CN115640690A - Engine water temperature prediction method and device based on fuzzy neural network - Google Patents
Engine water temperature prediction method and device based on fuzzy neural network Download PDFInfo
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Abstract
The application relates to a fuzzy neural network-based engine water temperature prediction method and device, wherein the method comprises the following steps: training test data by using a preset fuzzy neural network algorithm to obtain a heat database of an engine of the whole vehicle under different boundaries; inputting the running condition boundary of the whole vehicle into a heat database of an engine of the whole vehicle under different whole vehicle boundaries to obtain the heat of the engine under a required load, and simulating the water temperature of the engine under the running condition of the whole vehicle based on the heat to obtain a simulation result; and updating the heat database of the engine of the whole vehicle under different boundaries according to the benchmarking result until a preset condition is reached to obtain a database for predicting the water temperature of the engine. Therefore, the technical problems that in the related art, the prediction of the water temperature of the engine depends on the engineering experience of an engineer, the limitation is large, manual errors are easy to cause, and the accuracy of the prediction result is poor are solved.
Description
Technical Field
The application relates to the technical field of automobile engines, in particular to an engine water temperature prediction method and device based on a fuzzy neural network.
Background
The automobile engine is a device for providing power for an automobile, is the heart of the automobile, determines the dynamic property, the economical efficiency, the stability and the environmental protection property of the automobile, and is easily damaged when the water temperature of the engine is too high or too low. The water temperature of the engine is reasonably kept, an effective control mode is implemented, the phenomenon that the water temperature of the engine is abnormal can be effectively avoided, damage to the engine is reduced, the efficiency of the engine is improved, and the performance of the engine is exerted to the maximum extent.
However, in the related art, the method for predicting the water temperature of the engine, whether the method is predicted by an engineer according to engineering experience or analyzed by a traditional CAE method, depends on the engineering experience of the engineer, the accuracy of the predicted value of the water temperature of the engine is determined by the engineering experience and engineering capacity of the engineer, and the method has great limitation, is easy to cause errors, is not beneficial to popularization and application, and needs to be improved.
Disclosure of Invention
The application provides an engine water temperature prediction method and device based on a fuzzy neural network, and aims to solve the technical problems that in the related art, prediction of the engine water temperature depends on engineering experience of engineers, limitation is large, manual errors are prone to being caused, and the accuracy of a prediction result is poor.
The embodiment of the first aspect of the application provides an engine water temperature prediction method based on a fuzzy neural network, which comprises the following steps: training the test data by using a preset fuzzy neural network algorithm to obtain a heat database of the engine of the whole vehicle under different boundaries; inputting the running condition boundary of the whole vehicle into a heat database of an engine of the whole vehicle under different whole vehicle boundaries to obtain the heat of the engine under a required load, and simulating the water temperature of the engine under the running condition of the whole vehicle on the basis of the heat to obtain a simulation result; and calibrating the simulation result and the test target, and updating a heat database of the engine of the whole vehicle under different boundaries according to the calibration result until a preset condition is reached to obtain a database for predicting the water temperature of the engine.
Optionally, in an embodiment of the present application, the training of the test data by using a preset fuzzy neural network algorithm to obtain a heat database of the engine of the entire vehicle under different boundaries includes: fuzzification processing is carried out on the engine rotating speed, the engine power, the average effective pressure, the vehicle speed, the gradient and the environment temperature to obtain input information; and obtaining output information according to the whole vehicle test heat of the engine, and performing neural network training according to the input information and the output information to generate the heat database.
Optionally, in an embodiment of the present application, the preset condition is that the number of updates reaches a preset number or the heat database reaches a convergence condition.
Optionally, in one embodiment of the present application, the vehicle integrity limit includes engine speed, engine power, mean effective pressure, vehicle speed, grade, and ambient temperature.
The embodiment of the second aspect of the present application provides an engine water temperature prediction device based on a fuzzy neural network, including: the training module is used for training the test data by utilizing a preset fuzzy neural network algorithm to obtain a heat database of the engine of the whole vehicle under different boundaries; the simulation module is used for inputting the running working condition boundary of the whole vehicle into a heat database of the engine of the whole vehicle under different whole vehicle boundaries to obtain the heat of the engine under the required load, and simulating the water temperature of the engine under the running working condition of the whole vehicle based on the heat to obtain a simulation result; and the prediction module is used for calibrating the simulation result and the test target, and updating the heat database of the engine of the whole vehicle at different boundaries according to the calibration result until a preset condition is reached to obtain a database for predicting the water temperature of the engine.
Optionally, in an embodiment of the present application, the training module includes: the processing unit is used for fuzzifying the engine rotating speed, the engine power, the average effective pressure, the vehicle speed, the gradient and the environment temperature to obtain input information; and the generating unit is used for obtaining output information according to the whole vehicle test heat of the engine, and carrying out neural network training according to the input information and the output information to generate the heat database.
Optionally, in an embodiment of the present application, the preset condition is that the number of updates reaches a preset number or the heat database reaches a convergence condition.
Optionally, in one embodiment of the present application, the vehicle integrity limit comprises engine speed, engine power, mean effective pressure, vehicle speed, grade, and ambient temperature.
An embodiment of a third aspect of the present application provides a vehicle, comprising: the engine water temperature prediction method based on the fuzzy neural network comprises the following steps of storing water, 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 engine water temperature prediction method based on the fuzzy neural network according to the embodiment.
A fourth aspect of the present application provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the fuzzy neural network-based engine water temperature prediction method as above.
According to the embodiment of the application, the fuzzy neural network algorithm can be utilized to train test data, a heat database of an engine of a whole vehicle under different boundaries is obtained, working condition boundaries required by running of the whole vehicle are substituted into the database to obtain corresponding heat, the water temperature of the engine is obtained through one-dimensional simulation, and the database is updated and perfected through simulation results and benchmarking of test targets, so that dependence on engineering experience of engineers is reduced, errors caused by insufficient engineering experience of the engineers are avoided, the culture period of the engineers is shortened, the accuracy and efficiency of prediction of the water temperature of the engine are further improved, and the engineers can exert the maximum performance of the engine according to a better formulated scheme in a pre-research stage. Therefore, the technical problems that in the related art, the prediction of the water temperature of the engine depends on the engineering experience of an engineer, the limitation is large, manual errors are easy to cause, and the accuracy of the prediction result is poor are solved.
Additional aspects and advantages of the present application 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 present application.
Drawings
The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flow chart of an engine water temperature prediction method based on a fuzzy neural network according to an embodiment of the present application;
FIG. 2 is a schematic diagram illustrating a fuzzy neural network based engine water temperature prediction method according to an embodiment of the present application;
FIG. 3 is a flow chart of engine water temperature prediction of a fuzzy neural network based engine water temperature prediction method according to an embodiment of the present application;
FIG. 4 is a flow chart of a fuzzy neural network based engine water temperature prediction method according to an embodiment of the present application;
FIG. 5 is a schematic structural diagram of an engine water temperature prediction device based on a fuzzy neural network according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a vehicle according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.
The method and the device for predicting the water temperature of the engine based on the fuzzy neural network according to the embodiment of the application are described below with reference to the accompanying drawings. In the method, test data can be trained by using a fuzzy neural network algorithm to obtain a heat database of the engine of the whole vehicle at different boundaries, working condition boundaries required by the whole vehicle running are substituted into the database to obtain corresponding heat, the water temperature of the engine is obtained through one-dimensional simulation, and the database is updated and perfected through simulation results and benchmarks of test targets, so that the dependence on engineering experience of an engineer is reduced, errors caused by insufficient engineering experience of the engineer are avoided, the culture period of the engineer is shortened, the accuracy and the efficiency of engine water temperature prediction are further improved, and the engineer can exert the maximum performance of the engine according to a better formulated scheme in a pre-research stage. Therefore, the technical problems that in the related art, the prediction of the water temperature of the engine depends on the engineering experience of an engineer, the limitation is large, manual errors are easy to cause, and the accuracy of the prediction result is poor are solved.
Specifically, fig. 1 is a schematic flowchart of a method for predicting engine water temperature based on a fuzzy neural network according to an embodiment of the present disclosure.
As shown in FIG. 1, the engine water temperature prediction method based on the fuzzy neural network comprises the following steps:
in step S101, a preset fuzzy neural network algorithm is used to train test data, and a heat database of the engine of the entire vehicle at different boundaries is obtained.
It is understood that engine water temperature is either too high or too low and is prone to damage to the engine: the engine is easy to generate spontaneous combustion phenomenon when the water temperature is too high, the abrasion of the engine is aggravated, the lubricating effect of engine oil is reduced, if the water temperature of the engine is too high, the automobile still continues to run, the oil consumption is increased, the engine is accelerated to age, and the damage is aggravated. When the water temperature of an automobile engine is too high for a long time, the temperature of an engine cylinder body is increased, and the cylinder cover of the engine cylinder body is deformed for a long time, so that the engine can be permanently damaged. When the engine is more serious, the phenomena of cylinder explosion and the like of the engine can be caused, and the engine cannot be started;
when the water temperature is too low, the fuel oil of the engine is insufficiently combusted, the working efficiency of the engine is reduced, carbon deposition is caused, the oil consumption is increased, the environment is polluted, and the abrasion of internal parts of the engine is greatly increased under the condition that the engine works at low water temperature for a long time.
Therefore, it is important to reasonably maintain the water temperature of the engine and to implement an effective control method. In order to avoid manual errors and reduce labor cost caused by over-dependence on the experience of engineers, the embodiment of the application can train test data by using a preset fuzzy neural network algorithm to obtain the heat database of the engine of the whole vehicle under different boundaries.
Optionally, in one embodiment of the present application, the vehicle integrity limit comprises engine speed, engine power, mean effective pressure, vehicle speed, grade, and ambient temperature.
In some embodiments, vehicle boundaries may include engine speed, engine power, mean effective pressure, vehicle speed, grade, and ambient temperature as inputs to facilitate subsequent database building and refinement.
Optionally, in an embodiment of the present application, training the test data by using a preset fuzzy neural network algorithm to obtain a heat database of the engine of the entire vehicle under different boundaries includes: fuzzification processing is carried out on the engine rotating speed, the engine power, the average effective pressure, the vehicle speed, the gradient and the environment temperature to obtain input information; and obtaining output information according to the whole vehicle test heat of the engine, and performing neural network training according to the input information and the output information to generate a heat database.
Specifically, as shown in fig. 2, in the embodiment of the present application, software such as Matlab may be used to train existing vehicle model test data, the engine speed, the engine power, the average effective pressure, the vehicle speed, the gradient, and the ambient temperature are used as inputs to perform fuzzification processing, the engine complete vehicle test heat is used as an output, after the fuzzification processing is completed, neural network training is performed to generate rule bases of engine heat corresponding to different experimental boundaries, and along with development of different complete vehicles of different platforms, a test may be substituted into a database to perform retraining, so as to continuously optimize the database, improve the accuracy of the database, and further improve the response speed by adding a particle swarm algorithm and the like.
In step S102, the whole vehicle running condition boundary is input into a heat database of the engine of the whole vehicle under different whole vehicle boundaries to obtain the heat of the engine under the required load, and the water temperature of the engine under the whole vehicle running condition is simulated based on the heat to obtain a simulation result.
As a possible implementation mode, the embodiment of the application can input the fuzzy neural network training database based on the boundaries such as the required rotating speed, the required power and the average effective pressure under the running condition of the whole vehicle to obtain the heat of the engine under the required load, and the water temperature simulation work of the engine under the running condition of the whole vehicle is carried out.
As shown in fig. 3, the embodiment of the present application may be implemented by using the whole car boundary of the pre-research stage, such as: the engine speed, the engine power, the average effective pressure, the vehicle speed, the gradient and the ambient temperature are brought into an established heat database to obtain corresponding engine heat, and analysis work is carried out through one-dimensional simulation software (such as KULI, GT or AMESim) to calculate the water temperature of the engine.
In step S103, the calibration simulation result and the test target are calibrated, and the heat database of the engine of the entire vehicle at different boundaries is updated according to the calibration result until a preset condition is reached, so as to obtain a database for predicting the water temperature of the engine.
For example, as shown in fig. 3, in the embodiment of the present application, at the later stage of the entire vehicle development stage, through the entire vehicle test verification, the entire vehicle test and the simulation are aligned, the simulation model is perfected, the entire vehicle test data is substituted into the database for retraining, the database is continuously perfected, and the database precision is improved.
Optionally, in an embodiment of the present application, the preset condition is that the number of updates reaches a preset number or the heat database reaches a convergence condition.
As a possible implementation manner, the preset condition in the embodiment of the present application may be that the update times reach a preset number or that the heat database reaches a convergence condition, so as to perfect self-learning of the heat database and improve the accuracy of the database.
The working principle of the engine water temperature prediction method based on the fuzzy neural network according to the embodiment of the present application is explained in detail with reference to fig. 2 to 4.
As shown in fig. 4, the embodiment of the present application may include the following steps:
step S401: training the test data based on a pre-trained fuzzy neural network algorithm to obtain a heat database. Specifically, as shown in fig. 2, in the embodiment of the present application, software such as Matlab may be used to train existing vehicle model test data, the engine speed, the engine power, the average effective pressure, the vehicle speed, the gradient, and the ambient temperature are used as inputs to perform fuzzification processing, the engine complete vehicle test heat is used as an output, after the fuzzification processing is completed, neural network training is performed to generate rule bases of engine heat corresponding to different experimental boundaries, and along with development of different complete vehicles of different platforms, a test may be substituted into a database to perform retraining, so as to continuously optimize the database, improve the accuracy of the database, and further improve the response speed by adding a particle swarm algorithm and the like.
Step S402: inputting the heat of the engine to the boundary of the whole vehicle, performing simulation work, and outputting the water temperature of the engine. As a possible implementation mode, the embodiment of the application can input the fuzzy neural network training database based on the boundaries such as the required rotating speed, the required power and the average effective pressure under the running condition of the whole vehicle to obtain the heat of the engine under the required load, and the water temperature simulation work of the engine under the running condition of the whole vehicle is carried out.
As shown in fig. 3, the embodiment of the present application may be implemented by using the whole car boundary of the pre-research stage, such as: the engine speed, the engine power, the average effective pressure, the vehicle speed, the gradient and the ambient temperature are brought into an established heat database to obtain corresponding engine heat, and analysis work is carried out through one-dimensional simulation software (such as KULI, GT or AMESim) to calculate the water temperature of the engine.
Step S403: and (5) performing test and simulation benchmarking, and continuously updating and perfecting the database. For example, as shown in fig. 3, in the embodiment of the present application, at the later stage of the entire vehicle development stage, through the entire vehicle test verification, the entire vehicle test and the simulation are aligned, the simulation model is perfected, the entire vehicle test data is substituted into the database for retraining, the database is continuously perfected, and the database precision is improved.
According to the engine water temperature prediction method based on the fuzzy neural network, provided by the embodiment of the application, test data can be trained by utilizing a fuzzy neural network algorithm to obtain a heat database of an engine of a whole vehicle under different boundaries, working condition boundaries required by the running of the whole vehicle are substituted into the database to obtain corresponding heat, the engine water temperature is obtained through one-dimensional simulation, and the database is updated and perfected through simulation results and benchmarks of test targets, so that the dependence on engineering experience of an engineer is reduced, errors caused by insufficient engineering experience of the engineer are avoided, the culture period of the engineer is shortened, the accuracy and efficiency of engine water temperature prediction are further improved, and the engineer can exert the maximum performance of the engine according to a better formulated scheme in a pre-research stage. Therefore, the technical problems that in the related art, the prediction of the water temperature of the engine depends on the engineering experience of an engineer, the limitation is large, manual errors are easy to cause, and the accuracy of the prediction result is poor are solved.
Next, an engine water temperature prediction apparatus based on a fuzzy neural network according to an embodiment of the present application will be described with reference to the accompanying drawings.
FIG. 5 is a block diagram illustrating an engine water temperature prediction device based on a fuzzy neural network according to an embodiment of the present application.
As shown in fig. 5, the engine water temperature prediction device 10 based on the fuzzy neural network includes: a training module 100, a simulation module 200, and a prediction module 300.
Specifically, the training module 100 is configured to train test data by using a preset fuzzy neural network algorithm to obtain a heat database of the engine of the entire vehicle at different boundaries.
The simulation module 200 is configured to input the boundary of the entire vehicle driving condition into a heat database of the engine of the entire vehicle under different boundaries of the entire vehicle to obtain heat of the engine under a required load, and simulate water temperature of the engine under the entire vehicle driving condition based on the heat to obtain a simulation result.
And the prediction module 300 is used for comparing the standard simulation result with the test target, updating the heat database of the engine of the whole vehicle at different boundaries according to the standard result until a preset condition is reached, and obtaining a database for predicting the water temperature of the engine.
Optionally, in an embodiment of the present application, the training module 100 includes: a processing unit and a generating unit.
The processing unit is used for fuzzifying the engine speed, the engine power, the average effective pressure, the vehicle speed, the gradient and the ambient temperature to obtain input information.
And the generating unit is used for obtaining output information according to the whole vehicle test heat of the engine, and carrying out neural network training according to the input information and the output information to generate a heat database.
Optionally, in an embodiment of the present application, the preset condition is that the number of updates reaches a preset number or the heat database reaches a convergence condition.
Optionally, in one embodiment of the present application, the vehicle integrity limit comprises engine speed, engine power, mean effective pressure, vehicle speed, grade, and ambient temperature.
It should be noted that the foregoing explanation of the embodiment of the engine water temperature prediction method based on the fuzzy neural network is also applicable to the engine water temperature prediction device based on the fuzzy neural network of this embodiment, and will not be described herein again.
According to the engine water temperature prediction device based on the fuzzy neural network, provided by the embodiment of the application, test data can be trained by utilizing a fuzzy neural network algorithm to obtain a heat database of an engine of a whole vehicle under different boundaries, working condition boundaries required by the running of the whole vehicle are substituted into the database to obtain corresponding heat, the engine water temperature is obtained through one-dimensional simulation, and the database is updated and perfected through simulation results and benchmarks of test targets, so that the dependence on engineering experience of an engineer is reduced, errors caused by insufficient engineering experience of the engineer are avoided, the culture period of the engineer is shortened, the accuracy and efficiency of engine water temperature prediction are further improved, and the engineer can exert the maximum performance of the engine according to a better formulated scheme in a pre-research stage. Therefore, the technical problems that in the related art, the prediction of the water temperature of the engine depends on the engineering experience of an engineer, the limitation is large, manual errors are easy to cause, and the accuracy of the prediction result is poor are solved.
Fig. 6 is a schematic structural diagram of a vehicle according to an embodiment of the present application. The vehicle may include:
a memory 601, a processor 602, and a computer program stored on the memory 601 and executable on the processor 602.
The processor 602 executes the program to implement the fuzzy neural network-based engine water temperature prediction method provided in the above-described embodiment.
Further, the vehicle further includes:
a communication interface 603 for communicating between the memory 601 and the processor 602.
The memory 601 is used for storing computer programs that can be run on the processor 602.
If the memory 601, the processor 602 and the communication interface 603 are implemented independently, the communication interface 603, the memory 601 and the processor 602 may be connected to each other through a bus and perform communication with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 6, but this is not intended to represent only one bus or type of bus.
Alternatively, in practical implementation, if the memory 601, the processor 602, and the communication interface 603 are integrated on a chip, the memory 601, the processor 602, and the communication interface 603 may complete communication with each other through an internal interface.
The processor 602 may be a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement embodiments of the present Application.
The present embodiment also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the fuzzy neural network-based engine water temperature prediction method as described above.
In the description of the present specification, reference to the description of "one embodiment," "some embodiments," "an example," "a specific example," or "some examples" or the like means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "N" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or N executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or N wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are well known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may also be stored in a computer-readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (10)
1. An engine water temperature prediction method based on a fuzzy neural network is characterized by comprising the following steps:
training test data by using a preset fuzzy neural network algorithm to obtain a heat database of an engine of the whole vehicle under different boundaries;
inputting the running condition boundary of the whole vehicle into a heat database of an engine of the whole vehicle under different whole vehicle boundaries to obtain the heat of the engine under the required load, and simulating the water temperature of the engine under the running condition of the whole vehicle on the basis of the heat to obtain a simulation result;
and calibrating the simulation result and the test target, and updating the heat database of the engine of the whole vehicle under different boundaries according to the calibration result until a preset condition is reached to obtain a database for predicting the water temperature of the engine.
2. The method of claim 1, wherein the training of the test data by using the predetermined fuzzy neural network algorithm to obtain the heat database of the engine of the whole vehicle under different boundaries comprises:
fuzzification processing is carried out on the engine rotating speed, the engine power, the average effective pressure, the vehicle speed, the gradient and the environment temperature to obtain input information;
and obtaining output information according to the whole vehicle test heat of the engine, and performing neural network training according to the input information and the output information to generate the heat database.
3. The method according to claim 1, wherein the predetermined condition is that the number of updates reaches a predetermined number or the heat database reaches a convergence condition.
4. The method of claim 1, wherein the vehicle integrity limit comprises engine speed, engine power, mean effective pressure, vehicle speed, grade, and ambient temperature.
5. An engine water temperature prediction device based on a fuzzy neural network is characterized by comprising:
the training module is used for training the test data by utilizing a preset fuzzy neural network algorithm to obtain a heat database of the engine of the whole vehicle under different boundaries;
the simulation module is used for inputting the running working condition boundary of the whole vehicle into a heat database of the engine of the whole vehicle under different whole vehicle boundaries to obtain the heat of the engine under the required load, and simulating the water temperature of the engine under the running working condition of the whole vehicle based on the heat to obtain a simulation result;
and the prediction module is used for calibrating the simulation result and the test target, and updating the heat database of the engine of the whole vehicle at different boundaries according to the calibration result until a preset condition is reached to obtain a database for predicting the water temperature of the engine.
6. The apparatus of claim 5, wherein the training module comprises:
the processing unit is used for fuzzifying the engine rotating speed, the engine power, the average effective pressure, the vehicle speed, the gradient and the environment temperature to obtain input information;
and the generating unit is used for obtaining output information according to the whole vehicle test heat of the engine, and performing neural network training according to the input information and the output information to generate the heat database.
7. The apparatus of claim 5, wherein the predetermined condition is that the number of updates reaches a predetermined number or the heat database reaches a convergence condition.
8. The apparatus of claim 5, wherein the vehicle integrity limit comprises engine speed, engine power, mean effective pressure, vehicle speed, grade, and ambient temperature.
9. A vehicle, characterized by comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the fuzzy neural network based engine water temperature prediction method of any one of claims 1-4.
10. A computer-readable storage medium having a computer program stored thereon, the program being executable by a processor for implementing the fuzzy neural network based engine water temperature prediction method of any one of claims 1-4.
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| CN202211324393.2A CN115640690A (en) | 2022-10-27 | 2022-10-27 | Engine water temperature prediction method and device based on fuzzy neural network |
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