition model. This can accumulate the independent damage under different operating conditions, truly reflecting the cumulative thermal fatigue degree that the IGBT module has endured since it was put into use, overcoming the limitation that a single assessment cannot characterize the overall aging state. Furthermore, based on this total lifetime decay percentage, the current remaining lifetime percentage is obtained using the remaining lifetime percentage calculation formula, realizing an intuitive quantification from cumulative damage to remaining usable lifetime, and providing clear health indicators for operation and maintenance decisions.

[0015] In one optional implementation, the step of predicting the maximum lifetime cycle count of the target IGBT module based on the junction temperature fluctuation difference and mean junction temperature of the target IGBT module in the substeady state, combined with the junction temperature fluctuation frequency of the corresponding IGBT module in the substeady state, using a pre-trained lifetime analytical model, includes: The Norris Landzberg lifetime analytical model was used as the prediction function to construct the initial lifetime analytical model; Based on power cycling experimental data of the target IGBT module, the parameters of the initial lifetime analytical model are corrected using parameter fitting methods to obtain a pre-trained lifetime analytical model. Based on the junction temperature fluctuation difference and average junction temperature of the target IGBT module in the substeady state, combined with the junction temperature fluctuation frequency of the corresponding inverter in the substeady state, the values ​​are substituted into the pre-trained lifetime analytical model to obtain the maximum lifetime cycle count of the target IGBT module in the current substeady state.

[0016] Through the above implementation method, the Norris Landzberg lifetime analytical model is used as the prediction function to construct an initial lifetime analytical model. This fully utilizes the advantages of the initial lifetime analytical model, which comprehensively considers junction temperature fluctuation difference, mean junction temperature, and fluctuation frequency, providing a standardized mathematical framework for subsequent parameter adjustment and lifetime prediction. Then, based on the power cycling experimental data of the target IGBT module, the parameters of the initial lifetime analytical model are corrected using parameter fitting methods to obtain a pre-trained lifetime analytical model. This allows the model parameters to match the physical characteristics of the actual device, improving the adaptability and prediction accuracy of the lifetime analytical model for specific IGBT modules. Finally, based on the junction temperature fluctuation difference and mean junction temperature of the target IGBT module in the substeady state, combined with the corresponding junction temperature fluctuation frequency, the pre-trained lifetime analytical model is substituted to obtain the maximum lifetime cycle count in the current substeady state, realizing the quantitative conversion from accurate junction temperature parameters to the inherent lifetime of the device.

[0017] In one optional implementation, the maximum lifetime cycle count of the target IGBT satisfies:

[0018] in, Indicates the maximum lifetime cycle count of the target IGBT; This represents the junction temperature fluctuation difference of the target IGBT under substeady state. This represents the average junction temperature fluctuation of the target IGBT under substeady state. Indicates the activation energy of the target IGBT; Represents the Boltzmann constant; A , α and β All are constants, obtained from power cycling experiments; f This indicates the frequency of junction temperature fluctuations.

[0019] Through the above implementation method, the Norris Landzberg lifetime analytical model is used as a quantitative expression for the maximum lifetime cycle count, and the three key parameters of junction temperature fluctuation difference, junction temperature mean and fluctuation frequency are coupled in a product form. This can comprehensively reflect the fatigue failure mechanism of IGBT modules under thermal cycling, transform the complex physical failure process into a calculable mathematical expression, and realize the standardized mapping from junction temperature parameters to cycle life.

[0020] Secondly, the present invention provides an inverter IGBT module lifespan estimation device, the device comprising: The numerical simulation module is used to perform numerical simulations based on the target IGBT module to be evaluated, using the constructed equivalent thermal network model and thermal coupling correction method, to obtain the junction temperature fluctuation difference and the average junction temperature of the target IGBT module under substeady state. The lifetime prediction module is used to predict the maximum lifetime cycle number of the target IGBT module based on the junction temperature fluctuation difference and the average junction temperature of the target IGBT module in the substeady state, combined with the junction temperature fluctuation frequency of the corresponding IGBT module in the substeady state, using a pre-trained lifetime analytical model. The attenuation calculation module is used to obtain the ratio of the actual number of cycles to the maximum number of cycles under the current steady-state operating conditions based on the maximum lifetime cycle count of the target IGBT module, combined with the corresponding IGBT module's current steady-state operating time and junction temperature fluctuation frequency, and using the lifetime attenuation ratio model to obtain the current lifetime attenuation of the target IGBT module. The lifetime assessment module is used to evaluate the current remaining lifetime percentage of the target IGBT module based on its current lifetime degradation and the historical lifetime degradation of the corresponding IGBT module, using a lifetime cumulative degradation superposition model.

[0021] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the inverter IGBT module lifetime estimation method described in the first aspect or any corresponding embodiment thereof.

[0022] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the inverter IGBT module lifetime estimation method described in the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0023] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0024] Figure 1 This is a schematic diagram of the first process of the inverter IGBT module lifetime estimation method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the second process of the inverter IGBT module lifetime estimation method according to an embodiment of the present invention; Figure 3 This is a fourth-order Foster thermal network model constructed in the inverter IGBT module lifetime estimation method according to an embodiment of the present invention; Figure 4 This is a control block diagram of the inverter in the inverter IGBT module lifetime estimation method according to an embodiment of the present invention; Figure 5 This is a comparison of the junction temperature fluctuation curves of the time-varying loss and custom loss of the IGBT in the inverter IGBT module lifetime estimation method according to an embodiment of the present invention. Figure 6 This is a schematic diagram of the internal thermal-fluid coupling of the IGBT module in the inverter IGBT module lifetime estimation method according to an embodiment of the present invention; Figure 7 This is a schematic diagram of the third process of the inverter IGBT module lifetime estimation method according to an embodiment of the present invention; Figure 8 This is a schematic diagram of the fourth process of the inverter IGBT module lifetime estimation method according to an embodiment of the present invention; Figure 9 This is a schematic diagram of the fifth process of the inverter IGBT module lifetime estimation method according to an embodiment of the present invention; Figure 10 This is a structural block diagram of an inverter IGBT module lifetime estimation device according to an embodiment of the present invention; Figure 11 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0027] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0028] In related technologies, disclosed IGBT module lifetime estimation methods typically assume that power loss is a constant value or a simple periodic function, failing to fully consider the time-varying power loss characteristics caused by sinusoidal modulation during actual inverter operation (such as the change of conduction loss with phase angle and the instantaneous distribution of switching losses). This leads to a deviation between the calculated junction temperature fluctuation and the actual value, thus affecting the accuracy of lifetime prediction. Furthermore, the junction temperature calculation process in these methods often focuses on the IGBT's own power loss, neglecting the thermal coupling effect of the freewheeling diode (FWD) within the same module, which is transferred to the IGBT chip through the substrate and filler material. Since the freewheeling diode operates during the negative half-cycle of the output current and generates significant losses in actual production, this heat is superimposed on the IGBT chip, causing the actual junction temperature of the IGBT module to be higher than the calculated value of the self-heating model. This results in an underestimation of the average junction temperature, further leading to inaccurate IGBT module lifetime predictions.

[0029] To overcome the shortcomings of the aforementioned related technologies, this application provides a method for estimating the lifetime of an inverter IGBT module. First, numerical simulation is performed using a constructed equivalent thermal network model and a thermal coupling correction method, fully considering time-varying power losses and thermal coupling effects during actual inverter operation, accurately obtaining the junction temperature fluctuation difference and average junction temperature under substeady-state conditions. Then, combining the junction temperature fluctuation frequency under substeady-state conditions, a pre-trained lifetime analytical model is used for prediction to obtain the maximum lifetime cycle count. Next, combining the current steady-state operating time and junction temperature fluctuation frequency, a lifetime decay ratio model is used to obtain the ratio of the actual cycle count to the maximum lifetime cycle count, quantifying the lifetime decay caused by a single steady-state operation. Finally, based on the current lifetime decay and combined with historical lifetime decay, a lifetime cumulative decay superposition model is used for evaluation to obtain the current remaining lifetime percentage, ensuring accurate estimation of the remaining lifetime of the IGBT module.

[0030] According to an embodiment of the present invention, an embodiment of an inverter IGBT module lifetime estimation method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0031] This embodiment provides a method for estimating the lifespan of an inverter IGBT module, which can be used in the server terminal of a booster station. Figure 1 This is a flowchart of an inverter IGBT module lifetime estimation method according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps: S101, based on the target IGBT module to be evaluated, numerical simulation is performed using the constructed equivalent thermal network model and thermal coupling correction method to obtain the junction temperature fluctuation difference and the average junction temperature of the target IGBT module under substeady state.

[0032] The target IGBT module is an insulated gate bipolar transistor and its packaged components for which lifetime estimation is to be performed.

[0033] The equivalent thermal network model is a simplified circuit model used to simulate the internal heat conduction process of an IGBT module, and it can be implemented as a fourth-order Foster thermal network model.

[0034] The thermal coupling correction method addresses the additional temperature rise effect caused by the heat generated by the freewheeling diode within the same IGBT module being transferred to the IGBT chip through the substrate. This can be achieved by utilizing the equivalent series thermal resistance. This indicates that it is used to superimpose and correct the junction temperature parameters of the IGBT module.

[0035] Substeady state is the dynamic equilibrium state in which the IGBT junction temperature rises and falls by the same magnitude in each basic cycle under given operating conditions.

[0036] Junction temperature fluctuation It is the difference between the maximum and minimum junction temperatures of an IGBT module within a basic cycle, used to characterize the thermal stress amplitude.

[0037] Average junction temperature It is the average junction temperature of an IGBT over a basic cycle, used to characterize the average temperature level.

[0038] S102, based on the junction temperature fluctuation difference and the average junction temperature of the target IGBT module in the substeady state, combined with the junction temperature fluctuation frequency of the corresponding IGBT module in the substeady state, the maximum lifetime cycle number of the target IGBT module is predicted using a pre-trained lifetime analytical model.

[0039] The junction temperature fluctuation frequency is the fundamental frequency of the inverter output current, and also the frequency of the periodic fluctuation of the junction temperature of the IGBT module.

[0040] The pre-trained lifetime analytical model can be implemented as a lifetime prediction function obtained by fitting parameters to power cycling experimental data based on the Norris Landzberg lifetime analytical model.

[0041] The maximum lifetime cycle count is the total number of power cycles that the IGBT module can withstand under the current substeady-state conditions.

[0042] S103. Based on the maximum lifetime cycle count of the target IGBT module, combined with the corresponding IGBT module's current steady-state operating time and junction temperature fluctuation frequency, the ratio of the actual cycle count to the maximum cycle count under the current steady-state operating conditions is obtained using the lifetime decay ratio model, thus obtaining the current lifetime decay of the target IGBT module.

[0043] The current steady-state operating time is the duration of continuous operation of the inverter under the substeady-state operating conditions at the current moment.

[0044] The actual number of thermal cycles is the total number of cycles of junction temperature fluctuation within the current operating time.

[0045] The lifetime decay ratio model defines the percentage of lifetime decay caused by a single operation as the ratio of the actual number of thermal cycles to the maximum number of lifetime cycles.

[0046] The current lifetime decay corresponds to the lifetime loss caused by the actual substeady-state operation at the current moment.

[0047] S104. Based on the current lifetime decay of the target IGBT module and combined with the historical lifetime decay of the corresponding IGBT module, the lifetime cumulative decay superposition model is used for evaluation to obtain the current remaining lifetime percentage of the target IGBT module.

[0048] Historical lifetime decay is the cumulative sum of lifetime decay during the period from the initial commissioning to the end of steady-state operation at the current moment.

[0049] The cumulative lifespan decay superposition model uses Miner's linear accumulation rule to directly add up the lifespan decay amounts under each independent operating condition to obtain the total decay percentage.

[0050] The current remaining lifetime percentage is the proportion of the IGBT module's remaining usable lifetime to its total lifetime at the current moment.

[0051] The inverter IGBT module lifetime estimation method provided in this embodiment first uses a constructed equivalent thermal network model and thermal coupling correction method for numerical simulation, fully considering the time-varying power loss and thermal coupling effect in actual inverter operation, to accurately obtain the junction temperature fluctuation difference and the average junction temperature under substeady state. Then, combined with the junction temperature fluctuation frequency under substeady state, a pre-trained lifetime analytical model is used for prediction to obtain the maximum lifetime cycle count. Next, combined with the current steady-state operating time and junction temperature fluctuation frequency, a lifetime decay ratio model is used to obtain the ratio of the actual cycle count to the maximum lifetime cycle count, quantifying the lifetime decay caused by a single steady-state operation. Finally, based on the current lifetime decay and combined with the historical lifetime decay, a lifetime cumulative decay superposition model is used for evaluation to obtain the current remaining lifetime percentage, ensuring an accurate estimation of the remaining lifetime of the IGBT module.

[0052] This embodiment provides a method for estimating the lifespan of an inverter IGBT module, which can be used in the server terminal of a booster station. Figure 2 This is a flowchart of an inverter IGBT module lifetime estimation method according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps: S201, based on the target IGBT module to be evaluated, numerical simulation is performed using the constructed equivalent thermal network model and thermal coupling correction method to obtain the junction temperature fluctuation difference and the average junction temperature of the target IGBT module under substeady state.

[0053] Specifically, S201 above includes: S2011, based on the target IGBT module, constructs a fourth-order Foster equivalent thermal network model for equivalence, and determines the parameters of the corresponding equivalent thermal network model.

[0054] For example, the equivalent thermal network model is a simplified circuit model used to simulate the internal heat conduction process of an IGBT module, and can be implemented as follows: Figure 3 The fourth-order Foster thermal network model shown includes four sets of thermal resistances ( , , and ) and heat capacity ( , , and ).

[0055] For example, the equivalent heat network model is expressed as follows: (1) (2) in, , , and These represent the heat capacities of each order in the equivalent heat network model; , , and These represent the thermal resistances of different orders in the equivalent heat network model, respectively. This represents the difference between the IGBT junction temperature and the ambient temperature. , and These represent the temperature difference between each intermediate layer of the equivalent thermal network model and the ambient temperature. This represents power loss that varies over time. , , and These represent the absolute temperatures of each node in the equivalent heat network model; Indicates ambient temperature.

[0056] The solution to the above system of time-domain differential equations depends on the initial state. Assume... , , and All are initial coefficients for the temperature difference across the thermal resistance in each order of the RC structure, satisfying the following: (3) When power loss When no such condition is generated, the unique solution to the above system of time-domain differential equations (1) is: (4) in, , , and These represent the four time constants of the fourth-order Foster model.

[0057] After obtaining the parameters of the fourth-order Foster model of the IGBT, the IGBT devices in the closed-loop inverter system are under fully known conditions. Under substeady-state conditions, the control system setpoint remains unchanged, the junction temperature fluctuation period of the IGBT is the same as the fundamental period, and the peak value remains constant.

[0058] Under these conditions, the inverter's control block diagram is as follows: Figure 4 As shown, the setpoints include the following parameters: DC bus voltage. Output current amplitude Output current frequency Switching frequency Adjustment system Output voltage and current phase angle ; Parameters of a fourth-order Foster thermal network Broken line equation.

[0059] S2012, based on the parameters and conduction voltage drop model of the inverter corresponding to the target IGBT module under substeady state, uses the integral formula of switching loss and conduction loss to obtain the average power loss.

[0060] The power loss of an IGBT module is divided into switching loss and conduction loss. For IGBTs, switching loss... Including its turn-on and turn-off losses, for FWD, only reverse recovery losses are considered. The switching energy loss for each fundamental cycle can be given by the device datasheet and calculations based on actual operating conditions, satisfying the following: (5) (6) in, and These represent the IGBT specifications given in the datasheet. for , for Energy loss during switching on and off under certain conditions. This indicates that the FWD given in the datasheet is... for , for Reverse recovery energy loss under certain conditions.

[0061] When the switching frequency is much greater than the fundamental frequency ( When ), the series can be approximated by the integral, resulting in: (7) (8) Integrating equations 7 and 8 above, we get: (9) (10) The typical conduction characteristics of power devices IGBT and FWD are approximated by piecewise linear equations, satisfying the following: (11) (12) in, and These represent the IGBT and FWD respectively in terms of current. The on-state voltage drop is below; , , and These are the coefficients of the IGBT, which can be estimated using the IV curve in the datasheet or measured using instruments.

[0062] Since the inverter operates in a high-frequency chopping state, the duty cycle calculation is strongly related to the phase of the output voltage and current. Therefore, the phase angle needs to be introduced into the current model, satisfying the following: (13) Combining equations 11 to 13, the conduction loss of the IGBT's FWD can be obtained, which satisfies: (14) (15) in, The range of values ​​is ,and The modulation function is expressed as follows: (16) Among them, the modulation function It depends on the modulation method; it uses a common modulation method. Represented as: (17) Integrating Equations 14 and 15 over the fundamental cycle yields the conduction loss energy of the IGBT and FWD in each fundamental cycle, expressed as: (18) (19) in, The coefficients specific to SVPWM modulation are expressed as follows: (20) S2013, based on the fourth-order Foster equivalent thermal network model and average power loss, is solved using boundary conditions and the fourth-order Runge-Kutta method to obtain the junction temperature time series and determine the junction temperature fluctuation difference and the mean junction temperature to be corrected.

[0063] For example, S2013 above includes: a1, based on the fourth-order Foster equivalent thermal network model and average power loss, uses boundary conditions and the fourth-order Runge-Kutta method to solve for the junction temperature time series, and determines the junction temperature fluctuation difference and mean junction temperature to be corrected, including: a2, based on the time constant and average power loss of the fourth-order Foster equivalent thermal network model, the boundary conditions of the heating and cooling stages are used to solve the problem, the initial values ​​of the temperature difference of each order are obtained, and a set of thermal network differential equations is constructed. a3. Based on the differential equations of the thermal network, the modulation function is equivalent to a sine function, and the fourth-order Runge-Kutta method is used for numerical integration to obtain the junction temperature values ​​at each time point, thus constructing a junction temperature time series. a4, based on the junction temperature time series, uses the maximum and minimum value extraction method to obtain the maximum and minimum values ​​of the junction temperature, and determines the junction temperature fluctuation difference and the mean junction temperature to be corrected.

[0064] The differential equations for temperature power loss at any time in a fourth-order Foster network are shown in Equation 1. When current flows through the IGBT, there is power loss on the IGBT, which will continuously generate heat. However, the generated power loss is time-varying. For the calculation of the initial value, it can be approximated as a constant integral, satisfying: (twenty one) in, Calculated from Equation 9, It can be calculated using Equation 18, by using the time-varying power loss in Equation 1. Equivalent substitution to a constant .

[0065] When current flows through the FWD, the IGBT does not generate losses and is in a state of natural cooling. The value is 0, and the equation is the zero-input response of the fourth-order Foster model.

[0066] The initial values ​​under both conditions are the temperature values ​​at their initial moments. Using Equation 3 as the initial value, the analytical solution of Equation 1 is calculated, yielding: (twenty two) Therefore, Substituting into Equation 22, we obtain the junction temperature differential equations of the IGBT under the conditions of the positive half-cycle of the output current (power loss and heat generation) and the negative half-cycle of the output current (natural cooling), which satisfy the following: (twenty three) (twenty four) because , , and These represent the initial temperature difference across the thermal resistance of the 4th-order Foster model. To construct a temperature difference elimination mechanism... , and The following conditions must be met: (25) (26) (27) When an IGBT enters a substeady state, the junction temperature is in dynamic equilibrium, meaning the junction temperature fluctuates within a fixed range. Therefore, the cooling curve and the heating curve should be each other's initial and final values. That is, the initial value of the cooling curve should be the final value of the heating curve, and the initial value of the heating curve should be the final value of the cooling curve.

[0067] Let time 0 be the starting point of the heating curve, and time 1 be the starting point of the cooling curve. / 2 f The initial and final temperature differences across each thermal resistance in the Foster model can be calculated, satisfying the following: (28) (29) (30) (31) Define time constant ratio , , and It satisfies the following: (32) Combining equations 28 to 31, we can solve for the initial values ​​and 1 of the heating equations at time 0. / 2 f The initial values ​​of the cooling equations at time t satisfy the following: (33) (34) During the current flow through the IGBT, power loss occurs on the device, and it is in a state of temperature rise. Taking SPWM as an example, the expression for power loss on the IGBT as a function of phase angle is: (35) The IGBT power loss expression includes a cosine component. During the temperature rise process, there will be periods where the power loss is less than the period maintaining the current junction temperature and periods where the power loss is greater than the average power loss. This can cause the IGBT junction temperature to be lower than the initial value of the heating equation and higher than the initial value of the cooling equation during the heating process. Figure 5 As shown. Therefore, using Equation 1 to find the maximum and minimum values ​​of the junction temperature, the following conditions are met: First, Equation 35 is simplified by using a constant coefficient equivalent to replace the modulation function, which satisfies the following: (36) (37) Substituting equations 36 and 37 into equation 35, the phase angle cosine function is equivalently transformed into a time sine function, satisfying the following: (38) in, t The range of values ​​is .

[0068] At this point, the zero point of power loss corresponds to the zero point of the differential equation. Combining Equation 1 and boundary condition Equation 33, the unknown is the temperature difference across the Foster thermal resistance, satisfying the following: (39) Substituting equation 39 into equations 1 and 33, we obtain the simplified system of differential equations and boundary conditions, which satisfy the following: (40) (41) Formula 38 Substituting into Equation 40, the numerical solution of the differential equation is obtained using the fourth-order Runge-Kutta method with a fixed step size. The numerical solution of Equation 40 is a vector, which can be represented by a matrix as follows: (42) in, This indicates rounding down to the nearest integer.

[0069] definition For vectors T The maximum value in, For vectors T The minimum value in the range can be used to calculate the junction temperature fluctuation difference of the IGBT: (43) The mean power loss and the junction temperature fluctuation midline under the actual power loss model almost coincide, such as Figure 5 As shown. Therefore, the uncorrected mean junction temperature can be directly obtained by taking the mean of the two initial values ​​from equations (34) and (35), and can be expressed as: (44) In summary, the average junction temperature fluctuation of an IGBT is independent of the device's switching frequency and the thermal capacity of the thermal model, and is only related to the average power loss and thermal resistance, and its value is half of the steady-state junction temperature value under the average power loss.

[0070] By using the time constant and average power loss of a fourth-order Foster equivalent thermal network model, and solving the boundary conditions of the heating and cooling stages, the initial values ​​of each temperature difference are obtained and a set of thermal network differential equations is constructed. This accurately describes the start and end states of the periodic fluctuations in junction temperature under substeady state, providing a reasonable initial value for subsequent numerical solutions. Furthermore, based on this set of differential equations, the modulation function is equivalent to a sine function and numerical integration is performed using the fourth-order Runge-Kutta method. This ensures computational accuracy while efficiently handling the nonlinear characteristics of time-varying power loss, obtaining the junction temperature values ​​at each time node and constructing a junction temperature time series. This truly reflects the continuous fluctuation process of junction temperature under sinusoidal modulation of the inverter, overcoming the deficiency of traditional constant loss assumption methods in capturing instantaneous fluctuation details. Finally, based on this junction temperature time series, the extreme values ​​of junction temperature are obtained using the maximum and minimum value extraction method, and the junction temperature fluctuation difference and mean value to be corrected are determined accordingly, providing accurate parameters for subsequent thermal coupling correction.

[0071] S2014, based on the junction temperature fluctuation difference and the average junction temperature to be corrected, combined with the equivalent series thermal resistance from the FWD to the IGBT of the target IGBT module and the average power loss of the FWD, the thermal coupling correction method is used to correct the junction temperature fluctuation difference and the average junction temperature, and the corrected junction temperature fluctuation difference and the average junction temperature are used as the junction temperature fluctuation difference and the average junction temperature of the target IGBT module in the substeady state.

[0072] For IGBT power modules, both the FWD chip and the IGBT chip are integrated on the same substrate, and their heat transfer paths will couple, such as... Figure 6As shown, a portion of the energy generated on the FWD is transferred to the IGBT chip, causing the IGBT junction temperature to be higher than when it operates independently, necessitating decoupling of the heat transfer path. The device is filled with silicone, which has extremely high equivalent thermal resistance and thermal capacity, approximating thermal insulation. This is often ignored when analyzing the junction temperature independently. However, considering the coupled thermal network, the heat flow transfer between different materials results in a heat transfer path distance within the device that is much greater than the device's stack thickness. Therefore, the equivalent thermal resistance and thermal capacity of the transfer path are much greater than those of the stacked structure. In this case, the thermal resistance and thermal capacity of the silicone cannot be ignored. The equivalent heat transfer network from the FWD to the IGBT is as follows: Figure 7 As shown, the equivalent thermal impedance of the coupled thermal path of a multi-chip module can be obtained through preliminary experiments to eliminate thermal resistance. The impact.

[0073] Specifically, the above-mentioned S2014 is implemented as follows: Because the thermal steady-state time constant of the transmission path is extremely large, the junction temperature fluctuation of the IGBT caused by the power loss of the FWD is extremely small and can be replaced by the average junction temperature fluctuation, which is only related to the average thermal resistance and power loss.

[0074] The junction temperature rise caused by FWD power loss can be expressed as: (45) in, This represents the sum of the total thermal resistances along the heat transfer path from the FWD to the IGBT. The expression for the average power loss during the negative half-cycle of the output current (FWD) is: (46) Therefore, based on the superposition principle, the expressions for correcting the junction temperature fluctuation difference and the average junction temperature of the IGBT can be as follows: (47) (48) Equations 47 and 48 are only related to the equivalent series thermal resistance of the transmission path and are not related to the thermal capacity. The equivalent thermal resistance of the transmission path is relatively large, which makes the aging of the solder layer have a very small impact on the equivalent thermal resistance of the transmission path.

[0075] Therefore, it can be directly measured in the preliminary experiment. The measurement process is as follows: At a constant current Under these conditions, the voltage drop on the FWD is calibrated using an infrared temperature probe. With FWD junction temperature The relationship can be represented as: (49) A constant current is applied to the IGBT in reverse direction using a constant current power supply. At this time, the current flows through the FWD chip but not through the IGBT chip; Once the IGBT module reaches thermal equilibrium, the junction temperature can be directly measured using the Miller voltage. The value measured by the on-state voltage measurement circuit is the negative value of the voltage drop across the FWD. The value measured by the Hall sensor is the negative value of the current passing through the FWD. ); The acquisition of can be represented as: (50) By constructing the target IGBT module as a fourth-order Foster equivalent thermal network model and determining its parameters, the internal heat conduction path of the device can be characterized with high computational efficiency and easily identifiable parameter forms. Then, based on the inverter's system parameters and on-state voltage drop model under substeady-state conditions, the average power loss is calculated using the integral formulas for switching losses and conduction losses. This fully considers actual operating conditions, obtaining heat source input values ​​that more closely resemble real-world operation and reducing errors caused by the constant loss assumption. Finally, based on the fourth-order Foster model and average power loss, boundary conditions and fourth-order Runge... The Kutta method is used to solve the junction temperature time series and determine the junction temperature fluctuation difference and mean junction temperature to be corrected. This can accurately capture the transient junction temperature response under time-varying power loss. Finally, the equivalent series thermal resistance from the freewheeling diode to the IGBT and the average power loss of the FWD are combined with the thermal coupling correction method to make corrections. This can quantify the additional temperature rise transferred from the freewheeling diode to the IGBT chip through the substrate and filler material within the same IGBT module. This compensates for the underestimation of junction temperature caused by ignoring the coupling heat flow in related lifetime estimation methods, thus obtaining a more accurate corrected junction temperature fluctuation difference and mean junction temperature, significantly improving the accuracy of IGBT module lifetime estimation.

[0076] S202, based on the junction temperature fluctuation difference and average junction temperature of the target IGBT module in substeady state, combined with the junction temperature fluctuation frequency of the corresponding IGBT module in substeady state, uses a pre-trained lifetime analytical model to predict the maximum lifetime cycle count of the target IGBT module. For details, please refer to [link to details]. Figure 1 S102 of the illustrated embodiment will not be described again here.

[0077] S203, based on the maximum lifetime cycle count of the target IGBT module, combined with the corresponding IGBT module's current steady-state operating time and junction temperature fluctuation frequency, uses a lifetime decay ratio model to obtain the ratio of the actual cycle count to the maximum cycle count under the current steady-state operating conditions, thus obtaining the current lifetime decay of the target IGBT module. For details, please refer to [link to relevant documentation]. Figure 1 S103 of the illustrated embodiment will not be described again here.

[0078] S204, based on the current lifetime degradation of the target IGBT module and combined with the historical lifetime degradation of the corresponding IGBT module, uses a lifetime cumulative degradation superposition model to evaluate and obtain the current remaining lifetime percentage of the target IGBT module. For details, please refer to [link to relevant documentation]. Figure 1 S104 of the illustrated embodiment will not be described again here.

[0079] This embodiment provides a method for estimating the lifespan of an inverter IGBT module, which can be used in the server terminal of a booster station. Figure 7 This is a flowchart of an inverter IGBT module lifetime estimation method according to an embodiment of the present invention, such as... Figure 7 As shown, the process includes the following steps: S701, based on the target IGBT module to be evaluated, uses a constructed equivalent thermal network model and a thermal coupling correction method to perform numerical simulations, obtaining the junction temperature fluctuation difference and average junction temperature of the target IGBT module under substeady-state conditions. For details, please refer to [link to relevant documentation]. Figure 1 S101 of the illustrated embodiment will not be described again here.

[0080] S702, based on the junction temperature fluctuation difference and average junction temperature of the target IGBT module in substeady state, combined with the junction temperature fluctuation frequency of the corresponding IGBT module in substeady state, uses a pre-trained lifetime analytical model to predict the maximum lifetime cycle number of the target IGBT module.

[0081] Specifically, the aforementioned S702 includes: S7021, using the Norris Landzberg lifetime analytical model as the prediction function, constructs the initial lifetime analytical model; S7022, based on power cycling experimental data of the target IGBT module, uses parameter fitting method to correct the parameters of the initial lifetime analytical model and obtains a pre-trained lifetime analytical model. S7023, based on the junction temperature fluctuation difference and the average junction temperature of the target IGBT module in the substeady state, combined with the junction temperature fluctuation frequency of the corresponding inverter in the substeady state, is substituted into the pre-trained lifetime analytical model to obtain the maximum lifetime cycle number of the target IGBT module in the current substeady state.

[0082] Specifically, the maximum lifetime cycle count of the target IGBT in S7032 above satisfies: (51) in, Indicates the maximum lifetime cycle count of the target IGBT; This represents the junction temperature fluctuation difference of the target IGBT under substeady state. This represents the average junction temperature fluctuation of the target IGBT under substeady state. Indicates the activation energy of the target IGBT; Represents the Boltzmann constant; A , α and β All are constants, obtained from power cycling experiments; f This indicates the frequency of junction temperature fluctuations.

[0083] Using the Norris Landzberg lifetime analytical model as a quantitative expression for the maximum lifetime cycle count, and coupling the three key parameters of junction temperature fluctuation difference, junction temperature mean, and fluctuation frequency in a product form, can comprehensively reflect the fatigue failure mechanism of IGBT modules under thermal cycling, transforming the complex physical failure process into a calculable mathematical expression, and realizing a standardized mapping from junction temperature parameters to cycle life.

[0084] By using the Norris-Landzberg lifetime analytical model as the prediction function to construct an initial lifetime analytical model, the advantages of the initial lifetime analytical model in comprehensively considering junction temperature fluctuation difference, junction temperature mean, and fluctuation frequency can be fully utilized, providing a standardized mathematical framework for subsequent parameter adjustment and lifetime prediction. Then, based on the power cycling experimental data of the target IGBT module, the parameters of the initial lifetime analytical model are corrected using parameter fitting methods to obtain a pre-trained lifetime analytical model. This allows the model parameters to match the physical characteristics of the actual device, improving the adaptability and prediction accuracy of the lifetime analytical model for specific IGBT modules. Finally, based on the junction temperature fluctuation difference and junction temperature mean of the target IGBT module in the substeady state, combined with the corresponding junction temperature fluctuation frequency, the pre-trained lifetime analytical model is substituted to obtain the maximum lifetime cycle count in the current substeady state, realizing the quantitative conversion from accurate junction temperature parameters to the inherent lifetime of the device.

[0085] S703, based on the maximum lifetime cycle count of the target IGBT module, combined with the corresponding IGBT module's current steady-state operating time and junction temperature fluctuation frequency, uses a lifetime decay ratio model to obtain the ratio of the actual cycle count to the maximum cycle count under the current steady-state operating conditions, thus calculating the current lifetime decay of the target IGBT module. For details, please refer to [link to relevant documentation]. Figure 1 S103 of the illustrated embodiment will not be described again here.

[0086] S704, based on the current lifetime degradation of the target IGBT module and combined with the historical lifetime degradation of the corresponding IGBT module, uses a lifetime cumulative degradation superposition model to evaluate and obtain the current remaining lifetime percentage of the target IGBT module. For details, please refer to [link to relevant documentation]. Figure 1 S104 of the illustrated embodiment will not be described again here.

[0087] This embodiment provides a method for estimating the lifespan of an inverter IGBT module, which can be used in the server terminal of a booster station. Figure 8 This is a flowchart of an inverter IGBT module lifetime estimation method according to an embodiment of the present invention, such as... Figure 8 As shown, the process includes the following steps: S801, based on the target IGBT module to be evaluated, uses a constructed equivalent thermal network model and a thermal coupling correction method to perform numerical simulations, obtaining the junction temperature fluctuation difference and average junction temperature of the target IGBT module under substeady-state conditions. For details, please refer to... Figure 1 S101 of the illustrated embodiment will not be described again here.

[0088] S802, based on the junction temperature fluctuation difference and average junction temperature of the target IGBT module in substeady state, combined with the junction temperature fluctuation frequency of the corresponding IGBT module in substeady state, uses a pre-trained lifetime analytical model to predict the maximum lifetime cycle count of the target IGBT module. For details, please refer to [link to relevant documentation]. Figure 1 S102 of the illustrated embodiment will not be described again here.

[0089] S803, based on the maximum lifetime cycle count of the target IGBT module, combined with the corresponding IGBT module's current steady-state operating time and junction temperature fluctuation frequency, uses a lifetime decay ratio model to obtain the ratio of the actual cycle count to the maximum cycle count under the current steady-state operating conditions, thus obtaining the current lifetime decay of the target IGBT module.

[0090] Specifically, the aforementioned S803 includes: S8031, based on the maximum lifetime cycle count of the target IGBT module, combined with the duration of the corresponding IGBT module's current steady-state operation and the junction temperature fluctuation frequency, the actual number of thermal cycles that have occurred under the current steady state is obtained using the actual cycle count calculation method; S8032, based on the actual number of thermal cycles that have occurred under the current steady state, combined with the maximum lifetime cycle count of the target IGBT module, uses the lifetime decay ratio model to calculate the ratio of the actual number of cycles to the maximum number of cycles, and obtains the lifetime decay percentage corresponding to the current sub-steady state operation; S8033, based on the percentage of lifetime decay corresponding to the current substeady-state operation, is substituted into the Norris Landzberg lifetime decay ratio model to obtain the current lifetime decay of the target IGBT module.

[0091] For example, the current lifetime degradation of the target IGBT module in S8033 above satisfies the following: (52) in, Indicates the first Percentage of lifetime degradation due to substeady-state operation; Indicates the first The junction temperature fluctuation frequency of the substeady-state operating output current; Indicates the first Junction temperature fluctuation under substeady-state operation; Indicates the first Average junction temperature under substeady-state operation.

[0092] Based on the maximum lifetime cycle count of the target IGBT module, combined with the duration of the current steady-state operation and the junction temperature fluctuation frequency, the actual number of thermal cycles that have occurred under the current steady state is obtained using the actual cycle count calculation method. This converts the actual operating time into a thermal cycle count that matches the lifetime model. Then, based on this actual thermal cycle count and the maximum lifetime cycle count, the ratio of the two is calculated using the lifetime decay ratio model to obtain the lifetime decay percentage corresponding to the current sub-steady-state operation. This can accurately quantify the relative damage caused to the IGBT module by a single steady-state operation. Finally, based on this lifetime decay percentage, it is substituted into the Norris Landzberg lifetime decay ratio model to obtain the current lifetime decay of the target IGBT module. Key parameters such as junction temperature fluctuation difference, average junction temperature, and fluctuation frequency are directly embedded into the decay calculation process, realizing a quantitative conversion from physical junction temperature to engineering lifetime loss.

[0093] S804, based on the current lifetime degradation of the target IGBT module and combined with the historical lifetime degradation of the corresponding IGBT module, uses a lifetime cumulative degradation superposition model to evaluate and obtain the current remaining lifetime percentage of the target IGBT module. For details, please refer to [link to relevant documentation]. Figure 1 S104 of the illustrated embodiment will not be described again here.

[0094] This embodiment provides a method for estimating the lifespan of an inverter IGBT module, which can be used in the server terminal of a booster station. Figure 9 This is a flowchart of an inverter IGBT module lifetime estimation method according to an embodiment of the present invention, such as... Figure 9 As shown, the process includes the following steps: S901, based on the target IGBT module to be evaluated, uses a constructed equivalent thermal network model and a thermal coupling correction method to perform numerical simulations, obtaining the junction temperature fluctuation difference and average junction temperature of the target IGBT module under substeady-state conditions. For details, please refer to... Figure 1 S101 of the illustrated embodiment will not be described again here.

[0095] S902, based on the junction temperature fluctuation difference and average junction temperature of the target IGBT module in substeady state, combined with the junction temperature fluctuation frequency of the corresponding IGBT module in substeady state, uses a pre-trained lifetime analytical model to predict the maximum lifetime cycle count of the target IGBT module. For details, please refer to [link to relevant documentation]. Figure 1 S102 of the illustrated embodiment will not be described again here.

[0096] S903, based on the maximum lifetime cycle count of the target IGBT module, combined with the corresponding IGBT module's current steady-state operating time and junction temperature fluctuation frequency, uses a lifetime decay ratio model to obtain the ratio of the actual cycle count to the maximum cycle count under the current steady-state operating conditions, thus calculating the current lifetime decay of the target IGBT module. For details, please refer to [link to relevant documentation]. Figure 1 S103 of the illustrated embodiment will not be described again here.

[0097] S904, based on the current lifetime decay of the target IGBT module and combined with the historical lifetime decay of the corresponding IGBT module, uses a lifetime cumulative decay superposition model to evaluate and obtain the current remaining lifetime percentage of the target IGBT module.

[0098] Specifically, the aforementioned S904 includes: S9041, based on the current lifetime decay of the target IGBT module and combined with the historical lifetime decay of the corresponding IGBT module, the total lifetime decay percentage of the target IGBT module at the current moment is obtained using the lifetime cumulative decay superposition model. S9042, based on the total lifetime decay percentage of the target IGBT module at the current moment, calculates the current remaining lifetime percentage of the target IGBT module using the remaining lifetime percentage.

[0099] For example, S904 can be implemented as follows: (53) in, Indicates the first Percentage of remaining lifetime after substeady-state operation; Indicates the first Percentage of lifetime degradation due to substeady-state operation; Indicates the first The junction temperature fluctuation frequency of the substeady-state operating output current; Indicates the first Junction temperature fluctuation under substeady-state operation; Indicates the first Average junction temperature under substeady-state operation.

[0100] Based on the current lifetime decay of the target IGBT module and its historical lifetime decay, a cumulative lifetime decay superposition model is used to obtain the current total lifetime decay percentage. This model can accumulate independent damage under different operating conditions, truly reflecting the cumulative thermal fatigue experienced by the IGBT module since it was put into use, overcoming the limitation that a single assessment cannot characterize the overall aging state. Furthermore, based on this total lifetime decay percentage, the remaining lifetime percentage is calculated using the remaining lifetime percentage calculation formula, achieving an intuitive quantification from accumulated damage to remaining usable life, providing a clear health indicator for operation and maintenance decisions.

[0101] This embodiment also provides an inverter IGBT module lifetime estimation device, which is used to implement the above embodiments and preferred embodiments, and will not be repeated as already described. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0102] This embodiment provides a device for estimating the lifespan of an inverter IGBT module, such as... Figure 10 As shown, it includes: The numerical simulation module 1010 is used to perform numerical simulation based on the target IGBT module to be evaluated, using the constructed equivalent thermal network model and thermal coupling correction method, to obtain the junction temperature fluctuation difference and the average junction temperature of the target IGBT module under substeady state. The lifetime prediction module 1020 is used to predict the maximum lifetime cycle number of the target IGBT module based on the junction temperature fluctuation difference and the average junction temperature of the target IGBT module under substeady state, combined with the junction temperature fluctuation frequency of the corresponding IGBT module under substeady state, using a pre-trained lifetime analytical model. The attenuation calculation module 1030 is used to obtain the ratio of the actual number of cycles to the maximum number of cycles under the current steady-state operating conditions based on the maximum lifetime cycle count of the target IGBT module, combined with the corresponding IGBT module's current steady-state operating time and junction temperature fluctuation frequency, and to obtain the current lifetime attenuation of the target IGBT module using a lifetime attenuation ratio model. The lifetime assessment module 1040 is used to assess the current remaining lifetime percentage of the target IGBT module based on its current lifetime decay rate and the historical lifetime decay rate of the corresponding IGBT module using a lifetime cumulative decay superposition model.

[0103] In some alternative implementations, the numerical simulation module 1010 includes: The model equivalent unit is used to construct a fourth-order Foster equivalent thermal network model based on the target IGBT module, and to determine the parameters of the corresponding equivalent thermal network model. The loss calculation unit is used to obtain the average power loss based on the parameters of the inverter corresponding to the target IGBT module in the substeady state and the on-state voltage drop model, using the integral formula of switching loss and on-state loss. The junction temperature parameter solving unit is used to solve the junction temperature time series based on the fourth-order Foster equivalent thermal network model and average power loss, using boundary conditions and the fourth-order Runge-Kutta method, and to determine the junction temperature fluctuation difference and the junction temperature mean to be corrected. The junction temperature parameter correction unit is used to correct the junction temperature fluctuation difference and the junction temperature mean based on the junction temperature fluctuation difference to be corrected and the equivalent series thermal resistance from the FWD to the IGBT of the target IGBT module and the average power loss of the FWD. The correction is performed using a thermal coupling correction method to obtain the corrected junction temperature fluctuation difference and the junction temperature mean, which are then used as the junction temperature fluctuation difference and the junction temperature mean of the target IGBT module in the substeady state.

[0104] In some optional implementations, the junction temperature parameter solving unit is specifically used for: Based on the time constant and average power loss of the fourth-order Foster equivalent thermal network model, the boundary conditions of the heating and cooling stages are used to solve the problem, obtain the initial values ​​of the temperature difference at each order, and construct a set of thermal network differential equations. Based on the differential equations of the thermal network, the modulation function is equivalent to a sine function, and the fourth-order Runge-Kutta method is used for numerical integration to obtain the junction temperature values ​​at each time point, thus constructing a junction temperature time series. Based on the junction temperature time series, the maximum and minimum junction temperatures are obtained using the maximum and minimum value extraction method, and the junction temperature fluctuation difference and the mean junction temperature to be corrected are determined.

[0105] In some alternative implementations, the attenuation calculation module 1030 includes: The current cycle calculation unit is used to calculate the actual number of thermal cycles that have occurred in the current steady state based on the maximum lifetime cycle count of the target IGBT module, combined with the duration of the corresponding IGBT module's current steady-state operation and the junction temperature fluctuation frequency, using the actual cycle count calculation method. The lifetime decay conversion unit is used to calculate the ratio of the actual number of thermal cycles to the maximum number of cycles of the target IGBT module based on the actual number of thermal cycles that have occurred under the current steady state, combined with the maximum number of lifetime cycles of the target IGBT module, and to obtain the lifetime decay percentage corresponding to the current sub-steady state operation. The attenuation prediction unit is used to input the percentage of lifetime attenuation corresponding to the current substeady-state operation into the Norris-Landzberg lifetime attenuation ratio model to obtain the current lifetime attenuation of the target IGBT module.

[0106] In some alternative implementations, the life assessment module 1040 includes: The lifetime decay accumulation unit is used to obtain the total lifetime decay percentage of the target IGBT module at the current moment by combining the current lifetime decay amount of the target IGBT module with the historical lifetime decay amount of the corresponding IGBT module and using the lifetime decay accumulation superposition model. The remaining lifetime calculation unit is used to calculate the current remaining lifetime percentage of the target IGBT module based on the total lifetime decay percentage of the target IGBT module at the current moment.

[0107] In some alternative implementations, the lifetime prediction module 1020 includes: The model building unit is used to construct an initial lifetime analytical model by using the Norris Landzberg lifetime analytical model as the prediction function. The parameter optimization unit is used to correct the parameters of the initial lifetime analytical model based on the power cycling experimental data of the target IGBT module, and obtain the pre-trained lifetime analytical model by using the parameter fitting method. The lifetime prediction unit is used to obtain the maximum number of lifetime cycles of the target IGBT module in the current substeady state by substituting the junction temperature fluctuation difference and the average junction temperature of the target IGBT module into the pre-trained lifetime analytical model, combined with the junction temperature fluctuation frequency of the corresponding inverter in the substeady state.

[0108] In some optional implementations, the maximum lifetime cycle count of the target IGBT obtained by the lifetime prediction unit satisfies:

[0109] in, Indicates the maximum lifetime cycle count of the target IGBT; This represents the junction temperature fluctuation difference of the target IGBT under substeady state. This represents the average junction temperature fluctuation of the target IGBT under substeady state. Indicates the activation energy of the target IGBT; Represents the Boltzmann constant; A , α and β All are constants, obtained from power cycling experiments; f This indicates the frequency of junction temperature fluctuations.

[0110] The inverter IGBT module life estimation device provided in this embodiment of the invention can execute the inverter IGBT module life estimation method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the above modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.

[0111] Figure 11 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0112] The following is a detailed reference. Figure 11 The diagram illustrates a structural schematic suitable for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, a graphics processing unit, etc.) 1101, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 1102 or a program loaded from memory 1108 into random access memory (RAM) 1103. The RAM 1103 also stores various programs and data required for the operation of the electronic device. The processor 1101, ROM 1102, and RAM 1103 are interconnected via a bus 1104. An input / output (I / O) interface 1105 is also connected to the bus 1104.

[0113] Typically, the following devices can be connected to I / O interface 1105: input devices 1106 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 1107 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 1108 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1109. Communication device 1109 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 11 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0114] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 1109, or installed from memory 1108, or installed from ROM 1102. When the computer program is executed by processor 1101, it performs the functions defined above in the inverter IGBT module lifetime estimation method of the embodiments of the present invention.

[0115] Figure 11 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0116] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the inverter IGBT module lifetime estimation method shown in the above embodiments is implemented.

[0117] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0118] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for estimating the lifespan of an inverter IGBT module, characterized in that, The method includes: Based on the target IGBT module to be evaluated, numerical simulation is performed using the constructed equivalent thermal network model and thermal coupling correction method to obtain the junction temperature fluctuation difference and the average junction temperature of the target IGBT module under substeady state. Based on the junction temperature fluctuation difference and mean junction temperature of the target IGBT module in substeady state, combined with the junction temperature fluctuation frequency of the corresponding IGBT module in substeady state, the maximum lifetime cycle number of the target IGBT module is predicted using a pre-trained lifetime analytical model. Based on the maximum lifetime cycle count of the target IGBT module, combined with the corresponding IGBT module's current steady-state operating time and junction temperature fluctuation frequency, the ratio of the actual cycle count to the maximum cycle count under the current steady-state operating conditions is obtained using the lifetime decay ratio model, thus yielding the current lifetime decay of the target IGBT module. Based on the current lifetime decay of the target IGBT module, combined with the historical lifetime decay of the corresponding IGBT module, an evaluation is performed using a lifetime cumulative decay superposition model to obtain the current remaining lifetime percentage of the target IGBT module.

2. The method according to claim 1, characterized in that, The target IGBT module under evaluation is numerically simulated using a constructed equivalent thermal network model and a thermal coupling correction method to obtain the junction temperature fluctuation difference and mean junction temperature of the target IGBT module under substeady state, including: Based on the target IGBT module, a fourth-order Foster equivalent thermal network model is constructed for equivalence, and the parameters of the corresponding equivalent thermal network model are determined. Based on the parameters and on-state voltage drop model of the inverter corresponding to the target IGBT module in substeady state, the average power loss is obtained by using the integral formula of switching loss and on-state loss. Based on the fourth-order Foster equivalent thermal network model and the average power loss, the junction temperature time series is obtained by using boundary conditions and the fourth-order Runge-Kutta method, and the junction temperature fluctuation difference and the mean junction temperature to be corrected are determined. Based on the junction temperature fluctuation difference and the average junction temperature to be corrected, combined with the equivalent series thermal resistance from the FWD to the IGBT of the target IGBT module and the average power loss of the FWD, the thermal coupling correction method is used to correct the junction temperature fluctuation difference and the average junction temperature, which are then used as the junction temperature fluctuation difference and the average junction temperature of the target IGBT module in the substeady state.

3. The method according to claim 2, characterized in that, The method, based on the fourth-order Foster equivalent thermal network model and the average power loss, uses boundary conditions and the fourth-order Runge-Kutta method to solve for the junction temperature time series, and determines the junction temperature fluctuation difference and the mean junction temperature to be corrected, including: Based on the time constant of the fourth-order Foster equivalent thermal network model and the average power loss, the initial values ​​of the temperature differences at each order are obtained by solving the boundary conditions of the heating and cooling stages, and a set of thermal network differential equations is constructed. Based on the aforementioned thermal network differential equations, the modulation function is equivalent to a sine function, and numerical integration is performed using the fourth-order Runge-Kutta method to obtain the junction temperature values ​​at each time point, thus constructing a junction temperature time series. Based on the junction temperature time series, the maximum and minimum junction temperatures are obtained using the maximum and minimum value extraction method, and the junction temperature fluctuation difference and the mean junction temperature to be corrected are determined.

4. The method according to claim 1, characterized in that, The method, based on the maximum lifetime cycle count of the target IGBT module, and combined with the corresponding IGBT module's current steady-state operating time and junction temperature fluctuation frequency, uses a lifetime decay ratio model to obtain the ratio of the actual cycle count to the maximum cycle count under the current steady-state operating conditions, thus obtaining the current lifetime decay of the target IGBT module, including: Based on the maximum lifetime cycle count of the target IGBT module, combined with the duration of the corresponding IGBT module's current steady-state operation and the junction temperature fluctuation frequency, the actual number of thermal cycles that have occurred under the current steady state is obtained using the actual cycle count calculation method. Based on the actual number of thermal cycles that have occurred under the current steady state, and combined with the maximum lifetime cycle count of the target IGBT module, the ratio of the actual number of cycles to the maximum number of cycles is calculated using the lifetime decay ratio model to obtain the lifetime decay percentage corresponding to the current sub-steady state operation. Based on the percentage of lifetime decay corresponding to the current substeady-state operation, the current lifetime decay of the target IGBT module is obtained by substituting it into the Norris Landzberg lifetime decay ratio model.

5. The method according to claim 1, characterized in that, The assessment, based on the current lifetime degradation of the target IGBT module and combined with the historical lifetime degradation of the corresponding IGBT module, utilizes a lifetime cumulative degradation superposition model to obtain the current remaining lifetime percentage of the target IGBT module, including: Based on the current lifetime decay of the target IGBT module, combined with the historical lifetime decay of the corresponding IGBT module, the total lifetime decay percentage of the target IGBT module at the current moment is obtained using the lifetime cumulative decay superposition model. Based on the total lifetime decay percentage of the target IGBT module at the current moment, the remaining lifetime percentage is calculated to obtain the current remaining lifetime percentage of the target IGBT module.

6. The method according to claim 1, characterized in that, The maximum lifetime cycle count of the target IGBT module is predicted using a pre-trained lifetime analytical model, based on the junction temperature fluctuation difference and mean junction temperature of the target IGBT module under substeady state, combined with the junction temperature fluctuation frequency of the corresponding IGBT module under substeady state. This prediction includes: The Norris Landzberg lifetime analytical model was used as the prediction function to construct the initial lifetime analytical model; Based on power cycling experimental data of the target IGBT module, the parameters of the initial lifetime analytical model are corrected using parameter fitting methods to obtain a pre-trained lifetime analytical model. Based on the junction temperature fluctuation difference and average junction temperature of the target IGBT module in the substeady state, combined with the junction temperature fluctuation frequency of the corresponding inverter in the substeady state, the values ​​are substituted into the pre-trained lifetime analytical model to obtain the maximum lifetime cycle count of the target IGBT module in the current substeady state.

7. The method according to claim 1 or 6, characterized in that, The maximum lifetime cycle count of the target IGBT satisfies: in, Indicates the maximum lifetime cycle count of the target IGBT; This represents the junction temperature fluctuation difference of the target IGBT under substeady state. This represents the average junction temperature fluctuation of the target IGBT under substeady state. Indicates the activation energy of the target IGBT; Represents the Boltzmann constant; A , α and β All are constants, obtained from power cycling experiments; f This indicates the frequency of junction temperature fluctuations.

8. A device for estimating the lifespan of an inverter IGBT module, characterized in that, The device includes: The numerical simulation module is used to perform numerical simulations based on the target IGBT module to be evaluated, using the constructed equivalent thermal network model and thermal coupling correction method, to obtain the junction temperature fluctuation difference and the average junction temperature of the target IGBT module under substeady state. The lifetime prediction module is used to predict the maximum lifetime cycle number of the target IGBT module based on the junction temperature fluctuation difference and the average junction temperature of the target IGBT module in the substeady state, combined with the junction temperature fluctuation frequency of the corresponding IGBT module in the substeady state, using a pre-trained lifetime analytical model. The attenuation calculation module is used to obtain the ratio of the actual number of cycles to the maximum number of cycles under the current steady-state operating conditions based on the maximum lifetime cycle count of the target IGBT module, combined with the corresponding IGBT module's current steady-state operating time and junction temperature fluctuation frequency, and using the lifetime attenuation ratio model to obtain the current lifetime attenuation of the target IGBT module. The lifetime assessment module is used to evaluate the current remaining lifetime percentage of the target IGBT module based on its current lifetime degradation and the historical lifetime degradation of the corresponding IGBT module, using a lifetime cumulative degradation superposition model.

9. An electronic device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the inverter IGBT module lifetime estimation method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the inverter IGBT module lifetime estimation method according to any one of claims 1 to 7.

Images