CN117291136B - Multi-objective optimization design method for high-power density thermal performance of energy storage converter - Google Patents
Multi-objective optimization design method for high-power density thermal performance of energy storage converter Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 35
- 238000005457 optimization Methods 0.000 title claims abstract description 29
- 238000004146 energy storage Methods 0.000 title claims abstract description 26
- 239000002245 particle Substances 0.000 claims abstract description 48
- 239000000463 material Substances 0.000 claims abstract description 17
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 12
- 230000002068 genetic effect Effects 0.000 claims abstract description 9
- 238000004088 simulation Methods 0.000 claims abstract description 4
- 239000003990 capacitor Substances 0.000 claims description 26
- 230000035772 mutation Effects 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000001816 cooling Methods 0.000 claims description 6
- 239000012530 fluid Substances 0.000 claims description 4
- 238000012938 design process Methods 0.000 claims description 3
- 230000006870 function Effects 0.000 claims description 3
- 230000017525 heat dissipation Effects 0.000 abstract description 5
- 238000010438 heat treatment Methods 0.000 abstract 1
- HBMJWWWQQXIZIP-UHFFFAOYSA-N silicon carbide Chemical compound [Si+]#[C-] HBMJWWWQQXIZIP-UHFFFAOYSA-N 0.000 description 15
- 229910010271 silicon carbide Inorganic materials 0.000 description 15
- 238000013178 mathematical model Methods 0.000 description 3
- XAGFODPZIPBFFR-UHFFFAOYSA-N aluminium Chemical compound [Al] XAGFODPZIPBFFR-UHFFFAOYSA-N 0.000 description 2
- 229910052782 aluminium Inorganic materials 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 239000003792 electrolyte Substances 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 229910000859 α-Fe Inorganic materials 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
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- 238000011161 development Methods 0.000 description 1
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- 238000005516 engineering process Methods 0.000 description 1
- 239000003365 glass fiber Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 150000003071 polychlorinated biphenyls Chemical class 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/30—Circuit design
- G06F30/39—Circuit design at the physical level
- G06F30/392—Floor-planning or layout, e.g. partitioning or placement
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/28—Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/30—Circuit design
- G06F30/39—Circuit design at the physical level
- G06F30/398—Design verification or optimisation, e.g. using design rule check [DRC], layout versus schematics [LVS] or finite element methods [FEM]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/08—Fluids
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2115/00—Details relating to the type of the circuit
- G06F2115/12—Printed circuit boards [PCB] or multi-chip modules [MCM]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/08—Thermal analysis or thermal optimisation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
Abstract
The invention discloses a high-power-density thermal performance multi-objective optimization design method of an energy storage converter, which comprises the steps of firstly calculating heat loss power of a main heating element of the energy storage converter, then calculating the bottom area of each power element to obtain two-dimensional coordinates of the power element on a PCB, establishing a thermal simulation three-dimensional model, taking calculated loss as a heat source, setting material properties and solving domain boundary conditions, carrying out grid division and CFD solving to obtain the highest temperature of a middle plane power element and the whole outer contour volume of the PCB, taking the highest temperature and the whole outer contour volume of the PCB as minimum optimization targets, optimizing the coordinates of each power element by adopting a multi-objective genetic particle swarm algorithm, and finally finely adjusting the layout of the elements to obtain the main topology thermal design layout of the energy storage converter meeting a plurality of optimization targets. Compared with the prior art, the invention can realize the high-power density structural design of the energy storage converter and meet the heat dissipation requirement of a compact structure.
Description
Technical Field
The invention belongs to the technical field of power electronic device thermal design, and particularly relates to a multi-objective optimization design method for high-power density thermal performance of an energy storage converter.
Background
In the field of electrical device design, efficient thermal design has been a critical issue. In electrical equipment, components such as a power semiconductor, an inductor, a capacitor and the like can generate certain loss in the working process, if a good heat dissipation effect cannot be ensured, heat can be accumulated to further raise the temperature of the components, the performance and the service life of the components are affected, and the service life and the efficiency of the whole equipment are further affected; if the rated operating temperature of the components is exceeded, the components are likely to be damaged and directly fail, and the safety and stability of the equipment operation are affected.
Particularly for the energy storage converter, due to the development trend and the requirement of high power density of a power electronic device, the area of a main topology Printed Circuit Board (PCB) of the energy storage converter is continuously reduced, the wiring density and the density of components are greatly increased, and the heat dissipation problem of the main topology PCB is gradually highlighted, so that the high power density heat management and structural design are required, the requirement of high power density is met, good heat dissipation can be realized, the working safety and stability of the energy storage converter are ensured, and the prior art lacks a systematic multi-objective optimization design method for the main topology thermal performance and the component layout of the energy storage converter.
Disclosure of Invention
Aiming at the defects of the existing energy storage converter thermal performance multi-objective optimization design technology, the invention provides a high-power density thermal performance multi-objective optimization design method of an energy storage converter, and aims to realize the high-power density structural design of the energy storage converter and meet the heat dissipation requirement of a compact structure.
The technical scheme is as follows: the invention discloses a multi-objective optimization design method for high-power density thermal performance of an energy storage converter, which comprises the following steps:
step 1: initializing temporary parametersTp、VpThe value is more than or equal to 1000;
step 2: performing heat loss calculation on power components of the energy storage converter, wherein the power components comprise a filter capacitor, a boost inductor, a filter inductor and a SiC MOSFET device;
step 3: assembling the SiC MOSFET device on a radiating fin, and calculating a filter capacitor, a boost inductor, a filter inductor and a radiating fin projection area on a Printed Circuit Board (PCB) plane;
step 4: initializing coordinates of power components in a two-dimensional plane of a Printed Circuit Board (PCB), and determining that the positions of the power components on the PCB are not interfered with each other according to the coordinates and calculated projection areas of a filter capacitor, a boost inductor, a filter inductor and a radiating fin;
step 5: positioning the positions of all power components on a Printed Circuit Board (PCB) according to the coordinates of the power components, setting an outer contour enveloping rectangle projected on the bottom surface of each power component after the coordinates are positioned as the area of the PCB, and establishing a thermal simulation three-dimensional model;
step 6: taking the heat loss of the power component obtained by solving as a heat source of a temperature field, and setting material properties and solving domain boundary conditions; dividing the solving domain grids, carrying out fluid dynamics CFD solving, and obtaining the highest temperature of the middle plane power deviceT；
Step 7: highest temperature of mid-plane power device based on acquisitionTIntegral outer contour volume of Printed Circuit Board (PCB)VJudging whether the thermal performance optimization design is finished or not, if not, executing the step 8, and if so, executing the step 10;
step 8: the highest temperature of the middle plane power device obtained by current solvingTIntegral outer contour volume of Printed Circuit Board (PCB)VAssigning temporary parameters respectivelyTp、Vp；
Step 9: the method comprises the steps of taking filter capacitance, boost inductance, filter inductance and radiating fin coordinates as decision variables, taking the current PCB size range as decision space, and taking the highest temperature of a middle plane power device as decision spaceTIntegral outer contour volume of Printed Circuit Board (PCB)VMinimum ofFor optimizing the target, carrying out unconstrained optimization iteration updating on the coordinates of a filter capacitor, a boost inductor, a filter inductor and a radiating fin based on a multi-target genetic particle swarm algorithm, determining that all power components on a Printed Circuit Board (PCB) are not interfered with each other according to the coordinates and the calculated projection areas of the filter capacitor, the boost inductor, the filter inductor and the radiating fin, and returning to the step 5 for loop iteration;
step 10: the design process ends.
Further, the method for determining the coordinates of the power component in the two-dimensional plane of the printed circuit board PCB in the step 4 is as follows:
the approximate cylindrical device is positioned by the center coordinates of the bottom surface, and whether interference occurs is determined by the diameter of the bottom surface; the approximate cuboid device is positioned by four vertex coordinates of the bottom surface projected rectangle; the rectangular direction of the bottom surface projection of the cuboid device only considers the arrangement direction of the long side of the cuboid device parallel to the edge of the rectangular printed circuit board PCB.
Further, the solution domain boundary condition set in the step 6 is set as follows: taking the surface of the space with the height of the largest component as a boundary condition according to the PCB area of the printed circuit board as the bottom surface; the material properties are set according to the actual power component materials.
Further, the middle plane in the steps 6 to 8 refers to a plane where the maximum height of the power components on the printed circuit board PCB is half.
Further, the specific process of performing unconstrained optimization and iterative updating of the filter capacitor, the boost inductor, the filter inductor and the cooling fin coordinates by the multi-target genetic particle swarm algorithm in the step 9 is as follows:
each particle position in the multi-target genetic particle swarm algorithm represents coordinates of a filter capacitor, a boost inductor, a filter inductor and a radiating fin in a Printed Circuit Board (PCB), and each particle fitness function is the highest temperature of a middle plane power device obtained by solving CFDTIntegral outer profile volume of PCBVMinimum 2 optimization objectives;
the cross operation performed on each particle position in the algorithm is as follows:
；
wherein,Pos 1 and (3) withPos 2 The positions of the parent particles are respectively indicated,Pos 1,cross and (3) withPos 2,cross The positions of the child particles after the crossover operation,αis [0,1]Random numbers in between;
randomly selecting 70% of individuals in the whole population to perform mutation operation:
；
wherein,Pos i is the first in the populationiThe position of the individual particles is determined,Pos i,mutate for the position of the particle after the mutation of the particle,σ m for the step size of the mutation,RN i random numbers are distributed for standard normal states;
the method for updating the particle position comprises the following steps:
；
in the method, in the process of the invention,Vel i (k) And (3) withPos i (k) Respectively the firstiThe particles are at the firstkThe speed and position in the iteration of the wheel,Vel i (k+1) andPos i (k+1) are respectively the firstiThe particles are at the firstk+Speed and position in 1 round of iteration,wfor the inertial weight of the particles,c 1 and (3) withc 2 The learning coefficients of the individuals and the groups are respectively obtained,r 1 (k) And (3) withr 2 (k) Is the firstkRandom numbers are uniformly distributed between 0 and 1 in the round of iteration,Pos i,pbest is the firstiThe individual optimal positions of the individual particles,Pos leader is the optimal position of the group.
The beneficial effects are that: the multi-objective optimization design problem of the main topology PCB of the electrical equipment such as the energy storage converter can be solved, the synchronous minimum design of the equipment volume and the highest temperature can be realized, and the multi-objective optimal main topology thermal design result can be obtained. The multi-objective optimization design flow can also be used for the multi-objective optimization design problem of main topology PCBs of other electrical equipment such as electric automobile chargers, photovoltaic inverters and the like, and has certain universality.
Drawings
FIG. 1 is a flow chart of a high power density thermal performance multi-objective optimization design of an energy storage converter;
FIG. 2 is a grid division diagram of a power component, wherein (a) is a SiC MOSFET device, (b) is a filter capacitor, and (c) is a filter inductor;
FIG. 3 is a graph of velocity vector direction and velocity profile of an air flow field within a chassis being solved;
FIG. 4 is a plot of the temperature field distribution in the mid-plane of the solution;
fig. 5 is a diagram of a main topology PCB layout of the energy storage converter after manual layout fine tuning of the optimum design.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
The following detailed description of the invention is provided in connection with the accompanying drawings to illustrate specific details of the design so that the invention may be more fully understood.
The invention provides a multi-objective optimization design method for high-power density thermal performance of an energy storage converter, which is shown in figure 1 and comprises the following steps:
step 1: initializing temporary parametersTp、VpThe value is more than or equal to 1000.
Step 2: the heat loss calculation is carried out on power components of the energy storage converter, the power components mainly comprise filter capacitors, boost inductors, filter inductors and SiC MOSFET devices, and the loss calculation method comprises the following steps:
the mathematical model of the heat loss power of the inductor is as follows:
in the method, in the process of the invention,P L heat loss power for inductance，I L Representing the current flowing through the inductor,R L representing the resistance of the resistive material in the inductor.
The mathematical model of the heat loss power of the SiC MOSFET device is as follows:
in the method, in the process of the invention,P sw the power is lost for the switch of the MOS tube,P con and the power is lost when the MOS tube is conducted.V sw Is the voltage at which the SiC MOSFET is switched,Q G is the charge number of the SiC MOSFET,f s is the switching frequency of the SiC MOSFET.I con Is the current through the SiC MOSFET in the on state,R mos the resistance value of the SiC MOSFET in the on state.
The mathematical model of the heat loss power of the capacitor is as follows:
in the method, in the process of the invention,P c as the heat loss power of the capacitor,I c representing the current flowing through the capacitor in the high frequency band,R c representing the resistance of the equivalent resistive material in the inductor.
Step 3: and assembling the SiC MOSFET device on the radiating fin, and calculating the projection area of the filter capacitor, the boost inductor, the filter inductor and the radiating fin on the plane of the printed circuit board PCB.
Step 4: initializing coordinates of a power component in a two-dimensional plane of a Printed Circuit Board (PCB), wherein the coordinates of the two-dimensional plane are positioned by the center coordinates of the bottom surface for the approximate cylindrical component, determining whether interference occurs by the diameter of the bottom surface, and positioning by the coordinates of four vertexes of a bottom surface projected rectangle for the approximate cuboid component, wherein the bottom surface projected rectangle direction of the cuboid component only considers the arrangement direction of the long side of the rectangular component and the edge of the rectangular PCB in parallel. And determining that the positions of all components on the PCB are not interfered with each other according to the coordinates, the estimated components and the bottom area of the radiating fin.
Step 5: and positioning the positions of all components on the PCB according to the coordinates of the power components, setting the outer contour enveloping rectangle projected on the bottom surface of each component positioned by the coordinates as the PCB area of the PCB, and establishing a thermal simulation three-dimensional model.
Step 6: and (3) taking the heat loss of each power component obtained by solving in the step (2) as a temperature field heat source, and setting material properties and solving domain boundary conditions. The material properties are set according to the actual component materials used. In this embodiment, the filter capacitor material property is set to be electrolyte (electrolyte), the boost inductor is set, the filter inductor material property is ferrite (ferrite), the SiC MOSFET device material property is silicon carbide SiC, the printed circuit board PCB material is set to be glass fiber FR4, the fin material is set to be aluminum (aluminum), the rectangular area determined by the PCB area and the highest component height is used as a solving area, two faces of the six faces of the rectangular are respectively set to be fan air inflow (inlet) and air outflow (outlet) in the direction perpendicular to the fin length, and the remaining four faces of the rectangular are set to be wall faces (wall).
Step 7: the grid division result of a typical power component is shown in fig. 2, (a) is a grid division result of a SiC MOSFET device, (b) is a grid division result of a filter capacitor, and (c) is a grid division result of a filter inductor, and then computational fluid dynamics CFD (computational fluid dynamics) is carried out on the grid-divided rectangular solution area by using Icepak software.
Step 8: as shown in fig. 3 and 4, the highest temperature of the mid-plane power component obtained by solving is obtainedTCalculating the overall outline volume of the PCBV. The middle plane refers to the plane of the printed circuit board PCB where half of the maximum height of the power components is located.
Step 9: judging whether the thermal performance optimization design is finished, in the embodiment, calculating the distance between the normalized pareto front obtained by the iterative calculation of the present round and the normalized pareto front obtained by the iterative calculation of the last round, wherein the normalization method adopts a maximum and minimum normalization method, if the distance is smaller than 0.001, the thermal performance optimization design is finished, if not, the step 10 is executed, and if so, the step 12 is executed.
Step 10: the highest temperature of the middle plane power device obtained by current solvingTIntegral outer profile volume of PCBVAssigning temporary parameters respectivelyTp、Vp。
Step 11: the method comprises the steps of taking filter capacitance, boost inductance, filter inductance and radiating fin coordinates as decision variables, taking the current PCB size range as a decision space, and taking the highest temperature of a middle plane power device as decision spaceTIntegral outer profile volume of PCBVAnd (5) minimizing the power components and parts to be optimized, carrying out unconstrained optimization iteration to update coordinates of the filter capacitor, the boost inductor, the filter inductor and the cooling fin based on the multi-objective genetic particle swarm algorithm, determining that the positions of the power components and parts on the PCB are not interfered with each other according to the coordinates and the calculated projection areas of the filter capacitor, the boost inductor, the filter inductor and the cooling fin, and returning to the step (5) for loop iteration.
Each particle position in the multi-target genetic particle swarm algorithm represents coordinates of a filter capacitor, a boost inductor, a filter inductor and a radiating fin in a Printed Circuit Board (PCB), and each particle fitness function is the highest temperature of a middle plane power device obtained by solving CFDTIntegral outer profile volume of PCBVMinimum 2 optimization objectives.
The cross operation performed on each particle position in the algorithm is as follows:
wherein,Pos 1 and (3) withPos 2 The positions of the parent particles are respectively indicated,Pos 1,cross and (3) withPos 2,cross The positions of the child particles after the crossover operation,αis [0,1]Random numbers in between.
Randomly selecting 70% of individuals in the whole population to perform mutation operation:
wherein,Pos i is the first in the populationiThe position of the individual particles is determined,Pos i,mutate for the position of the particle after the mutation of the particle,σ m for the step size of the mutation,RN i random numbers are normally distributed for a standard.
The method for updating the particle position comprises the following steps:
in the method, in the process of the invention,Vel i (k) And (3) withPos i (k) Respectively the firstiThe particles are at the firstkThe speed and position in the iteration of the wheel,Vel i (k+1) andPos i (k+1) are respectively the firstiThe particles are at the firstk+Speed and position in 1 round of iteration,wfor the inertial weight of the particles,c 1 and (3) withc 2 The learning coefficients of the individuals and the groups are respectively obtained,r 1 (k) And (3) withr 2 (k) Is the firstkRandom numbers are uniformly distributed between 0 and 1 in the round of iteration,Pos i,pbest is the firstiThe individual optimal positions of the individual particles,Pos leader is the optimal position of the group.
Step 12: and manually fine-tuning the optimized layout, as shown in fig. 5, and ending the design process.
The foregoing has shown and described the basic principles and main features of the present invention and the advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (4)
1. The multi-objective optimization design method for the high-power density thermal performance of the energy storage converter is characterized by comprising the following steps of:
step 1: initializing temporary parametersTp、VpThe value is more than or equal to 1000;
step 2: performing heat loss calculation on power components of the energy storage converter, wherein the power components comprise a filter capacitor, a boost inductor, a filter inductor and a SiC MOSFET device;
step 3: assembling the SiC MOSFET device on a radiating fin, and calculating a filter capacitor, a boost inductor, a filter inductor and a radiating fin projection area on a Printed Circuit Board (PCB) plane;
step 4: initializing coordinates of power components in a two-dimensional plane of a Printed Circuit Board (PCB), and determining that the positions of the power components on the PCB are not interfered with each other according to the coordinates and calculated projection areas of a filter capacitor, a boost inductor, a filter inductor and a radiating fin;
step 5: positioning the positions of all power components on a Printed Circuit Board (PCB) according to the coordinates of the power components, setting an outer contour enveloping rectangle projected on the bottom surface of each power component after the coordinates are positioned as the area of the PCB, and establishing a thermal simulation three-dimensional model;
step 6: taking the heat loss of the power component obtained by solving as a heat source of a temperature field, and setting material properties and solving domain boundary conditions; dividing the solving domain grids, carrying out fluid dynamics CFD solving, and obtaining the highest temperature of the middle plane power deviceT；
Step 7: highest temperature of mid-plane power device based on acquisitionTIntegral outer contour volume of Printed Circuit Board (PCB)VJudging whether the thermal performance optimization design is finished or not, if not, executing the step 8, and if so, executing the step 10;
step 8: the highest temperature of the middle plane power device obtained by current solvingTIntegral outer contour volume of Printed Circuit Board (PCB)VAssigning temporary parameters respectivelyTp、Vp；
Step 9: the method comprises the steps of taking filter capacitance, boost inductance, filter inductance and radiating fin coordinates as decision variables, taking the current PCB size range as decision space, and taking the highest temperature of a middle plane power device as decision spaceTIntegral outer contour volume of Printed Circuit Board (PCB)VThe minimum is an optimization target, unconstrained optimization iteration updating of the filter capacitor, the boost inductor, the filter inductor and the cooling fin coordinate is carried out based on a multi-target genetic particle swarm algorithm, each power component on the printed circuit board PCB is determined to be not interfered with each other according to the coordinate and the calculated projection area of the filter capacitor, the boost inductor, the filter inductor and the cooling fin, and the step 5 is returned to the loop iteration;
the specific process of performing unconstrained optimization iterative updating of the filter capacitor, the boost inductor, the filter inductor and the cooling fin coordinate by the multi-target genetic particle swarm algorithm in the step 9 is as follows:
each particle position in the multi-target genetic particle swarm algorithm represents coordinates of a filter capacitor, a boost inductor, a filter inductor and a radiating fin in a Printed Circuit Board (PCB), and each particle fitness function is the highest temperature of a middle plane power device obtained by solving CFDTIntegral outer profile volume of PCBVMinimum 2 optimization objectives;
the cross operation performed on each particle position in the algorithm is as follows:
；
wherein,Pos 1 and (3) withPos 2 The positions of the parent particles are respectively indicated,Pos 1,cross and (3) withPos 2,cross The positions of the child particles after the crossover operation,αis [0,1]Random numbers in between;
randomly selecting 70% of individuals in the whole population to perform mutation operation:
；
wherein,Pos i is the first in the populationiThe position of the individual particles is determined,Pos i,mutate for the position of the particle after the mutation of the particle,σ m for the step size of the mutation,RN i random numbers are distributed for standard normal states;
the method for updating the particle position comprises the following steps:
；
in the method, in the process of the invention,Vel i (k) And (3) withPos i (k) Respectively the firstiThe particles are at the firstkThe speed and position in the iteration of the wheel,Vel i (k+1) andPos i (k+1) are respectively the firstiThe particles are at the firstk+Speed and position in 1 round of iteration,wfor the inertial weight of the particles,c 1 and (3) withc 2 The learning coefficients of the individuals and the groups are respectively obtained,r 1 (k) And (3) withr 2 (k) Is the firstkRandom numbers are uniformly distributed between 0 and 1 in the round of iteration,Pos i,pbest is the firstiThe individual optimal positions of the individual particles,Pos leader is the optimal position of the group;
step 10: the design process ends.
2. The method for optimizing design of high power density thermal performance of the energy storage converter according to claim 1, wherein the method for determining coordinates of the power components in the two-dimensional plane of the printed circuit board PCB in the step 4 is as follows:
the approximate cylindrical device is positioned by the center coordinates of the bottom surface, and whether interference occurs is determined by the diameter of the bottom surface; the approximate cuboid device is positioned by four vertex coordinates of the bottom surface projected rectangle; the rectangular direction of the bottom surface projection of the cuboid device only considers the arrangement direction of the long side of the cuboid device parallel to the edge of the rectangular printed circuit board PCB.
3. The method for optimizing design of high power density thermal performance of energy storage converter according to claim 1, wherein the solution domain boundary condition set in the step 6 is set as follows: taking the surface of the space with the height of the largest component as a boundary condition according to the PCB area of the printed circuit board as the bottom surface; the material properties are set according to the actual power component materials.
4. The method for optimizing design of high power density thermal performance of an energy storage converter according to claim 1, wherein the middle plane in the steps 6 to 8 is a plane of half of the maximum height of power components on a printed circuit board PCB.
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| CN113283073A (en) * | 2021-05-19 | 2021-08-20 | 合肥工业大学 | Multi-objective optimization design method for three-phase high-frequency high-power transformer |
| CN115397187A (en) * | 2022-04-07 | 2022-11-25 | 安世半导体科技(上海)有限公司 | Radiator for vehicle power module and design method thereof |
| CN115774980A (en) * | 2021-09-08 | 2023-03-10 | 航天科工惯性技术有限公司 | Thermal design method based on high-power-density PCB |
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| CN113283073A (en) * | 2021-05-19 | 2021-08-20 | 合肥工业大学 | Multi-objective optimization design method for three-phase high-frequency high-power transformer |
| CN115774980A (en) * | 2021-09-08 | 2023-03-10 | 航天科工惯性技术有限公司 | Thermal design method based on high-power-density PCB |
| CN115397187A (en) * | 2022-04-07 | 2022-11-25 | 安世半导体科技(上海)有限公司 | Radiator for vehicle power module and design method thereof |
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| "基于CFD的储能变流器功率单元结构和散热研究";黄立平等;《电力电子技术》;第57卷(第3期);第69-72页 * |
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