CN116611304B - Method and device for predicting electric corrosion risk of vehicle bearing - Google Patents
Method and device for predicting electric corrosion risk of vehicle bearing Download PDFInfo
- Publication number
- CN116611304B CN116611304B CN202310893713.4A CN202310893713A CN116611304B CN 116611304 B CN116611304 B CN 116611304B CN 202310893713 A CN202310893713 A CN 202310893713A CN 116611304 B CN116611304 B CN 116611304B
- Authority
- CN
- China
- Prior art keywords
- model
- distribution
- current
- shaft
- voltage
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 74
- 230000007797 corrosion Effects 0.000 title claims abstract description 67
- 238000005260 corrosion Methods 0.000 title claims abstract description 67
- 238000009826 distribution Methods 0.000 claims abstract description 133
- 238000004088 simulation Methods 0.000 claims abstract description 39
- 230000005284 excitation Effects 0.000 claims description 46
- 230000005672 electromagnetic field Effects 0.000 claims description 24
- 238000004364 calculation method Methods 0.000 claims description 17
- 230000005540 biological transmission Effects 0.000 claims description 15
- 238000004590 computer program Methods 0.000 claims description 10
- 239000010687 lubricating oil Substances 0.000 claims description 9
- 239000004020 conductor Substances 0.000 claims description 8
- 239000011810 insulating material Substances 0.000 claims description 8
- 230000008569 process Effects 0.000 claims description 8
- 238000009434 installation Methods 0.000 claims description 6
- 238000003860 storage Methods 0.000 claims description 6
- 238000011161 development Methods 0.000 abstract description 22
- 238000010586 diagram Methods 0.000 description 10
- 239000003921 oil Substances 0.000 description 6
- 238000011156 evaluation Methods 0.000 description 5
- 239000000463 material Substances 0.000 description 5
- 238000005457 optimization Methods 0.000 description 5
- 238000005096 rolling process Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 4
- 230000006872 improvement Effects 0.000 description 4
- 239000002184 metal Substances 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 238000009413 insulation Methods 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 238000004422 calculation algorithm Methods 0.000 description 2
- 230000008878 coupling Effects 0.000 description 2
- 238000010168 coupling process Methods 0.000 description 2
- 238000005859 coupling reaction Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 241001391944 Commicarpus scandens Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000012790 confirmation Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000002500 effect on skin Effects 0.000 description 1
- 238000010891 electric arc Methods 0.000 description 1
- 238000005530 etching Methods 0.000 description 1
- 230000001939 inductive effect Effects 0.000 description 1
- 238000003475 lamination Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000012827 research and development Methods 0.000 description 1
- 230000002441 reversible effect Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 230000001052 transient effect Effects 0.000 description 1
- 238000004804 winding Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
- G06T17/20—Finite element generation, e.g. wire-frame surface description, tesselation
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C60/00—Computational materials science, i.e. ICT specially adapted for investigating the physical or chemical properties of materials or phenomena associated with their design, synthesis, processing, characterisation or utilisation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Computing Systems (AREA)
- Geometry (AREA)
- Computer Graphics (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Software Systems (AREA)
- Computer Hardware Design (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Rolling Contact Bearings (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
Abstract
The application provides a method and a device for predicting the risk of electric corrosion of a vehicle bearing, wherein the method comprises the following steps: acquiring a three-dimensional finite element model, a power switch tube model and a power supply model of an electric driving system of the electric automobile; calculating the distribution of low-frequency shaft current and shaft voltage at different frequencies/moments according to the three-dimensional finite element model; calculating the distribution of high-frequency shaft current and shaft voltage according to the field path collaborative simulation circuit and the three-dimensional finite element model; calculating a complete shaft current distribution and a complete shaft voltage distribution according to the distribution of the low-frequency shaft current and the shaft voltage and the distribution of the high-frequency shaft current and the shaft voltage; and predicting the bearing electric corrosion risk according to the complete shaft current distribution and the complete shaft voltage distribution to obtain a prediction result. Therefore, the method and the device can predict and evaluate the current of the motor shaft of the drive motor in the early development stage of the electric drive system of the new energy automobile, define the risk positions of the current and the voltage of the shaft, and improve the development efficiency.
Description
Technical Field
The application relates to the technical field of automobile detection, in particular to a method and a device for predicting the risk of electric corrosion of a vehicle bearing.
Background
With the rapid development of electric vehicles, users put forward higher demands on charging performance of electric vehicles. The electric automobile driving motor has the advantages that shaft voltage exists between the motor rotating shaft and the motor shell, if the gap resistance of a bearing oil film is smaller, even the shaft voltage breaks through an oil film, a conductive loop is formed among the motor rotating shaft, the bearing inner ring, the bearing outer ring, the bearing rolling body and the motor shell, shaft current is generated, small and deep etching points or strip arc scratches are generated on the surfaces of the bearing position and the bearing inner ring due to arc discharge, the bearing reliability is reduced, and the normal operation and the service life of the bearing even the motor are affected. In the existing method, an insulating device or a conductive device is usually added according to the past experience of a technician in the motor structure design stage, the added effect cannot be predicted and estimated in the forward design stage, and the addition, the rectification and the test confirmation can only be carried out in the driving motor physical stage. However, in practice, it is found that the existing method relies on subjective experience to judge that the bearing electric corrosion condition cannot be predicted and estimated, and the automobile development efficiency is reduced.
Disclosure of Invention
The embodiment of the application aims to provide a vehicle bearing electric corrosion risk prediction method and device, which can predict and evaluate the current of a drive motor shaft in the early development stage of a new energy automobile electric drive system, determine the risk positions of the shaft current and the shaft voltage, and improve the development efficiency.
An embodiment of the present application provides a method for predicting risk of galvanic corrosion of a vehicle bearing, including:
acquiring a three-dimensional finite element model, a power switch tube model and a power supply model of an electric driving system of the electric automobile;
calculating the distribution of low-frequency shaft current and shaft voltage at different frequencies/moments according to the three-dimensional finite element model;
generating a field path collaborative simulation circuit according to the three-dimensional finite element model, the power switch tube model and the power supply model;
calculating the distribution of high-frequency shaft current and shaft voltage according to the field path co-simulation circuit and the three-dimensional finite element model;
calculating a complete shaft current distribution and a complete shaft voltage distribution according to the distribution of the low-frequency shaft current and the shaft voltage and the distribution of the high-frequency shaft current and the shaft voltage;
and predicting the bearing electric corrosion risk according to the complete shaft current distribution and the complete shaft voltage distribution to obtain a prediction result.
In the implementation process, the method can preferentially acquire a three-dimensional finite element model, a power switch tube model and a power supply model of the electric driving system of the electric automobile; then, calculating the distribution of low-frequency shaft current and shaft voltage at different frequencies/moments according to the three-dimensional finite element model; generating a field path collaborative simulation circuit according to the three-dimensional finite element model, the power switch tube model and the power supply model; then, according to the field path collaborative simulation circuit and the three-dimensional finite element model, calculating the distribution of high-frequency shaft current and shaft voltage; calculating complete shaft current distribution and complete shaft voltage distribution according to the distribution of the low-frequency shaft current and shaft voltage and the distribution of the high-frequency shaft current and shaft voltage; and finally, predicting the bearing electric corrosion risk according to the complete shaft current distribution and the complete shaft voltage distribution to obtain a prediction result. Therefore, the method can predict and evaluate the current of the motor shaft of the drive motor in the early development stage of the electric drive system of the new energy automobile, define the risk positions of the current and the voltage of the shaft, and improve the development efficiency.
Further, the calculating the distribution of the low-frequency axis current and the axis voltage at different frequencies/moments according to the three-dimensional finite element model includes:
adding voltage excitation to a three-phase busbar port in the three-dimensional finite element model to obtain a first target model;
and solving the low-frequency electromagnetic field of the first target model to obtain the distribution of low-frequency shaft current and shaft voltage under different frequencies/moments.
Further, the generating a field cooperative simulation circuit according to the three-dimensional finite element model, the power switch tube model and the power supply model includes:
adding a DC busbar and an AC busbar in the three-dimensional finite element model into a lumped port to obtain a second target model;
carrying out high-frequency full-wave electromagnetic field solving on the second target model to obtain a transmission parameter equivalent model;
acquiring a power switching tube model and a power supply model;
and generating a field path collaborative simulation circuit according to the transmission parameter equivalent model, the power switch tube model and the power supply model.
Further, the calculating the distribution of the high-frequency axis current and the axis voltage according to the field path co-simulation circuit and the three-dimensional finite element model comprises the following steps:
Solving common mode voltage and common mode current on the field-path co-simulation circuit busbar;
determining a common mode voltage excitation from the common mode voltage and a common mode current excitation from the common mode current;
adding the common mode voltage excitation and the common mode current excitation on an alternating current busbar and a direct current busbar in the three-dimensional finite element model to obtain a third target model;
and carrying out high-frequency full-wave electromagnetic field distribution solving treatment on the common mode current excitation to obtain the distribution of high-frequency shaft current and shaft voltage under different frequencies/time points.
Further, the method further comprises:
judging whether the risk of bearing electric corrosion exists or not according to the prediction result;
and if not, outputting the prediction result.
Further, the method further comprises:
when judging that the risk of bearing electric corrosion exists, acquiring model adjustment data aiming at the three-dimensional finite element model; the model adjustment data comprise type adjustment data of an insulating device/a conductive device, installation positions of the insulating device/the conductive device, insulating material adjustment data, conductive material parameter adjustment data and lubricating oil parameter adjustment data, output voltage adjustment data of a power supply, switching frequency adjustment data of a switching tube and nonlinear model adjustment data corresponding to different switching tubes;
And optimizing the three-dimensional finite element model according to the model adjustment data to obtain a new three-dimensional finite element model, and executing the distribution of low-frequency axis current and axis voltage under different frequencies/moments according to the three-dimensional finite element model.
A second aspect of the embodiments of the present application provides a vehicle bearing galvanic corrosion risk prediction apparatus, including:
the model acquisition unit is used for acquiring a three-dimensional finite element model, a power switch tube model and a power supply model of the electric driving system of the electric automobile;
a first calculation unit for calculating the distribution of low-frequency axis current and axis voltage at different frequencies/moments according to the three-dimensional finite element model;
the generating unit is used for generating a field path collaborative simulation circuit according to the three-dimensional finite element model, the power switch tube model and the power supply model;
the second calculation unit is used for calculating the distribution of the high-frequency shaft current and the shaft voltage according to the field path co-simulation circuit and the three-dimensional finite element model;
a third calculation unit for calculating a complete shaft current distribution and a complete shaft voltage distribution according to the distribution of the low-frequency shaft current and shaft voltage and the distribution of the high-frequency shaft current and shaft voltage;
And the prediction unit is used for predicting the bearing electric corrosion risk according to the complete shaft current distribution and the complete shaft voltage distribution to obtain a prediction result.
In the implementation process, the device can acquire a three-dimensional finite element model, a power switch tube model and a power supply model of the electric driving system of the electric automobile through a model acquisition unit; calculating the distribution of low-frequency shaft current and shaft voltage at different frequencies/moments according to a three-dimensional finite element model by a first calculation unit; generating a field path collaborative simulation circuit according to the three-dimensional finite element model, the power switch tube model and the power supply model through a generating unit; calculating the distribution of the high-frequency shaft current and the shaft voltage according to the field path co-simulation circuit and the three-dimensional finite element model through a second calculation unit; calculating, by a third calculation unit, a complete shaft current distribution and a complete shaft voltage distribution from the distribution of the low-frequency shaft current and the shaft voltage and the distribution of the high-frequency shaft current and the shaft voltage; and predicting the bearing electric corrosion risk according to the complete shaft current distribution and the complete shaft voltage distribution by a prediction unit to obtain a prediction result. Therefore, the device can predict and evaluate the current of the motor shaft of the drive motor in the early development stage of the electric drive system of the new energy automobile, and the risk positions of the current and the voltage of the shaft are clear, so that the development efficiency is improved.
Further, the first computing unit includes:
the adding subunit is used for adding voltage excitation to the three-phase busbar port in the three-dimensional finite element model to obtain a first target model;
and the calculating subunit is used for solving the low-frequency electromagnetic field of the first target model to obtain the distribution of low-frequency shaft current and shaft voltage under different frequencies/moments.
Further, the generating unit includes:
the adding subunit is used for adding the direct current busbar and the alternating current busbar in the three-dimensional finite element model into the lumped port to obtain a second target model;
the first solving subunit is used for solving the high-frequency full-wave electromagnetic field of the second target model to obtain a transmission parameter equivalent model;
the acquisition subunit is used for acquiring the power switching tube model and the power supply model;
and the generation subunit is used for generating a field path collaborative simulation circuit according to the transmission parameter equivalent model, the power switch tube model and the power supply model.
Further, the second calculation unit includes:
the second solving subunit is used for solving common-mode voltage and common-mode current on the field-path co-simulation circuit busbar;
a determining subunit for determining a common mode voltage excitation from the common mode voltage and a common mode current excitation from the common mode current;
The excitation adding subunit is used for adding the common-mode voltage excitation and the common-mode current excitation on an alternating current busbar and a direct current busbar in the three-dimensional finite element model to obtain a third target model;
the second solving subunit is further configured to perform a high-frequency full-wave electromagnetic field distribution solving process on the common-mode current excitation, and obtain distributions of high-frequency axis currents and axis voltages at different frequencies/moments.
Further, the vehicle bearing galvanic corrosion risk prediction apparatus further includes:
the judging unit is used for judging whether the risk of bearing electric corrosion exists according to the prediction result;
and the output unit is used for outputting the prediction result when the risk of bearing electric corrosion does not exist.
Further, the vehicle bearing galvanic corrosion risk prediction apparatus further includes:
the data acquisition unit is used for acquiring model adjustment data aiming at the three-dimensional finite element model when the bearing electric corrosion risk exists; the model adjustment data comprise type adjustment data of an insulating device/a conductive device, installation positions of the insulating device/the conductive device, insulating material adjustment data, conductive material parameter adjustment data and lubricating oil parameter adjustment data, output voltage adjustment data of a power supply, switching frequency adjustment data of a switching tube and nonlinear model adjustment data corresponding to different switching tubes;
And the optimizing unit is used for optimizing the three-dimensional finite element model according to the model adjustment data to obtain a new three-dimensional finite element model, and triggering the first calculating unit to execute the operation of calculating the distribution of the low-frequency axis current and the axis voltage under different frequencies/time according to the three-dimensional finite element model.
A third aspect of the embodiments of the present application provides an electronic device, including a memory and a processor, where the memory is configured to store a computer program, and the processor is configured to execute the computer program to cause the electronic device to execute the method for predicting risk of galvanic corrosion of a vehicle bearing according to any one of the first aspect of the embodiments of the present application.
A fourth aspect of the embodiments of the present application provides a computer readable storage medium storing computer program instructions which, when read and executed by a processor, perform the method for predicting risk of galvanic corrosion of a vehicle bearing according to any one of the first aspect of the embodiments of the present application.
The beneficial effects of this application are: the method and the device predict and evaluate the current of the motor shaft of the drive motor in the early development stage of the electric drive system of the new energy automobile, determine the risk positions of the current and the voltage of the shaft, and improve the development efficiency.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a method for predicting the risk of galvanic corrosion of a vehicle bearing according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of another method for predicting risk of galvanic corrosion of a vehicle bearing according to an embodiment of the disclosure;
fig. 3 is a schematic structural diagram of a device for predicting risk of galvanic corrosion of a vehicle bearing according to an embodiment of the present application;
FIG. 4 is a schematic structural diagram of another apparatus for predicting risk of galvanic corrosion of a vehicle bearing according to an embodiment of the disclosure;
fig. 5 is an exemplary flowchart of a method for predicting risk of galvanic corrosion of a vehicle bearing according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.
Example 1
Referring to fig. 1, fig. 1 is a flowchart of a method for predicting risk of galvanic corrosion of a vehicle bearing according to the present embodiment. The method for predicting the electric corrosion risk of the vehicle bearing comprises the following steps:
s101, acquiring a three-dimensional finite element model, a power switch tube model and a power supply model of an electric driving system of the electric automobile.
S102, calculating the distribution of low-frequency axis current and axis voltage at different frequencies/moments according to the three-dimensional finite element model.
S103, generating a field path collaborative simulation circuit according to the three-dimensional finite element model, the power switch tube model and the power supply model.
S104, calculating the distribution of the high-frequency shaft current and the shaft voltage according to the field path co-simulation circuit and the three-dimensional finite element model.
S105, calculating a complete shaft current distribution and a complete shaft voltage distribution according to the distribution of the low-frequency shaft current and the shaft voltage and the distribution of the high-frequency shaft current and the shaft voltage.
S106, bearing electric corrosion risk prediction is carried out according to the complete shaft current distribution and the complete shaft voltage distribution, and a prediction result is obtained.
In the embodiment, the three-dimensional finite element model and the power tube switching circuit of the motor and the electric controller utilized by the method are obtained in the forward research and development stage of the electric drive system, and the insulating device/conducting device is directly modified on the three-dimensional finite element model, so that the type and the mounting position of the insulating device/conducting device can be adjusted, different insulating materials, conducting materials and lubricating oil can be replaced by adjusting material parameters, the method is particularly suitable for comparing and optimizing various motor shaft current improvement schemes, and the motor shaft current improvement scheme with the lowest cost can be obtained in the forward development early stage of the motor. Meanwhile, the power switching circuit is provided with different output voltages of a power supply, nonlinear models of different power switching tubes are replaced or switching frequencies are adjusted, and therefore accurate prediction, evaluation and optimization of motor shaft current of a motor in multiple working conditions on a high-voltage platform with the voltage of 800V or even higher can be achieved.
Meanwhile, the method calculates and obtains the distribution condition of the shaft current through a three-dimensional finite element model of a complete electric automobile electric drive system consisting of a stator assembly, a rotor assembly, a motor shell, a motor input shaft, a motor rotating shaft, a bearing inner ring, an outer ring and rolling bodies (a common bearing or an insulating bearing) which are connected, and a front end cover and a rear end cover.
In addition, the method can also obtain low-frequency shaft current distribution density through a low-frequency solving algorithm comprising skin effect, proximity effect and eddy current loss of stator/rotor lamination of a stator winding, obtain high-frequency shaft current distribution density through a high-frequency full-wave electromagnetic field solving algorithm comprising capacitive coupling and inductive coupling, respectively carry out vector superposition on shaft current and shaft voltage distribution density on each metal part of the electric drive, and obtain complete motor shaft current and shaft voltage distribution density, the maximum value and the position of the motor shaft current and shaft voltage distribution density, so that the motor shaft current analysis and evaluation of motor shaft current with different temperatures and different humidities can be realized, and the prediction evaluation and optimization of motor shaft current under the multi-working condition, full-frequency range and full-working environment are realized.
In this embodiment, the execution subject of the method may be a computing device such as a computer or a server, which is not limited in this embodiment.
In this embodiment, the execution body of the method may be an intelligent device such as a smart phone or a tablet computer, which is not limited in this embodiment.
Therefore, by implementing the vehicle bearing electric corrosion risk prediction method described in the embodiment, the current of the drive motor shaft can be predicted and estimated in the early development stage of the electric drive system of the new energy automobile, the risk positions of the shaft current and the shaft voltage can be defined, and the development efficiency is improved.
Example 2
Referring to fig. 2, fig. 2 is a flowchart of a method for predicting risk of galvanic corrosion of a vehicle bearing according to the present embodiment. The method for predicting the electric corrosion risk of the vehicle bearing comprises the following steps:
s201, acquiring a three-dimensional finite element model, a power switch tube model and a power supply model of an electric driving system of the electric automobile.
In the embodiment, the method can introduce a three-dimensional finite element digital model of the electric driving system of the electric automobile and simplify the model.
In this embodiment, the method may introduce a three-dimensional finite element digital model of an electric drive system of an electric vehicle. The digital-analog converter comprises a stator assembly, a rotor assembly, a motor shell, a motor input shaft, a motor rotating shaft, a connecting bearing inner ring, a connecting bearing outer ring, a connecting bearing rolling body (which can be a common bearing or an insulating bearing), a front end cover, a rear end cover and the like, and simplifies the model.
And S202, adding voltage excitation to a three-phase busbar port in the three-dimensional finite element model to obtain a first target model.
In the embodiment, the method can add voltage excitation to the three-phase busbar port in the three-dimensional finite element model of the electric drive system, solve the low-frequency electromagnetic field and obtain the distribution of low-frequency shaft current and shaft voltage under different frequencies/moments.
In the embodiment, the method can add low-frequency voltage excitation to the three-phase busbar port in the three-dimensional finite element model of the electric drive system, and the frequency range is as follows: 1Hz-6000Hz. Then, the low frequency electromagnetic field is solved, and the distribution of the low frequency axis current and/or axis voltage at different frequencies/moments is obtained.
S203, carrying out low-frequency electromagnetic field solving on the first target model to obtain the distribution of low-frequency shaft current and shaft voltage under different frequencies/moments.
S204, adding the direct current busbar and the alternating current busbar in the three-dimensional finite element model into the lumped port to obtain a second target model.
S205, carrying out high-frequency full-wave electromagnetic field solving on the second target model to obtain a transmission parameter equivalent model.
In the embodiment, the method can be used for solving the high-frequency full-wave electromagnetic field by adding lumped ports into the direct current busbar and the alternating current busbar in the three-dimensional finite element model of the electric drive system, and the transmission parameter equivalent model is obtained.
In the embodiment, the method adds a lumped port to a direct current busbar and an alternating current busbar in a three-dimensional finite element model of an electric drive system to solve a high-frequency full-wave electromagnetic field, and the frequency range is set as follows: 100Hz-300MHz, and obtaining a transmission parameter equivalent model.
S206, acquiring a power switch tube model and a power supply model.
S207, generating a field path collaborative simulation circuit according to the transmission parameter equivalent model, the power switch tube model and the power supply model.
S208, solving common mode voltage and common mode current on the field circuit co-simulation circuit busbar.
In the embodiment, the method can form a field circuit collaborative simulation circuit by a transmission parameter equivalent model, a power switch tube nonlinear model and a power supply model so as to solve common mode voltage and common mode current on the busbar.
S209, determining common-mode voltage excitation according to the common-mode voltage and determining common-mode current excitation according to the common-mode current.
And S210, adding common-mode voltage excitation and common-mode current excitation on the alternating current busbar and the direct current busbar in the three-dimensional finite element model to obtain a third target model.
S211, carrying out high-frequency full-wave electromagnetic field distribution solving processing on common-mode current excitation, and obtaining the distribution of high-frequency shaft current and shaft voltage under different frequencies/time points.
In the embodiment, common mode voltage excitation and common mode current excitation are added to an alternating current busbar and a direct current busbar in a three-dimensional finite element model of the electric drive system, field distribution of a high-frequency full-wave electromagnetic field is solved, and distribution of high-frequency shaft current and/or shaft voltage under different frequencies/moments is obtained.
S212, calculating the complete shaft current distribution and the complete shaft voltage distribution according to the distribution of the low-frequency shaft current and the shaft voltage and the distribution of the high-frequency shaft current and the shaft voltage.
In this embodiment, the method may respectively perform vector superposition on the calculated distribution densities of the high-frequency and low-frequency axial currents and/or axial voltages of the surfaces of the electrically driven metal bodies at the same frequency, so as to obtain a complete axial current distribution and a complete axial voltage distribution (i.e., obtain the axial current and/or axial voltage distribution densities of the surfaces of the metal bodies in the whole frequency range).
In the present embodiment, when the maximum value of the shaft current density is less than 0.1A/mm 2 The risk of exceeding the standard of the shaft current does not exist, and rectification is not needed;
when the maximum value of the shaft current density is less than 0.3A/mm 2 The risk of exceeding the standard of the shaft current possibly exists, a gray area or a gray belt along the rolling direction of the rolling body can appear on the bearing contact surface, the position of the maximum value of the shaft voltage is the position where the oil film is easy to break down, and proper correction measures are adopted aiming at the position and the distribution condition of the maximum value of the shaft current and/or the shaft voltage;
when the maximum value of the shaft current density is more than 0.5A/mm 2 The risk of exceeding the standard of the shaft current exists, the trace of the rubbing plate lines can appear on the contact surface of the bearing, the position of the maximum value of the shaft voltage is the position of oil film breakdown, at the moment, the vibration noise of the bearing and the local overhigh temperature can be caused, the bearing is invalid, and the rectifying and modifying measures must be adopted.
In this embodiment, the decision threshold of the maximum value of the shaft current density and the shaft voltage may be set according to the specific requirements of the driving motor and conditions such as the operation environment.
S213, bearing electric corrosion risk prediction is carried out according to the complete shaft current distribution and the complete shaft voltage distribution, and a prediction result is obtained.
S214, judging whether the bearing is at risk of electric corrosion according to the prediction result, if so, executing steps S215-S216; if not, step S217 is performed.
In this embodiment, the method can be used for the axial current distribution density exceeding 0.3A/mm 2 When the shaft voltage reaches or exceeds the insulation pressure of the oil film, the shaft voltage damages the insulation performance of the oil film, and the high temperature generated by partial discharge of the shaft current has the risk of bearing electric corrosion.
In this embodiment, the location of the transient maximum of the shaft current and/or shaft voltage on the three-dimensional finite element model is the location where there is a risk of bearing galvanic corrosion.
S215, obtaining model adjustment data for the three-dimensional finite element model.
In this embodiment, the model adjustment data includes type adjustment data of an insulating device/conductive device, installation positions of the insulating device/conductive device, insulating material adjustment data, conductive material parameter adjustment data, lubricating oil parameter adjustment data, output voltage adjustment data of a power supply, switching frequency adjustment data of a switching tube, and nonlinear model adjustment data corresponding to different switching tubes.
And S216, carrying out optimization processing on the three-dimensional finite element model according to the model adjustment data to obtain a new three-dimensional finite element model, and executing the step S202.
In this embodiment, the method may adjust the type and installation position of the insulating device/conductive device, or modify the material parameters of the insulating material, conductive material, and lubricating oil; or modifying the output voltage of the power supply, the switching frequency of the power tube and even modifying nonlinear models corresponding to different switching tubes, and repeating the steps S202-S214 to realize the prediction, evaluation and optimization of shaft currents of various schemes. Wherein, the material parameters mainly comprise dielectric constant, conductivity, insulation pressure resistance and the like.
In this embodiment, when there is a risk of exceeding the standard of the shaft current, the improvement is mainly performed by the following measures, so that the maximum value and distribution of the shaft current and/or the shaft voltage can meet the requirements:
adding an insulating device/conducting device into the three-dimensional finite element model of the electric drive system, and adjusting the position and the type of the insulating device/conducting device;
in an insulating device/conducting device of a three-dimensional finite element model of an electric drive system, modifying material parameters of an insulating material or a conducting material;
changing the lubricating oil and modifying the material parameters corresponding to the lubricating oil;
Changing a nonlinear model of the power switch tube suitable for different working voltages;
adjusting PWM waveform and switching frequency of the switching circuit;
and modifying the structures of the direct current busbar and the alternating current busbar in the three-dimensional finite element model of the electric drive system.
S217, outputting a prediction result.
Referring to fig. 5, fig. 5 shows an exemplary flow diagram of the method.
In the embodiment, the method is suitable for predicting and evaluating the current of the driving motor shaft of the new energy automobile.
In the embodiment, the method can utilize a three-dimensional finite element model of an electric drive system comprising a motor rotating shaft, a motor input shaft and an alternating current/direct current busbar, carry out simulation calculation by adding a low-frequency voltage source excitation in the alternating current busbar and adding a common-mode voltage and common-mode current excitation source from a collaborative simulation circuit in the direct current busbar and the alternating current, obtain the distribution of low-frequency shaft current and high-frequency shaft current on the surface of each metal of the drive motor, obtain accurate shaft voltage and shaft current distribution conditions by vector superposition of shaft voltage and shaft current distribution density, accurately predict the maximum value of the shaft voltage and the shaft current and the position of the shaft voltage and the shaft current, judge the degree of bearing electric corrosion by the distribution density of the shaft current, and realize reduction of motor shaft current in a multi-working condition, full-frequency band and full-working environment drive motor through modifying circuit parameters such as the output voltage of a power supply power tube switching frequency, a nonlinear model of a switching tube and the like, and propose an optimal rectifying scheme.
In this embodiment, the method may also be applied to the analysis of the drive motor shaft current of other new energy automobiles and hybrid electric vehicles. Likewise, the method can be also suitable for analyzing the shaft current of a water-cooled motor and an oil-cooled N-in-one motor; likewise, the method can also be applied to the analysis of the drive motor shaft current of 800V or even higher voltage platforms; the method is also suitable for analyzing the shaft current of the generator, and the shaft current of the generator can be analyzed and evaluated according to the method by only deleting the power switch circuit and the power supply in the field cooperative simulation circuit and replacing the power switch circuit and the power supply with corresponding equivalent loads and respectively adding excitation at the torque and the rotating speed ports of the motor equivalent model.
By implementing the implementation mode, the prediction and evaluation of the current of the motor shaft of the drive motor can be performed in the early development stage of the electric drive system of the new energy automobile, the risk positions of the current and the voltage of the shaft are clear, and the influence of different devices on the shaft current is predicted.
In this embodiment, the execution subject of the method may be a computing device such as a computer or a server, which is not limited in this embodiment.
In this embodiment, the execution body of the method may be an intelligent device such as a smart phone or a tablet computer, which is not limited in this embodiment.
Therefore, by implementing the vehicle bearing electric corrosion risk prediction method described in the embodiment, the current of the drive motor shaft can be predicted and estimated in the early development stage of the electric drive system of the new energy automobile, the risk positions of the shaft current and the shaft voltage can be defined, and the development efficiency is improved.
Example 3
Referring to fig. 3, fig. 3 is a schematic structural diagram of a device for predicting risk of electric corrosion of a vehicle bearing according to the present embodiment. As shown in fig. 3, the vehicle bearing galvanic corrosion risk prediction apparatus includes:
the model obtaining unit 310 is configured to obtain a three-dimensional finite element model, a power switch tube model and a power supply model of the electric driving system of the electric automobile;
a first calculation unit 320 for calculating the distribution of the low-frequency axis current and the axis voltage at different frequencies/moments according to a three-dimensional finite element model;
the generating unit 330 is configured to generate a field path co-simulation circuit according to the three-dimensional finite element model, the power switching tube model and the power supply model;
a second calculation unit 340 for calculating a distribution of high-frequency axis current and axis voltage according to the field-path co-simulation circuit and the three-dimensional finite element model;
A third calculation unit 350 for calculating a complete axis current distribution and a complete axis voltage distribution from the distribution of the low frequency axis current and the axis voltage and the distribution of the high frequency axis current and the axis voltage;
and the prediction unit 360 is used for predicting the bearing electric corrosion risk according to the complete shaft current distribution and the complete shaft voltage distribution, so as to obtain a prediction result.
In this embodiment, the explanation of the apparatus for predicting the risk of galvanic corrosion of a vehicle bearing may refer to the description in embodiment 1 or embodiment 2, and the description is not repeated in this embodiment.
Therefore, the vehicle bearing electric corrosion risk prediction device described in the embodiment can predict and evaluate the current of the drive motor shaft in the early development stage of the electric drive system of the new energy automobile, define the risk positions of the shaft current and the shaft voltage, and improve the development efficiency.
Example 4
Referring to fig. 4, fig. 4 is a schematic structural diagram of a device for predicting risk of galvanic corrosion of a vehicle bearing according to the present embodiment. As shown in fig. 4, the vehicle bearing galvanic corrosion risk prediction apparatus includes:
the model obtaining unit 310 is configured to obtain a three-dimensional finite element model, a power switch tube model and a power supply model of the electric driving system of the electric automobile;
A first calculation unit 320 for calculating the distribution of the low-frequency axis current and the axis voltage at different frequencies/moments according to a three-dimensional finite element model;
the generating unit 330 is configured to generate a field path co-simulation circuit according to the three-dimensional finite element model, the power switching tube model and the power supply model;
a second calculation unit 340 for calculating a distribution of high-frequency axis current and axis voltage according to the field-path co-simulation circuit and the three-dimensional finite element model;
a third calculation unit 350 for calculating a complete axis current distribution and a complete axis voltage distribution from the distribution of the low frequency axis current and the axis voltage and the distribution of the high frequency axis current and the axis voltage;
and the prediction unit 360 is used for predicting the bearing electric corrosion risk according to the complete shaft current distribution and the complete shaft voltage distribution, so as to obtain a prediction result.
As an alternative embodiment, the first computing unit 320 includes:
the adding subunit 321 is configured to add voltage excitation to a three-phase busbar port in the three-dimensional finite element model, so as to obtain a first target model;
and the calculating subunit 322 is configured to perform low-frequency electromagnetic field solution on the first target model, so as to obtain the distribution of low-frequency axis current and axis voltage at different frequencies/moments.
As an alternative embodiment, the generating unit 330 includes:
the adding subunit 331 is configured to add the dc busbar and the ac busbar in the three-dimensional finite element model to the lumped port, so as to obtain a second target model;
a first solving subunit 332, configured to perform high-frequency full-wave electromagnetic field solving on the second target model, so as to obtain a transmission parameter equivalent model;
an acquisition subunit 333, configured to acquire a power switching tube model and a power supply model;
the generating subunit 334 is configured to generate a field path co-simulation circuit according to the transmission parameter equivalent model, the power switch tube model, and the power supply model.
As an alternative embodiment, the second calculating unit 340 includes:
the second solving subunit 341 is configured to solve a common-mode voltage and a common-mode current on the field-path co-simulation circuit busbar;
a determining subunit 342 for determining a common mode voltage excitation from the common mode voltage and a common mode current excitation from the common mode current;
an excitation adding subunit 343, configured to add common-mode voltage excitation and common-mode current excitation to the ac busbar and the dc busbar in the three-dimensional finite element model, so as to obtain a third target model;
the second solving subunit 341 is further configured to perform a high-frequency full-wave electromagnetic field distribution solving process on the common-mode current excitation, and obtain distributions of high-frequency axis currents and axis voltages at different frequencies/moments.
As an alternative embodiment, the apparatus for predicting risk of galvanic corrosion of a vehicle bearing further includes:
a judging unit 370 for judging whether there is a risk of bearing galvanic corrosion according to the prediction result;
and an output unit 380 for outputting a prediction result when there is no risk of bearing galvanic corrosion.
As an alternative embodiment, the apparatus for predicting risk of galvanic corrosion of a vehicle bearing further includes:
a data acquisition unit 390 for acquiring model adjustment data for the three-dimensional finite element model when there is a risk of bearing galvanic corrosion; the model adjustment data comprise type adjustment data of an insulating device/a conductive device, installation positions of the insulating device/the conductive device, insulating material adjustment data, conductive material parameter adjustment data and lubricating oil parameter adjustment data, output voltage adjustment data of a power supply, switching frequency adjustment data of switching tubes and nonlinear model adjustment data corresponding to different switching tubes;
the optimizing unit 400 is configured to perform optimization processing on the three-dimensional finite element model according to the model adjustment data, obtain a new three-dimensional finite element model, and trigger the first calculating unit 320 to perform an operation of calculating the distribution of the low-frequency axis current and the axis voltage at different frequencies/moments according to the three-dimensional finite element model.
In this embodiment, the explanation of the apparatus for predicting the risk of galvanic corrosion of a vehicle bearing may refer to the description in embodiment 1 or embodiment 2, and the description is not repeated in this embodiment.
Therefore, the vehicle bearing electric corrosion risk prediction device described in the embodiment can predict and evaluate the current of the drive motor shaft in the early development stage of the electric drive system of the new energy automobile, define the risk positions of the shaft current and the shaft voltage, and improve the development efficiency.
An embodiment of the present application provides an electronic device, including a memory and a processor, where the memory is configured to store a computer program, and the processor is configured to execute the computer program to cause the electronic device to execute a method for predicting an electrical corrosion risk of a vehicle bearing in embodiment 1 or embodiment 2 of the present application.
The present embodiment provides a computer-readable storage medium storing computer program instructions that, when read and executed by a processor, perform the vehicle bearing galvanic corrosion risk prediction method of embodiment 1 or embodiment 2 of the present application.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other manners as well. The apparatus embodiments described above are merely illustrative, for example, flow diagrams and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments of the present application may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely exemplary embodiments of the present application and is not intended to limit the scope of the present application, and various modifications and variations may be suggested to one skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application. It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Claims (6)
1. A method for predicting the risk of galvanic corrosion of a vehicle bearing, comprising:
acquiring a three-dimensional finite element model, a power switch tube model and a power supply model of an electric driving system of the electric automobile;
calculating the distribution of low-frequency shaft current and shaft voltage at different frequencies/moments according to the three-dimensional finite element model;
generating a field path collaborative simulation circuit according to the three-dimensional finite element model, the power switch tube model and the power supply model;
calculating the distribution of high-frequency shaft current and shaft voltage according to the field path co-simulation circuit and the three-dimensional finite element model;
calculating a complete shaft current distribution and a complete shaft voltage distribution according to the distribution of the low-frequency shaft current and the shaft voltage and the distribution of the high-frequency shaft current and the shaft voltage;
carrying out bearing electric corrosion risk prediction according to the complete shaft current distribution and the complete shaft voltage distribution to obtain a prediction result;
the calculating the distribution of the low-frequency shaft current and the shaft voltage under different frequencies/time points according to the three-dimensional finite element model comprises the following steps:
adding voltage excitation to a three-phase busbar port in the three-dimensional finite element model to obtain a first target model;
Carrying out low-frequency electromagnetic field solution on the first target model to obtain the distribution of low-frequency shaft current and shaft voltage under different frequencies/moments;
wherein, according to the three-dimensional finite element model, the power switch tube model and the power supply model, generating a field path collaborative simulation circuit comprises:
adding a DC busbar and an AC busbar in the three-dimensional finite element model into a lumped port to obtain a second target model;
carrying out high-frequency full-wave electromagnetic field solving on the second target model to obtain a transmission parameter equivalent model;
acquiring a power switching tube model and a power supply model;
generating a field path collaborative simulation circuit according to the transmission parameter equivalent model, the power switch tube model and the power supply model;
the method for calculating the distribution of the high-frequency shaft current and the shaft voltage according to the field path co-simulation circuit and the three-dimensional finite element model comprises the following steps:
solving common mode voltage and common mode current on the field-path co-simulation circuit busbar;
determining a common mode voltage excitation from the common mode voltage and a common mode current excitation from the common mode current;
adding the common mode voltage excitation and the common mode current excitation on an alternating current busbar and a direct current busbar in the three-dimensional finite element model to obtain a third target model;
And carrying out high-frequency full-wave electromagnetic field distribution solving treatment on the common mode current excitation to obtain the distribution of high-frequency shaft current and shaft voltage under different frequencies/time points.
2. The method of predicting risk of galvanic corrosion of a vehicle bearing of claim 1, further comprising:
judging whether the risk of bearing electric corrosion exists or not according to the prediction result;
and if not, outputting the prediction result.
3. The method of predicting risk of galvanic corrosion of a vehicle bearing of claim 2, further comprising:
when judging that the risk of bearing electric corrosion exists, acquiring model adjustment data aiming at the three-dimensional finite element model; the model adjustment data comprise type adjustment data of an insulating device/a conductive device, installation positions of the insulating device/the conductive device, insulating material adjustment data, conductive material parameter adjustment data and lubricating oil parameter adjustment data, output voltage adjustment data of a power supply, switching frequency adjustment data of a switching tube and nonlinear model adjustment data corresponding to different switching tubes;
and optimizing the three-dimensional finite element model according to the model adjustment data to obtain a new three-dimensional finite element model, and executing the distribution of low-frequency axis current and axis voltage under different frequencies/moments according to the three-dimensional finite element model.
4. A vehicle bearing galvanic corrosion risk prediction apparatus, characterized in that the vehicle bearing galvanic corrosion risk prediction apparatus comprises:
the model acquisition unit is used for acquiring a three-dimensional finite element model, a power switch tube model and a power supply model of the electric driving system of the electric automobile;
a first calculation unit for calculating the distribution of low-frequency axis current and axis voltage at different frequencies/moments according to the three-dimensional finite element model;
the generating unit is used for generating a field path collaborative simulation circuit according to the three-dimensional finite element model, the power switch tube model and the power supply model;
the second calculation unit is used for calculating the distribution of the high-frequency shaft current and the shaft voltage according to the field path co-simulation circuit and the three-dimensional finite element model;
a third calculation unit for calculating a complete shaft current distribution and a complete shaft voltage distribution according to the distribution of the low-frequency shaft current and shaft voltage and the distribution of the high-frequency shaft current and shaft voltage;
the prediction unit is used for predicting the bearing electric corrosion risk according to the complete shaft current distribution and the complete shaft voltage distribution to obtain a prediction result;
wherein the first computing unit includes:
The adding subunit is used for adding voltage excitation to the three-phase busbar port in the three-dimensional finite element model to obtain a first target model;
the calculating subunit is used for solving the low-frequency electromagnetic field of the first target model to obtain the distribution of low-frequency shaft current and shaft voltage under different frequencies/moments;
wherein the generating unit includes:
the adding subunit is used for adding the direct current busbar and the alternating current busbar in the three-dimensional finite element model into the lumped port to obtain a second target model;
the first solving subunit is used for solving the high-frequency full-wave electromagnetic field of the second target model to obtain a transmission parameter equivalent model;
the acquisition subunit is used for acquiring the power switching tube model and the power supply model;
the generation subunit is used for generating a field path collaborative simulation circuit according to the transmission parameter equivalent model, the power switch tube model and the power supply model;
wherein the second computing unit includes:
the second solving subunit is used for solving common-mode voltage and common-mode current on the field-path co-simulation circuit busbar;
a determining subunit for determining a common mode voltage excitation from the common mode voltage and a common mode current excitation from the common mode current;
The excitation adding subunit is used for adding the common-mode voltage excitation and the common-mode current excitation on an alternating current busbar and a direct current busbar in the three-dimensional finite element model to obtain a third target model;
the second solving subunit is further configured to perform a high-frequency full-wave electromagnetic field distribution solving process on the common-mode current excitation, and obtain distributions of high-frequency axis currents and axis voltages at different frequencies/moments.
5. An electronic device comprising a memory for storing a computer program and a processor that runs the computer program to cause the electronic device to perform the vehicle bearing galvanic corrosion risk prediction method according to any one of claims 1 to 3.
6. A readable storage medium having stored therein computer program instructions which, when read and executed by a processor, perform the vehicle bearing galvanic corrosion risk prediction method according to any one of claims 1 to 3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310893713.4A CN116611304B (en) | 2023-07-20 | 2023-07-20 | Method and device for predicting electric corrosion risk of vehicle bearing |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310893713.4A CN116611304B (en) | 2023-07-20 | 2023-07-20 | Method and device for predicting electric corrosion risk of vehicle bearing |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116611304A CN116611304A (en) | 2023-08-18 |
| CN116611304B true CN116611304B (en) | 2023-12-26 |
Family
ID=87685755
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310893713.4A Active CN116611304B (en) | 2023-07-20 | 2023-07-20 | Method and device for predicting electric corrosion risk of vehicle bearing |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116611304B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109885881A (en) * | 2019-01-18 | 2019-06-14 | 北京交通大学 | A kind of determination method for the critical shaft current causing bearing galvano-cautery |
| CN112926225A (en) * | 2021-04-12 | 2021-06-08 | 北京交通大学 | Modeling and parameter measuring method for high-frequency shaft current of alternating current motor |
| CN114662239A (en) * | 2022-03-28 | 2022-06-24 | 广东电网有限责任公司 | Reactor reactance parameter calculation method, device and equipment based on field path coupling |
| CN116050186A (en) * | 2023-03-29 | 2023-05-02 | 长安新能源南京研究院有限公司 | Method, device, equipment and medium for predicting risk of part radiation emission electromagnetic field |
| CN116338367A (en) * | 2023-05-31 | 2023-06-27 | 广汽埃安新能源汽车股份有限公司 | Method and device for evaluating electromagnetic compatibility of whole vehicle |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107393001B (en) * | 2017-07-26 | 2019-01-29 | 新疆金风科技股份有限公司 | The complementing method and equipment in SRTM data invalid region |
-
2023
- 2023-07-20 CN CN202310893713.4A patent/CN116611304B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109885881A (en) * | 2019-01-18 | 2019-06-14 | 北京交通大学 | A kind of determination method for the critical shaft current causing bearing galvano-cautery |
| CN112926225A (en) * | 2021-04-12 | 2021-06-08 | 北京交通大学 | Modeling and parameter measuring method for high-frequency shaft current of alternating current motor |
| CN114662239A (en) * | 2022-03-28 | 2022-06-24 | 广东电网有限责任公司 | Reactor reactance parameter calculation method, device and equipment based on field path coupling |
| CN116050186A (en) * | 2023-03-29 | 2023-05-02 | 长安新能源南京研究院有限公司 | Method, device, equipment and medium for predicting risk of part radiation emission electromagnetic field |
| CN116338367A (en) * | 2023-05-31 | 2023-06-27 | 广汽埃安新能源汽车股份有限公司 | Method and device for evaluating electromagnetic compatibility of whole vehicle |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116611304A (en) | 2023-08-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Muetze et al. | Practical rules for assessment of inverter-induced bearing currents in inverter-fed AC motors up to 500 kW | |
| TWI727957B (en) | Method and system for estimating charging state or discharging depth of battery | |
| Busse et al. | An evaluation of the electrostatic shielded induction motor: a solution for rotor shaft voltage buildup and bearing current | |
| Rahman et al. | Design of high-efficiency and high-torque-density switched reluctance motor for vehicle propulsion | |
| Demircalı et al. | Influence of the temperature on energy management in battery-ultracapacitor electric vehicles | |
| Ahola et al. | Normalization-based approach to electric motor BVR related capacitances computation | |
| CN107331923A (en) | Electric automobile power battery temprature control method and device | |
| Magdun et al. | Calculation of roller and ball bearing capacitances and prediction of EDM currents | |
| CN116611304B (en) | Method and device for predicting electric corrosion risk of vehicle bearing | |
| JP2001116811A (en) | Micro-short-circuited cell detecting method and cell short circuit detecting method | |
| DE10357984A1 (en) | Steady state battery terminal voltage determining method for hybrid electric vehicle, involves calculating steady state battery terminal voltage based on equivalent charge resistance and effective no load charge voltage | |
| JP2011064471A (en) | State estimation method and state estimation device of power storage device | |
| Êvo et al. | Inverter-Induced Bearing Currents: A Thorough Study of the Cause-and-Effect Chains | |
| Bukya et al. | A study on safety issues and analytical evaluation of stresses for HVDC cable in electrical vehicle | |
| CN110794210B (en) | Method and device for judging voltage harmonic isolation effect, power supply system, computer equipment and storage medium | |
| El‐Kharashi et al. | Approach to measure the degree of the unbalanced power in induction motor for energy‐efficient use | |
| CN116861752A (en) | Prediction method and device for electric corrosion of vehicle bearing | |
| Gehring et al. | Modeling of the automotive 14 V power net for voltage stability analysis | |
| CN115510725A (en) | Converter transformer valve side sleeve insulation state analysis method and device | |
| JP2024508855A (en) | Battery heating method, apparatus, device and storage medium | |
| KR20050017478A (en) | A method for calculating available power for a battery | |
| Wang et al. | Calculation of high frequency bearing currents of PWM inverter-fed VF induction motor | |
| Reis et al. | Genetic Algorithm-Based Commutation Angle Control for Torque Ripple Mitigation in Switched Reluctance Motor Drives | |
| Costabile et al. | An accurate evaluation of electric discharge machining bearings currents in inverter-driven induction motors | |
| Liu et al. | The effect of end windings and the bearing labyrinth seals on stray capacitances and bearing voltage ratio of AC motors |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |