Multi-model self-adaptive control method for heat dissipation of liquid cooling machine and related equipment

Abstract

The invention relates to the technical field of power electronics, and discloses a multi-model self-adaptive control method for heat dissipation of a liquid cooling machine and related equipment, according to the method, an improved prediction function controller is subdivided into a plurality of sub-space controllers, and each sub-controller is optimized according to specific operation or power requirements. After receiving set data, the sub-controllers generate hydraulic oil flow velocity data through a switching mechanism and input the hydraulic oil flow velocity data to the gun line temperature module and the corresponding sub-space prediction models. By comparing the actual temperature with the predicted temperature, an estimated error is calculated, and error compensation is obtained. And the compensation is subjected to rolling optimization and is fed back to a switching mechanism by adopting a modal switching strategy. According to the switching mechanism, an optimal subspace controller is selected, optimal regulation and control over the gun line temperature are achieved, and therefore multi-model self-adaptive control over heat dissipation of the liquid cooling machine is completed. According to the invention, not only is the heat dissipation flexibility of the liquid cooling machine improved, but also efficient heat dissipation under different operation conditions is ensured.

Description

Technical Field

[0001] The present invention relates to the field of power electronics technology, and in particular to a multi-model adaptive control method for heat dissipation of a liquid cooler and related equipment. Background Art

[0002] With the vigorous development of the electric vehicle industry, the charging pile industry has ushered in unprecedented development opportunities. Against this background, intelligence has become an important development trend in the charging pile industry, and the research and development and application of smart charging piles have increasingly attracted widespread attention in the industry. Smart charging piles can not only improve charging efficiency, but also reduce operating costs through intelligent management, providing users with a more convenient and safe charging experience.

[0003] During the operation of charging piles, heat dissipation has always been one of the key factors restricting its performance improvement. At present, the heat dissipation methods of charging piles are mainly divided into two categories: fan heat dissipation and liquid cooling heat dissipation. Fan heat dissipation takes away the heat inside the charging pile through the rotation of the fan, while liquid cooling heat dissipation uses the circulation of coolant to achieve the heat dissipation effect. These two heat dissipation methods have been widely used in existing charging piles, but most of them adopt a fixed heat dissipation mode, that is, after the charging pile is started, the fan continues to run at a fixed speed, or the liquid cooling oil circulates at a fixed flow rate.

[0004] However, this fixed heat dissipation method has exposed some problems in practical applications. Especially when the charging vehicle enters the later charging stage, as the charging current decreases significantly, the heat generation inside the charging pile also decreases accordingly, and the temperature rise is low. At this time, if a high-speed fan or a high-flow liquid cooling oil is still used for heat dissipation, it will not only cause energy waste, but also increase the internal consumption of the charging pile and reduce its overall operating efficiency. At the same time, in conventional technologies, a single prediction model is often used for a single controller. Therefore, when the controller is operated under different power requirements, it cannot be effectively controlled and the overall coordination is poor. Summary of the invention

[0005] In order to overcome the defects of the above-mentioned prior art, the purpose of the present invention is to provide a multi-model adaptive control method and related equipment for liquid cooler heat dissipation, so as to solve the technical problem of how to perform multi-model collaborative control adjustment on the liquid cooler heat dissipation according to the actual heat dissipation requirements of the charging pile.

[0006] The present invention is achieved through the following technical solutions: In a first aspect, the present invention provides a multi-model adaptive control method for heat dissipation of a liquid cooler, comprising: Allocating the improved prediction function controller into a number of improved prediction function subspace controllers; Several improved prediction function subspace controllers input setting data, and correspondingly generate several hydraulic oil flow rate data of liquid cooling oil through a switching mechanism; They are respectively input into the gun line temperature input module and the corresponding subspace prediction model according to several hydraulic oil flow rates; Calculate several estimation errors by calculating the actual temperature data output by several gun line temperature data and the predicted temperature data output by the corresponding subspace prediction model; Obtain several error compensations according to several estimation errors; After several error compensations are optimized by rolling, they are feedback to the switching mechanism by adopting a mode switching strategy. The switching mechanism selects the optimal improved prediction function subspace controller for several improved prediction function subspace controllers to achieve the optimal control of the gun line temperature, and completes the multi-model adaptive control of the liquid cooler heat dissipation.

[0007] Preferably, in the step of equally dividing the improved prediction function controller into several improved prediction function sub-controllers, the working space of the controlled system is equally divided into several subspaces, corresponding subspace prediction models are established for several subspaces, and several corresponding improved prediction function subspace controllers are set according to several subspace prediction models.

[0008] Preferably, in the step of inputting set data into several improved prediction function subspace controllers, the set data is a reference trajectory constructed by the gun line temperature set value in the actual use of the charging gun line and determined according to the gun line temperature model.

[0009] Preferably, in the process of calculating several estimation errors by calculating the actual temperature data output by several gun line temperature data and the predicted temperature data output by the corresponding subspace prediction model, several estimation errors are obtained by the difference between the actual temperature data output by several gun line temperature data and the predicted temperature data output by the corresponding subspace prediction model.

[0010] Preferably, in the process of obtaining several error compensations according to several estimation errors, it includes: Construct a state space equation, and solve several error compensations through the state space equation according to several estimation errors.

[0011] Preferably, in the step of optimizing several error compensations by rolling, the process of rolling optimization includes: Obtain the corrected gun line temperature data according to several error compensations, and the sum of squares of the difference between the gun line temperature data and the reference trajectory set temperature reaches the minimum value.

[0012] Preferably, in the step of feedback to the switching mechanism by adopting a mode switching strategy, the mode switching strategy includes: The switching mechanism selects the improved predictive function subspace controller corresponding to the subspace prediction model with the minimum performance index value at each moment as the optimal improved predictive function subspace controller, and controls the actual gun line temperature according to the switched optimal improved predictive function subspace controller to achieve optimal regulation.

[0013] In a second aspect, the present invention also provides a multi-model adaptive control system for liquid cooler heat dissipation, including: A controller allocation module for allocating the improved predictive function controller into several improved predictive function subspace controllers; A data generation module for inputting set data into several improved predictive function subspace controllers and correspondingly generating several hydraulic oil flow rate data of the liquid cooling oil through the switching mechanism; A data input module for respectively inputting according to several hydraulic oil flow rates into the gun line temperature input module and the corresponding subspace prediction model; An error acquisition module for calculating several predicted errors by calculating the actual temperature data output by several gun line temperature data and the predicted temperature data output by the corresponding subspace prediction model; An error compensation acquisition module for correspondingly obtaining several error compensations according to several predicted errors; A multi-model adaptive control module for feeding several error compensations back to the switching mechanism after rolling optimization by adopting a mode switching strategy. The switching mechanism selects the optimal improved predictive function subspace controller from several improved predictive function subspace controllers to achieve optimal regulation of the gun line temperature, and completes the multi-model adaptive control of the liquid cooler heat dissipation.

[0014] In a third aspect, the present invention also provides a mobile terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the multi-model adaptive control method for liquid cooler heat dissipation as described above are implemented.

[0015] In a fourth aspect, the present invention also provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the multi-model adaptive control method for liquid cooler heat dissipation as described above are implemented.

[0016] Compared with the prior art, the present invention has the following beneficial technical effects: The present invention provides a multi-model adaptive control method for the heat dissipation of a liquid cooler. By allocating an improved predictive function controller into several improved predictive function subspace controllers, each subspace controller can be optimized for specific operating conditions or power requirements. Through a switching mechanism, the optimal improved predictive function subspace controller is selected based on the estimated error and error compensation. The switching mechanism not only considers the performance of a single controller but also the overall coordination of the system. Through continuous optimization and switching, the overall performance of the system can be optimized.

[0017] Furthermore, through multiple subspace prediction models, a modal switching strategy is fed back to the switching mechanism. The switching mechanism selects the optimal improved predictive function subspace controller from several improved predictive function subspace controllers to achieve optimal regulation of the gun line temperature. It can adjust the flow rate of the liquid cooling oil in real time according to the upper limit of the gun line set temperature. When the vehicle charging current is small, the gun line generates less heat and the temperature is low, so the flow rate of the liquid cooling oil is small, and the power consumption of the liquid cooler is reduced, thereby reducing the power consumption of the entire DC charging pile. BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 is a flowchart of the multi-model adaptive control method for the heat dissipation of a liquid cooler in an embodiment of the present invention; Figure 2 is a schematic diagram of the principle of the multi-model adaptive control method for the heat dissipation of a liquid cooler in an embodiment of the present invention; Figure 3 is a schematic diagram of the principle of the multi-model adaptive control system for the heat dissipation of a liquid cooler in an embodiment of the present invention; In the figure: 1, allocation module; 2, data generation module; 3, data input module; 4, error acquisition module; 5, error compensation acquisition module; 6, multi-model adaptive control module. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0019] The purpose of the present invention is to provide a multi-model adaptive control method and related equipment for the heat dissipation of a liquid cooler to solve the technical problem of how to perform multi-model collaborative control adjustment on the heat dissipation of the liquid cooler according to the actual heat dissipation requirements of the charging pile.

[0020] The following further describes the present invention in detail with reference to the accompanying drawings: Embodiment 1 See Figure 1 andFigure 2 , in an embodiment of the present invention, a multi-model adaptive control method for liquid cooler heat dissipation is provided, including: Step 1, allocate the improved predictive function controller to several improved predictive function subspace controllers; Specifically, evenly divide the working space of the controlled system into several subspaces, establish corresponding subspace prediction models for the several subspaces, and set corresponding several improved predictive function subspace controllers according to the several subspace prediction models.

[0021] Step 2, input the set data into several improved predictive function subspace controllers, and generate several hydraulic oil flow rate data of the liquid cooling oil correspondingly through the switching mechanism; Specifically, the set data is a reference trajectory constructed by the set value of the gun line temperature in the actual use of the charging gun line and determined according to the gun line temperature model.

[0022] Among them, the dynamic response of the predictive function control is mainly affected by the reference trajectory. The gun line temperature can select the following reference trajectory in the form of a first-order exponential.

[0023] (1) The matrix form is: (2) Where ， c(k + i) is the tracking set value at the k + i moment, β is the attenuation coefficient, T r is the expected closed-loop response time, that is, after obtaining the actual temperature of the gun line, the time required for the gun line temperature to drop to the expected temperature, ypav(k) is the corrected output value of the gun line temperature process; L is the coefficient vector in front of the gun line temperature set value, and E is the attenuation coefficient vector.

[0024] Among them, the gun line temperature control input can adopt a first-order pure delay function, that is, a step function. The number of basis functions being 1 can ensure that the module has good tracking performance. (First-order pure delay model) Therefore, the control input can be seen in the following formula: (3) Where, u(k + i) is the control input of the gun line temperature at the k + i moment; P is the optimization time domain length, and u is the weighting coefficient.

[0025] Step 4, input the several hydraulic oil flow rates into the gun line temperature input module and the corresponding subspace prediction models respectively; Step 4, calculate the actual temperature data output by the several gun line temperature data and the predicted temperature data output by the corresponding subspace prediction models to obtain several estimated errors; Specifically, a number of estimation errors are obtained from the difference between the actual temperature data output by a number of gun line temperature data and the predicted temperature data output by the corresponding subspace prediction model.

[0026] Step 5: Obtain a number of error compensations corresponding to the number of estimation errors; Specifically, a state space equation is constructed, and a number of error compensations are obtained by solving the state space equation according to the number of estimation errors.

[0027] Among them, the expression of the state space equation is as follows: (4) In the formula, Tm is the time constant of the first-order transfer function model, Km is the model gain.

[0028] From formula (4), Xm(k + i) at time k + i can be deduced as the following formula: (5) (6) Among them, Xm(k + i) is the state value of the liquid cooler at time k + i, which refers to the rotational speed of the liquid cooler here. A, B, and C are three constant coefficients in the calculation formula of the liquid cooler state value, which can be set according to the actual situation. gk(i) is the error compensation of the liquid cooler.

[0029] Step 6: A number of error compensations are fed back to the switching mechanism through rolling optimization and then adopt a modal switching strategy. The switching mechanism selects the optimal improved prediction function subspace controller for a number of improved prediction function subspace controllers to achieve optimal control of the gun line temperature, and completes the multi-model adaptive control of the liquid cooler heat dissipation.

[0030] Specifically, the process of the rolling optimization includes: Obtain the corrected gun line temperature data according to a number of error compensations, and the sum of squares of the difference between the gun line temperature data and the reference trajectory set temperature reaches the minimum value.

[0031] Among them, when there is a pure time delay in the rise of the liquid cooling oil flow rate, the Smith prediction compensation idea can be borrowed to make a certain correction to the liquid cooling oil flow rate - gun line temperature prediction model. The formula is as follows (7) In the formula, D is the number of pure time delay steps, Td is the lag time (the liquid cooling oil flow rate rise time provided by the liquid cooler manufacturer), ypav(k) represents the gun line temperature output that corrects the pure time delay. Written in matrix form as: (8) During the actual operation of the whole machine, there may be a certain error between the output of the gun line temperature model and the actual detected gun line temperature (this error value may also be affected by the environment). After error compensation, it can be expressed as: (9) In the formula ej(k) is the corresponding coefficient, ne is the order of the error extrapolator. In this embodiment, there is only 1 variable, which is the liquid cooling oil flow rate, and it can be taken as e(k+i)=e(k) .

[0032] Among them, through internal rolling optimization, the sum of the squares of the differences between the predicted gun line temperature output and the reference trajectory set temperature at several fitting points in the optimization time domain reaches the minimum value. In this paper, the predictive function control algorithm is improved by adding the characteristics of the generalized fractional order PI parameters, as shown in the following formula: (10) (11) In the formula v , r is the weighting coefficient, λ is the integral order, u is the differential order, k i is the integral proportional constant, k d is the differential proportional constant. Simplify the subspace prediction model to make q0=d0=1 , and combine formula (3), formula (4) and formula (8) to obtain the subspace prediction model expression: (12) Set , and substitute formula (3) and formula (12) into formula (11) to obtain the following formula: (13) Specifically, the mode switching strategy includes: The switching mechanism selects the improved predictive function subspace controller corresponding to the subspace prediction model with the minimum performance index value at each moment as the optimal improved predictive function subspace controller, and controls the actual gun line temperature according to the switched optimal improved predictive function subspace controller to achieve optimal regulation.

[0033] The multi-model method has good control for uncertain systems. Under different power demand operations, the liquid cooling oil flow rate - gun line temperature prediction module has different models, and different adjustments of the actual output liquid cooling oil flow rate with reference to different models will have different effects, as shown in Table 1.

[0034]

[0035] Table 1 shows the mathematical model of a certain model at different power outputs obtained in the laboratory The switching mechanism determines the selection of the optimal controller at different times. In this embodiment, a switching performance index is designed, and the switching mechanism selects the sub-controller corresponding to the sub-model with the minimum performance index value at each moment. The difference ei(k) between the actual system output and the model output is used to determine the system matching degree, and the switching algorithm is as follows: (14) In the formula, the residual e between the output of each sub-model and the system output i (k)=y pav (k)-y i (k), λ1, λ2, ρ are adjustable parameters, and λ1>0, λ2>0, ρ ∈ [0,1], k0 is the moment when switching to the i-th sub-model. In this embodiment, i = 4 is selected.

[0036] k The value of the switching variable at a certain moment w i is: (15) In the formula H ( x ) is the Heaviside unit step function, which is defined as follows: (16) When the value of the switching variable wi is 1, the controller will switch to the i-th sub-controller to control the actual gun line temperature.

[0037] During the switching scheduling process, in order to prevent the controller from making frequent switches due to factors such as the natural environment temperature and air volume, it is necessary to use a correction criterion to make certain corrections to the switching method. A waiting period can be used to improve the switching method. Here, a waiting period T min >0 (T min = 42s is selected in this article) is given. Its connotation is that no re-switching is allowed within T min after the controller switches, which is beneficial to ensuring the control quality of the system. In this embodiment, the subspace prediction model can be obtained by the manufacturer using variables such as the gun line output current, gun line usage duration, and gun line length and testing in different environments to obtain test data.

[0038] In summary, the liquid cooler heat dissipation multi-model adaptive control method provided by this embodiment allocates the improved predictive function controller to several improved predictive function subspace controllers, and each subspace controller can be optimized for specific operating conditions or power requirements. Through the switching mechanism, the optimal improved predictive function subspace controller is selected based on the estimated error and error compensation. The switching mechanism not only considers the performance of a single controller but also the overall system coordination. Through continuous optimization and switching, the overall performance of the system can be optimized.

[0039] Embodiment 2 According to Figure 3 As shown, the present invention also provides a liquid cooler heat dissipation multi-model adaptive control system, including: The controller allocation module 1 is used to allocate the improved predictive function controller to several improved predictive function subspace controllers; The data generation module 2 is used to input set data to several improved predictive function subspace controllers and correspondingly generate several hydraulic oil flow rate data of the liquid cooling oil through the switching mechanism; The data input module 3 is used to input according to several hydraulic oil flow rates respectively into the gun line temperature input module and the corresponding subspace prediction model; The error acquisition module 4 is used to calculate several estimated errors by calculating the actual temperature data output by several gun line temperature data and the predicted temperature data output by the corresponding subspace prediction model; The error compensation acquisition module 5 is used to obtain several error compensations according to several estimated errors; The multi-model adaptive control module 6 is used to feedback several error compensations to the switching mechanism after rolling optimization by using the modal switching strategy, and the switching mechanism selects the optimal improved predictive function subspace controller from several improved predictive function subspace controllers to achieve the optimal control of the gun line temperature and complete the control of the liquid cooler heat dissipation multi-model adaptive.

[0040] Embodiment 3 The present invention also provides a mobile terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, such as the liquid cooler heat dissipation multi-model adaptive control program.

[0041] When the processor executes the computer program, the steps of the above liquid cooler heat dissipation multi-model adaptive control method are implemented, such as: Allocating the improved predictive function controller to several improved predictive function subspace controllers; Inputting set data to several improved predictive function subspace controllers and correspondingly generating several hydraulic oil flow rate data of the liquid cooling oil through the switching mechanism; Input corresponding to several hydraulic oil flow rates into the gun line temperature input module and the corresponding subspace prediction model respectively; Calculate a number of estimated errors by calculating the actual temperature data output by a number of gun line temperature data and the predicted temperature data output by the corresponding subspace prediction model; Obtain a number of error compensations corresponding to the number of estimated errors; A number of error compensations are fed back to the switching mechanism by adopting a mode switching strategy after rolling optimization. The switching mechanism selects the optimal improved prediction function subspace controller from a number of improved prediction function subspace controllers to achieve optimal control of the gun line temperature, and completes the multi-model adaptive control of the liquid cooler heat dissipation.

[0042] Alternatively, when the processor executes the computer program, it realizes the functions of each module in the above system. For example: The controller allocation module 1 is used to allocate the improved prediction function controller to a number of improved prediction function subspace controllers; The data generation module 2 is used to input setting data into a number of improved prediction function subspace controllers, and correspondingly generate a number of hydraulic oil flow rate data of the liquid cooling oil through the switching mechanism; The data input module 3 is used to input corresponding to several hydraulic oil flow rates into the gun line temperature input module and the corresponding subspace prediction model respectively; The error acquisition module 4 is used to calculate a number of estimated errors by calculating the actual temperature data output by a number of gun line temperature data and the predicted temperature data output by the corresponding subspace prediction model; The error compensation acquisition module 5 is used to obtain a number of error compensations corresponding to the number of estimated errors; The multi-model adaptive control module 6 is used to feed back a number of error compensations to the switching mechanism by adopting a mode switching strategy after rolling optimization. The switching mechanism selects the optimal improved prediction function subspace controller from a number of improved prediction function subspace controllers to achieve optimal control of the gun line temperature, and completes the multi-model adaptive control of the liquid cooler heat dissipation.

[0043] Exemplarily, the computer program can be divided into one or more modules / units. The one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program in the mobile terminal.

[0044] For example, the computer program can be divided into a controller allocation module 1, a data generation module 2, a data input module 3, an error acquisition module 4, an error compensation acquisition module 5, and a multi-model adaptive control module 6; The functions of each module are as follows: The controller allocation module 1 is used to allocate the improved predictive function controller into several improved predictive function subspace controllers; The data generation module 2 is used to input the set data into several improved predictive function subspace controllers, and correspondingly generate several hydraulic oil flow rate data of the liquid-cooled oil through the switching mechanism; The data input module 3 is used to respectively input according to several hydraulic oil flow rates into the gun line temperature input module and the corresponding subspace prediction model; The error acquisition module 4 is used to calculate several predicted errors by calculating the actual temperature data output by several gun line temperature data and the predicted temperature data output by the corresponding subspace prediction model; The error compensation acquisition module 5 is used to correspondingly obtain several error compensations according to several predicted errors; The multi-model adaptive control module 6 is used to feedback several error compensations to the switching mechanism by adopting a mode switching strategy after rolling optimization. The switching mechanism selects the optimal improved predictive function subspace controller from several improved predictive function subspace controllers to achieve the optimal regulation of the gun line temperature, and completes the multi-model adaptive control of the liquid chiller heat dissipation.

[0045] The mobile terminal can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The mobile terminal may include, but is not limited to, a processor and a memory.

[0046] The processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the mobile terminal, and connects various parts of the entire mobile terminal through various interfaces and lines.

[0047] The memory can be used to store the computer program and / or module. The processor realizes various functions of the mobile terminal by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory.

[0048] The memory may mainly include a program storage area and a data storage area. Among them, the program storage area may store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area may store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0049] Embodiment 4 The present invention also provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps of the liquid cooling machine heat dissipation multi-model adaptive control method are implemented.

[0050] If the modules / units integrated in the mobile terminal are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0051] Based on such an understanding, to implement all or part of the processes in the above method, the present invention can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above aggregation reinforcement learning resource scheduling method can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc.

[0052] The computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disc, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0053] It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0054] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific implementation manners of the present invention, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A multi-model adaptive control method for the heat dissipation of a liquid chiller, characterized in that, Including: Allocating the improved predictive function controller into several improved predictive function subspace controllers; Several improved predictive function subspace controllers input the set data and correspondingly generate several hydraulic oil flow rate data of the liquid-cooled oil through the switching mechanism; Inputting according to several hydraulic oil flow rates respectively into the gun line temperature input module and the corresponding subspace prediction model; Calculating the actual temperature data output by several gun line temperature data and the predicted temperature data output by the corresponding subspace prediction model to obtain several prediction errors; Obtaining several error compensations according to several prediction errors; Several error compensations are fed back to the switching mechanism through rolling optimization and adopting a modal switching strategy. The switching mechanism selects the optimal improved predictive function subspace controller among several improved predictive function subspace controllers to achieve optimal control of the gun line temperature, completing the multi-model adaptive control of the liquid cooler heat dissipation.

2. The multi-model adaptive control method for liquid cooler heat dissipation according to claim 1, wherein In the step of equally dividing the improved predictive function controller into several improved predictive function sub-controllers, the working space of the controlled system is equally divided into several subspaces, several subspaces establish corresponding subspace prediction models, and several corresponding improved predictive function subspace controllers are set according to several subspace prediction models.

3. The multi-model adaptive control method for the heat dissipation of a liquid chiller according to claim 1, characterized in that In the step of several improved predictive function subspace controllers inputting the set data, the set data is a reference trajectory constructed by the gun line temperature set value in the actual use of the charging gun line and determined according to the gun line temperature model.

4. A multi-model adaptive control method for liquid cooler heat dissipation according to claim 1, characterized in that, In the step of calculating the actual temperature data output by several gun line temperature data and the predicted temperature data output by the corresponding subspace prediction model to obtain several prediction errors, several prediction errors are obtained by the difference between the actual temperature data output by several gun line temperature data and the predicted temperature data output by the corresponding subspace prediction model.

5. A multi-model adaptive control method for the heat dissipation of a liquid chiller according to claim 1, characterized in that In the step of obtaining several error compensations according to several prediction errors, it includes: Constructing a state space equation and solving several error compensations through the state space equation according to several prediction errors.

6. The multi-model adaptive control method for the heat dissipation of a liquid chiller according to claim 1, characterized in that, In the step of several error compensations undergoing rolling optimization, the process of rolling optimization includes: Obtaining the corrected gun line temperature data according to several error compensations, and the sum of squares of the difference between the gun line temperature data and the reference trajectory set temperature reaches the minimum value.

7. A multi-model adaptive control method for the heat dissipation of a liquid chiller according to claim 1, characterized in that In the step of adopting a modal switching strategy and feeding it back to the switching mechanism, the modal switching strategy includes: The switching mechanism selects the improved predictive function subspace controller corresponding to the subspace prediction model with the minimum performance index value at each moment as the optimal improved predictive function subspace controller, and controls the actual gun line temperature according to the switched optimal improved predictive function subspace controller to achieve optimal control.

8. A multi-model adaptive control system for the heat dissipation of a liquid chiller, characterized in that, Including: A controller allocation module for allocating the improved predictive function controller into several improved predictive function subspace controllers; A data generation module for several improved predictive function subspace controllers to input the set data and correspondingly generate several hydraulic oil flow rate data of the liquid-cooled oil through the switching mechanism; A data input module for inputting according to several hydraulic oil flow rates respectively into the gun line temperature input module and the corresponding subspace prediction model; An error acquisition module, configured to calculate a plurality of estimated errors by calculating the actual temperature data output by a plurality of gun line temperature data and the predicted temperature data output by the corresponding subspace prediction model; An error compensation acquisition module, configured to correspondingly obtain a plurality of error compensations according to the plurality of estimated errors; A multi-model adaptive control module, configured to feedback a plurality of error compensations to a switching mechanism after rolling optimization by adopting a mode switching strategy, and the switching mechanism selects an optimal improved prediction function subspace controller from a plurality of improved prediction function subspace controllers to achieve optimal regulation of the gun line temperature, thereby completing the multi-model adaptive control of the liquid cooler heat dissipation.

9. A mobile terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, the steps of the multi-model adaptive control method for liquid cooler heat dissipation according to any one of claims 1-7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, the steps of the multi-model adaptive control method for liquid cooler heat dissipation according to any one of claims 1-7 are implemented.

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