CN114992808A - Heat pump air conditioner heat management control method and system based on combined intelligent algorithm - Google Patents
Heat pump air conditioner heat management control method and system based on combined intelligent algorithm Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/89—Arrangement or mounting of control or safety devices
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60H—ARRANGEMENTS OF HEATING, COOLING, VENTILATING OR OTHER AIR-TREATING DEVICES SPECIALLY ADAPTED FOR PASSENGER OR GOODS SPACES OF VEHICLES
- B60H1/00—Heating, cooling or ventilating [HVAC] devices
- B60H1/00642—Control systems or circuits; Control members or indication devices for heating, cooling or ventilating devices
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
- F24F11/46—Improving electric energy efficiency or saving
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
- F24F11/72—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure
- F24F11/74—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure for controlling air flow rate or air velocity
- F24F11/77—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure for controlling air flow rate or air velocity by controlling the speed of ventilators
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
- F24F11/80—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air
- F24F11/86—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air by controlling compressors within refrigeration or heat pump circuits
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- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
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Abstract
The invention belongs to the field of heat pump air conditioner heat management control, and provides a heat pump air conditioner heat management control method and a heat pump air conditioner heat management control system based on a combined intelligent algorithm, wherein the method adopts an artificial fish swarm algorithm to improve a wolf algorithm, foraging behavior of artificial fish swarm is introduced into the wolf algorithm, a regression type support vector machine is optimized through the improved wolf algorithm, so that a heat pump air conditioner heat management control algorithm is obtained, a heat pump air conditioner simulation model is built through one-dimensional simulation software, compressor rotating speed and fan rotating speed under the condition of real vehicle operation data are obtained through simulation, a heat pump air conditioner performance prediction sample library is constructed based on the data, a combined intelligent algorithm is adopted to train the sample library, compressor rotating speed and fan rotating speed prediction models under different operation conditions are obtained, real-time operation data are input into the prediction model, the compressor rotating speed and the fan rotating speed of the heat pump air conditioner are obtained, and a heat pump air conditioner heat management control strategy is formed, therefore, the heat pump air conditioner heat management control method and system based on the combined intelligent algorithm are obtained.
Description
Technical Field
The invention belongs to the field of heat pump air conditioner heat management control, and particularly relates to a heat pump air conditioner heat management control method and system based on a combined intelligent algorithm.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
The traditional fuel oil vehicle has the problem of exhaust emission inevitably in the driving process, and in the face of increasingly strict emission regulations and energy environment problems, the electric vehicle becomes a new industry, can realize zero emission in the driving process, and is more and more widely applied. For electric vehicles, the insufficient endurance mileage and difficulty in improvement are the main factors currently limiting the development of pure electric vehicles, under the technical background that the battery technology has no breakthrough progress. The air conditioning system is used as an energy consumption system of the electric automobile, and the reduction of the energy consumption is vital to the improvement of the endurance mileage of the electric automobile.
At present, an air conditioning system of an electric automobile generally meets the heating requirement of a passenger compartment through vapor compression type air conditioning in summer and PTC heating in winter, but the battery endurance mileage is reduced by using the PTC heating. At present, the heat pump air conditioner is a novel method for solving the problem of winter endurance of the electric automobile, and because the heat pump air conditioner system is a nonlinear complex system, the power consumption is overhigh by adopting the existing threshold control or P ID control.
Disclosure of Invention
In order to solve at least one technical problem in the background technology, the invention provides a heat pump air conditioner heat management control method and system based on a combined intelligent algorithm, which can ensure that the temperature of a passenger compartment quickly and accurately reaches a set temperature by adjusting parameters such as the rotating speed of a compressor, the air volume of a heat exchanger in a vehicle and the like so as to ensure that an air conditioning system is in an optimal working state in real time.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a heat pump air conditioner heat management control method based on a combined intelligent algorithm, which comprises the following steps:
acquiring running data of a real vehicle to be detected;
obtaining the actual rotating speeds of the heat pump air conditioner compressor and the fan according to the actual vehicle operation data to be detected and the compressor rotating speed and fan rotating speed prediction model under different trained working conditions;
the construction process of the compressor rotating speed and fan rotating speed prediction model under different working conditions is as follows: the method adopts an artificial fish swarm algorithm to improve a wolf algorithm, optimizes a regression type support vector machine algorithm based on the improved wolf algorithm, and specifically comprises the following steps: introducing foraging behavior of an artificial fish school into a wolf algorithm, regarding each wolf of the wolf algorithm as an artificial fish, regarding the distance between each dominant wolf and a prey as the visual field range of the artificial fish, and searching for an optimal fitness result in the visual field range of each wolf;
and controlling the heat pump air-conditioning compressor and the fan to operate according to the actual rotating speeds of the heat pump air-conditioning compressor and the fan. .
The second aspect of the invention provides a heat pump air conditioner heat management control system based on a combined intelligent algorithm, which comprises:
the operation data acquisition module is used for acquiring the operation data of the real vehicle to be detected;
the intelligent algorithm prediction module is used for obtaining the actual rotating speeds of the heat pump air conditioner compressor and the fan according to the running data of the real vehicle to be detected and the prediction model of the rotating speeds of the compressor and the fan under different working conditions after training;
the construction process of the compressor rotating speed and fan rotating speed prediction model under different working conditions is as follows: the method adopts an artificial fish swarm algorithm to improve a wolf algorithm, optimizes a regression type support vector machine algorithm based on the improved wolf algorithm, and specifically comprises the following steps: introducing foraging behavior of an artificial fish school into a wolf algorithm, regarding each wolf of the wolf algorithm as an artificial fish, regarding the distance between each dominant wolf and a prey as the visual field range of the artificial fish, and searching for an optimal fitness result in the visual field range of each wolf;
and the control module is used for controlling the operation of the heat pump air-conditioning compressor and the fan according to the actual rotating speeds of the heat pump air-conditioning compressor and the fan.
A third aspect of the invention provides a computer-readable storage medium.
A computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps in the heat pump air conditioner thermal management control method based on a combined intelligence algorithm as described above.
A fourth aspect of the invention provides a computer apparatus.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the heat pump air conditioner thermal management control method based on the combined intelligent algorithm as described above when executing the program.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, the foraging behavior of the artificial fish school algorithm is introduced through the Hui wolf algorithm, and the foraging behavior of the artificial fish school algorithm enables the global search capability of the algorithm in the early stage of iteration to be enhanced, so that a solution space can be explored more fully, and the problem that the Hui wolf algorithm is easy to fall into a local optimal solution is solved; compared with the existing wolf algorithm, the improved wolf algorithm is adopted to improve a regression type support vector machine to train a database, a compressor rotating speed and fan rotating speed prediction model is constructed, and a heat pump air conditioner heat management control strategy is obtained, so that the system can reduce the battery energy consumption to the maximum extent and increase the endurance mileage of the electric vehicle while meeting the heating requirement of a passenger compartment.
The regression-type support vector machine is optimized based on the improved wolf algorithm to obtain the combined intelligent algorithm, compared with the original regression-type support vector machine algorithm, the combined intelligent algorithm has better regression effect, can more accurately predict the rotating speed of the heat pump air conditioner compressor and the rotating speed of the fan, can reduce the energy consumption to the maximum extent while meeting the heating requirement of a passenger compartment, and provides a basis for the research and development of a control strategy of a thermal management system.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are included to illustrate an exemplary embodiment of the invention and not to limit the invention.
FIG. 1 is a flow chart of a heat pump air conditioner heat management control method based on a combined intelligent algorithm.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
In order to improve the control precision as much as possible on the premise of ensuring that a passenger compartment meets the temperature requirement, the invention provides a heat pump air conditioner heat management control method and a heat pump air conditioner heat management control system based on a combined intelligent algorithm, a heat pump air conditioner simulation model is built through one-dimensional simulation software, the rotating speed of a compressor and the rotating speed of a fan under the conditions of different vehicle speeds, environment temperatures, the air intake of an evaporator, the air intake of a condenser, the temperature of the passenger compartment, environment humidity and the like are obtained, a sample library is obtained by adopting the data construction, an improved wolf algorithm is adopted to improve a regression type support vector machine to train the sample library, the foraging behavior of an artificial fish swarm is introduced into the wolf algorithm, each wolf of the wolf algorithm sees an artificial fish as each wolf, the distance between each alpha wolf and a prey is regarded as the visual field range of the artificial fish, each wolf tries to search the position with high adaptability in the visual field range, a prediction model of the rotating speed of the compressor and the rotating speed of the fan is built to obtain a heat pump air conditioner heat management control strategy, so that the system can reduce the energy consumption of the battery to the maximum extent and increase the endurance mileage of the electric automobile while meeting the heating requirement of a passenger compartment.
Example one
As shown in fig. 1, the present embodiment provides a heat pump air conditioner thermal management control method based on a combined intelligent algorithm, including the following steps:
step 1: acquiring running data of a real vehicle to be detected;
in the implementation, the method can be applied to electric vehicles or plug-in hybrid electric vehicles and the like, and corresponding real vehicle operation data can be acquired according to actual scenes.
Step 2: obtaining actual rotating speeds of the heat pump air conditioner compressor and the fan according to the actual vehicle operation data to be detected and the compressor rotating speed and fan rotating speed prediction model under different trained working conditions;
the construction process of the compressor rotating speed and fan rotating speed prediction model under different working conditions is as follows: the method comprises the following steps of improving a wolf algorithm by adopting an artificial fish swarm algorithm, optimizing a regression support vector machine algorithm based on the improved wolf algorithm to obtain an intelligent combination algorithm, wherein the improvement of the wolf algorithm by adopting the artificial fish swarm algorithm comprises the following steps: introducing foraging behavior of an artificial fish school into a wolf algorithm, regarding each wolf of the wolf algorithm as an artificial fish, regarding the distance between each dominant wolf and a prey as the visual field range of the artificial fish, and searching the optimal result of fitness of each wolf in the visual field range of the wolf;
and step 3: the heat pump air-conditioning compressor and the fan are controlled to operate according to the actual rotating speed of the heat pump air-conditioning compressor and the fan, so that the system can meet the heating requirement of a passenger compartment, reduce the energy consumption of the battery to the maximum extent and increase the endurance mileage of the electric automobile.
As one or more embodiments, in step 1, the operation data of the real vehicle to be detected includes real-time vehicle speed, ambient temperature, air intake of the evaporator, air intake of the condenser, temperature of the passenger compartment, ambient humidity, and the like.
The heat pump air conditioning system specifically comprises an evaporator, a condenser, an electronic fan, an electric compressor, an electronic expansion valve, a four-way valve and other components.
As one or more embodiments, in step 2, the finding of the fitness optimization result by each wolf in the visual field of each wolf specifically includes:
the improved wolf algorithm is obtained by introducing the foraging behavior of the artificial fish school into the wolf algorithm.
The wolf algorithm introduces foraging behavior of artificial fish herds: each wolf of the wolf algorithm sees an artificial fish, and the distance between each alpha wolf and a prey is regarded as the visual field range of the artificial fish.
In the early stage of iteration, each wolf is formulated in its visual field
Moving towards the direction of the prey, and trying to find a position with high fitness.
The method specifically comprises the following steps:
if at X j Has a fitness value higher than X α Then the positions of the α wolf, β wolf, δ wolf are updated as:
if at X j The value of the adaptability is better than that of X β And is inferior to X α Then, the positions of the β wolf and the δ wolf are updated as follows:
if X j The value of the adaptability is better than that of X δ And is inferior to X β Then, the position of δ wolf is updated as:
the fitness value is updated as: f (X) δ )＝F(X j )。
Wherein i is the current iteration number, X α ，X β ，X δ ，X j The positions of alpha wolf, beta wolf, delta wolf and the current search agent, F (X), respectively α )，F(X β )，F(X δ )，F(X j ) Fitness values of alpha wolf, beta wolf, delta wolf and the current search agent, step is the iteration step, D α Is the distance between the wolf and the prey at the current moment, rand is [0,1 ]]A random value in between.
The gray wolf algorithm divides wolf groups into four grades of alpha, beta, delta and omega according to the habit of the gray wolf, wolfs of all grades are matched with each other to finish the predation process, each wolf group has a dominant wolf, namely alpha wolf which is a sole-finder in the wolf group and is responsible for all decisions of the groups, the second rank in the gray wolf group is beta wolf, the beta wolf in the groups can assist the decisions of alpha and feed back the information of other grades of wolf groups to the alpha wolf, the third rank is delta wolf, the delta wolf obeys the commands of the alpha wolf and the beta wolf, and the actions of the omega wolf can be commanded at the same time.
The introduced artificial fish swarm algorithm is an intelligent algorithm based on fish swarm behavior, and the optimal solution is obtained by searching in a feasible domain through simulating foraging, swarm clustering, rear-end collision and random behavior of artificial fish.
A fish group containing N artificial fishes in the water area { (x) i ，y i ) 1, 2,.., N }, wherein
J variables are the positions of certain artificial fish, namely feasible solutions; y is i Is the food concentration at that location, i.e., the value of the objective function.
Setting the current position of the artificial fish as x i The artificial fish executes foraging, random and rear-end actions after judging by searching the food concentration, crowdedness and the like of other positions of the artificial fish in the visual field range. The foraging behavior of the artificial fish shoal is that the artificial fish x is in a visible space i Finding position x v With higher concentration of food y v When the fish tries for the maximum number trynum, no better solution is found, and the artificial fish performs random action. Foraging behavior formula is x v ＝x i +rand×visual，
y v ＞y i . Wherein rand is a number [ -1,1 [ ]]And 1 × n random vectors in between, step is the moving step, and visual is the field of view.
The improved grey wolf algorithm is higher in overall optimizing capacity, is not prone to falling into a local optimal solution, is higher in convergence speed, has more accurate optimizing capacity compared with the grey wolf algorithm, can accurately predict the rotating speed of the heat pump air conditioner compressor and the rotating speed of the fan, can reduce energy consumption to the maximum extent while meeting the heating requirement of a passenger compartment, and provides a basis for the research and development of a control strategy of a thermal management system.
As one or more embodiments, an improved grey wolf algorithm is adopted to optimize a regression support vector machine algorithm to obtain a combined intelligent algorithm, the population number, the iteration times and the range of the variance g of a defined parameter penalty factor c and a radial basis kernel function are set, and c and g are used as the position coordinates of alpha wolf in the improved grey wolf algorithm;
the regression root mean square error of the regression support vector machine algorithm is used as a target function, the variance g and the penalty factor c of the radial basis kernel function of the regression support vector machine are optimized through an improved wolf algorithm to obtain the values of the optimal penalty factor c and the variance g of the radial basis kernel function, the improved wolf algorithm outputs the result with the optimal fitness and the corresponding position after iteration is completed, the optimal position is the final alpha wolf position, namely, the value of the penalty factor c corresponding to the minimum value of the target function is used as the abscissa of the alpha wolf, the value of the variance g of the radial basis kernel function is used as the ordinate of the alpha wolf, and the final alpha wolf position is obtained according to the abscissa and the ordinate of the alpha wolf.
And training the sample library by adopting a combined intelligent algorithm to obtain a prediction model of the rotating speed of the heat pump air conditioner compressor and the rotating speed of the fan.
The scheme has the advantages that compared with the original regression type support vector machine algorithm, the regression effect of the combined intelligent algorithm is better, the rotating speed of the heat pump air conditioner compressor and the rotating speed of the fan can be more accurately predicted, the heating requirement of the passenger compartment is met, the energy consumption can be reduced to the maximum extent, and a basis is provided for research and development of a control strategy of a thermal management system.
As one or more embodiments, a combined intelligent algorithm is adopted to train a sample library to obtain prediction models of the rotating speed of the compressor and the rotating speed of the fan under different working conditions, wherein the adopted sample library is obtained by establishing a simulation model of the heat pump air conditioner through one-dimensional simulation software, and the specific data comprises the following data: the method comprises the following steps of constructing a sample base by adopting the data of the motor compressor rotating speed and the electronic fan rotating speed under the conditions of vehicle speed, ambient temperature, evaporator air intake, condenser air intake, passenger compartment temperature, ambient humidity and the like, and randomly generating a training set and a testing set.
Example two
The embodiment provides a heat pump air conditioner thermal management control system based on a combined intelligent algorithm, which comprises:
the operation data acquisition module is used for acquiring operation data of the real vehicle to be detected;
the intelligent algorithm prediction module is used for obtaining the actual rotating speeds of the heat pump air conditioner compressor and the fan according to the running data of the real vehicle to be detected and the prediction model of the rotating speeds of the compressor and the fan under different working conditions after training;
the construction process of the compressor rotating speed and fan rotating speed prediction model under different working conditions is as follows: the method adopts an artificial fish school algorithm to improve a wolf algorithm, optimizes a regression type support vector machine algorithm based on the improved wolf algorithm, and specifically comprises the following steps: introducing foraging behavior of an artificial fish school into a wolf algorithm, regarding each wolf of the wolf algorithm as an artificial fish, regarding the distance between each dominant wolf and a prey as the visual field range of the artificial fish, and searching the optimal result of fitness of each wolf in the visual field range of the wolf;
and the control strategy output module is used for controlling the actual rotating speed of the heat pump air-conditioning compressor and the fan to operate according to the actual rotating speed of the heat pump air-conditioning compressor and the fan.
EXAMPLE III
The present embodiment provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps in the heat pump air conditioner thermal management control method based on a combined intelligent algorithm as described above.
Example four
The embodiment provides a computer device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the steps in the heat pump air conditioner thermal management control method based on the combined intelligent algorithm.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The heat pump air conditioner heat management control method based on the combined intelligent algorithm is characterized by comprising the following steps of:
acquiring running data of a real vehicle to be detected;
obtaining the actual rotating speeds of the heat pump air conditioner compressor and the fan according to the running data of the real vehicle to be detected and the prediction models of the rotating speeds of the compressor and the fan under different working conditions after training;
the construction process of the compressor rotating speed and fan rotating speed prediction model under different working conditions is as follows: the method adopts an artificial fish swarm algorithm to improve a wolf algorithm, optimizes a regression type support vector machine algorithm based on the improved wolf algorithm, and specifically comprises the following steps: introducing foraging behavior of an artificial fish school into a wolf algorithm, regarding each wolf of the wolf algorithm as an artificial fish, regarding the distance between each dominant wolf and a prey as the visual field range of the artificial fish, and searching the optimal result of fitness of each wolf in the visual field range of the wolf;
and controlling the heat pump air-conditioning compressor and the fan to operate according to the actual rotating speeds of the heat pump air-conditioning compressor and the fan.
2. The heat pump air conditioner heat management control method based on the combined intelligent algorithm, as claimed in claim 1, wherein the foraging behavior of the artificial fish school is introduced into the mausoleum algorithm, each wolf of the mausoleum algorithm is regarded as an artificial fish, the distance between each dominant wolf and prey is regarded as the visual field range of the artificial fish, and each wolf finds the result with the best fitness within the visual field range thereof, which specifically includes:
each wolf is in the field of visionIn the enclosure according to the formula
Moving towards the direction of a prey and trying to find a position with high fitness;
if at X j Has a fitness value higher than X α Then, the positions of α wolf, β wolf, δ wolf are updated as follows:
if at X j The fitness value of (A) is better than that of (X) β And is inferior to X α Then, the positions of the β wolf and the δ wolf are updated as follows:
if X j The value of the adaptability is better than that of X δ And is inferior to X β Then, the location of δ wolf is updated as:
the fitness value is updated as: f (X) δ )＝F(X j )，
Where i is the current iteration number, X α ，X β ，X δ ，X j The positions of alpha wolf, beta wolf, delta wolf and the current search agent, F (X), respectively α )，F(X β )，F(X δ )，F(X j ) Respectively alpha wolf, beta wolf, delta wolf and current searchThe fitness value of the cable agent is that step is iteration step length and rand is 0,1]A random value in between.
3. The heat pump air conditioner heat management control method based on combinational intelligent algorithm of claim 2, characterized in that the gray wolf algorithm divides the wolf group into four grades α, β, δ and ω according to the habit of gray wolfs, the wolfs of each grade cooperate with each other to complete the predation process, each wolf group has a dominant wolf, i.e. α wolf, which is the sole referee of the wolf group and is responsible for all decisions of the group, the second rank in the gray wolf group is β wolf, the β wolf in the group will assist the decision of α and feed back the other grade wolf group information to α wolf, the third rank is δ wolf, δ wolf follows the commands of α wolf and β wolf, and also commands the behavior of ω wolf.
4. The heat pump air conditioner heat management control method based on the combined intelligent algorithm according to claim 2, wherein the foraging behavior for introducing the artificial fish school specifically comprises:
in the visible space, the artificial fish x i Finding position x v With higher concentration of food y v When the artificial fish moves to the direction, a better solution is not found after the maximum number of times of trying, and the artificial fish performs random behavior;
wherein the foraging behavior formula is as follows:
x v ＝x i +rand×visual，
wherein rand is a number [ -1,1 [ ]]A random vector of 1 × b in between, step is the moving step, visual is the field of view,
j variables are the positions of certain artificial fish, namely feasible solutions; y is i Is the food concentration at that location, i.e., the value of the objective function.
5. The heat pump air conditioner thermal management control method based on combined intelligent algorithm as claimed in claim 1, wherein, optimizing regression type support vector machine algorithm based on improved wolf algorithm comprises:
the regression root mean square error of the regression support vector machine algorithm is used as a target function, the variance and the penalty factor of the radial basis kernel function of the regression support vector machine are optimized through the improved wolf algorithm, the value of the penalty factor corresponding to the minimum value of the target function is used as the abscissa of the alpha wolf, the value of the variance of the radial basis kernel function is used as the ordinate of the alpha wolf, and the final position of the alpha wolf is obtained according to the abscissa and the ordinate of the alpha wolf.
6. The heat pump air conditioner heat management control method based on the combined intelligent algorithm as claimed in claim 1, wherein the operation data of the real vehicle to be detected comprises real-time vehicle speed, ambient temperature, evaporator intake, condenser intake, passenger compartment temperature and ambient humidity.
7. The heat pump air conditioner heat management control method based on the combined intelligent algorithm as claimed in claim 1, wherein when the compressor rotation speed and fan rotation speed prediction models under different working conditions are trained, the training sample library specifically includes operation data of a real vehicle to be detected and the rotation speed of the electric compressor and the rotation speed of the electric fan, which are obtained by simulating the operation data of the real vehicle to be detected by building a heat pump air conditioner simulation model through one-dimensional simulation software.
8. A heat pump air conditioner heat management control system based on a combined intelligent algorithm is characterized by comprising:
the operation data acquisition module is used for acquiring operation data of the real vehicle to be detected;
the intelligent algorithm prediction module is used for obtaining the actual rotating speeds of the heat pump air conditioner compressor and the fan according to the running data of the real vehicle to be detected and the prediction model of the rotating speeds of the compressor and the fan under different working conditions after training;
the construction process of the compressor rotating speed and fan rotating speed prediction model under different working conditions is as follows: the method adopts an artificial fish swarm algorithm to improve a wolf algorithm, optimizes a regression type support vector machine algorithm based on the improved wolf algorithm, and specifically comprises the following steps: introducing foraging behavior of an artificial fish school into a wolf algorithm, regarding each wolf of the wolf algorithm as an artificial fish, regarding the distance between each dominant wolf and a prey as the visual field range of the artificial fish, and searching the optimal result of fitness of each wolf in the visual field range of the wolf;
and the control strategy output module is used for controlling the operation of the heat pump air-conditioning compressor and the fan according to the actual rotating speeds of the heat pump air-conditioning compressor and the fan.
9. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the steps of the method for heat pump air conditioner thermal management control based on a combined intelligence algorithm according to any one of claims 1-7.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the method for heat pump air conditioner thermal management control based on a combined intelligent algorithm according to any one of claims 1-7.
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