IGBT temperature rapid prediction method

Abstract

The invention discloses an IGBT temperature rapid prediction method, and relates to the technical field of power electronic device reliability, and the method comprises the steps: building an IGBT module finite element thermal model based on the geometric dimension and internal characteristics of an IGBT module; acquiring a temperature field sampling snapshot based on the IGBT module finite element thermal model; extracting an evolution mode of an IGBT module temperature field in the sampling snapshot based on a dynamic mode decomposition method; and selecting a dominant mode through the mode energy ratio, predicting the temperature field evolution of the IGBT module through the dominant mode, and generating a temperature field visualization cloud picture. Compared with the prior art, the method has the advantages that the chip temperature and internal temperature data of the IGBT module can be obtained, meanwhile, the temperature field cloud picture can be visualized, the calculation efficiency is improved, and the space occupancy rate of a disk is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power electronic device reliability, and in particular to a method for quickly predicting IGBT temperature. Background Art

[0002] Insulated-gate bipolar transistor (IGBT) power modules, due to their high power density and high efficiency, have become a core component in electric vehicle power converters. Research has shown that the lifespan of a power module is closely related to its maximum junction temperature and its temperature fluctuation, which can be quantified using lifetime prediction models. Accurately predicting the temperature field of the IGBT module is crucial to effectively predict the lifespan of the power module and improve the overall reliability of the power electronics system.

[0003] The finite element method (FEM) is a numerical solution based on materials and structures. It can accurately solve the temperature and stress characteristics of IGBTs. However, due to the large amount of computing resources consumed by the finite element analysis method and the long analysis time, it is difficult to use it for transient analysis of the junction temperature of IGBT modules.

[0004] Therefore, it is necessary to propose a fast prediction method for IGBT temperature field to improve the calculation efficiency while ensuring the calculation accuracy. Summary of the Invention

[0005] The problem to be solved by the present invention is to provide a method for quickly predicting IGBT temperature, which can greatly improve the calculation efficiency and ensure the calculation accuracy.

[0006] The present invention adopts the following technical solution: a method for quickly predicting IGBT temperature, comprising the following steps:

[0007] Step 1: Build a 3D finite element thermal model of the IGBT and define material properties and boundary conditions;

[0008] Step 2: Obtain the junction-to-case thermal resistance response curve of the IGBT through finite element simulation and compare it with the junction-to-case thermal resistance curve in the data sheet to verify the accuracy of the three-dimensional finite element thermal model;

[0009] Step 3: Obtain a sampling snapshot of the IGBT temperature field through finite element simulation, where the sampling interval is Δt and the number of sampling snapshots is n;

[0010] Step 4: Establish a temperature field prediction model for the IGBT based on the dynamic mode decomposition method and extract the temperature field evolution pattern;

[0011] Step 5: Calculate the energy of the temperature field evolution modes and sort them, and select the top k dominant modes;

[0012] Step 6: Use the first k dominant modes to predict the IGBT temperature field snapshot;

[0013] Step 7: Extract the mesh data of the IGBT's three-dimensional finite element thermal model and generate a temperature field visualization cloud map based on the predicted IGBT temperature field snapshot.

[0014] Preferably, in step 1, the three-dimensional finite element thermal model is constructed by the following sub-steps:

[0015] Step 1.1: Based on the internal structural features and geometric dimensions of the IGBT module, use 3D drawing software to create an IGBT 3D structural model in actual proportion.

[0016] Step 1.2: Mesh the IGBT 3D structure model and set the boundary conditions and material properties of the IGBT 3D structure model;

[0017] Step 1.3: Set the IGBT chip as a heat source and set the boundary condition to power loss.

[0018] Preferably, in step 4, the temperature field prediction model is constructed by the following sub-steps:

[0019] Step 4.1: Obtain a sampling snapshot of the IGBT temperature field based on finite element simulation and construct a temperature data matrix T as the input of the temperature field prediction model:

[0020] T=[T1 T2 T3...T i ...T n ]

[0021] Among them, T i represents the i-th temperature data, i=1,2,...,n;

[0022] Step 4.2: Decompose the temperature data matrix into two time-shift matrices T α and T β ,

[0023] T α =[T1,...,T i ,...,T n-1 ]

[0024] T β =[T2,...,T i+1 ...,T n ]

[0025] Step 4.3: Use the truncated SVD technique to calculate the time-shift matrix T α Perform low-dimensional decomposition;

[0026]

[0027] in, denote the left singular vector matrix and the right singular vector matrix respectively, represents the singular value matrix, the superscript * represents conjugate transpose;

[0028] Step 4.4, construct the temperature field evolution matrix A:

[0029]

[0030] Step 4.5: Construct a similar low-dimensional matrix of the evolution matrix A

[0031]

[0032] Step 4.6, respectively obtain the matrix The coefficient matrix λ and the evolution mode matrix Φ of :

[0033]

[0034] Where W represents the eigenvector matrix, λ j , Φ j represent the j-th coefficient and evolution mode respectively.

[0035] Preferably, in step 5, the energy of each evolution mode of the temperature field is calculated:

[0036]

[0037] Among them, ||·|| F represents the Frobenius norm;

[0038] Sort the modes according to the energy of each evolving mode and select the top k modes as the dominant modes:

[0039] Φ d =[Φ d1 Φ d2 ...Φ dk ]

[0040] Preferably, in step 6, the first k dominant modes are used to predict the IGBT temperature field snapshot, and the temperature field prediction model predicts the i-th IGBT snapshot, which is expressed as:

[0041]

[0042] Among them, Φ dj represents the jth dominant mode, λ dj represents the coefficient corresponding to the jth dominant mode, and T1 represents the initial temperature field snapshot.

[0043] Preferably, in the finite element simulation, the heat source input is the power loss of the IGBT module; the sampling interval Δt is 1 / 2 of the input power loss period;

[0044] The temperature data matrix T contains n sampling snapshots, each sampling snapshot contains the temperature data of all grid points; the number of sampling snapshots n is 3 / 10 of the number of required prediction snapshots.

[0045] Preferably, the time-shift matrix T is processed using the truncated SVD technique. α Perform low-dimensional decomposition, and the number of truncated singular values is r, which is half of the number of sampling snapshots n. The number of dominant modes k is selected based on the mode energy accumulation rate, which can be selected when the mode energy accumulation rate reaches a stable state and no longer increases significantly.

[0046] Preferably, in step 7, a temperature field visualization cloud map is generated using Tecplot software.

[0047] The technical solution of the present invention further provides: an electronic device, comprising:

[0048] one or more processors;

[0049] a storage device having one or more programs stored thereon;

[0050] When the one or more programs are executed by the one or more processors, the one or more processors implement any of the above-mentioned methods for quickly predicting the temperature of an IGBT.

[0051] The technical solution of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps in any of the above-mentioned IGBT temperature rapid prediction methods are implemented.

[0052] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0053] 1. The IGBT temperature rapid prediction method proposed in the present invention can improve simulation efficiency as much as possible while ensuring simulation accuracy by adopting a dynamic modal decomposition method.

[0054] 2. The IGBT temperature rapid prediction method proposed in the present invention can predict the IGBT module temperature field including the temperature inside the module package, not just the junction temperature.

[0055] 3. The IGBT temperature rapid prediction method proposed in the present invention only needs to save a sampling snapshot and an evolution pattern matrix, which greatly saves the disk space required for saving temperature field data. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1This is a flowchart of the method for quickly predicting IGBT temperature according to the present invention;

[0057] Figure 2 A schematic diagram of transient temperature response calculation according to an embodiment of the present invention;

[0058] Figure 3 Schematic diagram of temperature field calculation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0060] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0061] In one embodiment of the present invention, see Figure 1 , provides a method for rapid prediction of IGBT temperature.

[0062] In this example, a finite element model of the IGBT was first constructed using Ansys software, defining material properties and boundary conditions. The finite element model discretizes the IGBT structure into multiple grid points and, in combination with the material thermal parameters and boundary conditions, solves the heat conduction equation for each grid point.

[0063] Then, through numerical analysis methods, the model can simulate the heat transfer process inside the IGBT, thereby calculating the transient changes in the IGBT temperature field. Through finite element simulation, sampling snapshots of the IGBT temperature field are obtained, with a sampling interval of Δt and a number of sampling snapshots of n.

[0064] Next, a temperature field prediction model of IGBT is built. The temperature field evolution mode is extracted from the acquired sampling snapshots using the dynamic modal decomposition method. The modes are sorted according to the mode energy and k dominant modes are selected to construct the temperature field prediction model.

[0065] Finally, based on the sampling snapshot and the temperature field prediction model, the future snapshot of the IGBT temperature field is predicted, the mesh data of the finite element model of the IGBT is extracted, and the temperature field visualization cloud map is generated in combination with the predicted temperature field snapshot.

[0066] The specific implementation steps of the method in this embodiment are as follows:

[0067] Step S1: Build an IGBT finite element model. The sub-steps include:

[0068] (1) Based on the internal structural characteristics and geometric dimensions of the IGBT, a 3D physical model of the IGBT is constructed using 3D drawing software in actual proportion;

[0069] (2) Meshing the established three-dimensional model and configuring the model’s boundary conditions and material property parameters in the finite element analysis software Ansys;

[0070] (3) Set the IGBT chip as the heat source and set the boundary condition to power loss.

[0071] Step S2: Building an IGBT module temperature field prediction model, including the following sub-steps:

[0072] (1) In MATLAB software, the temperature data from the 1st to the nth sampling snapshots are constructed into a temperature data matrix T as the input of the prediction model:

[0073] T=[T1 T2 T3...T i ...T n ]

[0074] (2) Decompose the temperature data matrix T into two time-shift matrices T α and T β , where T α is the temperature data of the 1st to n-1st sampling snapshots, T β Temperature data for the 2nd to nth sampling snapshots

[0075] T α =[T1,...,T i ,...,T n-1 ]

[0076] T β =[T2,...,T i+1 ...,T n ]

[0077] (3) Using the truncated SVD technique to calculate the time-shift matrix T α Perform low-dimensional decomposition;

[0078]

[0079] (4) Construct the temperature field evolution matrix A:

[0080]

[0081] (5) Construct a similar low-dimensional matrix of matrix A

[0082]

[0083] (6) Obtain matrices respectively The coefficient matrix λ and the evolution mode matrix Φ of :

[0084]

[0085] Step S3: Calculate the energy of each evolution mode:

[0086]

[0087] Sort the modes according to their energy and select the top k modes as the dominant modes:

[0088] Φ d =[Φ d1 Φ d2 ...Φ dk ]

[0089] Step S4: construct a temperature field prediction model based on the dominant mode to predict the i-th IGBT snapshot:

[0090]

[0091] In this embodiment, Figure 2 The figure shows the transient temperature response of the maximum temperature point, which can accurately obtain the temperature fluctuation and temperature range; the temperature field calculation results are shown in Figure 3 As shown, the temperature data of the entire IGBT module can be obtained, including the temperature of components such as the chip, substrate, and solder.

[0092] In particular, in this embodiment, in step S2, the time shift matrix T α Use the truncated SVD technique, where the number of truncated singular values is r, and it is recommended that r be half the number of sampling snapshots n;

[0093] In step S2, the selection of the dominant mode number k is determined based on the mode energy accumulation rate. When the mode energy accumulation rate reaches a stable state and no longer increases significantly, the mode energy accumulation rate can be calculated as:

[0094]

[0095] Among them, E dj represents the energy of the j-th dominant mode.

[0096] In an embodiment of the present invention, an electronic device is also provided, comprising: one or more processors; a storage device on which one or more programs are stored; when the one or more programs are executed by the one or more processors, the one or more processors implement any of the IGBT temperature rapid prediction methods in the above embodiments.

[0097] In an embodiment of the present invention, a computer-readable storage medium is further provided, on which a computer program is stored. When the program is executed by a processor, the steps of any of the IGBT temperature rapid prediction methods in the above embodiments are implemented.

[0098] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and modifications fall within the scope of the invention as claimed.

Claims

1. A method for quickly predicting IGBT temperature, characterized in that: The steps include: Step 1: Build a 3D finite element thermal model of the IGBT and define material properties and boundary conditions; Step 2: Obtain the junction-to-case thermal resistance response curve of the IGBT through finite element simulation and compare it with the junction-to-case thermal resistance curve in the data sheet to verify the accuracy of the three-dimensional finite element thermal model; Step 3: Obtain a sampling snapshot of the IGBT temperature field through finite element simulation, where the sampling interval is Δt and the number of sampling snapshots is n; Step 4: Establish a temperature field prediction model for the IGBT based on the dynamic mode decomposition method and extract the temperature field evolution pattern; Step 5: Calculate the energy of the temperature field evolution modes and sort them, and select the top k dominant modes; Step 6: Use the first k dominant modes to predict the IGBT temperature field snapshot; Step 7: Extract the mesh data of the IGBT's three-dimensional finite element thermal model and generate a temperature field visualization cloud map based on the predicted IGBT temperature field snapshot.

2. The IGBT temperature rapid prediction method according to claim 1, characterized in that: In step 1, the three-dimensional finite element thermal model is constructed in the following sub-steps: Step 1.1: Based on the internal structural features and geometric dimensions of the IGBT module, use 3D drawing software to create an IGBT 3D structural model in actual proportion. Step 1.2: Mesh the IGBT 3D structure model and set the boundary conditions and material properties of the IGBT 3D structure model; Step 1.3: Set the IGBT chip as a heat source and set the boundary condition to power loss.

3. The IGBT temperature rapid prediction method according to claim 1, characterized in that: In step 4, the temperature field prediction model is constructed by the following sub-steps: Step 4.1: Obtain a sampling snapshot of the IGBT temperature field based on finite element simulation and construct a temperature data matrix T as the input of the temperature field prediction model: T=[T1 T2 T3...T i ...T n ] Among them, T i Represents the snapshot data of the i-th temperature field, i = 1, 2, ..., n; Step 4.2: Decompose the temperature data matrix into two time-shift matrices T α and T β : T α =[T1,...,T i ,...,T n-1 ] T β =[T2,...,T i+1 ...,T n ] Step 4.3: Use the truncated SVD technique to calculate the time-shift matrix T α Perform low-dimensional decomposition; in, denote the left singular vector matrix and the right singular vector matrix respectively, represents the singular value matrix, the superscript * represents conjugate transpose; Step 4.4, construct the temperature field evolution matrix A: Step 4.5: Construct a similar low-dimensional matrix of the evolution matrix A Step 4.6, respectively obtain the matrix The coefficient matrix λ and the evolution mode matrix Φ of : Where W represents the eigenvector matrix, λ j , Φ j represent the j-th coefficient and evolution mode respectively.

4. The IGBT temperature rapid prediction method according to claim 3, characterized in that: In step 5, the energy of each evolution mode of the temperature field is calculated: Among them, ||·|| F represents the Frobenius norm; Sort the modes according to the energy of each evolving mode and select the top k modes as the dominant modes: F d =[Φ d1 F d2 ... F dk ]。 5. The IGBT temperature rapid prediction method according to claim 4, characterized in that: In step 6, the first k dominant modes are used to predict the IGBT temperature field snapshot, and the temperature field prediction model predicts the i-th IGBT snapshot, which is expressed as: Among them, Φ dj represents the jth dominant mode, λ dj represents the coefficient corresponding to the jth dominant mode, and T1 represents the initial temperature field snapshot.

6. The IGBT temperature rapid prediction method according to claim 1, characterized in that: In the finite element simulation, the heat source input is the power loss of the IGBT module; the sampling interval Δt is 1 / 2 of the input power loss period; The temperature data matrix T includes n sampling snapshots, each sampling snapshot includes temperature data of all grid points; the number of sampling snapshots n is 3 / 10 of the number of required prediction snapshots.

7. The IGBT temperature rapid prediction method according to claim 6, characterized in that: The time-shift matrix T is transformed using the truncated SVD technique. α Perform low-dimensional decomposition, and the number of truncated singular values is r, which is half of the number of sampling snapshots n; The selection of the dominant mode number k is determined by the mode energy accumulation rate. When the mode energy accumulation rate reaches a stable state, the mode energy accumulation rate is calculated as follows: Among them, E dj represents the energy of the j-th dominant mode.

8. The IGBT temperature rapid prediction method according to claim 1, characterized in that: In step 7, generate a temperature field visualization cloud map using Tecplot software.

9. An electronic device, characterized in that: include: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the IGBT temperature rapid prediction method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the program is executed by a processor, the steps in the IGBT temperature rapid prediction method according to any one of claims 1 to 8 are implemented.

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