An IGBT module thermal parameter extraction method based on working condition excitation
By acquiring the junction temperature dynamic curve under IGBT module operating conditions, and utilizing the adaptive truncation mean algorithm and deconvolution operation, the problem of extracting thermal parameters in the shutdown state using traditional methods is solved, achieving accurate thermal parameter extraction in the operating state, with high precision and engineering applicability.
Patent Information
- Application Number
- CN202610726041.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-05-25
- Publication Date
- 2026-08-25
AI Technical Summary
Traditional structure function methods are difficult to extract thermal parameters under the actual operating conditions of IGBT modules because they require constant power loss when the module is stopped, which cannot adapt to the actual operating conditions of time-varying power loss.
A method based on operating condition excitation is adopted. By collecting the junction temperature dynamic curve of the IGBT module when the operating conditions change, the exponential component is separated by an adaptive truncation mean algorithm, and the time constant spectrum is obtained by deconvolution operation, so as to extract the thermal resistance and heat capacity parameters.
Thermal parameters can be extracted without stopping the IGBT module during normal operation. The accuracy of the separated signal is high, and the results are close to those of traditional methods with an error of less than 0.7%, which verifies the accuracy and engineering convenience of the method.
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Figure CN122634858A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of thermal management technology for power semiconductor devices, specifically a method for extracting thermal parameters of power semiconductor devices. Background Technology
[0002] Insulated Gate Bipolar Transistors (IGBTs), as core switching devices in power electronic converter systems, are widely used in new energy power generation, electric vehicles, and energy storage. With the increasing demands for power density and operational lifespan, the reliability of IGBT modules has become a critical bottleneck restricting the safe operation of the entire system. During operation, IGBT modules repeatedly endure thermal cycling shocks caused by conduction and switching losses, leading to periodic shear strain in different internal material layers due to mismatched coefficients of thermal expansion, ultimately inducing thermal-mechanical fatigue. The most direct macroscopic manifestation of this aging process is the change in thermal parameters. For example, solder layer cracks or voids increase contact thermal resistance, and material fatigue or delamination alters local thermal capacity. Therefore, accurately extracting the thermal resistance and thermal capacity parameters of each material layer in the IGBT module is not only necessary for thermal design verification but also a core prerequisite for achieving health status assessment and remaining life prediction.
[0003] Existing methods for extracting thermal parameters of IGBT modules mainly include analytical calculation, finite element simulation, and experimental measurement. Analytical calculation and finite element simulation require knowledge of parameters such as the thermal conductivity, specific heat capacity, effective heat transfer area, and physical dimensions of each layer of material within the module. These parameters are difficult to obtain when the device is intact, thus these two methods are mostly used in the preliminary design and optimization stages of modules, and are difficult to apply to extracting thermal parameters of already packaged or operating devices. Experimental measurement, on the other hand, uses information obtained from experimental measurements to indirectly obtain the thermal parameters of the IGBT module. Among these, the structure function method mathematically analyzes the transient thermal impedance curve of the device, transforming it into an integral or differential structure function along the heat flow path, thereby separating the thermal resistance and heat capacity parameters of different material layers and contact interfaces. The structure function method can obtain complete material thermal parameter information without damaging the device packaging, and is therefore widely used in scenarios such as factory evaluation, accelerated aging test monitoring, and thermal network model calibration.
[0004] However, extracting thermal parameters using the traditional structure function method faces significant application bottlenecks. A key step in this method is obtaining the junction temperature dynamic response curve of the device under step power loss excitation. To obtain this curve, it is typically necessary to apply a constant power loss while the device is completely off and measure the complete transition of the junction temperature from the initial steady state to the new steady state, with a sufficiently long sampling time to ensure the junction temperature reaches a steady state. In actual operation, the power loss of the IGBT module varies with the system operating conditions and is not a constant step function. Taking a typical three-phase two-level inverter as an example, its power loss is approximately a superposition of a sinusoidal half-wave and a high-frequency ripple, with the junction temperature fluctuating periodically at the fundamental frequency of the output current. Under such time-varying power loss, the operating IGBT module does not exhibit ideal step power loss and a corresponding junction temperature dynamic response curve; therefore, the traditional structure function method struggles to extract the thermal parameters of the IGBT module during system operation. Summary of the Invention
[0005] A method for extracting thermal parameters of an IGBT module based on operating condition excitation includes the following steps: S1: Collect the junction temperature dynamic curve when the operating condition of the IGBT module changes, and simultaneously obtain the output current and average junction temperature throughout the process;
[0006] S2: Based on the output current and average junction temperature obtained in step S1, estimate the change in the magnitude of the equivalent power loss caused by the change in operating conditions.
[0007] S3: The junction temperature dynamic curve obtained by S1 is processed by an adaptive truncation mean algorithm, and the exponential component is separated based on the processing result.
[0008] S4: Convert the exponential component obtained in step S3 into a convolutional form at logarithmic time, and use deconvolution operation to obtain the time constant spectrum;
[0009] S5: Perform mathematical transformation on the time constant spectrum obtained in step S4 to obtain the integral structure function curve, and identify the thermal resistance and thermal capacity parameters of the IGBT module based on the inflection point on the structure function curve.
[0010] Preferably, the change in the magnitude of the equivalent power loss caused by the change in operating conditions is specifically as follows:
[0011] The actual power loss within one fundamental cycle is equivalent to a sinusoidal half-wave form, and the equivalence principle is that the total energy within one cycle is equal.
[0012] Based on the effective value of the inverter output current, DC bus voltage, and switching frequency operating parameters, and combined with the on-state voltage drop curve and switching energy curve provided in the IGBT device datasheet, the total energy of conduction loss and switching loss in one fundamental cycle is calculated. At the same time, the integral energy of the equivalent sinusoidal half-wave model in the same cycle is made equal to the total energy, and the amplitude is solved.
[0013] Calculate the difference between the equivalent power loss amplitude before and after the change in operating conditions, and use the calculation result as the change in the equivalent power loss amplitude.
[0014] Preferably, the equivalence principle is that the total energy within one cycle is equal, specifically as follows:
[0015] ;
[0016] in, Indicates the magnitude of equivalent power loss. Indicates the fundamental angular frequency of the output current. Indicates instantaneous conduction loss. Indicates the energy loss during activation. This indicates that energy consumption is turned off. Indicates the switching frequency.
[0017] Preferably, the calculation result is used as the change in the equivalent power loss amplitude, as follows:
[0018] ;
[0019] in, This represents the equivalent power loss amplitude before the change in operating conditions. This indicates the magnitude of the equivalent power loss after the operating conditions change. This represents the change in the magnitude of the calculated equivalent power loss.
[0020] Preferably, the separation of the exponential component based on the processing result is as follows:
[0021] The junction temperature dynamic curve is divided into multiple complete periods according to the fundamental period length, forming a matrix;
[0022] For each phase of the data vector, outliers are removed using an adaptive criterion based on the absolute deviation of the median, and the truncated mean is calculated. The calculated truncated mean is then used as the estimate of the periodic component of the corresponding phase.
[0023] The preliminary separation index component is obtained by subtracting the estimated value of the periodic component from the original junction temperature signal;
[0024] The periodic component estimate and the exponentially decaying component are iteratively updated until the change in the periodic estimate between two adjacent iterations is less than a preset threshold, and the final exponentially decaying component is obtained.
[0025] Using the assumption that the exponential component value of the last period in the data matrix is approximately zero, the offset compensation is calculated, and the entire exponential decay component is corrected.
[0026] Preferably, the formation of the matrix is as follows:
[0027] Assuming the equivalent power loss amplitude changes abruptly, and the junction temperature dynamic curve is a superposition of exponential and periodic components, then:
[0028] ;
[0029] in, The periodic component representing the junction temperature curve includes the amplitude after changes in ambient temperature and operating conditions. The steady-state junction temperature resulting from the equivalent power loss, Represents the exponential component. , These represent the thermal resistance and heat capacity of the one-dimensional Foster model, respectively. Indicates the start time. Indicates the order of the Foster network;
[0030] For the discrete junction temperature signal obtained by sampling It consists of three parts, specifically:
[0031] ;
[0032] in, , and Representing the periodic component, exponential component, and zero-mean noise component respectively, the sampled signal is divided according to the period length. Divided into A complete cycle is obtained, and the portion at the end that is less than one cycle is discarded to form a matrix. , Indicates the first The first cycle Sampled values on each phase.
[0033] Preferably, the step of using the calculated truncated mean as the periodic component estimate of the corresponding phase is as follows:
[0034] For each fixed phase Choose phase in All sampled values over each period constitute a data vector, and the median and median absolute deviation of the data vector are calculated.
[0035] And introduce a threshold factor Data points are filtered based on the introduced threshold factor;
[0036] Calculate the arithmetic mean of the data points after the data point selection is completed, and use the calculated arithmetic mean as the estimated value of the periodic component of the corresponding phase.
[0037] Preferably, the calculation of the median and median absolute deviation of the data vector is as follows:
[0038] Choose phase in All sampled values over each period constitute a data vector. 𝑖 𝑖 ;
[0039] Calculating the median and absolute deviation of the median of the data vector, we have:
[0040] ;
[0041] Based on the introduced threshold factor for data point selection, we have:
[0042] ;
[0043] in, This means calculating using the median. Indicates the absolute deviation of the median. Indicates the sampled signal. A matrix representing the sampled signals. This represents the set of data points to be retained. This represents the threshold factor.
[0044] Preferably, the step of using the calculated arithmetic mean as the estimated value of the periodic component of the corresponding phase is as follows:
[0045] ;
[0046] in, A matrix representing the sampled signals. This represents the set of data points to be retained. This represents the estimated value of the calculated periodic component.
[0047] Preferably, the method of obtaining the time constant spectrum using deconvolution operation is as follows:
[0048] Converting the separated and corrected exponential components into a logarithmic-time convolution form, and then representing the exponential components as discrete convolution forms based on the logarithmic time and logarithmic time constant, we have:
[0049] ;
[0050] in, This indicates the set logarithmic time. This represents the set logarithmic time constant. This represents the convolutional form of the separated and corrected exponential components in logarithmic time. Represents the fundamental current angular frequency. Represents the time constant spectrum. This represents the change in the magnitude of the equivalent power loss.
[0051] Compared with the prior art, the present invention has the following beneficial effects:
[0052] 1. This invention utilizes the power loss changes naturally generated by the adjustment of operating conditions during equipment operation as an excitation source. The junction temperature dynamic response curve under the excitation source replaces the transient thermal impedance curve that can only be obtained by applying constant power in the shutdown state in the traditional structure function method. This realizes the extraction of IGBT module thermal parameters in normal operation without shutdown or external excitation.
[0053] 2. This invention addresses the mixed characteristics of junction temperature signals under operating conditions, which simultaneously contain large periodic fluctuations and exponential trends. It introduces a signal separation algorithm based on adaptive truncation mean. Compared with the traditional fixed threshold separation method, the signal separation algorithm based on adaptive truncation mean uses the absolute deviation of the median to adjust the pruning ratio on different phases, which can extract the exponential component from the strong periodic background and ensure the accuracy of signal separation.
[0054] 3. This invention constructs a convolution equation in logarithmic time and uses deconvolution to solve for the time constant spectrum. This enables accurate reconstruction of the time constant spectrum and integral structure function curve reflecting the thermal network characteristics of an IGBT module from a mixed junction temperature signal containing strong periodic components and noise. The extraction results are almost identical to those obtained using the traditional shutdown structure function method (total thermal resistance error less than 0.7%), verifying the equivalence of the method in terms of accuracy and its superior engineering convenience compared to traditional methods. Attached Figure Description
[0055] Figure 1 This is a schematic diagram of the overall method steps of the IGBT module thermal parameter extraction method based on operating condition excitation according to the present invention.
[0056] Figure 2 This is a schematic diagram showing the variation of the equivalent power loss amplitude with the inverter output current amplitude in the IGBT module thermal parameter extraction method based on operating condition excitation according to the present invention.
[0057] Figure 3 This is a schematic diagram of the power loss waveform of an IGBT module in inverter operation, based on the IGBT module thermal parameter extraction method of the present invention, which is based on operating condition excitation.
[0058] Figure 4 This is a schematic diagram of the exponential component extraction process based on adaptive truncation mean in the IGBT module thermal parameter extraction method based on operating condition excitation of the present invention.
[0059] Figure 5This is the junction temperature waveform after the operating condition is changed in the IGBT module thermal parameter extraction method based on operating condition excitation according to the present invention.
[0060] Figure 6 The graph shows the solution results of the time constant spectrum and structure function of the IGBT module thermal parameter extraction method based on operating condition excitation according to the present invention.
[0061] Figure 7 The figure shows the solution result of the integral structure function curve under the influence of noise in the IGBT module thermal parameter extraction method based on operating condition excitation of the present invention.
[0062] Figure 8 The figure shows the solution results of the integral structure function curves under different operating conditions of the IGBT module thermal parameter extraction method based on operating condition excitation according to the present invention.
[0063] Figure 9 The figure shows the solution results of thermal resistance under different operating conditions for the IGBT module thermal parameter extraction method based on operating condition excitation according to the present invention. Detailed Implementation
[0064] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and should not be construed as limiting the scope of the invention.
[0065] Reference Figures 1 to 9 As an embodiment of the present invention, a method for extracting thermal parameters of an IGBT module based on operating condition excitation is provided, comprising the following steps:
[0066] S1: When the operating conditions of the IGBT module change, the dynamic curve of the junction temperature during the process is collected, and the output current and average junction temperature of the whole process are obtained.
[0067] Specifically, during the junction temperature transition process under changing operating conditions, the junction temperature transient change curve of the IGBT module is acquired using a junction temperature measurement device at a sampling frequency of not less than 10kHz.
[0068] In addition, the junction temperature measurement device can be a non-invasive temperature measurement device based on the thermo-electrical parameter method, or other junction temperature measurement equipment with sufficient sampling frequency and accuracy. The specific device can be set by the implementer according to the actual application scenario.
[0069] Simultaneously, the output current amplitude and the average junction temperature before and after the transition phase are recorded throughout the entire process.
[0070] Furthermore, a change in the operating conditions of the IGBT module refers to a change in the inverter's output current command, which alters the power loss amplitude of the IGBT module, thereby causing the junction temperature to transition from one steady state to another.
[0071] Furthermore, the junction temperature of an IGBT module is affected by the power loss amplitude, which in turn is determined by factors such as output current, DC bus voltage, and switching frequency. While the DC bus voltage and switching frequency typically remain constant, the output current can be adjusted by changing control commands, such as... Figure 2 As shown, the equivalent power loss amplitude of an IGBT module is greatly affected by the output current amplitude. The equivalent power loss amplitude is sensitive to the output current amplitude; changing the output current can change the power loss, thereby causing a change in the junction temperature.
[0072] Therefore, the change in operating conditions mentioned in this invention specifically refers to changes in operating conditions that cause a change in the amplitude of the output current.
[0073] S2: Estimate the change in the magnitude of equivalent power loss caused by changes in operating conditions.
[0074] Specifically, the estimation of the change in equivalent power loss amplitude caused by the change in operating conditions is based on the output current and average junction temperature obtained from S1. The specific implementation is as follows:
[0075] S2.1: The power loss of the IGBT module in inverter operation is equivalent to a half-sine wave.
[0076] Specifically, for a three-phase two-level inverter with bipolar SPWM modulation, the power loss of the IGBT module is mainly concentrated in the positive half-cycle of the output current, manifesting as the superposition of a sinusoidal half-wave and high-frequency ripple, such as... Figure 3 As shown;
[0077] To reduce model complexity, the actual power loss within one fundamental cycle is equivalent to a sinusoidal half-wave form, with the equivalence principle being that the total energy within one cycle is equal.
[0078] Without considering the initial phase, the specific expression for the equivalent power loss over one fundamental period is:
[0079] ;
[0080] in, Indicates the magnitude of equivalent power loss. Indicates the fundamental angular frequency of the output current. Indicates instantaneous conduction loss. Indicates the energy loss during activation. This indicates that energy consumption is turned off. Indicates the switching frequency.
[0081] S2.2: Calculate the equivalent power loss amplitude.
[0082] Specifically, the calculation of the equivalent power loss amplitude is based on the inverter's output current RMS value, DC bus voltage, switching frequency and other operating parameters, and combined with the conduction voltage drop curve and switching energy curve provided in the IGBT device datasheet. The total energy of conduction loss and switching loss in one fundamental cycle is calculated, and the integral energy of the equivalent sinusoidal half-wave model in the same cycle is made equal to the total energy, thereby solving for the amplitude.
[0083] S2.3: Determine the change in the magnitude of the equivalent power loss.
[0084] Specifically, determining the change in the equivalent power loss amplitude involves calculating the difference between the equivalent power loss amplitudes before and after the change in operating conditions, and using the calculation result as the change in the equivalent power loss amplitude. Therefore:
[0085] ;
[0086] in, This represents the equivalent power loss amplitude before the change in operating conditions. This indicates the magnitude of the equivalent power loss after the operating conditions change. This represents the change in the magnitude of the calculated equivalent power loss.
[0087] S3: Determine the exponential component using the adaptive truncation mean algorithm.
[0088] Specifically, the exponential component is determined using an adaptive truncation mean algorithm. This involves processing the junction temperature dynamic curve obtained from S1 using the adaptive truncation mean algorithm and separating the exponential component based on the processing result. Figure 4 As shown, the specific implementation is as follows:
[0089] S3.1: Periodic division.
[0090] Specifically, periodic segmentation involves dividing the junction temperature dynamic curve into multiple complete periods according to the fundamental period length, and forming a matrix, as follows:
[0091] The equivalent power loss amplitude is set to a step change, and the junction temperature dynamic curve is a superposition of exponential and periodic components, such as... Figure 5 As shown, we have:
[0092] ;
[0093] in, The periodic component representing the junction temperature curve includes the amplitude after changes in ambient temperature and operating conditions. The steady-state junction temperature resulting from the equivalent power loss, Represents the exponential component. , These represent the thermal resistance and heat capacity of the one-dimensional Foster model, respectively.
[0094] For the discrete junction temperature signal obtained by sampling It consists of three parts, specifically:
[0095] ;
[0096] in, , and These represent the periodic component, exponential component, and zero-mean noise component, respectively. For curves containing periodic components, the strict repeatability of periodic signals can be utilized to divide the sampled signals according to the period length. Divided into A complete cycle is obtained, and the portion at the end that is less than one cycle is discarded to form a matrix. Its elements are Indicates the first The first cycle Sampled values on each phase.
[0097] S3.2: Adaptive truncation of mean to estimate periodic components.
[0098] Specifically, adaptive truncated mean estimation of periodic components involves, for each phase of the data vector, removing outliers using an adaptive criterion based on the absolute deviation of the median, calculating the truncated mean, and using this calculated truncated mean as the estimate of the periodic components for the corresponding phase, as follows:
[0099] For each fixed phase Choose phase in All sampled values over each period constitute a data vector. ;
[0100] And by calculating the median and absolute deviation of the median of the data vector, we have:
[0101] ;
[0102] And introduce a threshold factor And the introduced threshold factor satisfies the formula The value range in this invention is: ;
[0103] Based on the introduced threshold factor for data point selection, we have:
[0104] ;
[0105] in, This means calculating using the median. Indicates the absolute deviation of the median. Indicates the sampled signal. A matrix representing the sampled signals. This represents the set of data points to be retained. Indicates the threshold factor;
[0106] Calculate the arithmetic mean of the data points after the data point selection is completed, and use the calculated arithmetic mean as the estimated value of the periodic component of the corresponding phase. Then:
[0107] ;
[0108] in, A matrix representing the sampled signals. This represents the set of data points to be retained. This represents the estimated value of the calculated periodic component.
[0109] S3.3: Preliminary separation of index components.
[0110] Specifically, the preliminary separation of the exponential component is obtained by subtracting the estimated value of the periodic component from the original junction temperature signal. Specifically:
[0111] ;
[0112] in, This represents the estimated value of the periodic component. This represents the original junction temperature signal. This indicates the initial separation of the exponential components.
[0113] S3.4: Iterative optimization.
[0114] Specifically, iterative optimization involves iteratively updating the periodic component estimate and the exponentially decaying component until the change in the periodic estimate between two consecutive iterations is less than a preset threshold, thus obtaining the final exponentially decaying component.
[0115] From the original signal Subtract the current exponential component estimate To obtain the purified periodic signal;
[0116] Then, the adaptive truncation mean operation of step S3.2 is performed again on the purified periodic signal to obtain updated periodic component estimates. ;
[0117] This process is repeated iteratively until the change in the period estimate between two consecutive iterations is less than a preset convergence threshold. Stop the iteration to obtain the final exponentially decaying component. .
[0118] S3.5: Offset compensation.
[0119] Specifically, offset compensation utilizes the assumption that the exponential component value of the last period in the data matrix is approximately zero to calculate the offset compensation amount and correct the entire exponential decay component, as follows:
[0120] Set the last cycle of the transition process The middle exponential component is close to zero, and the offset compensation is calculated. ;
[0121] The entire exponentially decaying component is arranged according to its corresponding phase. Subtract compensation amount Obtain the exponentially decaying component after offset correction;
[0122] Based on the obtained offset-corrected exponential decay component, offset compensation is performed, specifically as follows:
[0123] If all the corrected exponential components are negative, it indicates that the equivalent power loss amplitude increases after the system operating conditions change, and the junction temperature shows an upward trend during the transition phase. Based on the symmetry of energy release and storage, the opposite number can be taken for the corrected exponential components that are all negative.
[0124] S4: Obtain the time constant spectrum.
[0125] Specifically, obtaining the time constant spectrum involves converting the exponential component obtained in step S3 into a convolutional form at logarithmic time, and then using deconvolution to obtain the time constant spectrum. The specific implementation is as follows:
[0126] Converting the separated and corrected exponential components into a logarithmic-time convolution form, and then representing the exponential components as discrete convolution forms based on the logarithmic time and logarithmic time constant, we have:
[0127] ;
[0128] in, This indicates the set logarithmic time. This represents the set logarithmic time constant. This represents the convolutional form of the separated and corrected exponential components in logarithmic time. Represents the fundamental current angular frequency. Represents the time constant spectrum. This represents the change in the magnitude of the equivalent power loss.
[0129] S5: Extraction of thermal resistance and thermal capacity parameters of IGBT module.
[0130] Specifically, the extraction of thermal resistance and thermal capacity parameters of the IGBT module involves mathematically transforming the time constant spectrum obtained in step S4 to obtain the integral structure function curve, and identifying the thermal resistance and thermal capacity parameters of the IGBT module based on the inflection points on the structure function curve.
[0131] Furthermore, to further demonstrate the correctness of the IGBT module thermal parameter extraction method proposed in this invention, the technical solution of this invention is compared with the time constant spectrum curve and integral structure function curve obtained by the traditional structure function method, such as... Figure 6 As shown, it is not difficult to find that the time constant spectrum curve and integral structure function curve obtained by the method proposed in this invention almost coincide with those obtained by the traditional structure function method. The thermal parameters of the IGBT module can be identified by the inflection point on the integral structure function curve. The inflection point position of the integral structure function curve obtained by the technical solution of this invention is close to the set value, indicating that the thermal parameters of the IGBT module extracted by the technical solution of this invention are close to the set value with a small error. Therefore, the correctness of the technical solution of this invention is proven.
[0132] Furthermore, the integral structure function curve solutions of the present invention are listed separately for signal-to-noise ratios of 20 and 10, as follows: Figure 7 As shown, it is easy to see that even when subjected to noise interference, the inflection point position of the integral structure function curve obtained by the method of the present invention is still close to the set value.
[0133] Furthermore, to demonstrate that the technical solution of this invention is affected by noise, thermal resistance and thermal capacity parameters identified using structure function curves are now provided, as shown in the table below:
[0134] parameter Setting value No noise 0.0052 0.0048 0.006 0.0081 0.0323 0.0322 0.0318 0.031 0.0176 0.018 0.018 0.0159 0.005 0.0055 0.0051 0.0052 0.2457 0.131 0.154 0.284 0.4404 0.552 0.532 0.71 1.6693 1.73 1.542 2.37 116.379 121.8 146.8 132.3 0.0601 0.0605 0.0609 0.0602
[0135] As can be seen from the table above, when subjected to noise interference, the thermal parameters extracted using the technical solution of the present invention are close to the set values with small errors, thus proving that the technical solution of the present invention is less affected by noise.
[0136] Furthermore, to verify the impact of the coupling effect between junction temperature and power loss on the technical solution of this invention, the results of solving the integral structure function curves and extracting thermal resistance under four different operating conditions are compared, as follows: Figures 8 to 9 As shown, the four operating conditions are as follows:
[0137] Operating condition 1: The effective value of the output current changes from 675A to 450A;
[0138] Operating condition 2: The effective value of the output current changes from 540A to 450A;
[0139] Operating condition 3: The effective value of the output current changes from 675A to 300A;
[0140] Operating condition 4: The effective value of the output current changes from 540A to 300A.
[0141] It should be noted that, based on the comparison of the integral structure function curves and thermal resistance extraction results under four different operating conditions, it is easy to see that when the output current is the same after the operating condition changes, the structure function curves obtained using the technical solution of this invention are almost the same, and the extracted thermal resistance is also approximately the same. This indicates that when the output current is the same after the operating condition changes, the junction temperature has the same effect on the thermal parameter extraction results. In addition, the error between the thermal resistance solution results and the set value under the four different operating conditions is small, indicating that the coupling effect between junction temperature and power loss has little impact on the technical solution of this invention.
[0142] Furthermore, if the aforementioned function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0143] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.
[0144] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.
[0145] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of protection claimed by the present invention.
Claims
1. A method for extracting thermal parameters of an IGBT module based on operating condition excitation, characterized in that: Includes the following steps: S1: Collect the junction temperature dynamic curve when the operating conditions of the IGBT module change, and simultaneously obtain the output current and average junction temperature throughout the process. S2: Based on the output current and average junction temperature obtained in step S1, estimate the change in the magnitude of the equivalent power loss caused by the change in operating conditions. S3: The junction temperature dynamic curve obtained by S1 is processed by an adaptive truncation mean algorithm, and the exponential component is separated based on the processing result. S4: Convert the exponential component obtained in step S3 into a convolutional form at logarithmic time, and use deconvolution operation to obtain the time constant spectrum; S5: Perform mathematical transformation on the time constant spectrum obtained in step S4 to obtain the integral structure function curve, and identify the thermal resistance and thermal capacity parameters of the IGBT module based on the inflection point on the structure function curve.
2. The method for extracting thermal parameters of an IGBT module based on operating condition excitation as described in claim 1, characterized in that: The estimated change in the magnitude of equivalent power loss caused by the change in operating conditions is as follows: The actual power loss within one fundamental cycle is equivalent to a sinusoidal half-wave form, and the equivalence principle is that the total energy within one cycle is equal. Based on the effective value of the inverter output current, DC bus voltage, and switching frequency operating parameters, and combined with the on-state voltage drop curve and switching energy curve provided in the IGBT device datasheet, the total energy of conduction loss and switching loss in one fundamental cycle is calculated. At the same time, the integral energy of the equivalent sinusoidal half-wave model in the same cycle is made equal to the total energy, and the amplitude is solved. Calculate the difference between the equivalent power loss amplitude before and after the change in operating conditions, and use the calculation result as the change in the equivalent power loss amplitude.
3. The method for extracting thermal parameters of an IGBT module based on operating condition excitation as described in claim 2, characterized in that: The equivalence principle states that the total energy within a period is equal, as detailed below: ； in, Indicates the magnitude of equivalent power loss. Indicates the fundamental angular frequency of the output current. Indicates instantaneous conduction loss. Indicates the energy loss during activation. This indicates that energy consumption is turned off. Indicates the switching frequency.
4. The method for extracting thermal parameters of an IGBT module based on operating condition excitation as described in claim 2, characterized in that: The calculation result is used as the change in the equivalent power loss amplitude, as detailed below: ； in, This represents the equivalent power loss amplitude before the change in operating conditions. This indicates the magnitude of the equivalent power loss after the operating conditions change. This represents the change in the magnitude of the calculated equivalent power loss.
5. The method for extracting thermal parameters of an IGBT module based on operating condition excitation as described in claim 1, characterized in that: The exponential component is separated based on the processing result, as detailed below: The junction temperature dynamic curve is divided into multiple complete periods according to the fundamental period length, forming a matrix; For each phase of the data vector, outliers are removed using an adaptive criterion based on the absolute deviation of the median, and the truncated mean is calculated. The calculated truncated mean is then used as the estimate of the periodic component of the corresponding phase. The preliminary separation index component is obtained by subtracting the estimated value of the periodic component from the original junction temperature signal; The periodic component estimate and the exponentially decaying component are iteratively updated until the change in the periodic estimate between two adjacent iterations is less than a preset threshold, and the final exponentially decaying component is obtained. Using the assumption that the exponential component value of the last period in the data matrix is approximately zero, the offset compensation is calculated, and the entire exponential decay component is corrected.
6. The method for extracting thermal parameters of an IGBT module based on operating condition excitation as described in claim 5, characterized in that: The matrix formation is as follows: Assuming the equivalent power loss amplitude changes abruptly, and the junction temperature dynamic curve is a superposition of exponential and periodic components, then: ； in, The periodic component representing the junction temperature curve includes the amplitude after changes in ambient temperature and operating conditions. The steady-state junction temperature resulting from the equivalent power loss, Represents the exponential component. , These represent the thermal resistance and heat capacity of the one-dimensional Foster model, respectively. Indicates the start time. Indicates the order of the Foster network; For the discrete junction temperature signal obtained by sampling It consists of three parts, specifically: ； in, , and Representing the periodic component, exponential component, and zero-mean noise component respectively, the sampled signal is divided according to the period length. Divided into A complete cycle is obtained, and the portion at the end that is less than one cycle is discarded to form a matrix. , Indicates the first The first cycle Sampled values on each phase.
7. The method for extracting thermal parameters of an IGBT module based on operating condition excitation as described in claim 5, characterized in that: The calculation of the truncated mean as the estimate of the periodic component of the corresponding phase is as follows: For each fixed phase Choose phase in All sampled values over each period constitute a data vector, and the median and median absolute deviation of the data vector are calculated. And introduce a threshold factor Data points are filtered based on the introduced threshold factor; Calculate the arithmetic mean of the data points after the data point selection is completed, and use the calculated arithmetic mean as the estimated value of the periodic component of the corresponding phase.
8. The method for extracting thermal parameters of an IGBT module based on operating condition excitation as described in claim 7, characterized in that: The calculation of the median and median absolute deviation of the data vector is as follows: Choose phase in All sampled values over each period constitute a data vector. ; Calculating the median and absolute deviation of the median of the data vector, we have: ； Based on the introduced threshold factor for data point selection, we have: ； in, This means calculating using the median. Indicates the absolute deviation of the median. Indicates the sampled signal. A matrix representing the sampled signals. This represents the set of data points to be retained. This represents the threshold factor.
9. The method for extracting thermal parameters of an IGBT module based on operating condition excitation as described in claim 8, characterized in that: The calculation of the arithmetic mean is used as the estimated value of the periodic component of the corresponding phase, as follows: ； in, A matrix representing the sampled signals. This represents the set of data points to be retained. This represents the estimated value of the calculated periodic component.
10. The method for extracting thermal parameters of an IGBT module based on operating condition excitation as described in claim 1, characterized in that: The method for obtaining the time constant spectrum using deconvolution is as follows: Converting the separated and corrected exponential components into a logarithmic-time convolution form, and then representing the exponential components as discrete convolution forms based on the logarithmic time and logarithmic time constant, we have: ； in, This indicates the set logarithmic time. This represents the set logarithmic time constant. This represents the convolutional form of the separated and corrected exponential components in logarithmic time. Represents the fundamental current angular frequency. Represents the time constant spectrum. This represents the change in the magnitude of the equivalent power loss.