A traction converter life prediction method and system based on a probabilistic damage model
By constructing a probabilistic damage model, the problem of neglecting factors in the life prediction of traction converters is solved, and more accurate life prediction and system behavior understanding are achieved. It is applicable to a variety of converter topologies.
Patent Information
- Application Number
- CN202411109496.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-13
- Publication Date
- 2025-11-11
- Estimated Expiration
- 2044-08-13
AI Technical Summary
In the existing technology, the life prediction method for traction converters ignores the complex environmental factors and the variability and randomness of the degradation process of internal semiconductor devices, resulting in a large deviation between the prediction results and the actual situation.
A method for predicting the lifespan of a traction converter based on a probabilistic damage model is constructed, including the construction of a power loss model, a thermal model, a degradation model, and an interdependent degradation relationship model, which are then combined with the limit state equation for lifespan prediction.
It improves the accuracy and realism of lifetime prediction, is applicable to complex real-world systems, provides a more realistic understanding of system behavior and maintenance strategies, and is suitable for converters with different topologies.
Smart Images
Figure CN119047161B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of prognosis and health management technology for traction converters, specifically to a method and system for predicting the lifespan of traction converters based on a probabilistic damage model. Background Technology
[0002] As a core component of the electric traction system, the traction converter is responsible for converting high-voltage electrical energy from the power grid into low-voltage electrical energy suitable for electric traction. Its performance and lifespan directly affect the operational stability and safety of the entire system. However, in actual operation, the traction converter faces a variety of complex and severe challenges. Traction converters typically consist of multiple power semiconductor devices. During long-term operation, these components are affected by various factors such as current, voltage, and temperature, gradually aging. This aging process exhibits significant randomness and uncertainty, and different components are interdependent, making the prediction of the entire traction converter's lifespan even more complex.
[0003] In existing technologies, the life prediction of traction converters mostly adopts simplified models or data-driven fitting methods. These models often ignore the variable environmental factors in the actual system and cannot reflect the complex mechanism of the entire degradation process of the traction converter, including the variability and randomness of the degradation process of internal semiconductor devices, as well as the randomness of failures, resulting in a large deviation between the prediction results and the actual situation.
[0004] Therefore, a more realistic life prediction method that closely resembles actual systems is needed to improve prediction accuracy, facilitate a deeper understanding of system behavior, and thereby improve maintenance strategies to ensure the continuous and stable operation of electric traction systems. Summary of the Invention
[0005] This invention provides a method and system for predicting the life of traction converters based on a probabilistic damage model, in order to solve the problems existing in the prior art.
[0006] To achieve the above objectives, the present invention employs the following technical solution:
[0007] In a first aspect, the present invention provides a method for predicting the lifespan of a traction converter based on a probabilistic damage model, comprising:
[0008] S1: Construct a power loss model for semiconductor devices in the traction converter bridge arm, and construct a thermal model for the semiconductor devices based on the power loss model;
[0009] S2: Based on the physics of solder cracking failure, a degradation model of the semiconductor device is constructed using temperature as the model input;
[0010] S3: Based on the thermal conductivity characteristics, construct the degradation factor, correlation variable relationship matrix, and dependency coefficient matrix; and construct a model of interdependent degradation relationships;
[0011] S4: Based on the thermal model, the degradation model, and the interdependent degradation relationship model, construct a probabilistic damage model;
[0012] S5: Construct the limit state equation based on the probabilistic damage model; and predict the life of the traction converter based on the limit state equation.
[0013] In a second aspect, this application provides a traction converter life prediction system based on a probabilistic damage model, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the method described in the first aspect above.
[0014] The present invention has the following beneficial effects:
[0015] The traction converter lifetime prediction method based on a probabilistic damage model provided in this application comprehensively considers multiple factors, including the variability and randomness of the degradation process of internal semiconductor devices, as well as the randomness of failures, making it suitable for the multi-factor environment of complex real-world systems. The probabilistic modeling approach makes the model closer to the operation of actual systems, improving its realism and practicality. By realistically simulating the interdependent degradation relationships within the traction converter, it surpasses the simplification of component independence in traditional models, improving the accuracy of lifetime prediction and contributing to a deeper understanding of system behavior and improved maintenance strategies. Furthermore, this traction converter lifetime prediction method based on a probabilistic damage model can also be applied to the lifetime prediction of other converters with different topologies. Attached Figure Description
[0016] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:
[0017] Figure 1 This is a flowchart of a traction converter life prediction method based on a probabilistic damage model according to a preferred embodiment of the present invention.
[0018] Figure 2 This is a schematic diagram of the traction converter topology according to a preferred embodiment of the present invention;
[0019] Figure 3 This is a schematic diagram of the thermal network model circuit of the traction converter according to a preferred embodiment of the present invention;
[0020] Figure 4This is a cross-sectional schematic diagram of the traction converter heat dissipation system according to a preferred embodiment of the present invention;
[0021] Figure 5 This is a schematic diagram of the operating conditions of the traction converter according to a preferred embodiment of the present invention;
[0022] Figure 6 This is an average temperature diagram of each unit in the traction converter during operation according to a preferred embodiment of the present invention;
[0023] Figure 7 This is a diagram showing the change in safety margin during operation of the traction converter according to a preferred embodiment of the present invention.
[0024] Figure 8 This is a graph showing the life prediction results of the traction converter according to a preferred embodiment of the present invention. Detailed Implementation
[0025] The technical solution of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and 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] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms "an" or "a," and similar terms do not indicate a quantity limitation, but rather indicate the presence of at least one. The terms "connected" or "linked," and similar terms, are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up," "down," "left," "right," etc., are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship also changes accordingly.
[0027] Please see Figure 1 This invention application provides a method for predicting the lifespan of a traction converter based on a probabilistic damage model, comprising:
[0028] S1: Construct a power loss model for semiconductor devices in the traction converter bridge arm, and construct a thermal model for the semiconductor devices based on the power loss model;
[0029] S2: Based on the physics of solder cracking failure, a degradation model of the semiconductor device is constructed using temperature as the model input;
[0030] S3: Based on the thermal conductivity characteristics, construct the degradation factor, correlation variable relationship matrix, and dependency coefficient matrix; and construct a model of interdependent degradation relationships;
[0031] S4: Based on the thermal model, the degradation model, and the interdependent degradation relationship model, construct a probabilistic damage model;
[0032] S5: Construct the limit state equation based on the probabilistic damage model; and predict the life of the traction converter based on the limit state equation.
[0033] The aforementioned probabilistic damage model-based traction converter life prediction method can accurately predict the remaining service life of various traction converters. This method allows for flexible setting of multiple power device models and test conditions; it can accurately predict lifespan under different operating conditions and loads according to different prediction needs, without requiring physical damage experiments or relying on a large amount of historical degradation data. This method can provide highly realistic and reliable lifespan prediction results for the monitoring and maintenance of various converter power systems containing power devices. Furthermore, the probabilistic damage model-based traction converter life prediction method of this invention demonstrates broad applicability. It is not only applicable to traction converters but can also be applied to other converter lifespan prediction scenarios containing power device structures and having different topologies, providing strong support for the health management and maintenance strategy formulation of various converter power systems.
[0034] In the complete example, first, please see Figure 2 and Figure 3 The power loss model and thermal model of the semiconductor devices in the traction converter bridge arm are constructed as follows:
[0035] The power conversion of a traction converter mainly consists of two parts: the rectification section and the inverter section. Let the phase arms of the rectification and inverter sections be a finite set W = {1,2,Lz,…,Z}, where z is the phase arm number and Z is the total number of phase arms. The value of z can be 1 to Z, representing the phase arms of the rectification section and the inverter section respectively. Each phase arm consists of power devices and semiconductor devices such as diodes. Let the semiconductor devices in the z-th phase arm be a finite set W. z = {1,2,Ln,…,N}, where n is the semiconductor device serial number and N is the total number of semiconductor devices. The value of n can be 1 to N, representing the power devices, diodes, and clamping diodes from top to bottom of the phase bridge arm, respectively.
[0036] Construct a power loss model for semiconductor devices in the traction converter arm:
[0037] The formula for calculating the power loss of the nth semiconductor device in the z-th phase bridge arm is as follows:
[0038]
[0039] in, Let i be the power loss of the nth semiconductor device in the z-th phase bridge arm. z It is the conduction current at the AC terminal of the z-th phase bridge arm; f sw It is the switching frequency; and These are the nth semiconductor device in the z-th phase bridge arm with a conduction current i z The on-state voltage drop and switching losses are as follows; z,n and χ z,n These represent the average duty cycle and conduction state of the nth semiconductor device in the z-th phase bridge arm operating mode, respectively.
[0040] Constructing a thermal model of semiconductor devices in the traction converter bridge arm:
[0041] Treating the junction temperature of the semiconductor device as the temperature of the solder under the chip, a thermal model of the nth semiconductor device in the z-th phase bridge arm is constructed, with the following formula:
[0042] T z,n =P loss,z,n (Z z,n,jc +Z z,n,ch )+T z h (2)
[0043] Among them, T z,n P loss,z,n Z z,n,jc and Z z,n,ch These are the temperature, power loss, thermal resistance from the chip to the substrate, and thermal resistance from the substrate to the heat sink of the nth semiconductor device in the z-th phase bridge arm, respectively. z h It is the temperature at the radiator of the z-th phase bridge arm.
[0044] Based on the physics of solder cracking failure, a degradation model for semiconductor devices is constructed using temperature as the model input:
[0045] Using the temperature of the semiconductor device as input, the Manson-Coffin lifetime equation describes the degradation process of the semiconductor device. The formula is as follows:
[0046]
[0047] Among them, L z,n Let ΔT be the crack length of the nth semiconductor device in the z-th phase bridge arm after degradation. z,n , and ΔN z,n These are the nth semiconductor device in the z-th phase bridge arm at temperature T. z,iThe temperature fluctuation range, average temperature, and crack length reached L. z,i The required number of loops; θ L,z,n Δε is the estimation error of the crack length of the nth semiconductor device in the z-th phase bridge arm; a and b are the estimation coefficients of the crack length, respectively; Δε p,z,n The solder of the nth semiconductor device in the z-th phase bridge arm at ΔT z,n , The cumulative plastic strain under the given conditions is expressed as:
[0048]
[0049] Where A, B, Q, and R are estimated coefficients for cumulative plastic strain; Δα is the difference in thermal expansion coefficients between the solder and the chip; θ ε,z,n It is the estimation error of the cumulative plastic strain of the nth semiconductor device in the z-th phase arm.
[0050] Formulas (3) to (4) constitute the degradation model of semiconductor devices in the traction converter bridge arm.
[0051] Please see Figure 4 Based on the thermal conductivity characteristics, a degradation factor, a correlation variable relationship matrix, and a dependency coefficient matrix are constructed; a model of interdependent degradation relationships is then built.
[0052] Constructing degradation factors:
[0053] The degradation factor of semiconductor devices is constructed using the following formula:
[0054]
[0055] Where, Θ z.n This represents the degradation factor of the nth semiconductor device in the z-th phase arm; This indicates that the variable within the first set of parentheses is differentiated from the variable within the second set of parentheses.
[0056] Construct a matrix of relationships between related variables:
[0057] Degraded semiconductor devices are used as source devices, and semiconductor devices affected by degradation are used as target devices. When the nth semiconductor device in the z-th phase arm is used as the target device, a correlation variable relationship matrix is constructed. Represented as:
[0058]
[0059] in, It is the vector of correlation variables between the source devices and the target devices, which consists of all semiconductor devices in the p-th phase bridge arm. Let be the correlation variable between the k-th semiconductor device in the p-th phase bridge arm as the source device and the target device; p belongs to the phase bridge arm set, i.e., p∈W, W={1,2,L,Z}; k belongs to the semiconductor set, i.e., k∈W z W z = {1, 2, L, N}. When p ≠ z and k ≠ n, it means that the source device and the target device are not the same device; when p = z and k = n, it means that the source device and the target device are the same device.
[0060] Related variables Initialize to 0. When active devices degrade, the associated variable... Updated to:
[0061]
[0062] Among them, Z p,k,jc and P loss,p,k ε represents the chip-to-substrate thermal resistance and power loss of the k-th semiconductor device in the p-th phase bridge arm, respectively; Ploss,p.k and Let be the power loss and thermal resistance increased due to the degradation of the k-th semiconductor device in the p-th phase bridge arm, respectively, expressed as:
[0063]
[0064] in, and It is temperature T p.k and conduction current i p The increments of on-state voltage drop and switching loss of the k-th semiconductor device in the p-th phase bridge arm are obtained from the device datasheet; Γ z,n and χ z,n These are the average duty cycle and conduction state of the nth semiconductor device in the z-th phase bridge arm operating mode, respectively; λ represents the damage level of the k-th semiconductor device in the p-th phase bridge arm at time t; k It is the coefficient of thermal resistance increment due to the degradation of the kth semiconductor device; This is the nonlinear relationship between the thermal resistance and degradation of the k-th semiconductor device in the p-th phase bridge arm, typically expressed as an exponential function:
[0065]
[0066] Formulas (6) to (10) constitute the relational matrix of related variables.
[0067] Construct the dependency coefficient matrix:
[0068] When the nth semiconductor device in the z-th phase bridge arm is used as the target device, the dependency coefficient matrix C z,n Represented as:
[0069]
[0070] in, It is the dependence coefficient of all semiconductor devices in the p-th phase bridge arm as source devices on the target device; It is the dependence coefficient of the k-th semiconductor device in the p-th phase bridge arm as the source device on the target device, expressed as:
[0071]
[0072] in, It is the random variable of the dependence of the k-th semiconductor device in the p-th phase bridge arm as the source device on the target device, which is related to the strain rate sensitivity of the unit material; It is the thermal diffusivity of the k-th semiconductor device in the p-th phase bridge arm as the source device to the target device.
[0073] Formulas (11) to (12) constitute the dependency coefficient matrix.
[0074] Constructing a model of interdependent degenerate relationships:
[0075] A model of the interdependent degradation relationship between damage increment and semiconductor device degradation factor, correlation variable matrix, and dependency coefficient matrix is constructed, expressed as:
[0076]
[0077] Among them, Ψ z,n This represents the damage increment of the nth semiconductor device in the z-th phase bridge arm due to source device degradation.
[0078] Formulas (5) to (13) constitute a model of interdependent degradation relationship.
[0079] Based on the thermal model, degradation model, and interdependent degradation relationship model, a probabilistic damage model is constructed:
[0080] Let the probabilistic damage of the traction converter be a finite set. Let the damage degree of the nth semiconductor device in the z-th phase bridge arm at time t be expressed as:
[0081]
[0082] in, This represents the initial state of the nth semiconductor device in the z-th phase bridge arm at t=0. η x,z,n The estimation error of the damage level of the nth semiconductor device in the z-th phase bridge arm is calculated as follows:
[0083] η x,z,n =(θ ε,z,n) b θ L,z,n (15)
[0084] Λ(t,T z,n ) is a nonlinear relationship related to the failure physics of unit materials, expressed as:
[0085] Λ(t,T z,n )=Λ(t-Δt,T z,n )+L z,n (16)
[0086] Where Δt is the time corresponding to the number of cycles ΔN under the same cumulative plastic strain.
[0087] Formulas (14) to (16) constitute the probabilistic damage model of the traction converter.
[0088] Based on the constructed probabilistic damage model, a limit state equation is established; the lifespan of the traction converter is predicted.
[0089] Based on the semiconductor device model used in the traction converter, the crack length failure criterion for the nth semiconductor device in the z-th phase bridge arm is defined as L. fail,z,n Construct the limit state equation, which is expressed as:
[0090]
[0091] Among them, M z,n (U,t) represents the safety margin of the nth semiconductor device in the z-th phase arm at time t, M z,n When (U,t)≤0, the nth semiconductor device in the z-th phase bridge arm fails, and U is the set of uncertainties, i.e. θ D This indicates the uncertainty of the solder failure threshold;
[0092] Traction converter life prediction:
[0093] According to the limit state equation, let P be the failure probability of the nth semiconductor device in the z-th phase arm at time t. F,z,n The formula is:
[0094]
[0095] Among them, f U,z,n (u,t) is the failure probability density function; u is an element of set U; set W F It is a failure set, consisting of M z,n It consists of all elements u that are (U,t)≤0.
[0096] Let the average lifespan of the traction converter be MTTF. sThe average lifespan of the z-th phase bridge arm is MTTF. z The average lifetime of the nth semiconductor device in the z-th phase bridge arm is MTTF. z,n The calculation formulas are as follows:
[0097]
[0098] Here, min{} represents taking the minimum value of the element within the parentheses.
[0099] Let R be the reliability of the traction converter at time t. s (t), the reliability of the z-th phase bridge arm at time t is R. z (t), where the reliability of the nth semiconductor device in the z-th phase arm at time t is R. z,n (t), the calculation formulas are as follows:
[0100]
[0101] Formulas (1) to (24) constitute a method for predicting the life of traction converters based on a probabilistic damage model.
[0102] The traction converter in the above embodiment refers to the traction converter in the CRH2 EMU, with a speed range of 150-300 km / h, and the operating conditions are as follows. Figure 5 As shown in the figure. The selected power device modules are IGBT module 5SNA1200E330100 and diode module 5SDD52N4000. The specific parameters of the experiment are shown in Table 1 and Table 2.
[0103] Table 1. One of the parameters of the experiment
[0104]
[0105] Table 2. Parameters of the experiment (Part 2)
[0106]
[0107]
[0108] Among them, the average temperature of each unit of each component during the operation of the traction converter is as follows: Figure 6 As shown, the operating temperatures of different units under different converter characteristics, such as rectification or inversion, are described. It can be seen that the average temperature of units 2, 3, 6, and 7 in components 1 and 2 reaches over 70 degrees Celsius. However, the lifespan of units 6 and 7 is significantly lower than that of units 2 and 3. This is related to the failure definition; units 6 and 7 are diode modules, which have a longer failure crack definition length. The safety margin changes during traction converter operation as shown... Figure 7 As shown, the traction converter is expected to fail at least in 4.5 years; the predicted lifespan of the traction converter is as follows. Figure 8 As shown, the remaining service life of different units under different converter characteristics is described.
[0109] This application also provides a traction converter life prediction system, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor performs the steps of the method. This traction converter life prediction system can implement various embodiments of the above-described traction converter life prediction method and achieve the same beneficial effects; further details are omitted here.
[0110] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.
Claims
1. A method for predicting the lifespan of a traction converter based on a probabilistic damage model, characterized in that, include: S1: Construct a power loss model for semiconductor devices in the traction converter bridge arm, and construct a thermal model for the semiconductor devices based on the power loss model; S2: Based on the physics of solder cracking failure, a degradation model of the semiconductor device is constructed using temperature as the model input; S3: Based on the thermal conductivity characteristics, construct the degradation factor, correlation variable relationship matrix, and dependency coefficient matrix; and construct a model of interdependent degradation relationships; S4: Based on the thermal model, the degradation model, and the interdependent degradation relationship model, construct a probabilistic damage model; S5: Construct the limit state equation based on the probabilistic damage model; and predict the lifespan of the traction converter based on the limit state equation; S3 includes: S31: Constructing semiconductor device degradation factors; S32: Construct the correlation matrix of variables; S33: Construct the dependency coefficient matrix; When the nth semiconductor device in the z-th phase bridge arm is used as the target device, the dependency coefficient matrix The following relationship must be satisfied: in, It is the dependence coefficient of all semiconductor devices in the p-th phase bridge arm as source devices on the target device; It is the dependence coefficient of the k-th semiconductor device in the p-th phase bridge arm as the source device on the target device, which satisfies the following relationship: in, It is the random variable of the dependence of the k-th semiconductor device in the p-th phase bridge arm as the source device on the target device, which is related to the strain rate sensitivity of the unit material; It is the thermal diffusivity of the k-th semiconductor device in the p-th phase bridge arm as the source device to the target device; The dependency coefficient matrix is constructed from formulas (11) to (12); S34: Construct a model of interdependent degenerate relationships; S5 includes: S51: Based on the semiconductor device model used in the traction converter, the crack length failure criterion for the nth semiconductor device in the z-th phase bridge arm is defined as L. fail,z,n Construct the limit state equations, which satisfy the following relationship: Among them, M z,n (U,t) represents the safety margin of the nth semiconductor device in the z-th phase arm at time t, M z,n When (U,t)≤0, the nth semiconductor device in the z-th phase bridge arm fails, and U is the set of uncertainties, i.e. θ D θ represents the uncertainty of the solder failure threshold. L,z,n θ is the estimation error of the crack length of the nth semiconductor device in the z-th phase bridge arm; ε,z,n It is the estimation error of the cumulative plastic strain of the nth semiconductor device in the z-th phase bridge arm. It represents the damage level of the nth semiconductor device in the z-th phase bridge arm at time t; S52: Perform traction converter life prediction; According to the limit state equation, let P be the failure probability of the nth semiconductor device in the z-th phase arm at time t. F,z,n It satisfies the following relationship: Among them, f U,z,n (u,t) is the failure probability density function; u is an element of set U; set W F It is a failure set, consisting of M z,n It consists of all elements u whose (U,t)≤0; Let the average lifespan of the traction converter be MTTF. s The average lifespan of the z-th phase bridge arm is MTTF. z The average lifetime of the nth semiconductor device in the z-th phase bridge arm is MTTF. z,n The calculation formulas are as follows: Where min{} represents taking the minimum value of the element within the parentheses; Let R be the reliability of the traction converter at time t. s (t), the reliability of the z-th phase bridge arm at time t is R. z (t), where the reliability of the nth semiconductor device in the z-th phase arm at time t is R. z,n (t), the calculation formulas are as follows: Perform lifetime prediction.
2. The traction converter life prediction method based on a probabilistic damage model according to claim 1, characterized in that, S1 includes: S11: The power conversion of the traction converter is defined as including a rectification section and an inverter section. Let the phase bridge arms of the rectification and inverter sections be a finite set W = {1, 2, ..., z, ..., Z}, where z is the phase bridge arm number and Z is the total number of phase bridge arms. The value of z is 1 to Z, representing the phase bridge arms of the rectification section and the inverter section respectively. Each phase bridge arm consists of power devices and diode semiconductor devices. Let the semiconductor devices in the z-th phase bridge arm be a finite set W. z ={1,2,…n,…,N}, where n is the semiconductor device serial number and N is the total number of semiconductor devices. The value of n is 1 to N, which represent the power devices, diodes and clamping diodes from top to bottom of the phase bridge arm, respectively. S12: Construct a power loss model for semiconductor devices in the traction converter bridge arm; The power loss of the nth semiconductor device in the z-th phase arm of the bridge is calculated according to the following relationship: in, Let i be the power loss of the nth semiconductor device in the z-th phase bridge arm. z It is the conduction current at the AC terminal of the z-th phase bridge arm; f sw It is the switching frequency; and These are the nth semiconductor device in the z-th phase bridge arm with a conduction current i z The on-state voltage drop and switching losses are as follows; z,n and χ z,n These are the average duty cycle and conduction state of the nth semiconductor device in the z-th phase bridge arm operating mode, respectively; S13: Construct a thermal model of the semiconductor device in the traction converter bridge arm based on the power loss of the nth semiconductor device in the z-th phase bridge arm; Assuming the junction temperature of the semiconductor device is the temperature of the solder under the chip, a thermal model of the nth semiconductor device in the z-th phase bridge arm is constructed, satisfying the following relationship: Among them, T z,n P loss,z,n Z z,n,jc and Z z,n,ch These are the temperature, power loss, thermal resistance from the chip to the substrate, and thermal resistance from the substrate to the heat sink of the nth semiconductor device in the z-th phase bridge arm, respectively. It is the temperature at the radiator of the z-th phase bridge arm.
3. The traction converter life prediction method based on a probabilistic damage model according to claim 1, characterized in that, S2 includes: S21: Using the temperature of the semiconductor device as input, the Manson-Coffey lifetime equation describes the degradation process of the semiconductor device, satisfying the following relationship: Among them, L z,n Let ΔT be the crack length of the nth semiconductor device in the z-th phase bridge arm after degradation. z,n , and ΔN z,n These are the nth semiconductor device in the z-th phase bridge arm at temperature T. z,i The temperature fluctuation range, average temperature, and crack length reached L. z,i The number of loops required; θ L,z,n Δε is the estimation error of the crack length of the nth semiconductor device in the z-th phase bridge arm; a and b are the estimation coefficients of the crack length, respectively; Δε p,z,n The solder of the nth semiconductor device in the z-th phase bridge arm at ΔT z,n , The cumulative plastic strain under the given conditions satisfies the following relationship: Where A, B, Q, and R are estimated coefficients for cumulative plastic strain; Δα is the difference in thermal expansion coefficients between the solder and the chip; θ ε,z,n It is the estimation error of the cumulative plastic strain of the nth semiconductor device in the z-th phase arm; S22: The degradation model of semiconductor devices in the traction converter bridge arm is constructed by formulas (3) to (4).
4. The traction converter life prediction method based on a probabilistic damage model according to claim 1, characterized in that, The semiconductor device degradation factor in S31 satisfies the following relationship: Where, Θ z.n This represents the degradation factor of the nth semiconductor device in the z-th phase arm; This indicates taking the derivative of the variable within the first set of parentheses with respect to the variable within the second set of parentheses. a and b are the estimated coefficients for crack length, and A, B, Q, and R are the estimated coefficients for cumulative plastic strain. and ΔN z,n These are the nth semiconductor device in the z-th phase bridge arm at temperature T. z,i The average temperature and crack length reached L z,i The required number of loops; S32 includes: Using the degraded semiconductor device as the source device and the semiconductor device affected by the degradation as the target device, a correlation variable relationship matrix is constructed when the nth semiconductor device in the z-th phase arm is used as the target device. The following relationship must be satisfied: in, It is the vector of correlation variables between the source devices and the target devices, which consists of all semiconductor devices in the p-th phase bridge arm. Let be the correlation variable between the k-th semiconductor device in the p-th phase bridge arm as the source device and the target device; p belongs to the phase bridge arm set, i.e., p∈W, W={1,2,…,Z}; k belongs to the semiconductor set, i.e., k∈W z W z = {1,2,…,N}, when p≠z and k≠n, it means that the source device and the target device are not the same device; when p=z and k=n, it means that the source device and the target device are the same device. Related variables Initialized to 0, the associated variable is set to zero when the active device degrades. Updated to: Among them, Z p,k,jc and P loss,p,k Z represents the chip-to-substrate thermal resistance and power loss of the k-th semiconductor device in the p-th phase bridge arm, respectively. z,n,ch It is the thermal resistance from the substrate to the heat sink of the nth semiconductor device in the z-th phase bridge arm; and Let be the power loss and thermal resistance increased due to the degradation of the k-th semiconductor device in the p-th phase bridge arm, respectively, satisfying the following relationship: in, and It is temperature T p.k and conduction current i p The increments of on-state voltage drop and switching loss of the k-th semiconductor device in the p-th phase bridge arm are obtained from the device datasheet; Γ z,n and χ z,n These are the average duty cycle and conduction state of the nth semiconductor device in the z-th phase bridge arm operating mode, respectively; λ represents the damage level of the k-th semiconductor device in the p-th phase bridge arm at time t; k It is the coefficient of thermal resistance increment due to the degradation of the kth semiconductor device; This is the nonlinear relationship between the thermal resistance and degradation of the k-th semiconductor device in the p-th phase bridge arm, typically expressed as an exponential function: Formulas (6) to (10) constitute a matrix of related variables; S34 includes: A model of the interdependent degradation relationship between damage increment and semiconductor device degradation factor, correlation variable matrix, and dependency coefficient matrix is constructed, satisfying the following relationship: Among them, Ψ z,n This represents the damage increment of the nth semiconductor device in the z-th phase bridge arm due to source device degradation; The interdependent degradation relationship model is constructed by formulas (5) to (13).
5. The traction converter life prediction method based on a probabilistic damage model according to claim 1, characterized in that, S4 includes: Let the probabilistic damage of the traction converter be a finite set. Let the damage degree of the nth semiconductor device in the z-th phase bridge arm at time t satisfy the following relationship: in, This represents the initial state of the nth semiconductor device in the z-th phase bridge arm at t=0. η x,z,n The estimation error of the damage level of the nth semiconductor device in the z-th phase bridge arm is calculated as follows: or x,z,n =(θ ε,z,n ) b i L,z,n (15) Where, θ ε,z,n θ is the estimation error of the cumulative plastic strain of the nth semiconductor device in the z-th phase arm. L,z,n Λ(t,T) represents the estimation error of the crack length of the nth semiconductor device in the z-th phase bridge arm, and b is the estimation coefficient of the crack length; z,n ) is a nonlinear relationship related to the physics of failure of a single material, expressed as: Λ(t,T z,n )=Λ(t-Δt,T z,n )+L z,n (16) Where Δt is the time corresponding to the number of cycles ΔN under the same cumulative plastic strain, T z,n It is the nth semiconductor device in the z-th phase bridge arm at temperature T z,i The temperature below, L z,n The crack length of the nth semiconductor device in the z-th phase bridge arm after degradation; The probabilistic damage model of the traction converter is constructed by formulas (14) to (16).
6. A traction converter life prediction system based on a probabilistic damage model, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of any of the methods described in claims 1-5.