Method for determining heat dissipation optimization parameters of module with fin needle type bottom plate

Abstract

The invention provides a method for determining heat dissipation optimization parameters of a module with a fin needle type bottom plate, and relates to the technical field of data processing, comprising the following steps: constructing a module heat dissipation model, and determining module power consumption, a thermal resistance upper limit and a flow resistance upper limit according to module operation conditions; optimization parameters are obtained, limit values of all the optimization parameters are determined, and the optimization parameters comprise the fin needle size, the fin needle distance, the bottom plate thickness and the bottom plate material; and taking the thermal resistance and the flow resistance as optimization targets respectively to adjust each optimization parameter for simulation, determining the influence degree of each optimization parameter on the optimization target, and generating a target result, thereby solving the problem that the existing module with a fin needle type bottom plate depends on artificial experience design and lacks parameter analysis, so that the heat dissipation effect of the module is poor.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method for determining heat dissipation optimization parameters of a module with a fin-pin type bottom plate. Background Art

[0002] Good thermal management is particularly important for the stability and reliability of power modules. Power semiconductor modules used in motor controllers for new energy vehicles face more complex operating environments and special application conditions, so they require better heat dissipation. The complex operating conditions of existing new energy vehicles pose great challenges to the performance and reliability of power module heat dissipation substrates, and heat dissipation substrates need to meet heat dissipation requirements.

[0003] The pin-fin baseplate is a common module heat dissipation baseplate. The high-power module equipped with the pin-fin heat dissipation structure is installed in the external waterway structure, and water flow can be passed to perform liquid cooling. Most of the existing baseplates only select the appropriate size according to the power module, and the structure of the baseplate itself, such as the arrangement of the fins and the thickness of the baseplate, will affect the heat dissipation effect of the power module. There is a lack of research on the optimization parameters of the baseplate, and the baseplate design is only adjusted by manual experience, which often results in poor heat dissipation. Summary of the invention

[0004] In order to overcome the above-mentioned technical defects, the purpose of the present invention is to provide a method for determining the heat dissipation optimization parameters of a module with a wing-pin type base plate, so as to solve the problem that the existing design of modules with a wing-pin type base plate relies on artificial experience and lacks analysis of the base plate parameters, resulting in poor module heat dissipation effect.

[0005] The present invention discloses a method for determining heat dissipation optimization parameters of a module with a fin-pin type base plate, comprising:

[0006] Construct a module heat dissipation model and determine the module power consumption, upper limit of thermal resistance and upper limit of flow resistance according to the module operating conditions;

[0007] Obtaining optimization parameters and determining the limit values ​​of each optimization parameter, wherein the optimization parameters include: fin pin size, fin pin spacing, bottom plate thickness, and bottom plate material;

[0008] The thermal resistance and flow resistance are used as optimization targets to adjust various optimization parameters for simulation, determine the influence of each optimization parameter on the optimization target, and generate the target result.

[0009] Preferably, the determining module power consumption includes:

[0010] The module includes an upper half bridge and a lower half bridge, and each half bridge includes a first chip and a second chip;

[0011] The module includes an upper half bridge and a lower half bridge, and each half bridge includes a first chip and a second chip;

[0012] Obtaining the on-state voltage drop, on-state current and switching frequency of the first chip and the second chip under the module operation condition;

[0013] The power consumption of the first chip and the power consumption of the second chip are calculated respectively according to the on-state voltage drop, on-state current and switching frequency of the first chip and the second chip using a preset integral operation rule, and the power consumption of the module is obtained by adding them up.

[0014] Preferably, calculating the upper limit of thermal resistance includes:

[0015] Get the limit temperature and coolant temperature of each chip;

[0016] Obtain the self-thermal resistance and mutual thermal resistance data sets of each chip through simulation;

[0017] Establishing a relationship function of the junction temperature of each chip relative to the module power consumption according to the self-thermal resistance and mutual thermal resistance data set and the coolant temperature, wherein the relationship function has a change parameter corresponding to each chip;

[0018] The threshold of the variable parameter in the relationship function is preset according to the limit temperature of each chip to calculate the upper limit of the thermal resistance of each chip.

[0019] Preferably, the relationship function between the junction temperature of any chip D1 and the power consumption of the module is expressed as:

[0020] R th_D1 =(T vj_D1 -T f ) / (P D1 +K1*P T1 +K2*P T2 +K2*P D2 );

[0021] Where T1 and T2 are first chips located in different half bridges; D1 and D2 are second chips located in different half bridges; chips D1 and T1 are located in one half bridge; D2 and T2 are located in the other half bridge; th_D1 is the self-thermal resistance of chip D1; T vj_D1 is the junction temperature of chip D1; P D1 is the power consumption of chip D1; P T1 is the power consumption of chip T1; P D2 is the power consumption of chip D2; P T2 is the power consumption of chip T2; K1 and K2 are variable parameters.

[0022] Preferably, the upper limit of the flow resistance is pre-acquired according to the water channel structure formed on the upper bottom plate of the module.

[0023] Preferably, before performing simulation by adjusting various optimization parameters with thermal resistance and flow resistance as optimization targets respectively:

[0024] The optimization parameters are associated with the optimization target, wherein the base plate thickness, base plate material, fin pin size, fin pin spacing and thermal resistance are associated, and the fin pin size, fin pin spacing and flow resistance are associated.

[0025] Preferably, determining the influence of each optimization parameter on the optimization target includes:

[0026] For any optimization parameter, determine the number of variables in the optimization parameter;

[0027] When the optimization parameter has a unique variable, the control variable is used to calculate the influence of the optimization parameter on the optimization target, wherein the optimization parameter with a unique variable includes the bottom plate thickness and the bottom plate material;

[0028] When the optimization parameter has at least two variables, a combined regression is performed on each variable to calculate the influence of the optimization parameter on the optimization target, wherein the optimization parameter with at least two variables includes: wing pin size and wing pin spacing.

[0029] Preferably, the combined regression calculation of the optimization parameters and the influence of the optimization parameters on the optimization target by various variables includes:

[0030] The mapping relationship between the combination of different variables and the optimization target is obtained through simulation;

[0031] Performing a multivariate regression fitting analysis on the mapping relationship to obtain a functional relationship between the optimization parameter and the optimization target;

[0032] The maximum and minimum values ​​of the functional relationship are solved to obtain the influence of the optimization parameters on the optimization target.

[0033] Preferably, the limit values ​​of the various variables are obtained according to the limit values ​​of the optimization parameters;

[0034] For each variable, a plurality of reference values ​​are formed within the limit value with a preset variation range to carry out orthogonal combination design, so as to obtain a plurality of combinations with different variables.

[0035] Preferably, it also includes:

[0036] For optimization parameters with at least two variables:

[0037] The grey correlation analysis method is used to determine the proportion of each variable in the optimization parameter in the degree of influence of the optimization parameter on the optimization target, and is associated with the variables of each optimization parameter to supplement the degree of influence of each optimization parameter on the optimization target to generate the target result.

[0038] Compared with the prior art, the above technical solution has the following beneficial effects:

[0039] The present application provides a method for determining the heat dissipation optimization parameters of a module with a wing-pin type base plate. By determining the upper limits of the thermal resistance and flow resistance of the module, a module heat dissipation simulation model is established. The thermal resistance and flow resistance are used as optimization targets, and the influence degree of each optimization parameter is analyzed to output a target result including each optimization parameter and the corresponding influence degree, so as to provide accurate data reference for subsequent optimization of the base plate design, so as to improve the heat dissipation effect by optimizing the base plate design, and solve the problem that the existing modules with wing-pin type base plates rely on manual experience design and the heat dissipation effect is not good enough. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a flow chart of an embodiment of a method for determining heat dissipation optimization parameters of a module with a fin-pin type base plate according to the present invention;

[0041] Figure 2 It is a flow chart for determining the influence of each optimization parameter on the optimization target in an embodiment of a method for determining heat dissipation optimization parameters of a module with a fin-pin type bottom plate according to the present invention;

[0042] Figure 3 It is a reference diagram of the change in the degree of influence of the optimization parameter thermal resistance taking the thermal conductivity of the base plate material as an example in an embodiment of the method for determining the heat dissipation optimization parameters of a module with a fin-pin type base plate described in the present invention. DETAILED DESCRIPTION

[0043] The advantages of the present invention are further described below in conjunction with the accompanying drawings and specific embodiments.

[0044] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0045] The terms used in this disclosure are for the purpose of describing specific embodiments only and are not intended to limit the disclosure. The singular forms of "a", "said" and "the" used in this disclosure and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0046] It should be understood that although the terms first, second, third, etc. may be used in the present disclosure to describe various information, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present disclosure, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0047] In the following description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present invention, and have no specific meanings. Therefore, "module" and "component" can be used interchangeably.

[0048] Embodiment: This embodiment discloses a method for determining the heat dissipation optimization parameters of a module with a wing-pin type base plate, and the optimization object is a (power) module with a wing-pin type base plate, wherein the optimization parameters include parameters that affect the heat dissipation effect of the wing-pin type base plate, including but not limited to wing pin size, wing pin spacing, base plate thickness, base plate material, etc. Further, the wing pin size also includes but is not limited to the following variables: for elliptical wing pins, including major semi-axis length, minor semi-axis length, wing pin length; for circular wing pins, including radius, wing pin length; for prismatic wing pins, including major diagonal length, short diagonal length, wing pin length; for wing pins of other shapes, other variables may also be included; wing pin spacing includes but is not limited to: wing pin transverse spacing, wing pin longitudinal spacing, etc.; It should be noted that, in addition to various optimization parameters, the target result finally output by this embodiment also includes the degree of influence of each optimization parameter on the heat dissipation effect (reflected in thermal resistance and flow resistance), so that the wing pin base plate is optimized according to the determined optimization parameters provided in this embodiment in the application scenario to improve the heat dissipation effect.

[0049] Specifically, the heat dissipation optimization parameter determination method of this embodiment is as follows: Figure 1 , including the following:

[0050] S1: Build a module heat dissipation model and determine the module power consumption, upper limit of thermal resistance and upper limit of flow resistance according to the module operating conditions;

[0051] In this embodiment, it is necessary to determine the power consumption of the chip according to the extreme operating conditions of the module operation, and determine the upper limit of the thermal resistance and flow resistance according to the power consumption and the extreme temperature that the chip in the module can withstand. Specifically, the module includes an upper half bridge and a lower half bridge, and each half bridge includes a first chip and a second chip; in this embodiment, taking the dual-chip ReoPACK power semiconductor module as an example, the upper and lower half bridges include a total of four chips, the first chip is an IGBT, the second chip is an FRD, and the first chip and the second chip are interchangeable.

[0052] It should be noted that the power consumption of the module is related to the chip layout of the module itself, and can be directly obtained or determined through model simulation as in the present embodiment. The specific method of determining the module power consumption includes: obtaining the on-state voltage drop, on-state current and switching frequency of the first chip and the second chip under the module operating conditions; calculating the power consumption of the first chip and the second chip respectively according to the on-state voltage drop, on-state current and switching frequency of the first chip and the second chip using a preset integral operation rule (which may include the following first operation rule and second operation rule, or the existing operation rule for calculating power consumption may be used), and adding them up to obtain the module power consumption.

[0053] Specifically, in the above dual-chip module example, the module power consumption includes IGBT loss and FRD loss. The IGBT loss includes conduction loss and switching loss. The FRD loss includes conduction loss and reverse recovery loss. Therefore, the following is performed to obtain the module power consumption:

[0054] Under the module operating conditions, obtain the on-state voltage drop, average on-state current and switching frequency of the IGBT chip, and the on-state voltage drop, forward on-state current and switching frequency of the FRD chip;

[0055] According to the on-state voltage drop V of the IGBT chip, the first operation rule is preset. CRon , average on-state current I Cav and switching frequency f sw Calculate the on-state power consumption P of the IGBT chip cond and switching power consumption P sw , add and determine the power consumption of the IGBT chip; in the above calculation process, the conduction loss occurs when the IGBT is turned on, and is caused by the forward voltage drop of the current passing through the device. The switching loss occurs when the IGBT is in the switching process, and is caused by the change of current and voltage. The switching loss consists of turn-on loss and turn-off loss, so the first operation rule is expressed as:

[0056] P cond =V CRon ×I Cav ×(1+cosθ) / 2;P sw =f sw ×(E on +E off ), cosθ is the power factor, E on 、E off They are E under different currents. on and E off The turn-on loss and turn-off loss are obtained by linear fitting as a function.

[0057] According to the on-state voltage drop V of the FRD chip, the second operation rule is preset. F , forward conduction current IF and switching frequency f sw The on-state power consumption and reverse recovery power consumption of the FRD chip are calculated, and the sum is used to determine the power consumption of the FRD chip; the sum is used to determine the power consumption of the module. In this process, the second operation rule is expressed as:

[0058] P condF =V F ×I F ×(1-cosθ) / 2;P rec =f sw ×E rec , cosθ is the power factor, E rec The reverse recovery loss of the diode is determined by linearly fitting the Erec at different currents into a function.

[0059] Therefore: The loss of IGBT chip P IGBT =P cond +P sw ; Loss of FRD chip P frd =P ondF +P swF .

[0060] As an example, the operating condition can be set as: DC side voltage U dc , output current I ac , switching frequency f sw , power factor PF: U dc =450V, I ac =428Arms, f sw =10kHz, PF=-1; thus, the losses of the IGBT and FRD of the half-bridge under this working condition can be calculated: the total IGBT loss of one half-bridge = 151W; the total FRD loss is 327W; the total IGBT loss of the other half-bridge is 155W, and the total FRD loss is 330W

[0061] Specifically, the upper limit of thermal resistance is calculated, including the following:

[0062] Get the extreme temperature and coolant temperature of each chip; for example, the extreme temperature that the FRD used in ReoPACK can withstand is 175°C, and the coolant temperature is 65°C;

[0063] The self-thermal resistance and mutual thermal resistance data sets of each chip are obtained through simulation. The self-thermal resistance is generated by the heat generated by each chip itself, and the mutual thermal resistance refers to the thermal resistance generated by the heat generated by other chips other than each chip itself. The self-thermal resistance and mutual thermal resistance affect the junction temperature of the chip.

[0064] Establishing a relationship function of the junction temperature of each chip relative to the module power consumption according to the self-thermal resistance and mutual thermal resistance data set and the coolant temperature, wherein the relationship function has a change parameter corresponding to each chip;

[0065] Specifically, the relationship function between the junction temperature of any chip D1 and the power consumption of the module is expressed as:

[0066] R th_D1 =(T vj_D1 -T f ) / (P D1 +K1*P T1 +K2*P T2 +K2*P D2 );

[0067] Where D1 and D2 are first chips located in different half bridges; T1 and T2 are second chips located in different half bridges; chips D1 and T1 are located in one half bridge; D2 and T2 are located in the other half bridge; R th_D1 is the self-thermal resistance of chip D1; T vj_D1 is the junction temperature of chip D1; P D1 is the power consumption of chip D1; P T1 is the power consumption of chip T1; P D2 is the power consumption of chip D2; P T2 is the power consumption of chip T2; K1 and K2 are variable parameters.

[0068] The thresholds of the variable parameters (K1, K2) in the relationship function are preset according to the limit temperature of each chip to calculate the upper limit of the self-thermal resistance of each chip.

[0069] As an example, the self-thermal resistance and mutual thermal resistance data set includes the mutual thermal resistance of D1T1 of 0.0553, the mutual thermal resistance of D1D2 of 0.0187, the mutual thermal resistance of D1T2 of 0.0202, and the self-thermal resistance of D1 of 0.2156. Therefore, K1>1 / 4 and K2>1 / 8 are preset, and the adjustment margin can be appropriately increased; then the upper limit of the thermal resistance of the FRD chip R th_D1 <R th_D1MAX 0.258K / W.

[0070] Specifically, the upper limit of flow resistance is obtained in advance according to the waterway structure formed on the bottom plate of the module. As an example, the waterway structure is required to withstand a breaking pressure of more than 2.5Mpa, and thus it is determined that: :8L / min is not greater than Δpmax25kpa. The specific setting of the waterway structure can be based on the actual scenario.

[0071] S2: Obtain optimization parameters and determine the limit values ​​of each optimization parameter, wherein the optimization parameters include: fin pin size, fin pin spacing, bottom plate thickness, and bottom plate material;

[0072] As an example, the following table (taking the heat dissipation base of the elliptical Pinfin as an example) shows:

[0073]

[0074] It should be noted that the pin-fin shape, size, spacing, thickness, material, etc. of the heat dissipation base plate are used as parameters affecting thermal resistance; the pin-fin shape, size, spacing, etc. of the heat dissipation base plate are used as parameters affecting flow resistance. Therefore, before adjusting each optimization parameter for simulation with thermal resistance and flow resistance as optimization targets respectively: the optimization parameters are associated with the optimization target, wherein the base plate thickness, base plate material, fin pin size, fin pin spacing are associated with thermal resistance, and the fin pin size and fin pin spacing are associated with flow resistance.

[0075] It should also be noted that other optimization parameters / variables may be added according to different designs.

[0076] S3: Taking thermal resistance and flow resistance as optimization targets, each optimization parameter is adjusted for simulation, the influence of each optimization parameter on the optimization target is determined, and the target result is generated.

[0077] The constructed module heat dissipation model is simulated, with thermal resistance and flow resistance as optimization targets. During the simulation, the flow resistance is determined by subtracting the average pressure drop at the water outlet from the average pressure drop at the water inlet, and the thermal resistance of each chip is determined separately for the upper / lower bridge IGBT / FRD heating source PD1.

[0078] Specifically, this embodiment needs to evaluate the influence of different optimization parameters on the optimization target, so as to subsequently optimize the design of the base plate based on each optimization parameter to improve the heat dissipation effect. Figure 2 ,include:

[0079] S31: For any optimization parameter, determine the number of variables in the optimization parameter;

[0080] S32: When the optimization parameter has a unique variable, the influence of the optimization parameter on the optimization target is calculated by using the control variable, wherein the optimization parameter with a unique variable includes the bottom plate thickness and the bottom plate material;

[0081] In this embodiment, for an optimization parameter with a unique variable, such as the thickness of the base plate, it is only necessary to control all other optimization parameters except the optimization parameter to remain unchanged, and only change the thickness of the base plate within the limit value of the base plate thickness, and then obtain the thermal resistance and flow resistance corresponding to the thickness of the base plate, and the degree of influence can be determined based on the change in thermal resistance / flow resistance corresponding to the change in the thickness of the base plate.

[0082] As an example, taking the thickness of the heat dissipation base of the elliptical Pinfin as an example, parametric analysis is performed within its limit range. The thickness changes from 1mm to 5mm, and the spacing is 0.5mm. The calculation shows that the base thickness is between 3-3.5mm, and the thermal resistance has a minimum value, which is the optimal solution for heat dissipation performance. The thermal resistance range is 0.202-0.223K / W, ΔRth=0.223-0.202=0.021K / W, which is compared with the upper limit of the thermal resistance obtained in S1, and the influencing factor of this parameter is obtained as A3=ΔRth / Rth D1MAX =0.021 / 0.258=0.08. Through multiple control variable analysis (bottom plate thickness 1.5-3 mm), it is found that A3 is between 0.067 and 0.088.

[0083] Take the base material as an example. Figure 3 As shown, the thermal conductivity is mainly considered. As the thermal conductivity increases, the heat dissipation performance will be better. Among the main materials currently used for heat dissipation base plates, the one with poor thermal conductivity is tertiary aluminum 100W / m·K, and the one with good thermal conductivity is oxygen-free copper 400W / m·K. From tertiary aluminum to oxygen-free copper, the thermal resistance changes in the range of 0.243-0.184K / W, ΔRth=0.233-0.184=0.039K / W, comparing it with the upper limit of thermal resistance, the influencing factor of the optimization parameter A4=ΔRth / Rth D1MAX =0.049 / 0.258=0.19. Through more data analysis, it is found that A4 is between 0.017 and 0.21.

[0084] S33: When the optimization parameter has at least two variables, a combined regression is performed on each variable to calculate the influence of the optimization parameter on the optimization target, wherein the optimization parameter with at least two variables includes: wing pin size and wing pin spacing.

[0085] Specifically, the combined regression calculation of each variable to calculate the optimization parameters and the degree of influence of the optimization parameters on the optimization target includes: obtaining the mapping relationship between the combination of different variables and the optimization target through simulation; performing multivariate regression fitting analysis on the mapping relationship to obtain the functional relationship between the optimization parameters and the optimization target; solving the maximum and minimum values ​​of the functional relationship to obtain the degree of influence of the optimization parameters on the optimization target.

[0086] Taking the optimization parameters as the pin size including the major semi-axis length a, the minor semi-axis length b, and the pinfin length h as an example, the functional relationship between the optimization parameters and the optimization target can be as follows: i=f(a, b, h), i=1, 2, where y1 is thermal resistance and y2 is flow resistance. Specifically, the above-mentioned limit values ​​of each variable are obtained according to the limit values ​​of the optimization parameters; for each variable, a plurality of reference values ​​are formed within the limit values ​​with a preset change range to perform orthogonal combination design, and several combinations with different variables are obtained, as shown in the following table:

[0087]

[0088] Solve the maximum and minimum values ​​of the fitting function yi, thermal resistance: y1 min (0.195K / W)~y1 max (0.226K / W); flow resistance: y2 min (11kPa)~y2 max (2kPa). Then the influence degree (factor) of the optimization parameter (fin pin size) on the thermal resistance is A1 = (y1 max -y1 min ) / Rth D1MAX =0.031 / 0.258=0.12; the influence factor (degree) of the optimization parameter (fin needle size) on the flow resistance is B1=(y2 max -y2 min ) / Δpmax=9 / 25=0.36.

[0089] For optimization parameters with multiple variables, each variable will have a certain impact on the heat dissipation effect, and the degree of influence of each variable is different. Therefore, in order to further determine the impact of each variable on the final heat dissipation effect to improve the accuracy of the optimization design, for optimization parameters with at least two variables, it also includes: S34 uses a grey correlation analysis method to determine the proportion of each variable in the optimization parameter in the degree of influence of the optimization parameter relative to the optimization target, and associates it to the variables of each optimization parameter to supplement the degree of influence of each optimization parameter on the optimization target to generate the target result.

[0090] Based on the above, the grey correlation analysis method determines the degree of association between each object (optimization parameter) and the reference object (optimization target) by comparing the development trend and pattern of each object (optimization parameter). It generally includes determining the analysis sequence, data preprocessing, and calculating the grey correlation coefficient, which reflects the similarity length between each sequence, the extreme correlation degree, and the sum evaluation of the weights calculated based on the management degree, and sorts and evaluates each object. As an example, the above-mentioned major semi-axis length a, minor semi-axis length b, and pinfin length h are subjected to grey correlation analysis: The grey correlation coefficient is written in the form of a matrix P The grey correlation coefficient matrix reflects the thermal resistance and flow resistance performance corresponding to each parameter combination. The average grey correlation coefficient ξ is obtained for the combination of a = 0.2 mm. 1_a0.2 and 2_a0.2, which reflects the thermal resistance and flow resistance performance when a = 0.2 mm. The amplitude of the average grey correlation coefficient of the corresponding parameter is used as the weight ratio of the sub-parameter. For example, the amplitude of the average grey correlation coefficient of the major semi-axis length a for thermal resistance is ξ 1_a5 -ξ 1_a5 =0.21, then its importance is A1_a=0.21, and the influence degree (weight / proportion) of other variables can be obtained in the same way.

[0091] Therefore, based on the above, the target results output by the method for determining the heat dissipation optimization parameters of a module with a wing-pin type base plate provided in this embodiment include the optimization parameters and the degree of influence corresponding to each optimization parameter. The degree of influence can be expressed by an influence factor, weight or proportion, which can reveal the intrinsic connection between the structure of the heat dissipation base plate and the thermal resistance and flow resistance, and determine the importance of the optimization parameters according to the rules, provide accurate data support, improve the efficiency and accuracy of the base plate optimization, avoid one-sided and blind structural optimization, and provide an accurate reference for base plate design, which is different from the existing method that can only rely on experience.

[0092] Based on the target results output by the method for determining the heat dissipation optimization parameters of a module with a fin-type base plate provided in this embodiment, the existing fin-type base plate can be optimized, including but not limited to adjusting the specific values ​​of each optimization parameter to meet certain thermal resistance and flow resistance, which can be set according to the actual application scenario.

[0093] As a further supplement, the optimization parameter determination method provided in this embodiment can be implemented on a computer device, which includes 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 cooperates with the simulation system to implement the above steps to output the target result.

[0094] It should be noted that the embodiments of the present invention have better practicability and do not impose any form of limitation on the present invention. Any technician familiar with the field may use the technical content disclosed above to change or modify it into an equivalent effective embodiment. However, any modification or equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A method for determining heat dissipation optimization parameters of a module with a fin-pin type base plate, characterized in that: include: Construct a module heat dissipation model and determine the module power consumption, upper limit of thermal resistance and upper limit of flow resistance according to the module operating conditions; Obtaining optimization parameters and determining the limit values ​​of each optimization parameter, wherein the optimization parameters include: fin pin size, fin pin spacing, bottom plate thickness, and bottom plate material; The thermal resistance and flow resistance are used as optimization targets to adjust various optimization parameters for simulation, determine the influence of each optimization parameter on the optimization target, and generate the target result.

2. The method for determining optimization parameters according to claim 1, characterized in that: The determining module power consumption includes: The module includes an upper half bridge and a lower half bridge, and each half bridge includes a first chip and a second chip; Obtaining the on-state voltage drop, on-state current and switching frequency of the first chip and the second chip under the module operation condition; The power consumption of the first chip and the power consumption of the second chip are calculated respectively according to the on-state voltage drop, on-state current and switching frequency of the first chip and the second chip using a preset integral operation rule, and the module power consumption is obtained by adding them together.

3. The method for determining optimization parameters according to claim 2, characterized in that: Calculate the upper limit of thermal resistance, including: Get the limit temperature and coolant temperature of each chip; Obtain the self-thermal resistance and mutual thermal resistance data sets of each chip through simulation; Establishing a relationship function of the junction temperature of each chip relative to the module power consumption according to the self-thermal resistance and mutual thermal resistance data set and the coolant temperature, wherein the relationship function has a change parameter corresponding to each chip; The threshold of the variable parameter in the relationship function is preset according to the limit temperature of each chip to calculate the upper limit of the thermal resistance of each chip.

4. The method for determining optimization parameters according to claim 3, characterized in that: The relationship function between the junction temperature of any chip D1 and the power consumption of the module is expressed as: R th_D1 =(T vj_D1 -T f ) / (P D1 +K1*P T1 +K2*P T2 +K2*P D2 ); Where T1 and T2 are first chips located in different half-bridges; D1 and D2 are second chips located in different half-bridges; chips D1 and T1 are located in one half-bridge; D2 and T2 are located in the other half-bridge; R th_D1 is the self-thermal resistance of chip D1; T vj_D1 is the junction temperature of chip D1; P D1 is the power consumption of chip D1; P T1 is the power consumption of chip T1; P D2 is the power consumption of chip D2; P T2 is the power consumption of chip T2; K1 and K2 are variable parameters.

5. The method for determining optimization parameters according to claim 1, characterized in that: The upper limit of the flow resistance is obtained in advance according to the water channel structure formed on the bottom plate of the module.

6. The method for determining optimization parameters according to claim 1, characterized in that: Before simulating by adjusting various optimization parameters with thermal resistance and flow resistance as optimization targets: The optimization parameters are associated with the optimization target, wherein the base plate thickness, base plate material, fin pin size, fin pin spacing and thermal resistance are associated, and the fin pin size, fin pin spacing and flow resistance are associated.

7. The method for determining optimization parameters according to claim 1, characterized in that: Determining the influence of each optimization parameter on the optimization target includes: For any optimization parameter, determine the number of variables in the optimization parameter; When the optimization parameter has a unique variable, the control variable is used to calculate the influence of the optimization parameter on the optimization target, wherein the optimization parameter with a unique variable includes the bottom plate thickness and the bottom plate material; When the optimization parameter has at least two variables, a combined regression is performed on each variable to calculate the influence of the optimization parameter on the optimization target, wherein the optimization parameter with at least two variables includes: wing pin size and wing pin spacing.

8. The method for determining optimization parameters according to claim 7, characterized in that: The combined regression calculation of the optimization parameters and the influence of the optimization parameters on the optimization target includes: The mapping relationship between the combination of different variables and the optimization target is obtained through simulation; Perform a multivariate regression fitting analysis on the mapping relationship to obtain a functional relationship between the optimization parameter and the optimization target; solve the maximum and minimum values ​​of the functional relationship to obtain the influence of the optimization parameter on the optimization target.

9. The method for determining optimization parameters according to claim 8, characterized in that: Obtain the limit values ​​of each variable according to the limit values ​​of the optimization parameters; For each variable, a plurality of reference values ​​are formed within the limit value with a preset variation range to carry out orthogonal combination design, so as to obtain a plurality of combinations with different variables.

10. The method for determining optimization parameters according to claim 8, characterized in that: Also includes: For optimization parameters with at least two variables: The grey correlation analysis method is used to determine the proportion of each variable in the optimization parameter in the degree of influence of the optimization parameter on the optimization target, and is associated with the variables of each optimization parameter to supplement the degree of influence of each optimization parameter on the optimization target to generate the target result.

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