CN113050426A - Genetic ant colony algorithm fused thermal management control method and system - Google Patents
Genetic ant colony algorithm fused thermal management control method and system Download PDFInfo
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- CN113050426A CN113050426A CN202110301556.4A CN202110301556A CN113050426A CN 113050426 A CN113050426 A CN 113050426A CN 202110301556 A CN202110301556 A CN 202110301556A CN 113050426 A CN113050426 A CN 113050426A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04B—POSITIVE-DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS
- F04B49/00—Control, e.g. of pump delivery, or pump pressure of, or safety measures for, machines, pumps, or pumping installations, not otherwise provided for, or of interest apart from, groups F04B1/00 - F04B47/00
- F04B49/06—Control using electricity
- F04B49/065—Control using electricity and making use of computers
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04D—NON-POSITIVE-DISPLACEMENT PUMPS
- F04D27/00—Control, e.g. regulation, of pumps, pumping installations or pumping systems specially adapted for elastic fluids
- F04D27/004—Control, e.g. regulation, of pumps, pumping installations or pumping systems specially adapted for elastic fluids by varying driving speed
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Abstract
The invention provides a heat management control method and system fusing a genetic ant colony algorithm, which are used for acquiring environmental temperature, environmental humidity, motor rotating speed, motor torque and motor water outlet temperature as control input parameters; and controlling based on control input parameters by using a support vector machine prediction model improved based on a fusion genetic ant colony algorithm, determining a fan duty ratio matched with the current working condition, and controlling the rotating speed of the electronic fan by using the fan duty ratio. The invention can reduce the energy consumption of the fan as much as possible while ensuring that the heat dissipation requirement of the motor is met, and has great application value for the effective work, energy conservation and emission reduction of the heat management system of the electric drive assembly.
Description
Technical Field
The invention belongs to the technical field of thermal management, particularly relates to a thermal management control method and system fusing a genetic ant colony algorithm, and more particularly relates to the technical field of controlling the rotating speed of an electronic fan of an electrically driven vehicle and engineering machinery under different working conditions according to signals of various sensors measured in real time.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The characteristics of low energy consumption and zero emission of electric automobiles and electric driving engineering machinery enable the electric automobiles and the electric driving engineering machinery to become an emerging industry which is not geared. For pure electric vehicles and electrically driven engineering machinery, the normal operation of key components such as motors is the basis for ensuring the safe operation of the key components, and the key point of the power system of the vehicles and the engineering machinery is. The motor and the controller thereof are used as key components for power conversion of the electric drive assembly, and the motor and the controller thereof must operate below the highest safe temperature to ensure normal operation of a rolling bearing, a motor winding and the like in the motor, and a set of complete and efficient thermal management system is the key for ensuring that the motor and the controller operate in a proper temperature range. It has become a focus of research to develop and design thermal management systems that meet thermal load requirements.
The electronic fan and the electronic water pump are indispensable rings of a thermal management system, the consumed power of the electronic fan and the electronic water pump is increased along with the increase of the rotating speed, the energy consumption of the electronic water pump is lower than that of the fan, and therefore the control of the rotating speed of the fan is an effective means for reducing the energy consumption. Existing electric drive assembly thermal management systems typically set the fan speed to several gears that do not meet specific operating condition requirements, and some operating conditions may include the following: firstly, the rotating speed of the fan is too low, and the refrigerating requirement cannot be met; secondly, the rotating speed of the fan is too high, which causes the waste of energy.
Disclosure of Invention
The invention aims to solve the problems and provides a heat management control method and system fusing a genetic ant colony algorithm.
According to some embodiments, the invention adopts the following technical scheme:
a thermal management control method fusing genetic ant colony algorithm comprises the following steps:
acquiring the ambient temperature, the ambient humidity, the motor rotating speed, the motor torque and the motor water outlet temperature as control input parameters;
and controlling based on control input parameters by using a support vector machine prediction model improved based on a fusion genetic ant colony algorithm, determining a fan duty ratio matched with the current working condition, and controlling the rotating speed of the electronic fan by using the fan duty ratio.
As an alternative implementation mode, the fusion genetic ant colony algorithm introduces the genetic algorithm into the ant colony algorithm, adopts a selection operator and a mutation operator of the genetic algorithm, sorts ant individuals according to the pheromone concentration when executing the selection operator, and selects individuals with pheromone concentration ranking in a preset range to update pheromone; when executing mutation operator, the gene value of the ant individual is actually mutated into other allele values at a set mutation rate.
In an alternative embodiment, in the improved support vector machine prediction model based on the fusion genetic ant colony algorithm, by setting initial parameters, a combination of a penalty factor and a variance parameter of a radial basis function is randomly generated as an initial population, and each generation of ants is subjected to transfer, selection, variation and pheromone release operations to find a combination of the penalty factor and the variance parameter of the radial basis function which minimizes the error of the support vector machine prediction model.
By way of further limitation, the initial parameters include, but are not limited to, population number, maximum number of iterations, pheromone volatility upper and lower limits, transition probability, mutation probability, and selection probability.
As an alternative embodiment, the improved support vector machine prediction model based on the fusion genetic ant colony algorithm is trained in advance, and training samples are obtained by historical environment temperature, environment humidity, motor rotating speed, motor torque, motor water outlet temperature detection data and simulation experiment data.
As a further limitation, the rotating speed of the electronic fan meeting the target requirement of the water outlet temperature of the motor under different working conditions is obtained by setting different environmental temperatures, environmental humidity, motor rotating speed, motor torque and electronic water pump rotating speed and combining with the simulation of the wind tunnel test data of the heat dissipation module, and the duty ratio of the fan serving as an output parameter is obtained by conversion.
A system for thermal management control incorporating genetic ant colony algorithms, comprising:
the system comprises an electric drive assembly thermal management system and a controller, wherein the controller is configured to acquire an ambient temperature, an ambient humidity, a motor rotating speed, a motor torque and a motor water outlet temperature as control input parameters;
and controlling based on control input parameters by using a support vector machine prediction model improved based on a fusion genetic ant colony algorithm, determining a fan duty ratio matched with the current working condition, and controlling the rotating speed of the electronic fan by using the fan duty ratio so as to control the thermal management system of the electric drive assembly.
As an alternative embodiment, the ambient temperature, the ambient humidity, the motor rotation speed, the motor torque and the motor outlet water temperature in the electric drive assembly thermal management system are acquired by corresponding sensors.
An electronic device comprises a memory, a processor and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the steps of the thermal management control method fusing genetic ant colony algorithm are completed.
A computer readable storage medium for storing computer instructions which, when executed by a processor, perform the steps of the above-described method for thermal management control incorporating genetic ant colony algorithm.
Compared with the prior art, the invention has the beneficial effects that:
the method improves the ant colony algorithm, the obtained genetic ant colony fusion algorithm is used for improving the support vector machine algorithm, and the obtained support vector machine prediction model is more accurate.
The method utilizes the support vector machine model improved by the genetic ant colony fusion algorithm to predict the fan rotating speed of the electric drive assembly heat management system, has smaller error, reasonably saves the power consumption of the fan and the electronic water pump on the premise of meeting the refrigeration requirement, provides a basis for the research and development of the control strategy of the heat management system, and has important significance for energy conservation and emission reduction.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
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 incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a schematic diagram of the control logic of the present invention.
The specific implementation mode is as follows:
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.
Aiming at an electric drive assembly thermal management system, the invention provides an electric drive assembly thermal management control strategy fusing a genetic ant colony algorithm, the ant colony algorithm is improved into the genetic ant colony fusion algorithm, and a prediction model of a support vector machine is formed based on the algorithm. And (4) carrying out simulation calculation by extracting sensor signals and combining the wind tunnel test data of the heat dissipation module to obtain sample data of the prediction model. And training the support vector machine prediction model by using the sample data to finally form a control strategy of the rotating speed of the electronic fan under each working condition.
A control strategy aiming at the rotating speed of an electronic fan enables the system to meet the refrigeration requirement and simultaneously reduces the energy consumption to the maximum extent. In the control strategy, the environmental temperature, the environmental humidity, the motor rotating speed, the motor torque and the motor water outlet temperature are used as input variables, and the electronic fan and electronic water duty ratio signals are used as output parameters, so that the support vector machine prediction model is trained, and finally, a control model of the fan rotating speed under each working condition is formed.
As shown in fig. 1, when a vehicle is in a certain working condition, the sensors monitor the ambient temperature, the ambient humidity, the motor outlet water temperature, the motor rotation speed, the motor torque, and the electronic water pump rotation speed as input parameters in real time, and the fan duty ratio as an output parameter, and train the support vector machine improved by the genetic ant colony fusion algorithm to obtain the trained support vector machine prediction model shown in fig. 1.
Specifically, when the genetic ant colony fusion algorithm introduces the genetic algorithm into the ant colony algorithm, a selection operator and a mutation operator of the genetic algorithm are adopted. When the selection operator is executed, the ant individuals are sorted according to the concentration of the pheromone, and the individual with the highest pheromone concentration rank is selected for pheromone updating. When executing mutation operator, the gene value of ant individual is varied with the mutation rate PmReal value variation is performed to other allele values. The individual i is updated as:
wherein the content of the first and second substances,
xijand xij' is the j-dimension position coordinate, x, of the i-th ant before and after variationmaxAnd xminThe ant positions are respectively the upper and lower limits of the corresponding dimension, rand is a random number of 0-1 generated randomly, the current iteration number iter and the maximum iteration number maxgen. Chinese patent medicineThe self-adaptive pheromone volatilization coefficient of the ant-colony-transfer fusion algorithm is
Wherein rhomax，rhominRespectively an upper limit and a lower limit of the volatilization coefficient of the pheromone. Pheromone update formula as taui'＝(1-rho)·taui+ΔtauiWherein, taking the maximum value as an example,
tauiand taui' the pheromone contents of the i-th ant before and after mutation, respectively. Delta tauiIs pheromone increment of the ith ant, omegaiThe rank of the first ant, L the number of ants selected after the selection operator is executed and participating in pheromone updating, and the rest ant-L ants do not update pheromone. f. ofmin(x)，fmax(x) Respectively fitness minimum and maximum.
A support vector machine improved by a genetic ant colony fusion algorithm randomly generates a penalty factor c and a variance g parameter combination of a radial basis kernel function as an initial population by setting initial parameters such as population number, maximum iteration times, pheromone volatilization upper and lower limits, transfer probability, variation probability, selection probability and the like, and searches for a penalty factor which minimizes the error of a support vector machine prediction model and a variance parameter combination of the radial basis kernel function through operations such as transfer, selection, variation, pheromone release and the like in each generation.
Of course, the electric drive assembly thermal management system of the present invention may be an existing system. The electric drive assembly comprises an electric automobile and an electric engineering machine, such as an electric bulldozer, an electric excavator, an electric loader, an electric road roller and the like.
The electric drive assembly heat management system comprises a motor radiator, an electronic water pump, an electronic fan, an air duct, a condenser and other parts, wherein the electronic water pump is set to be in three gears of low speed, medium speed and high speed.
The prediction model training sample is obtained through one-dimensional and three-dimensional fluid mechanics combined operation. According to a control strategy for an electronic fan in an electric drive assembly thermal management system, the electronic fan rotating speed meeting the target requirement of the water outlet temperature of a motor under different working conditions is obtained through simulation by setting different environmental temperatures, environmental humidity, motor rotating speeds, motor torques and electronic water pump rotating speeds, the fan duty ratio serving as an output parameter is obtained through conversion, and the data are used as a training sample of a support vector machine prediction model.
The electric drive assembly thermal management system forms a control strategy for the electronic fan: when a vehicle is in a certain working condition, the environmental temperature, the environmental humidity, the motor water outlet temperature, the motor rotating speed, the motor torque and the electronic water pump rotating speed are collected by the sensors to serve as input variables, the duty ratio of the fan is output, and a support vector machine prediction model improved by a genetic ant colony fusion algorithm is trained to obtain a fan control strategy ConStrMach _ fan of the electric drive assembly thermal management system.
The control strategy of the rotating speed of the electronic fan, the radiator, the electronic fan, the electronic water pump and the like form a thermal management control strategy and system for the electric drive assembly, and the control strategy and system can meet the refrigeration requirement of the motor and reduce energy consumption.
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 an entirely hardware embodiment, an entirely 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, CD-ROM, 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.
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.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (10)
1. A thermal management control method fusing genetic ant colony algorithm is characterized by comprising the following steps: the method comprises the following steps:
acquiring the ambient temperature, the ambient humidity, the motor rotating speed, the motor torque and the motor water outlet temperature as control input parameters;
and controlling based on control input parameters by using a support vector machine prediction model improved based on a fusion genetic ant colony algorithm, determining a fan duty ratio matched with the current working condition, and controlling the rotating speed of the electronic fan by using the fan duty ratio.
2. The method for controlling heat management by fusing genetic ant colony algorithm as claimed in claim 1, wherein: the fusion genetic ant colony algorithm introduces the genetic algorithm into the ant colony algorithm, adopts a selection operator and a mutation operator of the genetic algorithm, sorts ant individuals according to the concentration of pheromones when executing the selection operator, and selects individuals with the concentration ranking of the pheromones in a preset range to update the pheromones; when executing mutation operator, the gene value of the ant individual is actually mutated into other allele values at a set mutation rate.
3. The method for controlling heat management by fusing genetic ant colony algorithm as claimed in claim 1, wherein: in the improved support vector machine prediction model based on the fusion genetic ant colony algorithm, initial parameters are set, a combination of a penalty factor and a variance parameter of a radial basis function is randomly generated to serve as an initial population, and each generation of ants are subjected to transfer, selection, variation and pheromone release operations to find the combination of the penalty factor and the variance parameter of the radial basis function, which enables the error of the support vector machine prediction model to be minimum.
4. The method for controlling heat management by fusing genetic ant colony algorithm as claimed in claim 3, wherein: the initial parameters include, but are not limited to, population number, maximum iteration number, pheromone volatilization upper and lower limits, transition probability, mutation probability, and selection probability.
5. The method for controlling heat management by fusing genetic ant colony algorithm as claimed in claim 1, wherein: the improved support vector machine prediction model based on the fusion genetic ant colony algorithm is trained in advance, and training samples are obtained by historical environmental temperature, environmental humidity, motor rotating speed, motor torque, motor water outlet temperature detection data and simulation experiment data.
6. The method for controlling heat management by fusing genetic ant colony algorithm as claimed in claim 5, wherein: the electronic fan rotating speed meeting the target requirement of the motor water outlet temperature under different working conditions is obtained by setting different environmental temperatures, environmental humidity, motor rotating speed, motor torque and electronic water pump rotating speed and combining with the simulation of the wind tunnel test data of the heat dissipation module, and the fan duty ratio serving as an output parameter is obtained by conversion.
7. A thermal management control system fusing genetic ant colony algorithm is characterized in that: the method comprises the following steps:
the system comprises an electric drive assembly thermal management system and a controller, wherein the controller is configured to acquire an ambient temperature, an ambient humidity, a motor rotating speed, a motor torque and a motor water outlet temperature as control input parameters;
and controlling based on control input parameters by using a support vector machine prediction model improved based on a fusion genetic ant colony algorithm, determining a fan duty ratio matched with the current working condition, and controlling the rotating speed of the electronic fan by using the fan duty ratio so as to control the thermal management system of the electric drive assembly.
8. The system according to claim 7, wherein the genetic ant colony algorithm is fused to the thermal management control system: and the environment temperature, the environment humidity, the motor rotating speed, the motor torque and the motor water outlet temperature in the electric drive assembly heat management system are acquired by corresponding sensors.
9. An electronic device, characterized by: comprising a memory and a processor and computer instructions stored on the memory and executed on the processor, which when executed by the processor, perform the steps of a method of thermal management control incorporating genetic ant colony algorithms as claimed in any one of claims 1 to 6.
10. A computer-readable storage medium characterized by: storing computer instructions for performing, when executed by a processor, the steps of a method for thermal management control incorporating genetic ant colony algorithms as claimed in any one of claims 1 to 6.
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| CN114992808A (en) * | 2022-06-15 | 2022-09-02 | 山东大学 | Heat pump air conditioner heat management control method and system based on combined intelligent algorithm |
| CN114992808B (en) * | 2022-06-15 | 2023-06-02 | 山东大学 | Heat pump air conditioner thermal management control method and system based on combined intelligent algorithm |
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