CN115169249A - Method for constructing reliability cycle working condition of electric drive system based on user big data - Google Patents
Method for constructing reliability cycle working condition of electric drive system based on user big data Download PDFInfo
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Abstract
The application discloses a method for constructing reliability cycle working conditions of an electric drive system based on user big data. The construction method of the reliability cycle working condition of the electric drive system based on the user big data comprises the following steps: acquiring basic operation data of each vehicle; dividing each basic operation data to obtain typical region data, wherein the typical region data comprises short-stroke fragment data; the following operations are performed for each set of typical region data: performing statistical calculation on the obtained fatigue damage information of each unit mileage, and selecting 95% quantile damage values as the fatigue damage values of the unit mileage of the typical area group; acquiring a typical area cycle condition; acquiring a unit mileage fatigue damage value based on a cycle working condition; acquiring a cyclic coefficient; and acquiring the final cycle condition of the typical area data. The unit mileage fatigue damage value of typical region and the unit mileage fatigue damage value based on the cycle condition are fused to obtain the cycle coefficient, and therefore more accurate final cycle condition is constructed.
Description
Technical Field
The application relates to the technical field of vehicle electric driving system analysis, in particular to a method and a device for constructing reliability cycle working conditions based on a user big data electric driving system.
Background
The electric drive system is one of the core assemblies of the pure electric vehicle, provides power for the vehicle and can recover brake energy. Compared with the traditional fuel vehicle, the pure electric vehicle has fewer gears and more concentrated torque, and the electric drive system has increasingly outstanding reliability problems due to quick response of the motor torque, high rotating speed and frequent recovery of braking energy. In order to solve the problem, a reliable cycle working condition of pure electric vehicle driving characteristics capable of representing 95% of user damage needs to be constructed, a whole vehicle model simulation platform is combined, time histories of information such as loads and rotating speeds are obtained, accurate input is provided for reliability design and test verification, failure modes of an electric driving system are fully detected in advance, and the reliability level of the system is improved.
The general method for constructing the characteristics of the running cycle conditions of the automobile is real automobile acquisition, and the method is realized by user investigation, planning of a running route, small sample data acquisition, short-stroke segment division, characteristic parameter extraction, cluster analysis and candidate condition reconstruction. The load of the driving system is related to objective conditions such as terrain and traffic and the driving behavior of a driver, and the practical vehicle collection has the limitations of small data sample size, short mileage, single planned route and the like, is difficult to cover the actual use conditions of a user, and cannot accurately reflect the characteristics of the user. Therefore, according to the regional characteristics of China, it is very important to establish a reliable cycle working condition which not only accords with the road driving characteristics of China, but also represents the damage of users.
The patent with the patent number of CN113688558 and the name of automobile running condition construction method and system based on big data samples provides an automobile running condition construction method and system based on big database samples, and provides a method and system for determining reasonable original data quantity according to an automobile running characteristic correlation analysis method, establishing an optimized working condition construction model, obtaining independent working conditions of each automobile, recombining the independent working conditions into optimized original data, and finally generating representative working conditions, so that the data for constructing the representative working conditions and the motion characteristics of each generation of automobiles are guaranteed to have higher correlation, and the single-machine running of single vehicle data has lower calculation requirement. However, the method is used for constructing the circulation working condition by taking the speed as the main characteristic parameter, can be used for oil consumption analysis, lacks load data in the construction of the characteristic parameter, is not equivalent to user damage calculation, and cannot be used for reliability analysis.
The patent number is CN113297795, and the name is a pure electric vehicle running condition construction method, and discloses a pure electric vehicle running condition construction method, which comprises the following processes of carrying out data acquisition on the pure electric vehicle running condition, dividing a test route into a plurality of short stroke segments, and obtaining characteristic parameters of the pure electric vehicle running condition from the plurality of short stroke segments; carrying out nonlinear dimensionality reduction on the characteristic parameters of the running working conditions of the pure electric vehicle through kernel principal component analysis, classifying the characteristic parameters subjected to nonlinear dimensionality reduction through a mixed clustering method, and screening a plurality of short stroke segments according to classification results and combination of the duration weight of each class in the working conditions and the Pearson correlation coefficient to construct a plurality of candidate working conditions of the pure electric vehicle; and calculating and comparing relative error values of the characteristic parameters in the plurality of candidate working conditions of the pure electric vehicle and the SAPD frequency value to construct the running working conditions of the pure electric vehicle. The method has the advantages that the construction accuracy of the working condition is higher, the actual driving characteristics of the electric automobile can be reflected better, the consistency of the obtained working condition curve and the actual working condition is stronger, but the method adopts the experimental vehicle with limited sample quantity to carry out data acquisition, a large number of data supports of typical users are lacked in the clustering process, and the constructed circulating working condition cannot be equivalent to the damage of the users and cannot be used as the input of reliability analysis.
Accordingly, a solution is desired to solve or at least mitigate the above-mentioned deficiencies of the prior art.
Disclosure of Invention
The invention aims to provide a method for constructing a reliability cycle condition of an electric drive system based on user big data to solve at least one technical problem.
In one aspect of the invention, a method for constructing a reliability cycle condition of an electric drive system based on user big data is provided, and the method for constructing the reliability cycle condition of the electric drive system based on the user big data comprises the following steps:
acquiring basic operation data of each vehicle in an area to be constructed;
dividing basic operation data of each vehicle to obtain at least one group of typical region data, wherein each group of typical region data comprises at least one short-stroke fragment data;
the following operations are performed for each group of typical region data:
acquiring unit mileage fatigue damage information corresponding to each short stroke fragment data according to each short stroke fragment data;
performing statistical calculation on the obtained fatigue damage information of each unit mileage, and selecting 95% quantile damage values as the fatigue damage values of the unit mileage of the typical area group;
acquiring a typical area cycle condition;
acquiring a unit mileage fatigue damage value based on a cycle working condition according to each short stroke fragment data;
acquiring a cyclic coefficient according to the unit mileage fatigue damage value based on the cyclic working condition and the unit mileage fatigue damage value of the typical region;
and acquiring the final cycle working condition of the typical region data according to the cycle coefficient and the typical region cycle working condition.
Optionally, the basic operation data includes vehicle speed information, motor torque information, motor speed information, operation time information, operation mileage information, acceleration information, and GPS signals.
Optionally, the dividing the basic operation data of each vehicle to obtain at least one group of typical region data, where each group of typical region data includes at least one short-trip segment data includes:
dividing basic operation data of each vehicle so as to obtain each short-stroke fragment data of the vehicle;
each short stroke fragment data is identified, so that each short stroke fragment data is assigned with a typical region label, and each short stroke fragment data with the same typical region label forms the typical region data.
Optionally, the obtaining of the unit-mileage fatigue damage information corresponding to each short-trip segment data according to each short-trip segment data includes:
acquiring travel mileage data, rotating speed data and torque data in the short stroke segment data;
constructing a failure mode model of each rotating component in the electric drive system;
and carrying out fatigue damage analysis based on the Miner linear damage principle, and obtaining unit mileage fatigue damage information corresponding to the short-stroke segment data according to the driving mileage data, the rotating speed data and the torque data.
Optionally, the obtaining of the typical region cycle condition includes:
extracting characteristic parameter data in each short-stroke fragment data;
and carrying out clustering analysis on the characteristic parameter data in each short-stroke fragment data, and selecting the characteristic parameter data in each short-stroke fragment data closest to a clustering center for recombination, thereby obtaining the circulation condition of the typical region.
Optionally, the cyclic coefficient obtained according to the cyclic condition-based unit mileage fatigue damage value and the typical region unit mileage fatigue damage value is obtained by using the following formula:
the cycle coefficient k = unit mileage fatigue damage value of typical region/unit mileage fatigue damage value based on cycle condition.
Optionally, the identifying each short-run fragment data so as to assign a typical region label to each short-run fragment data, and the composing the typical region data by each short-run fragment data with the same typical region label comprises:
the following operations are performed for each short run segment data:
acquiring a GPS signal in each short-stroke fragment data;
acquiring the area position of the short stroke fragment data according to the GPS signal;
obtaining a typical area comparison table, wherein the typical area comparison table comprises a preset area position and a typical area label corresponding to a preset typical area;
and acquiring a typical region label corresponding to a preset region position which is the same as the region position of the short-stroke fragment data.
Optionally, the dividing the basic operation data of each vehicle, so as to obtain each short-trip fragment data of the vehicle includes:
acquiring starting information of a vehicle at a first time point;
acquiring power-off information of the vehicle after starting at a second time point after the first time point;
acquiring state information of the vehicle between a first time point and a second time point;
and judging whether the vehicle at least comprises an idle speed section and a motion section between the first time point and the second time point according to the state information, and if so, acquiring basic operation data between the first time point and the second time point as short-stroke section data.
And dividing a complete kinematic segment, including an idle section and a motion section. Generally, the data between two idle points can be selected as a kinematic segment in the algorithm. The complete kinematic segment should include the idling, accelerating, uniform and decelerating micro segments. The high voltage electrical signal needs to be continuously effective during the short stroke duration.
Optionally, after the extracting the feature parameter data in each short-stroke segment data, the acquiring the typical region cycle condition further includes:
and performing principal component analysis on the characteristic parameter data in each extracted short-stroke fragment data so as to obtain the characteristic parameter data in each short-stroke fragment data screened by the principal component analysis.
The application still provides a system reliability cycle operating mode construction device is driven to big data electricity based on user, it includes to drive system reliability cycle operating mode construction device to drive to the big data electricity based on user:
the system comprises a basic operation data acquisition module, a building module and a building module, wherein the basic operation data acquisition module is used for acquiring basic operation data of each vehicle in an area to be built;
the system comprises a dividing module, a calculating module and a processing module, wherein the dividing module is used for dividing basic operation data of each vehicle so as to obtain at least one group of typical region data, and each group of typical region data comprises at least one short-stroke fragment data;
a single set of typical region operation modules for operating on each set of typical region data separately, the single set of typical region operation modules comprising the following modules:
the unit mileage fatigue damage information acquisition module is used for acquiring unit mileage fatigue damage information corresponding to each short stroke fragment data according to each short stroke fragment data;
the fatigue damage value acquisition module is used for carrying out statistical calculation on the acquired fatigue damage information of each unit mileage, and selecting 95% quantile damage values as the unit mileage fatigue damage values of the typical area group;
the typical region cycle working condition acquisition module is used for acquiring typical region cycle working conditions;
the unit mileage fatigue damage value acquisition module based on the cycle working condition is used for acquiring a cycle coefficient according to the unit mileage fatigue damage value based on the cycle working condition and the unit mileage fatigue damage value of a typical area;
the cyclic coefficient acquisition module is used for acquiring a cyclic coefficient according to the cyclic working condition-based unit mileage fatigue damage value and the unit mileage fatigue damage value of the typical region;
and the final cycle working condition acquisition module is used for acquiring the final cycle working condition of the typical region data according to the cycle coefficient and the typical region cycle working condition.
Advantageous effects
According to the construction method of the reliability cycle working condition of the electric drive system based on the user big data, vehicle driving working conditions are classified according to the characteristics of typical urban road conditions in China, short travel segment databases of four typical areas including urban areas, mountainous areas, high speed areas and suburban areas are obtained, hypothesis testing is carried out on unit mileage fatigue damage of the short travel segment of each typical area, the unit mileage fatigue damage value of 95% quantiles is taken as a target fatigue damage value of the typical area, and then the unit mileage fatigue damage value of the constructed typical area cycle working condition is fused with the unit mileage fatigue damage value to obtain a cycle coefficient, so that the reliability cycle working condition representing 95% of user damage level is accurately constructed.
Drawings
FIG. 1 is a schematic flow chart diagram of a method for constructing reliability cycle conditions of an electric drive system based on user big data according to an embodiment of the application.
FIG. 2 is a schematic diagram of an electronic device capable of implementing a method for building a reliability cycle of an electric drive system based on big data of a user according to an embodiment of the present application.
Fig. 3 is a schematic diagram of short stroke segment division according to an embodiment of the present application.
Fig. 4 is a schematic diagram of an SN curve (stress life curve) according to an embodiment of the present application.
Detailed Description
In order to make the implementation objects, technical solutions and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be described in more detail below with reference to the drawings in the embodiments of the present application. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are a subset of the embodiments in the present application and not all embodiments in the present application. The embodiments described below with reference to the accompanying drawings are illustrative and intended to explain the present application and should not be construed as limiting the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort belong to the protection scope of the present application. Embodiments of the present application will be described in detail below with reference to the accompanying drawings.
FIG. 1 is a schematic flow chart of a method for constructing reliability cycle conditions of an electric drive system based on user big data according to an embodiment of the present application.
The method for constructing the reliability cycle condition of the electric drive system based on the big data of the user as shown in FIG. 1 comprises the following steps:
step 1: acquiring basic operation data of each vehicle in an area to be constructed;
step 2: dividing basic operation data of each vehicle to obtain at least one group of typical region data, wherein each group of typical region data comprises at least one short-stroke fragment data;
the following operations are performed for each set of typical region data:
and step 3: acquiring unit mileage fatigue damage information corresponding to each short-stroke fragment data according to each short-stroke fragment data;
and 4, step 4: performing statistical calculation on the obtained fatigue damage information of each unit mileage, and selecting 95% quantile damage values as the fatigue damage values of the unit mileage of the typical area group;
and 5: acquiring a typical area cycle condition;
step 6: acquiring a unit mileage fatigue damage value based on a cycle working condition according to each short stroke fragment data;
and 7: acquiring a cyclic coefficient according to the unit mileage fatigue damage value based on the cyclic working condition and the unit mileage fatigue damage value of the typical region;
and step 8: and acquiring the final cycle working condition of the typical region data according to the cycle coefficient and the typical region cycle working condition.
According to the construction method of the reliability cycle working condition of the electric drive system based on the user big data, vehicle driving working conditions are classified according to the characteristics of typical urban road conditions in China, short travel segment databases of four typical areas including urban areas, mountainous areas, high speed areas and suburban areas are obtained, hypothesis testing is carried out on unit mileage fatigue damage of the short travel segment of each typical area, the unit mileage fatigue damage value of 95% quantiles is taken as a target fatigue damage value of the typical area, and then the unit mileage fatigue damage value of the constructed typical area cycle working condition is fused with the unit mileage fatigue damage value to obtain a cycle coefficient, so that the reliability cycle working condition representing 95% of user damage level is accurately constructed.
In the present embodiment, the basic operation data includes vehicle speed information, motor torque information, motor rotational speed information, operation time information, operation mileage information, acceleration information, and GPS signals.
In this embodiment, the dividing the basic operation data of each vehicle to obtain at least one group of typical region data, where each group of typical region data includes at least one short-trip segment data includes:
dividing basic operation data of each vehicle so as to obtain each short-stroke fragment data of the vehicle;
each short stroke fragment data is identified, so that each short stroke fragment data is assigned with a typical region label, and each short stroke fragment data with the same typical region label forms the typical region data.
In this embodiment, the acquiring, according to each short-trip segment data, unit-mileage fatigue damage information corresponding to each short-trip segment data includes:
acquiring travel mileage data, rotating speed data and motor torque data in the short stroke segment data;
constructing a failure mode model of each rotating component in the electric drive system;
and carrying out fatigue damage analysis based on the Miner linear damage principle, and obtaining unit mileage fatigue damage information corresponding to the short stroke fragment data according to the driving mileage data, the rotating speed data and the torque data.
In this embodiment, the obtaining of the typical region cycle condition includes:
extracting characteristic parameter data in each short-stroke fragment data;
and carrying out clustering analysis on the characteristic parameter data in each short-stroke fragment data, and selecting the characteristic parameter data in each short-stroke fragment data closest to a clustering center for recombination, thereby obtaining the circulation condition of the typical region.
In this embodiment, the cyclic coefficient obtained according to the unit mileage fatigue damage value based on the cyclic working condition and the unit mileage fatigue damage value of the typical region is obtained by using the following formula:
the cycle coefficient k = unit mileage fatigue damage value of typical region/unit mileage fatigue damage value based on cycle condition.
In this embodiment, the identifying each short-run fragment data so as to assign a typical region label to each short-run fragment data, and the composing the typical region data by each short-run fragment data having the same typical region label includes:
the following operations are performed for each short run fragment data:
acquiring a GPS signal in each short-stroke fragment data;
acquiring the area position of the short stroke fragment data according to the GPS signal;
acquiring a typical area comparison table, wherein the typical area comparison table comprises a preset area position and a typical area label corresponding to a preset typical area;
and acquiring a typical region label corresponding to a preset region position which is the same as the region position of the short-stroke fragment data.
In the present embodiment, dividing the basic operation data of each vehicle so as to acquire each short trip segment data of the vehicle includes:
acquiring starting information of a vehicle at a first time point;
acquiring power-off information of the vehicle after starting at a second time point after the first time point;
acquiring state information of the vehicle between a first time point and a second time point;
and judging whether the vehicle at least comprises an idle speed section and a motion section between the first time point and the second time point according to the state information, and if so, acquiring basic operation data between the first time point and the second time point as short-stroke section data.
It is understood that the kinematic segment is a basic unit of the driving condition construction, and refers to a state change process that the automobile undergoes from one idling speed to the next idling speed. Referring to fig. 3, in this embodiment, the starting point and the end point of the short stroke are determined by assisting the high voltage power-on signal in addition to the vehicle speed being 0 as the determination condition, and the high voltage power-on signal is continuously effective in the whole short stroke, and when the high voltage power-on signal is identified as 0 and the speed is 0, the high voltage power-on signal is determined as the end point determination condition of the short stroke.
And dividing a complete kinematic segment, including an idle section and a motion section. Generally, the data between two idle points can be selected as a kinematic segment in the algorithm. The complete kinematic segment should include the idling, accelerating, uniform and decelerating micro segments. The high voltage electrical signal needs to be continuously effective during the short stroke duration.
In this embodiment, after the extracting the feature parameter data in each short-stroke segment data, the acquiring a typical region cycle condition further includes:
and performing principal component analysis on the characteristic parameter data in each extracted short-stroke fragment data so as to obtain the characteristic parameter data in each short-stroke fragment data screened by the principal component analysis.
Specifically, the characteristic parameters (such as average speed, average torque, maximum speed, maximum acceleration, average acceleration and the like) are calculated for each short stroke, and then nonlinear dimension reduction is performed on the characteristic parameters through principal component analysis, so that the main characteristics are reserved, and the calculation cost and the complexity of problems are reduced.
The present application is further exemplified by the following examples, and it should be understood that the examples are not intended to limit the present application in any way.
Step 1: the method includes the steps that basic operation data of each vehicle in an area to be built are obtained, for example, basic operation data of each vehicle in Beijing city in the process of driving at ordinary times are obtained, it can be understood that each vehicle can upload the basic operation data in the process of driving at ordinary times in a cloud uploading mode, and only the data need to be obtained from a cloud.
Step 2: dividing basic operation data of each vehicle to obtain at least one group of typical region data, wherein each group of typical region data comprises at least one short-stroke fragment data; specifically, basic operation data of each vehicle is divided, so that each short-stroke fragment data of the vehicle is obtained;
each short stroke fragment data is identified, so that each short stroke fragment data is assigned with a typical region label, and each short stroke fragment data with the same typical region label forms the typical region data.
In this embodiment, the following principle is adopted for segment division:
and dividing a complete kinematic segment, including an idle segment and a motion segment. Generally, the data between two idle points can be selected as a kinematic segment in the algorithm. The complete kinematic segment should include the idling, accelerating, uniform and decelerating micro segments. The high voltage electrical signal needs to be continuously effective during the short stroke duration.
In the embodiment, it is assumed that the vehicle a is divided into 100 short-trip fragment data, the vehicle B is divided into 100 short-trip fragment data, all the 100 short-trip fragment data of the vehicle a need to be identified, and a typical region to which each short-trip fragment data belongs is determined, and all the 100 short-trip fragment data of the vehicle B need to be identified, and a typical region to which each short-trip fragment data belongs is determined.
In this embodiment, a typical area is identified by a GPS, for example, GPS information transmitted by one piece of short-trip segment data of the vehicle a is determined by map search that the position is in a certain mountain area in beijing, and the short-trip segment data is considered to belong to mountain area typical data.
In the present embodiment, the typical roads of the user are divided into 4 types, which are main urban areas, high speed, mountain areas, suburban areas (including national roads, provincial roads, rural villages, etc.), respectively. 4) Adopting a typical city surrounding high-speed or loop circuit as a suburban boundary, and further intersecting with a national road bank to obtain a main urban road and a suburban road bank; the highway library is a high-speed part collection of roads in provincial regions, including suburban high speed, high speed around cities and the like; the ordinary road administrative grade division uses G, S, X, Y, C, Z to distinguish national road, provincial road, county road, rural road, village road and special road, mainly covers suburb except high speed, other part roads of mountain area, mountain area road uses DEM data, obtains the mountain area in typical city in ARCGIS software, thus obtains the mountain area road bank; and acquiring a GPS constraint set through ARCGIS software as a short-stroke screening condition, thereby realizing the identification and classification of the short-stroke segment of the typical region.
When each short-trip segment data of each vehicle is acquired, and each short-trip segment data is subjected to typical region division, each group of typical region data can be acquired, for example, in the present embodiment, the typical region data includes 4 groups, that is, main urban region typical region data, high-speed typical region data, mountain region typical region data, and suburban region typical region data.
The following operations are performed for each set of typical region data:
and step 3: the unit mileage fatigue damage information corresponding to each short stroke fragment data is obtained according to each short stroke fragment data, specifically, rotating speed data and motor torque information in each short stroke fragment data of each typical region are extracted, a failure mode model of a rotating component such as an axle tooth is constructed, fatigue damage analysis is carried out based on a Miner linear damage principle, and meanwhile, the driving mileage of the short stroke fragment is extracted, and the unit mileage fatigue damage value is obtained.
For example, the torque and the rotating speed of each short stroke are used as input, the rotating rain flow counting is adopted to obtain the cycle number of each torque interval, and then the fatigue damage value of each short stroke is obtained based on the linear damage principle.
In the present embodiment, the fatigue damage per unit history is obtained using the following formula:
n i is a torque T i Number of revolutions of the hour, N i Is a torque T i The fatigue life of the steel sheet, k, is the power exponent of the S-N curve (the curve shown in FIG. 4).
And 4, step 4: and performing statistical calculation on the obtained fatigue damage information of each unit mileage, and selecting 95% quantile damage values as the unit mileage fatigue damage values of the group of typical areas.
And 5: acquiring a typical region cycle condition, specifically, acquiring the typical region cycle condition by adopting the following method:
the characteristic parameter data in each short stroke segment data is extracted, and in the embodiment, the vehicle running characteristic parameters with large reliability cycle condition correlation are selected and constructed, wherein the vehicle running characteristic parameters mainly comprise 5 speed parameters, 5 torque parameters, 8 acceleration parameters, 5 time parameters, 1 mileage parameter and 24 characteristic parameters. In the present embodiment, specifically, the 5 speeds include: average speed, running speed, minimum speed, maximum speed, speed standard deviation; the 5 torques include: average positive torque, average negative torque, maximum positive torque, maximum negative torque, torque standard deviation; the 8 accelerations include: the acceleration control system comprises a maximum acceleration, a maximum deceleration, an acceleration section average acceleration, a deceleration section average deceleration, a positive acceleration standard deviation, a negative acceleration standard deviation, an acceleration standard deviation and a relative positive acceleration; time 5 includes: acceleration time, deceleration time, uniform speed time, positive torque time and negative torque time; mileage 1 includes: and (4) driving mileage.
In this embodiment, after the above feature parameter data in each short-run segment data is obtained, 8) Principal Component Analysis (PCA) is used to reduce the dimension, and with 0.85 as the boundary line of the variance contribution rate, the first few principal components satisfying 0.85 are selected to replace all the principal components, so as to reduce the subsequent calculation amount, which is also the purpose of dimension reduction. Specifically, in the present embodiment, the principal component analysis reduces the original 24-dimensional feature parameter matrix to 5 dimensions (assuming that the contribution ratio of 0.85 is 5 dimensions), and reduces the amount of computation.
And carrying out clustering analysis on the characteristic parameter data in each short-stroke fragment data, and selecting the characteristic parameter data in each short-stroke fragment data closest to a clustering center for recombination, thereby obtaining the circulation condition of the typical region. And classifying the short-stroke fragments in each typical region by adopting K-means clustering analysis. Wherein data closest to the cluster center in each cluster group (the data representing representative data of the feature) is acquired.
Step 6: and acquiring a unit-mileage fatigue damage value based on the cycle working condition according to each short-stroke fragment data, and specifically, carrying out the unit-mileage fatigue damage calculation based on the cycle working condition on the constructed cycle working condition by adopting the unit-mileage fatigue damage formula.
And 7: and acquiring a cycle coefficient according to the unit-mileage fatigue damage value based on the cycle condition and the unit-mileage fatigue damage value of the typical region, wherein the cycle coefficient k = the unit-mileage fatigue damage value of the typical region/the unit-mileage fatigue damage value based on the cycle condition.
And step 8: and (4) acquiring a final cycle working condition according to the cycle coefficient and the typical region cycle working condition, specifically, multiplying the typical region cycle working condition constructed in the step (5) by a corresponding coefficient k to obtain the final cycle working condition representing the road characteristics of each typical region.
The application also provides a device for constructing the reliability cycle working condition of the electric driving system based on the user big data, which comprises a basic operation data acquisition module, a division module and a single group of typical region operation modules, wherein the single group of typical region operation modules comprises a unit mileage fatigue damage value acquisition module based on the cycle working condition, a unit mileage fatigue damage information acquisition module, a fatigue damage value acquisition module, a typical region cycle working condition acquisition module, a cycle coefficient acquisition module and a final cycle working condition acquisition module; wherein,
the basic operation data acquisition module is used for acquiring basic operation data of each vehicle in the area to be constructed;
the dividing module is used for dividing basic operation data of each vehicle so as to obtain at least one group of typical region data, wherein each group of typical region data comprises at least one short-stroke fragment data;
a single set of typical region operation modules for operating on each set of typical region data separately, the single set of typical region operation modules comprising the following modules:
the unit mileage fatigue damage information acquisition module is used for acquiring unit mileage fatigue damage information corresponding to each short stroke fragment data according to each short stroke fragment data;
the fatigue damage value acquisition module is used for carrying out statistical calculation on the acquired fatigue damage information of each unit mileage, and selecting 95% of quantiles of damage values as the unit mileage fatigue damage values of the typical area group;
the typical region cycle working condition acquisition module is used for acquiring typical region cycle working conditions;
the unit mileage fatigue damage value acquisition module based on the cycle working condition is used for acquiring a cycle coefficient according to the unit mileage fatigue damage value based on the cycle working condition and the unit mileage fatigue damage value of a typical area;
the cyclic coefficient acquisition module is used for acquiring a cyclic coefficient according to the cyclic working condition-based unit mileage fatigue damage value and the unit mileage fatigue damage value of the typical area;
and the final cycle working condition acquisition module is used for acquiring the final cycle working condition of the typical region data according to the cycle coefficient and the typical region cycle working condition.
It will be appreciated that the above description of the method applies equally to the description of the apparatus.
The application further provides an electronic device which comprises a storage, a processor and a computer program which is stored in the storage and can run on the processor, and the method for constructing the reliability cycle condition of the electric drive system based on the user big data is realized when the processor executes the computer program.
The application also provides a computer-readable storage medium, which stores a computer program, and the computer program can realize the above method for constructing the reliability cycle condition of the electric drive system based on the big data of the user when being executed by a processor.
FIG. 2 is an exemplary block diagram of an electronic device capable of implementing a method for building a reliability cycle of an electric drive system based on user big data according to an embodiment of the present application.
As shown in fig. 2, the electronic device includes an input device 501, an input interface 502, a central processor 503, a memory 504, an output interface 505, and an output device 506. The input interface 502, the central processing unit 503, the memory 504 and the output interface 505 are connected to each other through a bus 507, and the input device 501 and the output device 506 are connected to the bus 507 through the input interface 502 and the output interface 505, respectively, and further connected to other components of the electronic device. Specifically, the input device 504 receives input information from the outside and transmits the input information to the central processor 503 through the input interface 502; the central processor 503 processes input information based on computer-executable instructions stored in the memory 504 to generate output information, temporarily or permanently stores the output information in the memory 504, and then transmits the output information to the output device 506 through the output interface 505; the output device 506 outputs the output information to the outside of the electronic device for use by the user.
That is, the electronic device shown in fig. 2 may also be implemented to include: a memory storing computer-executable instructions; and one or more processors that when executing computer executable instructions may implement the method for building a reliability cycle condition for an electric drive system based on user big data described in conjunction with fig. 1.
In one embodiment, the electronic device shown in fig. 2 may be implemented to include: a memory 504 configured to store executable program code; one or more processors 503 configured to execute the executable program code stored in the memory 504 to perform the method for building the reliability cycle of the electric drive system based on the big data of the user in the above embodiment.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media include both non-transitory and non-transitory, removable and non-removable media that implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Furthermore, it will be obvious that the term "comprising" does not exclude other elements or steps. A plurality of units, modules or devices recited in the device claims may also be implemented by one unit or overall device by software or hardware.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, 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 identified 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.
The Processor in this embodiment may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may be used to store computer programs and/or modules, and the processor may implement various functions of the apparatus/terminal device by running or executing the computer programs and/or modules stored in the memory, as well as by invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
In this embodiment, the module/unit integrated with the apparatus/terminal device may be stored in a computer-readable storage medium if it is implemented in the form of a software functional unit and sold or used as a separate product. Based on such understanding, all or part of the flow in the method according to the embodiments of the present invention may also be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of the embodiments of the method. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic diskette, optical disk, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), electrical carrier wave signal, telecommunications signal, software distribution medium, etc. It should be noted that the computer readable medium may contain content that is appropriately increased or decreased as required by legislation and patent practice in the jurisdiction. Although the present application has been described with reference to the preferred embodiments, it is not intended to limit the present application, and those skilled in the art can make variations and modifications without departing from the spirit and scope of the present application.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Furthermore, it will be obvious that the term "comprising" does not exclude other elements or steps. A plurality of units, modules or devices recited in the device claims may also be implemented by one unit or overall device by software or hardware.
Although the invention has been described in detail hereinabove with respect to a general description and specific embodiments thereof, it will be apparent to those skilled in the art that modifications or improvements may be made thereto based on the invention. Accordingly, it is intended that all such modifications and alterations be included within the scope of this invention as defined in the appended claims.
Claims (10)
1. The method for constructing the reliability cycle condition of the electric drive system based on the user big data is characterized by comprising the following steps of:
acquiring basic operation data of each vehicle in an area to be constructed;
dividing basic operation data of each vehicle to obtain at least one group of typical region data, wherein each group of typical region data comprises at least one short-stroke fragment data;
the following operations are performed for each group of typical region data:
acquiring unit mileage fatigue damage information corresponding to each short stroke fragment data according to each short stroke fragment data;
performing statistical calculation on the obtained fatigue damage information of each unit mileage, and selecting 95% quantile damage values as the fatigue damage values of the unit mileage of the typical area group;
acquiring a typical area cycle condition;
acquiring a unit mileage fatigue damage value based on a cycle working condition according to each short stroke fragment data;
acquiring a cyclic coefficient according to the unit mileage fatigue damage value based on the cyclic working condition and the unit mileage fatigue damage value of the typical region;
and acquiring the final cycle working condition of the typical region data according to the cycle coefficient and the typical region cycle working condition.
2. The method for constructing the reliability cycle condition of the electric drive system based on the user big data as claimed in claim 1, wherein the basic operation data comprises vehicle speed information, motor torque information, motor rotation speed information, operation time information, operation mileage information, acceleration information and GPS signals.
3. The method for constructing the reliability cycle condition of the electric drive system based on the user big data as claimed in claim 2, wherein the dividing the basic operation data of each vehicle to obtain at least one group of typical region data, each group of typical region data including at least one short stroke fragment data comprises:
dividing basic operation data of each vehicle so as to obtain each short-stroke fragment data of the vehicle;
each short stroke fragment data is identified, so that each short stroke fragment data is assigned with a typical region label, and each short stroke fragment data with the same typical region label forms the typical region data.
4. The method for constructing the reliability cycle condition of the electric drive system based on the user big data as claimed in claim 3, wherein the obtaining the fatigue damage information per unit mileage corresponding to each short stroke segment data according to each short stroke segment data comprises:
acquiring travel mileage data, rotating speed data and torque data in the short stroke segment data;
constructing a failure mode model of each rotating component in the electric drive system;
and carrying out fatigue damage analysis based on the Miner linear damage principle, and obtaining unit mileage fatigue damage information corresponding to the short stroke fragment data according to the driving mileage data, the rotating speed data and the torque data.
5. The method for constructing the reliability cycle condition of the electric drive system based on the big data of the user according to claim 4, wherein the obtaining the typical region cycle condition comprises:
extracting characteristic parameter data in each short-stroke fragment data;
and carrying out cluster analysis on the characteristic parameter data in each short-stroke fragment data, and selecting the characteristic parameter data in each short-stroke fragment data closest to a cluster center for recombination, thereby obtaining the circulation condition of a typical region.
6. The method for constructing the reliability cycle condition of the electric drive system based on the big data of the user as claimed in claim 5, wherein the cycle coefficient obtained according to the fatigue damage per unit mileage value based on the cycle condition and the fatigue damage per unit mileage value of the typical area is obtained by the following formula:
the cycle coefficient k = unit mileage fatigue damage value of typical region/unit mileage fatigue damage value based on cycle condition.
7. The method for constructing the reliability cycle condition of the electric drive system based on the user big data as claimed in claim 3, wherein the identifying each short stroke fragment data, so as to assign a typical region label to each short stroke fragment data, and the composing of the typical region data by each short stroke fragment data with the same typical region label comprises:
the following operations are performed for each short run segment data:
acquiring a GPS signal in each short-stroke fragment data;
acquiring the area position of the short stroke fragment data according to the GPS signal;
obtaining a typical area comparison table, wherein the typical area comparison table comprises a preset area position and a typical area label corresponding to a preset typical area;
and acquiring a typical region label corresponding to a preset region position which is the same as the region position of the short-stroke fragment data.
8. The method for constructing the reliability cycle condition of the electric drive system based on the user big data as claimed in claim 7, wherein the step of dividing the basic operation data of each vehicle so as to obtain each short stroke fragment data of the vehicle comprises the following steps:
acquiring starting information of a vehicle at a first time point;
acquiring power-off information of the vehicle after starting at a second time point after the first time point;
acquiring state information of the vehicle between a first time point and a second time point;
and judging whether the vehicle at least comprises an idle speed section and a motion section between the first time point and the second time point according to the state information, and if so, acquiring basic operation data between the first time point and the second time point as short-stroke section data.
9. The method for constructing the reliability cycle condition of the electric drive system based on the user big data as claimed in claim 5, wherein after the extracting the feature parameter data in each short stroke segment data, the obtaining the typical region cycle condition further comprises:
and performing principal component analysis on the characteristic parameter data in each extracted short-stroke fragment data so as to obtain the characteristic parameter data in each short-stroke fragment data screened by the principal component analysis.
10. The utility model provides a based on big data electricity of user drives system reliability cycle operating mode and constructs device which characterized in that, it includes to drive system reliability cycle operating mode based on big data electricity of user constitutes device:
the system comprises a basic operation data acquisition module, a basic operation data acquisition module and a basic operation data acquisition module, wherein the basic operation data acquisition module is used for acquiring basic operation data of each vehicle in an area to be constructed;
the system comprises a dividing module, a calculating module and a processing module, wherein the dividing module is used for dividing basic operation data of each vehicle so as to obtain at least one group of typical region data, and each group of typical region data comprises at least one short-stroke fragment data;
a single set of typical region manipulation modules for manipulating each set of typical region data separately, the single set of typical region manipulation modules comprising the following modules:
the unit mileage fatigue damage information acquisition module is used for acquiring unit mileage fatigue damage information corresponding to each short-stroke fragment data according to each short-stroke fragment data;
the fatigue damage value acquisition module is used for carrying out statistical calculation on the acquired fatigue damage information of each unit mileage, and selecting 95% quantile damage values as the unit mileage fatigue damage values of the typical area group;
the typical region cycle working condition acquisition module is used for acquiring typical region cycle working conditions;
the unit mileage fatigue damage value acquisition module based on the cycle working condition is used for acquiring a cycle coefficient according to the unit mileage fatigue damage value based on the cycle working condition and the unit mileage fatigue damage value of a typical area;
the cyclic coefficient acquisition module is used for acquiring a cyclic coefficient according to the cyclic working condition-based unit mileage fatigue damage value and the typical area-based unit mileage fatigue damage value;
and the final cycle working condition acquisition module is used for acquiring the final cycle working condition of the typical region data according to the cycle coefficient and the typical region cycle working condition.
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