CN114239350A - Water cooling plate optimization design method and device based on multi-objective optimization and storage medium - Google Patents
Water cooling plate optimization design method and device based on multi-objective optimization and storage medium Download PDFInfo
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Abstract
The invention discloses a multi-objective optimization-based water cooling plate optimization design method, a device and a storage medium, wherein the method comprises the following steps: sampling the pipe diameter of an input variable main flow channel, the pipe diameter of a branch inlet necking, the total flow of cooling liquid and the temperature of a cooling liquid inlet; carrying out simulation analysis on the input variable, the maximum temperature of the optimized target battery, the temperature difference of the battery, the pressure drop of the water cooling plate and the flow field uniformity of the water cooling plate through a finite element simulation model to obtain a relation matrix between the input variable and the optimized target; establishing a water cooling plate optimization model according to the relation matrix, and training to obtain a functional relation between the input variable and an optimization target; performing iterative optimization calculation by using a genetic algorithm to obtain an optimal parameter combination between an input variable and an optimization target; and obtaining the optimal design scheme of the water cooling plate according to the optimal parameter combination. The method adopts the neural network model to carry out multi-objective optimization on the water-cooling plate, effectively reduces the optimization period of the water-cooling plate and obviously improves the optimization efficiency.
Description
Technical Field
The invention relates to the technical field of power batteries, in particular to a water-cooling plate optimization design method and device based on multi-objective optimization and a storage medium.
Background
Lithium ion batteries have gradually replaced other batteries to become the mainstream vehicle power batteries at present due to the advantages of high specific power, large capacity density, long service life, low self-discharge rate, long storage time and the like. The lithium ion battery is sensitive to temperature, and the optimal working temperature range is 15-35 ℃. The charging and discharging performance, safety, aging and the like of the battery are greatly influenced by overhigh temperature, so that the charging time of the whole vehicle is prolonged, the driving performance is reduced, and the safety and the service life are reduced. Therefore, a reasonably efficient battery cooling system is generally required to ensure that the temperature of the battery is controlled within a reasonable range during operation.
The mainstream battery cooling modes at home and abroad include air cooling, liquid cooling, phase change material cooling, heat pipe cooling and the like. The air cooling scheme needs to design and reserve the space of the air channel, so that the defects of low space utilization rate, low heat dissipation efficiency, poor battery temperature consistency and the like of the battery pack are caused, and the air cooling scheme is gradually abandoned by the market. The phase-change material cooling and heat pipe cooling scheme still stays at the scheme design and sample test verification stage at present, and is not widely applied to batch vehicle power batteries. The liquid cooling scheme has the characteristics of high heat dissipation efficiency, good temperature consistency and the like, and is the most mainstream cooling mode of the power battery of the electric automobile at present.
One of key components in a liquid cooling system is a water cooling plate, and the current mainstream optimized design method of the water cooling plate is to design the structure of the water cooling plate in 3D software, introduce the water cooling plate into finite element simulation software for simulation analysis, return the water cooling plate to the 3D design software for optimized design according to a simulation result, and then perform simulation verification. However, the method has long design and optimization period, and due to the adoption of discrete optimization design, the optimal solution is difficult to optimize, and the optimization efficiency is low.
Disclosure of Invention
The technical problem to be solved by the embodiment of the invention is to provide a water-cooling plate optimization design method, a device and a storage medium based on multi-objective optimization, and a neural network model is adopted to perform multi-objective optimization on the water-cooling plate, so that the optimization period of the water-cooling plate is effectively reduced, and the optimization efficiency is remarkably improved.
In order to achieve the above object, an embodiment of the present invention provides a water-cooling plate optimization design method based on multi-objective optimization, including:
sampling an input variable at a first sampling interval; the input variables comprise the variable pipe diameter of a main flow channel, the necking pipe diameter of a branch inlet, the total flow of cooling liquid and the temperature of the cooling liquid inlet;
carrying out simulation analysis on the relation between the input variable and the optimization target through a finite element simulation model to obtain a relation matrix between the input variable and the optimization target; wherein the optimization target comprises the highest temperature of the battery, the temperature difference of the battery, the pressure drop of the water cooling plate and the flow field uniformity of the water cooling plate;
establishing a water cooling plate optimization model according to the relation matrix, and training the water cooling plate optimization model to obtain a functional relation between the input variable and the optimization target;
adjusting sampling intervals, sampling the input variables at second sampling intervals, and performing iterative optimization calculation on the water-cooling plate optimization model by using a genetic algorithm to obtain an optimal parameter combination between the input variables and the optimization target; wherein the second sampling interval is less than the first sampling interval;
and obtaining the optimal design scheme of the water cooling plate according to the optimal parameter combination.
As an improvement of the above, the method further comprises:
carrying out parameterization processing on the input variable:
Dij_min≤Dij≤Dij_max
Umn_min≤Umn≤Umn_max
Qtotal_q≤Qtotal_q_max
Tt_min≤Tt≤Tt_max
wherein D isijIs a branch inlet with a necking pipe diameter and UmnIs the main streamChanging pipe diameter, Qtotal_qIs the total flow rate, T, of the cooling liquidtIs the coolant inlet temperature;
carrying out parameterization processing on the optimization target:
ΔPmax≤ΔPmax_limit
Tmax≤Tmax_limit
ΔTmax≤ΔTmax_limit
wherein, Δ PmaxFor water-cooled plate pressure drop, TmaxIs the maximum temperature, Δ T, of the batterymaxIs the temperature difference of the battery,
For water-cooled plate flow field uniformity, QiIs the branch flow,
Is the branch average flow.
As an improvement of the above scheme, the obtaining a relationship matrix between the input variable and the optimization target by performing simulation analysis on the relationship between the input variable and the optimization target through a finite element simulation model specifically includes:
carrying out battery highest temperature simulation analysis on the input variable through the finite element simulation model to obtain a first relation matrix between the input variable and the battery highest temperature;
performing battery temperature difference simulation analysis on the input variable through the finite element simulation model to obtain a second relation matrix between the input variable and the battery temperature difference;
performing water-cooling plate pressure drop simulation analysis on the input variable through the finite element simulation model to obtain a third relation matrix between the input variable and the water-cooling plate pressure drop;
and carrying out water-cooling plate flow field uniformity simulation analysis on the input variable through the finite element simulation model to obtain a fourth relation matrix between the input variable and the water-cooling plate flow field uniformity.
As an improvement of the above scheme, the establishing a water-cooling plate optimization model according to the relationship matrix, and training the water-cooling plate optimization model to obtain a functional relationship between the input variable and the optimization target specifically includes:
establishing a first water-cooling plate optimization model according to the first relation matrix and the second relation matrix;
training the first water-cooling plate optimization model to obtain a functional relation among the variable pipe diameter of the main runner, the necking pipe diameter of the branch inlet, the total flow of the cooling liquid, the inlet temperature of the cooling liquid, the highest temperature of the battery and the temperature difference of the battery, namely:
establishing a second water-cooling plate optimization model according to the third relation matrix and the fourth relation matrix;
training the second water-cooling plate optimization model to obtain the functional relation between the variable pipe diameter of the main runner and the necking pipe diameter of the branch inlet and the pressure drop of the water-cooling plate and the flow field uniformity of the water-cooling plate, namely:
ΔPmax＝f(Umn,Dij)。
as an improvement of the above scheme, the iterative optimization calculation specifically includes:
initializing the input variable;
performing optimization calculation on the water cooling plate optimization model by using a genetic algorithm;
and respectively judging whether the flow field uniformity of the water-cooling plate, the pressure drop of the water-cooling plate, the highest temperature of the battery and the temperature difference of the battery meet preset conditions or not to obtain the optimal parameter combination between the input variable and the optimization target.
As an improvement of the above scheme, the initializing the input variable specifically includes:
and respectively randomly distributing a random number to the variable pipe diameter of the main flow channel, the necking pipe diameter of the branch inlet, the total flow of the cooling liquid and the temperature of the cooling liquid inlet, and setting an error function and calculation precision.
As an improvement of the above scheme, the respectively determining whether the uniformity of the flow field of the water-cooling plate, the pressure drop of the water-cooling plate, the maximum temperature of the battery, and the temperature difference of the battery satisfy preset conditions to obtain an optimal parameter combination between the input variable and the optimization target specifically includes:
judging whether the uniformity of the flow field of the water cooling plate meets a first preset condition or not;
judging whether the pressure drop of the water cooling plate meets a second preset condition or not;
if the flow field uniformity of the water cooling plate meets a first preset condition and the pressure drop of the water cooling plate meets a second preset condition, obtaining an optimal parameter combination of the variable pipe diameter of the main runner and the necking pipe diameter of the branch inlet;
if the uniformity of the flow field of the water cooling plate does not meet a first preset condition or the pressure drop of the water cooling plate does not meet a second preset condition, returning to the optimization calculation of the water cooling plate optimization model by using a genetic algorithm until the uniformity of the flow field of the water cooling plate meets the first preset condition and the pressure drop of the water cooling plate meets the second preset condition;
judging whether the highest temperature of the battery meets a third preset condition or not;
judging whether the battery temperature difference meets a fourth preset condition or not;
if the highest temperature of the battery meets a third preset condition and the temperature difference of the battery meets a fourth preset condition, obtaining a parameter combination with the optimal total flow rate of the cooling liquid and the optimal inlet temperature of the cooling liquid;
and if the highest temperature of the battery does not meet a third preset condition or the temperature difference of the battery does not meet a fourth preset condition, returning to the optimization calculation of the water cooling plate optimization model by using a genetic algorithm until the highest temperature of the battery meets the third preset condition and the temperature difference of the battery meets the fourth preset condition.
The embodiment of the invention also provides a water-cooling plate optimal design device based on multi-objective optimization, which comprises the following components:
the sampling module is used for sampling the input variable at a first sampling interval; the input variables comprise the variable pipe diameter of a main flow channel, the necking pipe diameter of a branch inlet, the total flow of cooling liquid and the temperature of the cooling liquid inlet;
the simulation module is used for carrying out simulation analysis on the relation between the input variable and the optimization target through a finite element simulation model to obtain a relation matrix between the input variable and the optimization target; wherein the optimization target comprises the highest temperature of the battery, the temperature difference of the battery, the pressure drop of the water cooling plate and the flow field uniformity of the water cooling plate;
the training module is used for establishing a water-cooling plate optimization model according to the relation matrix and training the water-cooling plate optimization model to obtain a functional relation between the input variable and the optimization target;
the optimization module is used for adjusting sampling intervals, sampling the input variables at second sampling intervals, and performing iterative optimization calculation on the water cooling plate optimization model by using a genetic algorithm to obtain an optimal parameter combination between the input variables and the optimization target; wherein the second sampling interval is less than the first sampling interval;
and the design module is used for obtaining the optimal design scheme of the water cooling plate according to the optimal parameter combination.
The embodiment of the invention also provides a water-cooling plate optimization design device based on multi-objective optimization, which comprises a processor, a memory and a computer program which is stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the water-cooling plate optimization design method based on multi-objective optimization is realized.
The embodiment of the invention also provides a computer-readable storage medium, which comprises a stored computer program, wherein when the computer program runs, the equipment where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned water-cooling plate optimization design methods based on multi-objective optimization.
Compared with the prior art, the water-cooling plate optimization design method, the water-cooling plate optimization design device and the storage medium based on multi-objective optimization provided by the embodiment of the invention have the beneficial effects that: by sampling an input variable at a first sampling interval; the input variables comprise the variable pipe diameter of a main flow channel, the necking pipe diameter of a branch inlet, the total flow of cooling liquid and the temperature of the cooling liquid inlet; carrying out simulation analysis on the relation between the input variable and the optimization target through a finite element simulation model to obtain a relation matrix between the input variable and the optimization target; wherein the optimization target comprises the highest temperature of the battery, the temperature difference of the battery, the pressure drop of the water cooling plate and the flow field uniformity of the water cooling plate; establishing a water cooling plate optimization model according to the relation matrix, and training the water cooling plate optimization model to obtain a functional relation between the input variable and the optimization target; adjusting sampling intervals, sampling the input variables at second sampling intervals, and performing iterative optimization calculation on the water-cooling plate optimization model by using a genetic algorithm to obtain an optimal parameter combination between the input variables and the optimization target; wherein the second sampling interval is less than the first sampling interval; and obtaining the optimal design scheme of the water cooling plate according to the optimal parameter combination. According to the embodiment of the invention, firstly, a finite element simulation model is used for carrying out simulation analysis on a plurality of input variables influencing the performance of the water cooling plate to obtain a relation matrix between the plurality of input variables and a plurality of optimization targets, and then a neural network model is used for carrying out multi-objective optimization on the water cooling plate to obtain the optimal solution among the highest temperature of the battery, the temperature difference of the battery, the pressure drop of the water cooling plate, the flow field uniformity of the water cooling plate and the design of the water cooling plate structure, so that the optimization period of the water cooling plate is effectively reduced, and the optimization efficiency is remarkably improved.
Drawings
FIG. 1 is a schematic flow chart of a preferred embodiment of a water-cooling plate optimization design method based on multi-objective optimization provided by the invention;
FIG. 2 is a diagram of a first optimization model of a water-cooling plate according to a preferred embodiment of the method for designing a water-cooling plate based on multi-objective optimization of the present invention;
FIG. 3 is a second optimization model diagram of the water-cooling plate according to the preferred embodiment of the method for designing the water-cooling plate based on multi-objective optimization of the present invention;
FIG. 4 is a schematic flow chart of iterative optimization calculation in a preferred embodiment of the method for optimally designing a water-cooling plate based on multi-objective optimization according to the present invention;
FIG. 5 is a comparison graph of the flow field uniformity of the water-cooling plate before and after optimization in an embodiment of the method for designing a water-cooling plate based on multi-objective optimization;
FIG. 6 is a comparison graph of the maximum temperature of the battery before and after optimization in a preferred embodiment of the method for optimally designing the water-cooling plate based on multi-objective optimization provided by the invention;
FIG. 7 is a comparison graph of the temperature difference between the batteries before and after optimization in a preferred embodiment of the method for optimally designing the water-cooling plate based on multi-objective optimization provided by the invention;
FIG. 8 is a flow channel structure diagram of a water-cooling plate optimized in an embodiment of the method for designing a water-cooling plate based on multi-objective optimization according to the present invention;
FIG. 9 is a schematic structural diagram of a preferred embodiment of a water-cooling plate optimization design device based on multi-objective optimization provided by the invention;
FIG. 10 is a schematic structural diagram of another preferred embodiment of the water-cooling plate optimization design device based on multi-objective optimization.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a schematic flow chart of a water-cooling plate optimization design method based on multi-objective optimization according to a preferred embodiment of the present invention. The water cooling plate optimization design method based on multi-objective optimization comprises the following steps:
s1, sampling the input variable at a first sampling interval; the input variables comprise the variable pipe diameter of a main flow channel, the necking pipe diameter of a branch inlet, the total flow of cooling liquid and the temperature of the cooling liquid inlet;
s2, carrying out simulation analysis on the relation between the input variable and the optimization target through a finite element simulation model to obtain a relation matrix between the input variable and the optimization target; wherein the optimization target comprises the highest temperature of the battery, the temperature difference of the battery, the pressure drop of the water cooling plate and the flow field uniformity of the water cooling plate;
s3, establishing a water-cooling plate optimization model according to the relation matrix, and training the water-cooling plate optimization model to obtain a functional relation between the input variable and the optimization target;
s4, adjusting sampling intervals, sampling the input variables at second sampling intervals, and performing iterative optimization calculation on the water cooling plate optimization model by using a genetic algorithm to obtain an optimal parameter combination between the input variables and the optimization target; wherein the second sampling interval is less than the first sampling interval;
and S5, obtaining the optimal design scheme of the water cooling plate according to the optimal parameter combination.
Specifically, in this embodiment, the optimization targets of the water-cooling plate are firstly determined to be the highest temperature of the battery, the temperature difference of the battery, the pressure drop of the water-cooling plate, the flow field uniformity of the water-cooling plate, and the sensitivity influence factors of the optimization targets, that is, the input variables are the variable pipe diameter of the main flow channel, the necking pipe diameter of the branch flow inlet, the total flow rate of the cooling liquid, and the temperature of the cooling liquid inlet.
Carrying out parameterization processing on the input variable:
Dij_min≤Dij≤Dij_max
Umn_min≤Umn≤Umn_max
Qtotal_Q≤Qtotal_q_max
Tt_min≤Tt≤Tt_max
wherein D isijIs a branch inlet with a necking pipe diameter and UmnChange the pipe diameter and Q of the main flow passagetotal_qIs the total flow rate, T, of the cooling liquidtIs the coolant inlet temperature.
It should be noted that m is the number of influence factors of the variable pipe diameter sensitivity of the main flow channel, and for example, m is 1-9; n is the variable pipe diameter variation range of the main flow channel, for example, U1n is more than or equal to 0.1mm and less than or equal to 7 mm; i is the number of influence factors of the necking sensitivity of the inlet of the branch, for example, i is 1-9; j is the change range of the pipe diameter of the branch inlet necking, such as D is more than or equal to 0.1mm1jLess than or equal to 4 mm; q is a sample of the change in inlet flow to the water-cooled panels, e.g. Qtotal_q_max10L/min; t is a sample value of the coolant inlet temperature, e.g. Tt_min＝10℃,Tt_max＝20℃。
Carrying out parameterization processing on the optimization target:
ΔPmax≤ΔPmax_limit
Tmax≤Tmax_limit
ΔTmax≤Tmax_limit
wherein, Δ PmaxFor water-cooled plate pressure drop, TmaxIs the maximum temperature, Δ T, of the batterymaxIs the temperature difference of the battery,
For water-cooled plate flow field uniformity, QiIs the branch flow,
Is the branch average flow.
Note that Δ Pmax_limitLimiting value, T, for the pressure drop of the water-cooled platemax_limitLimiting value T of the maximum temperature of the batterymax_limitIs the limit value of the temperature difference of the battery,
For the uniformity of the flow field of the water-cooled plate, in this embodiment, it is preferable
Therefore, in the multi-objective optimization of water-cooled panels, it is desirable to meet
ΔPmax≤ΔPmax_limit
Tmax≤Tmax_limit
ΔTmax≤Tmax_limit
After the input variables and optimization objectives are determined, the input variables are sampled at large sampling intervals by experimentally designing the DOE matrix. For example, the main parameter affecting the flow field uniformity and pressure drop of the water cooling plate is the change of the main flow channel into the pipe diameter UmnAnd branch inlet throat diameter DijWherein U1n is more than or equal to 0.1mm and less than or equal to 7mm, D is more than or equal to 0.1mm1jThe sampling interval is less than or equal to 4mm, the sampling value n can be 1mm and 5mm, the sampling value j can be 1mm and 3mm, and the sampling is carried out in a mode of larger sampling interval to form a 2X2 sampling matrix.
Specifically, in S2, the performing simulation analysis on the relationship between the input variable and the optimization target through the finite element simulation model to obtain a relationship matrix between the input variable and the optimization target specifically includes:
s21, carrying out battery highest temperature simulation analysis on the input variable through the finite element simulation model to obtain a first relation matrix between the input variable and the battery highest temperature;
s22, performing battery temperature difference simulation analysis on the input variable through the finite element simulation model to obtain a second relation matrix between the input variable and the battery temperature difference;
s23, performing water-cooling plate pressure drop simulation analysis on the input variable through the finite element simulation model to obtain a third relation matrix between the input variable and the water-cooling plate pressure drop;
and S24, performing water-cooling plate flow field uniformity simulation analysis on the input variable through the finite element simulation model to obtain a fourth relation matrix between the input variable and the water-cooling plate flow field uniformity.
Illustratively, a finite element CAE simulation model is established, and simulation analysis of the maximum temperature of the battery, the temperature difference of the battery, the pressure drop of the water cooling plate and the flow field uniformity of the water cooling plate is respectively carried out on a coarse 3D digital analog sample established by DOE, so as to obtain positive/negative correlation function relationship matrixes between simulation input and simulation output, namely a first relationship matrix, a second relationship matrix, a third relationship matrix and a fourth relationship matrix. For example, increase the input variable main runner pipe diameter UmnThe pressure drop delta P of the optimized target water cooling plate can be reducedmaxAnd (5) equaling the relation matrix.
Specifically, in S3, the establishing a water-cooling plate optimization model according to the relationship matrix, and training the water-cooling plate optimization model to obtain a functional relationship between the input variable and the optimization target specifically includes:
s31, establishing a first water-cooling plate optimization model according to the first relation matrix and the second relation matrix;
s32, training the first water-cooling plate optimization model to obtain a functional relationship between the variable pipe diameter of the main flow channel, the throat pipe diameter of the branch inlet, the total flow rate of the cooling liquid, the inlet temperature of the cooling liquid, the maximum temperature of the battery, and the temperature difference of the battery, that is:
s33, establishing a second water-cooling plate optimization model according to the third relation matrix and the fourth relation matrix;
s34, training the second water-cooling plate optimization model to obtain a functional relation between the main runner variable pipe diameter and the branch inlet throat pipe diameter, the pressure drop of the water-cooling plate and the flow field uniformity of the water-cooling plate, namely:
ΔPmax＝f(Umn,Dij)。
referring to fig. 2, fig. 2 is a diagram of an optimization model of a first water-cooling plate in a preferred embodiment of a water-cooling plate optimization design method based on multi-objective optimization according to the present invention. Maximum battery temperature T in the optimization targetmaxAnd temperature difference Δ TmaxNot only depends on input variable main runner to change pipe diameter UmnAnd branch inlet throat diameter DijAnd also on the total flow rate Q of the cooling liquidtotal_qAnd coolant inlet temperature Tt. Therefore, T is established according to a first relation matrix between the input variable and the highest temperature of the battery and a second relation matrix between the input variable and the temperature difference of the batterymax、ΔTmaxAnd Umn,Dij,
TtThe first water-cooling plate optimization model is trained through sample data, and a relation network among input variables, hidden layers and optimization targets is established.
Referring to fig. 3, fig. 3 is a diagram of a second water-cooling plate optimization model in a preferred embodiment of a water-cooling plate optimization design method based on multi-objective optimization according to the present invention. Optimizing water-cooled plate flow field uniformity in a target
And pressure drop Δ P of water-cooled platemaxMainly depends on input variable main runner variable pipe diameter UmnAnd branch inlet throat diameter Dij. Therefore, the third relation matrix between the input variable and the pressure drop of the water cooling plate and the fourth relation matrix between the input variable and the flow field uniformity of the water cooling plate are established
ΔPmaxAnd Umn、DijAnd training a second water-cooling plate optimization model through sample data to establish a relation network among the input variable, the hidden layer and the optimization target.
It should be noted that the hidden layer is an intermediate layer of the neural network, and can provide multiple layers of neurons, and the neurons in different layers are fully connected, but the neurons in the same layer are independent of each other. According to the data of the input layer, the neuron units in the output layer can be coded through the neuron units in each hidden layer; when the output layer cannot obtain a proper value, correcting the matrix weight and the threshold value of each layer by the output error, and then returning to the hidden layer and the input layer respectively; when the output layer gets the appropriate values, the matrix weights and thresholds for each layer are also returned to the hidden layer and the input layer.
Specifically, after a functional relation between an input variable and an optimization target is obtained by training a water-cooling plate optimization model, a sampling interval is adjusted, the input variable is sampled at a small sampling interval, iterative optimization calculation is carried out on the water-cooling plate optimization model by using a genetic algorithm, and an optimal parameter combination between the input variable and the optimization target is obtained. For example, the main flow channel is changed into the pipe diameter U according to the input variablemnThe range of U1n is more than or equal to 0.1mm and less than or equal to 7mm, the range of D1j is more than or equal to 0.1mm and less than or equal to 4mm, and the small interval sampling means that n can beSampling every 0.01mm and sampling every 0.01mm j are used, so that a 690X390 sampling matrix can be formed. In addition, consider UmnM variables and D inijThe matrix structure is 690X390 m i. And (4) carrying out automatic iterative optimization calculation on the trained neural network model by using a genetic algorithm to obtain an optimal parameter combination.
Specifically, in S4, the iterative optimization calculation specifically includes:
s41, initializing the input variables;
s42, performing optimization calculation on the water-cooling plate optimization model by using a genetic algorithm;
and S43, respectively judging whether the flow field uniformity of the water cooling plate, the pressure drop of the water cooling plate, the highest temperature of the battery and the temperature difference of the battery meet preset conditions, and obtaining the optimal parameter combination between the input variable and the optimization target.
Specifically, S41 initializes the input variable, specifically:
and respectively randomly distributing a random number to the variable pipe diameter of the main flow channel, the necking pipe diameter of the branch inlet, the total flow of the cooling liquid and the temperature of the cooling liquid inlet, and setting an error function and calculation precision.
Specifically, S43 respectively determines whether the uniformity of the flow field of the water-cooling plate, the pressure drop of the water-cooling plate, the maximum temperature of the battery, and the temperature difference of the battery satisfy preset conditions, to obtain an optimal parameter combination between the input variable and the optimization target, and specifically includes:
judging whether the uniformity of the flow field of the water cooling plate meets a first preset condition or not;
judging whether the pressure drop of the water cooling plate meets a second preset condition or not;
if the flow field uniformity of the water cooling plate meets a first preset condition and the pressure drop of the water cooling plate meets a second preset condition, obtaining an optimal parameter combination of the variable pipe diameter of the main runner and the necking pipe diameter of the branch inlet;
if the uniformity of the flow field of the water cooling plate does not meet a first preset condition or the pressure drop of the water cooling plate does not meet a second preset condition, returning to the optimization calculation of the water cooling plate optimization model by using a genetic algorithm until the uniformity of the flow field of the water cooling plate meets the first preset condition and the pressure drop of the water cooling plate meets the second preset condition;
judging whether the highest temperature of the battery meets a third preset condition or not;
judging whether the battery temperature difference meets a fourth preset condition or not;
if the highest temperature of the battery meets a third preset condition and the temperature difference of the battery meets a fourth preset condition, obtaining a parameter combination with the optimal total flow rate of the cooling liquid and the optimal inlet temperature of the cooling liquid;
and if the highest temperature of the battery does not meet a third preset condition or the temperature difference of the battery does not meet a fourth preset condition, returning to the optimization calculation of the water cooling plate optimization model by using a genetic algorithm until the highest temperature of the battery meets the third preset condition and the temperature difference of the battery meets the fourth preset condition.
For example, referring to fig. 4, fig. 4 is a schematic flowchart of iterative optimization calculation in a preferred embodiment of a method for designing a water-cooling plate based on multi-objective optimization according to the present invention.
R0: initializing input variables to change the pipe diameter U of the main flow channelmnAnd the diameter D of the necking of the branch inletijTotal flow rate Q of cooling liquidtotal_qCoolant inlet temperature TtRespectively randomly distributing a random number, setting an error function and giving calculation precision;
r1: optimizing and calculating the water cooling plate multi-target optimization model by using a genetic algorithm;
r2: judging the uniformity of the flow field of the water cooling plate
Whether the first preset condition is met
Wherein
If not satisfied
Go to the next iteration v + ═ 1 where
v＝f(j,n)
If it is satisfied with
Proceeding to the next step;
r3: judging whether the pressure drop of the water cooling plate meets a second preset condition delta Pmax≤ΔPmax_limit，
If Δ P is not satisfiedmax≤ΔPmax_limitGo to the next iteration v + ═ 1 where
v＝f(j,n)
If Δ P is satisfiedmax≤ΔPmax_limitProceeding to the next step;
r4: determining the diameter D of a branch inlet throatijAnd the main runner changes the pipe diameter UmnA combination of parameters of (1);
r5: judging whether the highest temperature of the battery meets a third preset condition Tmax≤Tmax_limit，
If T is not satisfiedmax≤Tmax_limitProceeding to the next iteration w + ═ 1; wherein
w＝f(q,t)
If T is satisfiedmax≤Tmax_limitProceeding to the next step;
r6: judging whether the battery temperature difference meets a fourth preset condition delta Tmax≤ΔTmax_limit，
If Δ T is not satisfiedmax≤ΔTmax_limitProceeding to the next iteration w + ═ 1; wherein
w＝f(q,t)
If Δ T is satisfiedmax≤ΔTmax_limitProceeding to the next step;
R7：determining the total flow rate Q of the cooling liquidtotal_qAnd coolant inlet temperature TtThe parameter combinations of (1).
Specifically, an optimal design scheme of the water cooling plate is obtained according to the optimal parameter combination, and 3D structure design is completed. The water-cooling plate digital design method based on multi-objective optimization can obtain the optimal solution among the highest temperature of the battery, the temperature difference of the battery, the pressure drop of the water-cooling plate, the flow field uniformity of the water-cooling plate and the design of the water-cooling plate structure, and the optimization design efficiency of the water-cooling plate is obviously improved.
According to the result of multi-objective optimization design, taking a battery system with 9 modules as an example, the uniformity of the flow field of the water cooling plate before the optimization of the liquid cooling system is poor, and the maximum flow deviation value is poor
Deviation value of minimum flow
The result is far greater than the optimization goal
The optimization method is adopted for optimization, and the flow field uniformity of the optimized water-cooling plate is
Meeting optimization objectives
The flow field uniformity ratio of the water-cooled plates before and after optimization is shown in fig. 5.
Under the DC charging working condition, the maximum temperature of the battery is T before optimizationmax_preT after optimization at 55 ℃max_optAt 45 ℃. The maximum temperature of the battery before and after the optimization is plotted in fig. 6.
Under the DC charging working condition, before the battery temperature difference is optimized, the temperature difference is delta Tmax_preNot less than 10 ℃, delta T after optimizationmax_optIs less than or equal to 3 ℃. Electricity before and after optimizationThe cell temperature differential pair is shown in figure 7.
The optimized water-cooling plate runner structure is shown in fig. 8.
According to the embodiment, firstly, a finite element simulation model is used for carrying out simulation analysis on a plurality of input variables influencing the performance of the water cooling plate to obtain a relation matrix between the plurality of input variables and a plurality of optimization targets, then a neural network model is used for carrying out multi-objective optimization on the water cooling plate to obtain the optimal solution between the highest temperature of the battery, the temperature difference of the battery, the pressure drop of the water cooling plate, the flow field uniformity of the water cooling plate and the structural design of the water cooling plate, the optimization period of the water cooling plate is effectively shortened, and the optimization efficiency is remarkably improved.
Correspondingly, the invention also provides a water-cooling plate optimal design device based on multi-objective optimization, which can realize all the processes of the water-cooling plate optimal design method based on multi-objective optimization in the embodiment.
Referring to fig. 9, fig. 9 is a schematic structural diagram of a preferred embodiment of a water-cooling plate optimization design device based on multi-objective optimization according to the present invention. The water-cooling plate optimal design device based on multi-objective optimization comprises:
a sampling module 901, configured to sample an input variable at a first sampling interval; the input variables comprise the variable pipe diameter of a main flow channel, the necking pipe diameter of a branch inlet, the total flow of cooling liquid and the temperature of the cooling liquid inlet;
a simulation module 902, configured to perform simulation analysis on a relationship between the input variable and an optimization target through a finite element simulation model to obtain a relationship matrix between the input variable and the optimization target; wherein the optimization target comprises the highest temperature of the battery, the temperature difference of the battery, the pressure drop of the water cooling plate and the flow field uniformity of the water cooling plate;
the training module 903 is used for establishing a water-cooling plate optimization model according to the relation matrix, and training the water-cooling plate optimization model to obtain a functional relation between the input variable and the optimization target;
an optimization module 904, configured to adjust a sampling interval, sample the input variable at a second sampling interval, and perform iterative optimization calculation on the water-cooling plate optimization model by using a genetic algorithm to obtain an optimal parameter combination between the input variable and the optimization target; wherein the second sampling interval is less than the first sampling interval;
and a design module 905 for obtaining an optimal design scheme of the water cooling plate according to the optimal parameter combination.
Preferably, the apparatus further comprises a parameterization processing module for:
carrying out parameterization processing on the input variable:
Dij_min≤Dij≤Dij_max
Umn_min≤Umn≤Umn_max
Qtotal_q≤Qtotal_q_max
Tt_min≤Tt≤Tt_max
wherein D isijIs a branch inlet with a necking pipe diameter and UmnChange the pipe diameter and Q of the main flow passagetotal_qIs the total flow rate, T, of the cooling liquidtIs the coolant inlet temperature;
carrying out parameterization processing on the optimization target:
ΔPmax≤ΔPmax_limit
Tmax≤Tmax_limit
ΔTmax≤ΔTmax_limit
wherein, Δ PmaxFor water-cooled plate pressure drop, TmaxIs the maximum temperature, Δ T, of the batterymaxIs the temperature difference of the battery,
For water-cooled plate flow field uniformity, QiIs the branch flow,
Is the branch average flow.
Preferably, the simulation module 902 specifically includes:
a first simulation unit 912, configured to perform a battery maximum temperature simulation analysis on the input variable through the finite element simulation model to obtain a first relationship matrix between the input variable and the battery maximum temperature;
the second simulation unit 922 is configured to perform battery temperature difference simulation analysis on the input variable through the finite element simulation model to obtain a second relationship matrix between the input variable and the battery temperature difference;
a third simulation unit 932, configured to perform water-cooling plate pressure drop simulation analysis on the input variable through the finite element simulation model to obtain a third relation matrix between the input variable and the water-cooling plate pressure drop;
a fourth simulation unit 942 is configured to perform simulation analysis on the flow field uniformity of the water-cooling plate on the input variable through the finite element simulation model, so as to obtain a fourth relationship matrix between the input variable and the flow field uniformity of the water-cooling plate.
Preferably, the training module 903 specifically includes:
the first modeling unit 913 is configured to establish a first water-cooling plate optimization model according to the first relation matrix and the second relation matrix;
a first training unit 923, configured to train the first water-cooling plate optimization model to obtain a functional relationship between the variable pipe diameter of the main flow channel, the throat pipe diameter of the branch inlet, the total flow rate of the cooling liquid, the inlet temperature of the cooling liquid, the maximum temperature of the battery, and the temperature difference of the battery, that is:
a second modeling unit 933, configured to establish a second water-cooling plate optimization model according to the third relation matrix and the fourth relation matrix;
a second training unit 943, configured to train the second water-cooling plate optimization model, to obtain a functional relationship between the main runner variable pipe diameter and the branch inlet throat pipe diameter and the pressure drop across the water-cooling plate and the uniformity of the flow field across the water-cooling plate, that is:
ΔPmax＝f(Umn,Dij)。
preferably, the iterative optimization calculation in the optimization module 904 specifically includes:
initializing the input variable;
performing optimization calculation on the water cooling plate optimization model by using a genetic algorithm;
and respectively judging whether the flow field uniformity of the water-cooling plate, the pressure drop of the water-cooling plate, the highest temperature of the battery and the temperature difference of the battery meet preset conditions or not to obtain the optimal parameter combination between the input variable and the optimization target.
Preferably, the initializing the input variable specifically includes:
and respectively randomly distributing a random number to the variable pipe diameter of the main flow channel, the necking pipe diameter of the branch inlet, the total flow of the cooling liquid and the temperature of the cooling liquid inlet, and setting an error function and calculation precision.
Preferably, the respectively determining whether the uniformity of the flow field of the water-cooling plate, the pressure drop of the water-cooling plate, the maximum temperature of the battery and the temperature difference of the battery meet preset conditions to obtain an optimal parameter combination between the input variable and the optimization target specifically includes:
judging whether the uniformity of the flow field of the water cooling plate meets a first preset condition or not;
judging whether the pressure drop of the water cooling plate meets a second preset condition or not;
if the flow field uniformity of the water cooling plate meets a first preset condition and the pressure drop of the water cooling plate meets a second preset condition, obtaining an optimal parameter combination of the variable pipe diameter of the main runner and the necking pipe diameter of the branch inlet;
if the uniformity of the flow field of the water cooling plate does not meet a first preset condition or the pressure drop of the water cooling plate does not meet a second preset condition, returning to the optimization calculation of the water cooling plate optimization model by using a genetic algorithm until the uniformity of the flow field of the water cooling plate meets the first preset condition and the pressure drop of the water cooling plate meets the second preset condition;
judging whether the highest temperature of the battery meets a third preset condition or not;
judging whether the battery temperature difference meets a fourth preset condition or not;
if the highest temperature of the battery meets a third preset condition and the temperature difference of the battery meets a fourth preset condition, obtaining a parameter combination with the optimal total flow rate of the cooling liquid and the optimal inlet temperature of the cooling liquid;
and if the highest temperature of the battery does not meet a third preset condition or the temperature difference of the battery does not meet a fourth preset condition, returning to the optimization calculation of the water cooling plate optimization model by using a genetic algorithm until the highest temperature of the battery meets the third preset condition and the temperature difference of the battery meets the fourth preset condition.
In specific implementation, the working principle, the control flow and the realized technical effect of the multi-objective optimization-based water-cooling plate optimal design device provided by the embodiment of the invention are the same as those of the multi-objective optimization-based water-cooling plate optimal design method in the embodiment, and are not described herein again.
Referring to fig. 10, fig. 10 is a schematic structural diagram of another preferred embodiment of a water-cooling plate optimization design device based on multi-objective optimization according to the present invention. The multi-objective optimization-based water-cooling plate optimization design device comprises a processor 1001, a memory 1002 and a computer program which is stored in the memory 1002 and configured to be executed by the processor 1001, wherein when the processor 1001 executes the computer program, the multi-objective optimization-based water-cooling plate optimization design method according to any one of the embodiments is realized.
Preferably, the computer program may be divided into one or more modules/units (e.g., computer program 1, computer program 2, … …) that are stored in the memory 1002 and executed by the processor 1001 to implement the present invention. The one or more modules/units can be a series of instruction sections of a computer program capable of achieving specific functions, and the instruction sections are used for describing the execution process of the computer program in the multi-objective optimization-based water-cooling plate optimization design device.
The Processor 1001 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, etc., the general purpose Processor may be a microprocessor, or the Processor 1001 may be any conventional Processor, the Processor 1001 is a control center of the apparatus, and various interfaces and lines are used to connect various parts of the apparatus.
The memory 1002 mainly includes a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function, and the like, and the data storage area may store related data and the like. In addition, the memory 1002 may be a high speed random access memory, a non-volatile memory such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card), and the like, or the memory 1002 may be other volatile solid state memory devices.
It should be noted that the aforementioned multi-objective optimization-based water-cooling plate optimization design device may include, but is not limited to, a processor and a memory, and those skilled in the art will understand that the schematic structure of fig. 10 is only an example of the aforementioned multi-objective optimization-based water-cooling plate optimization design device, and does not constitute a limitation of the aforementioned multi-objective optimization-based water-cooling plate optimization design device, and may include more or less components than those shown in the drawings, or may combine some components, or may differ.
The embodiment of the invention also provides a computer-readable storage medium, which comprises a stored computer program, wherein when the computer program runs, the equipment where the computer-readable storage medium is located is controlled to execute the method for optimally designing the water-cooling plate based on multi-objective optimization, which is described in any one of the above embodiments.
The embodiment of the invention provides a multi-objective optimization-based water-cooling plate optimization design method, a multi-objective optimization-based water-cooling plate optimization design device and a storage medium, wherein input variables are sampled at first sampling intervals; the input variables comprise the variable pipe diameter of the main flow channel, the necking pipe diameter of a branch inlet, the total flow of the cooling liquid and the temperature of the cooling liquid inlet; carrying out simulation analysis on the relation between an input variable and an optimization target through a finite element simulation model to obtain a relation matrix between the input variable and the optimization target; the optimization target comprises the highest temperature of the battery, the temperature difference of the battery, the pressure drop of a water cooling plate and the flow field uniformity of the water cooling plate; establishing a water cooling plate optimization model according to the relation matrix, and training the water cooling plate optimization model to obtain a functional relation between the input variable and the optimization target; adjusting sampling intervals, sampling input variables at a second sampling interval, and performing iterative optimization calculation on a water-cooling plate optimization model by using a genetic algorithm to obtain an optimal parameter combination between the input variables and the optimization target; wherein the second sampling interval is less than the first sampling interval; and obtaining the optimal design scheme of the water cooling plate according to the optimal parameter combination. According to the embodiment of the invention, firstly, a finite element simulation model is used for carrying out simulation analysis on a plurality of input variables influencing the performance of the water cooling plate to obtain a relation matrix between the plurality of input variables and a plurality of optimization targets, and then a neural network model is used for carrying out multi-objective optimization on the water cooling plate to obtain the optimal solution among the highest temperature of the battery, the temperature difference of the battery, the pressure drop of the water cooling plate, the flow field uniformity of the water cooling plate and the design of the water cooling plate structure, so that the optimization period of the water cooling plate is effectively reduced, and the optimization efficiency is remarkably improved.
It should be noted that the above-described system embodiments are merely illustrative, where the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. In addition, in the drawings of the embodiment of the system provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, and may be specifically implemented as one or more communication buses or signal lines. One of ordinary skill in the art can understand and implement it without inventive effort.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (10)
1. A water cooling plate optimization design method based on multi-objective optimization is characterized by comprising the following steps:
sampling an input variable at a first sampling interval; the input variables comprise the variable pipe diameter of a main flow channel, the necking pipe diameter of a branch inlet, the total flow of cooling liquid and the temperature of the cooling liquid inlet;
carrying out simulation analysis on the relation between the input variable and the optimization target through a finite element simulation model to obtain a relation matrix between the input variable and the optimization target; wherein the optimization target comprises the highest temperature of the battery, the temperature difference of the battery, the pressure drop of the water cooling plate and the flow field uniformity of the water cooling plate;
establishing a water cooling plate optimization model according to the relation matrix, and training the water cooling plate optimization model to obtain a functional relation between the input variable and the optimization target;
adjusting sampling intervals, sampling the input variables at second sampling intervals, and performing iterative optimization calculation on the water-cooling plate optimization model by using a genetic algorithm to obtain an optimal parameter combination between the input variables and the optimization target; wherein the second sampling interval is less than the first sampling interval;
and obtaining the optimal design scheme of the water cooling plate according to the optimal parameter combination.
2. The method for optimally designing the water-cooling plate based on the multi-objective optimization as recited in claim 1, further comprising the following steps of:
carrying out parameterization processing on the input variable:
Dij_min≤Dij≤Dij_max
Umn_min≤Umn≤Umn_max
Qtotal_q≤Qtotal_q_max
Tt_min≤Tt≤Tt_max
wherein D isijIs a branch inlet with a necking pipe diameter and UmnChange the pipe diameter and Q of the main flow passagetotal_aIs the total flow rate, T, of the cooling liquidtIs the coolant inlet temperature;
carrying out parameterization processing on the optimization target:
ΔPmax≤ΔPmax_limit
Tmax≤Tmax_limit
ΔTmax≤ΔTmax_limit
wherein, Δ PmaxFor water-cooled plate pressure drop, TmaxIs the maximum temperature, Δ T, of the batterymaxIs the temperature difference of the battery,
For water-cooled plate flow field uniformity, QiIs the branch flow,
Is the branch average flow.
3. The method for optimally designing the water-cooling plate based on the multi-objective optimization as claimed in claim 1, wherein the relationship between the input variable and the optimization target is simulated and analyzed through a finite element simulation model to obtain a relationship matrix between the input variable and the optimization target, and the method specifically comprises the following steps:
carrying out battery highest temperature simulation analysis on the input variable through the finite element simulation model to obtain a first relation matrix between the input variable and the battery highest temperature;
performing battery temperature difference simulation analysis on the input variable through the finite element simulation model to obtain a second relation matrix between the input variable and the battery temperature difference;
performing water-cooling plate pressure drop simulation analysis on the input variable through the finite element simulation model to obtain a third relation matrix between the input variable and the water-cooling plate pressure drop;
and carrying out water-cooling plate flow field uniformity simulation analysis on the input variable through the finite element simulation model to obtain a fourth relation matrix between the input variable and the water-cooling plate flow field uniformity.
4. The method according to claim 3, wherein the step of establishing a water cooling plate optimization model according to the relationship matrix and training the water cooling plate optimization model to obtain a functional relationship between the input variable and the optimization target specifically comprises:
establishing a first water-cooling plate optimization model according to the first relation matrix and the second relation matrix;
training the first water-cooling plate optimization model to obtain a functional relation among the variable pipe diameter of the main runner, the necking pipe diameter of the branch inlet, the total flow of the cooling liquid, the inlet temperature of the cooling liquid, the highest temperature of the battery and the temperature difference of the battery, namely:
establishing a second water-cooling plate optimization model according to the third relation matrix and the fourth relation matrix;
training the second water-cooling plate optimization model to obtain the functional relation between the variable pipe diameter of the main runner and the necking pipe diameter of the branch inlet and the pressure drop of the water-cooling plate and the flow field uniformity of the water-cooling plate, namely:
ΔPmax＝f(Umn,Dij)。
5. the method for optimally designing the water-cooling plate based on the multi-objective optimization as claimed in claim 4, wherein the iterative optimization calculation specifically comprises the following steps:
initializing the input variable;
performing optimization calculation on the water cooling plate optimization model by using a genetic algorithm;
and respectively judging whether the flow field uniformity of the water-cooling plate, the pressure drop of the water-cooling plate, the highest temperature of the battery and the temperature difference of the battery meet preset conditions or not to obtain the optimal parameter combination between the input variable and the optimization target.
6. The method for optimally designing the water-cooling plate based on the multi-objective optimization as claimed in claim 5, wherein the initialization of the input variables is specifically as follows:
and respectively randomly distributing a random number to the variable pipe diameter of the main flow channel, the necking pipe diameter of the branch inlet, the total flow of the cooling liquid and the temperature of the cooling liquid inlet, and setting an error function and calculation precision.
7. The multi-objective optimization-based water cooling plate optimization design method of claim 5, wherein the step of respectively judging whether the uniformity of the flow field of the water cooling plate, the pressure drop of the water cooling plate, the maximum temperature of the battery and the temperature difference of the battery meet preset conditions to obtain an optimal parameter combination between the input variable and the optimization target specifically comprises the following steps:
judging whether the uniformity of the flow field of the water cooling plate meets a first preset condition or not;
judging whether the pressure drop of the water cooling plate meets a second preset condition or not;
if the flow field uniformity of the water cooling plate meets a first preset condition and the pressure drop of the water cooling plate meets a second preset condition, obtaining an optimal parameter combination of the variable pipe diameter of the main runner and the necking pipe diameter of the branch inlet;
if the uniformity of the flow field of the water cooling plate does not meet a first preset condition or the pressure drop of the water cooling plate does not meet a second preset condition, returning to the optimization calculation of the water cooling plate optimization model by using a genetic algorithm until the uniformity of the flow field of the water cooling plate meets the first preset condition and the pressure drop of the water cooling plate meets the second preset condition;
judging whether the highest temperature of the battery meets a third preset condition or not;
judging whether the battery temperature difference meets a fourth preset condition or not;
if the highest temperature of the battery meets a third preset condition and the temperature difference of the battery meets a fourth preset condition, obtaining a parameter combination with the optimal total flow rate of the cooling liquid and the optimal inlet temperature of the cooling liquid;
and if the highest temperature of the battery does not meet a third preset condition or the temperature difference of the battery does not meet a fourth preset condition, returning to the optimization calculation of the water cooling plate optimization model by using a genetic algorithm until the highest temperature of the battery meets the third preset condition and the temperature difference of the battery meets the fourth preset condition.
8. The utility model provides a water-cooling board optimal design device based on multi-objective optimization which characterized in that includes:
the sampling module is used for sampling the input variable at a first sampling interval; the input variables comprise the variable pipe diameter of a main flow channel, the necking pipe diameter of a branch inlet, the total flow of cooling liquid and the temperature of the cooling liquid inlet;
the simulation module is used for carrying out simulation analysis on the relation between the input variable and the optimization target through a finite element simulation model to obtain a relation matrix between the input variable and the optimization target; wherein the optimization target comprises the highest temperature of the battery, the temperature difference of the battery, the pressure drop of the water cooling plate and the flow field uniformity of the water cooling plate;
the training module is used for establishing a water-cooling plate optimization model according to the relation matrix and training the water-cooling plate optimization model to obtain a functional relation between the input variable and the optimization target;
the optimization module is used for adjusting sampling intervals, sampling the input variables at second sampling intervals, and performing iterative optimization calculation on the water cooling plate optimization model by using a genetic algorithm to obtain an optimal parameter combination between the input variables and the optimization target; wherein the second sampling interval is less than the first sampling interval;
and the design module is used for obtaining the optimal design scheme of the water cooling plate according to the optimal parameter combination.
9. A multi-objective optimization-based water-cooling plate optimization design device, which comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to realize the multi-objective optimization-based water-cooling plate optimization design method according to any one of claims 1 to 7.
10. A computer-readable storage medium, comprising a stored computer program, wherein when the computer program runs, the computer-readable storage medium controls a device to execute the method according to any one of claims 1 to 7.
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| CN115397187B (en) * | 2022-04-07 | 2023-09-05 | 安世半导体科技(上海)有限公司 | Design method of radiator for vehicle power module |
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