CN108509738A - High-power island special power supply module radiating device and its temprature control method - Google Patents
High-power island special power supply module radiating device and its temprature control method Download PDFInfo
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Abstract
The invention discloses a kind of high-power island special power supply module radiating device and its temprature control methods, as to specific position temperature analysis obtained by temperature monitoring module, establish the thermal model of airtight cavity, particle cluster algorithm is recycled to be modified institute's established model, model is set more accurately to describe machine box temperature changing process, the reliability of its adaptivity and PREDICTIVE CONTROL is improved, main control module realizes the adjusting to different air ducts and inside cavity fan wind-force based on Model Predictive Control from particle group optimizing method.Radiator structure of the present invention is combined with control strategy, radiator structure promotes even heat to be distributed, it quickly removes, enhances the independent control of component for each wind-force and mutual cooperation significantly improves radiating efficiency, realize effective heat dissipation of the high-power island special power supply in hot environment.
Description
Technical Field
The invention relates to a high-power heat dissipation device for a special island power supply module and a temperature control method thereof, which are suitable for heat dissipation treatment of the special island power supply.
Background
In the field of power electronics, power devices and inductance and capacitance generate heat due to self-loss. The heat will cause the temperature of the device to rise, the light person will not work normally and the service life will be affected, the heavy person will damage immediately, therefore, each device will take corresponding heat dissipation measures. The currently known heat dissipation technologies are respectively natural convection cooling using heat dissipation fins; forced convection cooling by adopting a radiating fin and a fan external force; cooling the flowing matter state change of the assembled heat conduction pipe; liquid cooling by a microtube cooling plate. The first two heat dissipation methods are mostly suitable for small power electronic devices, and the latter two heat dissipation methods are mostly adopted for power type semiconductor devices or modules.
The sea island environment is severe, the annual average humidity is as high as 85%, the surface temperature in summer is as high as 60 ℃, the day and night temperature difference is large, and the long-term reliable operation of power supply equipment is challenged by high salt mist, high humidity and high temperature difference: outdoor water logging leads to equipment power tube short circuit exploder, and high salt fog leads to the circuit board to be corroded and causes the exploder to damage, and the excess temperature leads to equipment rapid ageing and trouble frequently. And the problems of heat dissipation and protection are mutually restricted, which is a difficult problem to be solved urgently by special power supplies of islands and shore bases.
Disclosure of Invention
The invention aims to solve the technical problem that the prior art is not enough, and provides a high-power island special power supply module heat dissipation device and a temperature control method thereof, so that the high-power island special power supply can effectively dissipate heat in a high-temperature environment.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows: a high-power island special power module heat dissipation device comprises a shell; at least two cooling air channels are arranged in the shell, the internal space of the shell is divided into at least three spaces by the at least two cooling air channels, and a heating element and a plurality of temperature sensors for measuring the heating element are arranged in each space; an internal fan is mounted in each housing; and a cooling air fan is arranged at least one end of the cooling air channel.
All temperature sensors are electrically connected with the main control module.
And (3) building a thermal coupling model for the specific structure of the high-power island special power supply, optimizing the thermal coupling model by adopting a particle swarm algorithm, and monitoring the temperature of the heat concentration part by aiming at the thermal coupling model to perform optimal estimation.
And a plurality of radiating pipes are uniformly arranged on the outer surface of the cooling air duct.
Correspondingly, the invention also provides a temperature control method of the high-power island special power supply module heat dissipation device, which is realized by seeking the objective function J (x (k), Uk) To solve for the optimal control input U for the prediction system at time kk(k) Thereby obtaining the state temperature at the k +1 moment; wherein the objective function J (x (k), Uk) The expression of (a) is:
wherein,model system output quantity, T, predicted for time k + sO(k + s) is the target temperature, γ, set at the time of k + sy，γuTo regulate factor, gammay＝150，γu＝0.1；Np，NcRespectively representing the number of the prediction time domain and the control time domain segments of the system, wherein Np is more than or equal to Nc and more than or equal to 1;the optimal control input for the prediction system at time k + s.
The objective function J (x (k), U)k) Comprises the following steps:
1) setting a target temperature T0Sampling the temperature T in the housing at time k of the initial staten(k) Sampling air temperature, initializing particle swarm, and setting acceleration weight coefficient c1、c2，c1＝c22, and an inertial weight w;
2) random sampling to obtain 20 sets of input quantitiesq 1,2, … … 20 for each groupObtain the corresponding state variableHere, q represents a q-th group of input control quantity particles in the particle swarm, and t represents the initial iteration step number of the particles; particles5-dimensional particles representing independent control quantities of 5 fans; the 5 fans include 4 cooling air fans and 1 internal fan;
3) initialization speedBy using the saidThe objective function J (x (k), U)k) Obtaining the optimal P of the particles of the populationjAnd global optimum Pg；
4) And (3) updating the speed and the position of the particles by using the following particle swarm updating formula:
Vj t+1＝wVj t+c1r1(Pj t-Lj t)+c2r2(Pg t-Lj t)；
Lj t+1＝Lj t+Vj t+1；
in the t-th iteration, Pj tRefers to the historical optimum position, P, of each particleg tThe historical best position of the population, Lj tIs the position of the jth particle, Vj tIs the moving speed of the jth particle, r1、r2Is in the range [0,1 ]]The random number in (1) introduces uncertain factors;
5) calculate each groupCorresponding state variableCalculating the target function value of the updated particle and judging whether to update the optimal P of the particlejAnd global optimum Pg(ii) a If the set maximum iteration times are reached, step 6); otherwise, adding 1 to the value of the iteration times t;
6) the optimized control input of the nonlinear system at the k moment is obtained through the stepsqx refers to the number of particle groups in the optimal state, namely the optimal solution matrixMatrix of optimal solutionThe first element in (1)Acting on the prediction system;
7) at time k +1, return to step 1).
Compared with the prior art, the invention has the beneficial effects that: the invention designs the air duct heat dissipation surface with uniform liquid heat conduction, increases the effective heat dissipation area, reduces the heat dissipation surface diffusion thermal resistance and enhances the wind power heat dissipation capability. Meanwhile, the temperature monitoring module for monitoring the temperatures of a plurality of specific positions and the main control module based on the model predictive control and particle swarm optimization method complement each other, the heat dissipation structure promotes the uniform distribution and the rapid dissipation of heat, the heat dissipation efficiency is obviously improved aiming at the independent control and the mutual coordination of each wind power enhancement assembly, and the effective heat dissipation of the high-power island special power supply in a high-temperature environment is realized.
Drawings
FIG. 1 is a schematic diagram of a high power module unit according to the present invention;
(1) the heat sensor is distributed at the concentrated part of the component, as shown in △ in the figure;
FIG. 2 is a schematic diagram showing the distribution of soaking tubes for liquid attached to the air duct according to the present invention;
FIG. 3 is a flow chart of mechanism model modification based on particle swarm optimization according to the present invention;
FIG. 4 is a flow chart of the particle swarm optimization algorithm of the present invention;
FIG. 5 is a basic schematic diagram of model predictive control according to the present invention.
Detailed Description
The module unit of the invention uniformly adopts the air duct, the cavity formed by the power supply top cover and the side panel as the package to form a multi-surface heat dissipation structure. The soaking pipe is embedded in the contact surface of the air channel and the power device, so that the heat of a point heat source is dispersed, the diffusion thermal resistance is reduced, and the heat dissipation efficiency is improved. The cooling air is rapidly conveyed through the cooling air duct inlet and outlet fans, so that the conducted heat is rapidly transferred to the outside of the module through cold heat exchange, and the purpose of overall heat dissipation of the power module is achieved. The thermal coupling model is established on the basis of historical data and equipment characteristics, and the device control part comprises a temperature monitoring module and a main control module and is matched with the established model to realize model prediction control. Meanwhile, the optimization problem is solved by combining a particle swarm optimization algorithm.
the temperature monitoring module comprises a temperature sensor, the No. 1 single chip microcomputer and the No. 1 communication module are connected with the main control module and are mainly responsible for monitoring the real-time temperature of the concentrated part of each power device in the machine box, as shown by a delta in figure 1, real-time temperature instruction signals are output, and the No. 1 communication module sends the real-time temperature signals to the main control module.
According to different distribution positions of internal power devices, heat pipe distribution paths have respective pertinence, and most paths are as shown in fig. 2, so that the heat dissipation efficiency is improved. In order to ensure the performance of the heat pipe, the minimum bending radius is three times of the diameter of the heat pipe, the bending times of the heat pipe are reduced, and the heat pipe is prevented from being excessively flattened.
According to Newton's heat dissipation theorem, the calculation formula for obtaining the convection heat dissipation of the surface of the power device is as follows:
Qs＝hAb(Tb-Ta)
wherein h is the heat exchange coefficient between the device and the air fluid, Ab is the heat dissipation area of the single power device, Ta is the temperature of the air fluid near the device, and T is the temperature of the air fluid near the devicebIs the device temperature.
Taking a cylindrical device as an example, D is the diameter thereof
kaNu is the Nu Su. In the range of the fan wind speed limit, the Reynolds number Re ranges from 3264.8 to 65295, and at the moment
Nu＝0.27Re0.63Pr0.63(Pr/Prw)0.25
Wherein Pr is the prandtl number of the air fluid at the inlet; pr (Pr) ofwIs the prandtl number of the air fluid in the vicinity of the device; re is Reynolds number, paIs the air density, μ is the air viscosity, vmaxIs the maximum wind speed in the tunnel and L is the distance between adjacent devices. The calculation can obtain:
order to
Finally obtaining the heat dissipated per unit time on the surface of the device as
Qs＝a1Ab(Tb-Ta)·v0.63
The heat generation quantity of the battery in unit time is obtained by an empirical formula
R is internal resistance of the device, dE/dTbIs the temperature coefficient of the device, generally determined by measuring the voltage variation with temperature, denoted as a2。
In order to show the relationship between internal resistance and temperature, let R ═ a3Tb+a4And the specific numerical value is obtained by data fitting. Therefore, the first and second electrodes are formed on the substrate,
Qp＝(-a2I+a3I2)Tb+a4I2
the thermal coupling model of the single battery can be obtained by the calculation
Is the average temperature of the device, cbM is the specific heat capacity of the devicebIs the device quality.
The thermal coupling model adopts ANSYS software to simulate the temperature change of the power device under the operation environment and the operation condition under the real condition, simulation data is used as a real value to verify the model precision, the particle swarm algorithm is subsequently adopted to carry out optimization correction, and FIG. 3 is a mechanism model correction flow chart based on the particle swarm algorithm.
The main control module comprises a No. 2 single chip microcomputer and a No. 2 communication module, and is connected with the wind power enhancing assembly. The wind power regulating device is mainly responsible for sending out a wind power regulating instruction according to a received real-time temperature signal. The temperature monitoring module and the main control module form closed-loop control, the adjusted junction temperature of the power device is fed back to the main control module, and the main control module is combined with model prediction control to determine whether the wind power of the wind power enhancing assembly needs to be further increased or reduced.
Taking the temperature T of the measured part1，T2……T20The state quantity can be obtained by sampling of a temperature monitoring module, the control quantity is the output wind speed u-v of the wind power enhancing assembly, and the temperature T of a certain position is selectednAs a control output variable, it differs from the set safety temperature Tn-T0As a constraint output condition for each wind amplifying assembly. I.e. y ═ Tn,Tn-T0]. Meanwhile, the wind speed control method further comprises control quantity constraint and control increment constraint, so that the wind speed of the wind power enhancement assembly is within a reasonable range, and the wind speed is prevented from being changed too severely.
Discretizing the temperature control system to obtain:
x(k+1)＝fk(x(k),u(k))·Δt+x(k) (1)
y(k)＝Cyx(k) (2)
wherein x (k), x (k +1) is the state variable at time k and time k +1, i.e. temperature, u (k) is the control input variable at time k, i.e. fan speed, Δ t represents the step length, fkExpressing the change rate of the state quantity at the moment k, obtained by a system state space equation, taking y (k) as a control output variable, and outputting a matrix
Definition of Np，NcRespectively representing a prediction time domain and a control time domain of the system, and knowing that Np is more than or equal to Nc is more than or equal to 1 according to a model prediction control theory. At the k sampling instants, the control sequence U (k) and the prediction output sequence Y (k) of the system are respectively as follows:
and (3) taking sampling time k, x (k) of the temperature monitoring module as a prediction starting point, wherein the value of the sampling time k, x (k) is equal to a value x (k | k) of the starting time of the prediction process, the state quantity x and the output quantity y of the system are calculated and updated according to the state variable value at the current time and the system input value at the previous time after a sampling time, and the updating processes are shown in formulas (3) and (4) with reference to formulas (1) and (2). The solved control quantity (wind speed) will act on the system at the next sampling moment. At the next sampling moment, the controller solves a new optimization control problem according to the new state measurement value, so that the value of the future sampling moment is solved, and fig. 4 is a basic schematic diagram of model prediction control of the invention.
x(k+1|k)＝fk(x(k|k),u(k|k))·Δt+x(k|k)
x(k+Nc|k)＝fk(x(k+Nc-1|k),u(k+Nc-1|k))·Δt+x(k+Nc-1|k) (3)
y(k+1|k)＝Cy(fk(x(k|k),u(k|k))·Δt+x(k|k))
y(k+Np|k)＝Cy(fk(x(k+Np-1|k),u(k+Np-1|k))·Δt+x(k+Np-1|k))
(4)
The objective function of the temperature controller is:
seek JminWherein T isOIn order to set the temperature control target,optimal control input for the prediction system at the time k + s; the first part on the right side of the formula (5) quickly and stably tracks the reference track, and the second part realizes the optimization target with low energy consumption and two adjustment factors gammay，γuAnd adjusting the tracking speed and the energy level.In the optimization process of the particle swarm optimization, the state variables are calculated by a model system from given initial quantities and control quantities, namely only U existskOne variable, so is equivalent to seeking J (U)k)min。
And solving the optimization problem by using a particle swarm optimization algorithm, and making up the problem that the model predictive control cannot obtain the control rate by directly solving the nonlinear programming problem due to nonlinearity and time domain constraint. The wind speed is set as particles, and the number of the particles is selected to be 20. The maximum number of iterations is 30, and the search is stopped when the termination condition is set to reach the maximum number of iterations. The acceleration weight coefficient is taken according to experience1＝c22, which represent the degree of influence of "self-experience" and "social experience", respectively, on particle velocity update, with "self-experience" referring to the historical optimal position for each particle, counted as pj"social experience" refers to the historical best position of the population, designated as pg. Speed clamp set to Vmax0.1, avoid crossing the area of the optimal solution directly too much. w is an inertial weight, and the change speed of different stages is adjusted, and is set to change in a linear decreasing trend. The particle fitness function is an objective function, and a particle swarm algorithm flowchart is shown in fig. 4.
The method comprises the following specific steps:
step 1: selecting and sampling proper parameters including parameters of particle swarm algorithm, setting target temperature T in case0Sampling of the temperature T in the case of the initial staten(k) And sampling the air temperature.
Step 2: random sampling to obtain 20 sets of input quantitiesFor each groupAccording to the established model system and each initial quantity, the corresponding state variable can be obtainedHere, i represents the ith group of input control amount particles in the particle group, and t represents the initial number of iteration steps of the particles. Here the particlesIs a 5-dimensional particle and represents the independent control quantity of 5 fans.
Step 3: initialization speedAnd calculating an objective function of the initial point, as shown in formula (5), to obtain the optimal P of the population particlesjAnd global optimum Pg。
Step 4: the particle swarm update formula is used for carrying out speed update and position update on the particles and simultaneously being limited by speed clamp
Vj t+1＝wVj t+c1r1(Pj t-Lj t)+c2r2(Pg t-Lj t) (6)
Lj t+1＝Lj t+Vj t+1(7)
Step 5: calculating each group according to the model systemCorresponding state variableCalculating an objective function and judging whether to update the particle optimal PjAnd global optimum Pg. If the set maximum iteration number is reached, executing step 6; otherwise, t is t +1, Step4 is executed.
Step 6: through the steps, the optimized control input at the k moment in the nonlinear system is obtainedix refers to the number of particle groups in the optimal state, i.e.The first element thereinThe input acts on the system.
Step 7: at time k +1, Step1 is repeated.
Above-mentioned wind-force reinforcing component adopts the outer wind channel that expands of the outer shape of expanding of arc with the wind channel intercommunication, considers the protective capacities, has adopted the mesh to filter the air as the air inlet screen panel for the stainless steel mesh of hexagon structure, but adjustable shelves three proofings fan assembly installs in wind-force reinforcing component. The model predictive control carries out targeted heat dissipation on the power device with higher temperature according to each fan gear instruction obtained by the temperature distribution in the case, thereby effectively reducing the nonuniformity of the temperature in the case.
Compared with the prior art, the invention has the beneficial effects that: by placing the temperature sensors at a plurality of specific locations, the thermal coupling model is completed. The air duct heat dissipation surface is designed to conduct heat uniformly through liquid, the effective heat dissipation area is increased, the heat dissipation surface diffusion thermal resistance is reduced, the encapsulation inductance double-side heat conduction method is designed, the heat conduction area and the heat conduction efficiency of a large heating element are increased, the wind power reinforcing assembly is communicated with the heat radiator through the outer expansion air duct in the shape of an arc-shaped outer expansion surface, and the wind power heat dissipation capacity is enhanced. The master control module based on model predictive control and the temperature monitoring module form closed-loop control, the wind power output by the wind power enhancement assemblies at all the positions is adjusted in real time, and the heat dissipation efficiency is greatly improved.
Claims (6)
1. A high-power island special power module heat dissipation device is characterized by comprising a shell (1); at least two cooling air channels (2) are arranged in the shell (1), the internal space of the shell (1) is divided into at least three spaces by the at least two cooling air channels (2), and each space is internally provided with a heating element (6) and a plurality of temperature sensors (7) for measuring the heating element (6); an internal fan (5) is arranged in each shell (1); and a cooling air fan (3) is arranged at least one end of the cooling air duct (2).
2. The high power island special power module heat sink of claim 1, wherein all temperature sensors are electrically connected to the master control module.
3. The heat dissipation device of claim 1, wherein a thermal coupling model is built for the specific structure of the high-power island special power supply, and meanwhile, a particle swarm optimization is adopted to optimize the thermal coupling model, and the thermal coupling model is used to monitor the temperature of the heat concentration part for optimal estimation.
4. Thermal coupling model the heat dissipation device of high-power island special power module as claimed in claim 1, wherein the cooling air duct (2) has a plurality of heat dissipation tubes (4) uniformly arranged on its outer surface.
5. A temperature control method for a high-power island special power supply module heat dissipation device is characterized in that an objective function J (x (k), U) is soughtk) To solve for the optimal control input U for the prediction system at time kk(k) Thereby obtaining the state temperature at the k +1 moment; wherein the objective function J (x (k), Uk) The expression of (a) is:
wherein,model system output quantity, T, predicted for time k + sO(k + s) is the target temperature, γ, set at the time of k + sy，γuTo regulate factor, gammay＝150，γu＝0.1；Np，NcRespectively representing the number of the prediction time domain and the control time domain segments of the system, wherein Np is more than or equal to Nc and more than or equal to 1;the optimal control input for the prediction system at time k + s.
6. The temperature control method of claim 6, wherein the objective function J (x (k), U)k) Comprises the following steps:
1) setting a target temperature T0Sampling the temperature T in the housing at time k of the initial staten(k) Sampling air temperature, initializing particle swarm, and setting acceleration weight coefficient c1、c2，c1＝c22, and an inertial weight w;
2) random sampling to obtain 20 sets of input quantitiesq 1,2, … … 20 for each groupObtain the corresponding state variableHere, q represents a q-th group of input control quantity particles in the particle swarm, and t represents the initial iteration step number of the particles; particles5-dimensional particles representing independent control quantities of 5 fans; the 5 fans include 4 cooling air fans and 1 internal fan;
3) initialization speedUsing the objective function J (x (k), U)k) Obtaining the optimal P of the particles of the populationjAnd global optimum Pg；
4) And (3) updating the speed and the position of the particles by using the following particle swarm updating formula:
in the t-th iteration, Pj tRefers to the historical optimum position, P, of each particleg tThe historical best position of the population, Lj tIs the position of the jth particle, Vj tIs the moving speed of the jth particle, r1、r2Is in the range [0,1 ]]The random number in (1) introduces uncertain factors;
5) calculate each groupCorresponding state variableCalculating the target function value of the updated particle and judging whether to update the optimal P of the particlejAnd global optimum Pg(ii) a If the set maximum iteration times are reached, step 6); otherwise, adding 1 to the value of the iteration times t;
6) the optimized control input of the nonlinear system at the k moment is obtained through the stepsqx refers to the number of particle groups in the optimal state, namely the optimal solution matrixMatrix of optimal solutionThe first element in (1)Acting on the prediction system;
7) at time k +1, return to step 1).
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| 茹敬佩: "基于风冷散热的电动汽车电池组热模型与温度控制研究", 《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑（月刊 ）》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113901591A (en) * | 2021-12-08 | 2022-01-07 | 中国空气动力研究与发展中心低速空气动力研究所 | Hot wire unit dry power calculation method and liquid water content calculation method based on same |
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