A method for constructing reliability cycle working conditions of an electric drive system based on user big data

Abstract

The application discloses a user big data-based electric drive system reliability cycle working condition construction method. The user big data-based electric drive system reliability cycle working condition construction method comprises the following steps: acquiring basic operation data of each vehicle; dividing each basic operation data to acquire typical region data, wherein the typical region data comprises short trip segment data; performing the following operation on each group of typical region data: performing statistical calculation on each unit mileage fatigue damage information acquired, and selecting a damage value at a 95% quantile as a unit mileage fatigue damage value of the group of typical regions; acquiring a typical region cycle working condition; acquiring a unit mileage fatigue damage value based on the cycle working condition; acquiring a cycle coefficient; and acquiring a final cycle working condition of the typical region data. The cycle coefficient is acquired by fusing the unit mileage fatigue damage value of the typical region and the unit mileage fatigue damage value based on the cycle working condition, so that a more accurate final cycle working condition is constructed.

Description

Technical Field

[0001] This application relates to the field of vehicle electric drive system analysis technology, specifically to a method and apparatus for constructing reliable cyclic operating conditions of electric drive systems based on user big data. Background Technology

[0002] The electric drive system is one of the core components of a pure electric vehicle, providing power and enabling regenerative braking. Compared to traditional gasoline vehicles, pure electric vehicles have fewer gears, more concentrated torque, and faster motor torque response, higher speeds, and frequent regenerative braking, making the reliability of the electric drive system increasingly prominent. To address this issue, it is necessary to construct a reliability cyclic operating condition that represents the driving characteristics of pure electric vehicles at 95% of user failure rates. This, combined with a vehicle model simulation platform, allows for the acquisition of time histories of load, speed, and other information. This provides accurate input for reliability design and experimental verification, enabling early detection of failure modes in the electric drive system and improving overall system reliability.

[0003] The typical approach to constructing vehicle driving cycle characteristics involves real-vehicle data collection, including user surveys, route planning, small-sample data collection, short-trip segmentation, feature parameter extraction, cluster analysis, and candidate condition reconstruction. However, the load on the drive system is related to both objective conditions such as terrain and traffic, as well as driver behavior. Real-vehicle data collection has limitations such as small sample size, short mileage, and limited route planning, making it difficult to cover actual user conditions and accurately reflect user characteristics. Therefore, considering my country's regional characteristics, creating reliability cycle conditions that both conform to the characteristics of road driving in my country and represent user damage is particularly important.

[0004] Patent CN113688558, entitled "A Method and System for Constructing Vehicle Driving Conditions Based on Big Data Samples," provides a method and system for constructing vehicle driving conditions based on a large database of samples. It proposes determining a reasonable amount of raw data using a vehicle driving characteristic correlation analysis method, establishing an optimized driving condition construction model, first obtaining independent driving conditions for each vehicle and then recombining them into optimized raw data, finally generating representative driving conditions. This ensures that the data used for constructing representative driving conditions has a high correlation with the motion characteristics of each generation of vehicles, and that single-vehicle data has low computational requirements for stand-alone operation. However, this patent constructs cyclic driving conditions using speed as the main characteristic parameter, which is suitable for fuel consumption analysis. The characteristic parameter construction lacks load data and does not include equivalent calculations for user damage, making it unsuitable for reliability analysis.

[0005] Patent CN113297795, entitled "A Method for Constructing Driving Conditions of Pure Electric Vehicles," discloses a method for constructing driving conditions of pure electric vehicles. The method includes the following steps: collecting data on driving conditions of pure electric vehicles; dividing the test route into several short-distance segments; obtaining characteristic parameters of the driving conditions of pure electric vehicles from these short-distance segments; performing nonlinear dimensionality reduction on the characteristic parameters of the driving conditions of pure electric vehicles using kernelized principal component analysis; classifying the nonlinearly dimensionality-reduced characteristic parameters using a hybrid clustering method; filtering several short-distance segments based on the classification results, combined with the weight of each category in the driving condition duration and the Pearson correlation coefficient, to construct multiple candidate driving conditions of pure electric vehicles; calculating and comparing the relative error values ​​and SAPD frequency values ​​of the characteristic parameters in the multiple candidate driving conditions of pure electric vehicles to construct the driving conditions of pure electric vehicles. This patent achieves higher accuracy in constructing operating conditions, better reflects the actual driving characteristics of electric vehicles, and obtains operating condition curves with stronger consistency with actual operating conditions. However, this patent uses experimental vehicles with a limited sample size for data collection. During the clustering process, it lacks data support from a large number of typical users, and the constructed cyclic operating conditions fail to be equivalent to user damage, making it unsuitable as input for reliability analysis.

[0006] Therefore, there is a need for a technical solution to address or at least mitigate the aforementioned shortcomings of existing technologies. Summary of the Invention

[0007] The purpose of this invention is to provide a method for constructing a reliability cycle operating condition for an electric drive system based on user big data, which at least solves one of the above-mentioned technical problems.

[0008] One aspect of the present invention provides a method for constructing a reliability cyclic operating condition for an electric drive system based on user big data, the method comprising:

[0009] Obtain basic operational data for each vehicle in the area to be constructed;

[0010] The basic operating data of each vehicle is divided to obtain at least one set of typical area data, and each set of typical area data includes at least one short-trip segment data.

[0011] Perform the following operations on each set of typical region data:

[0012] Based on the data of each short-stroke segment, obtain the fatigue damage information per unit mileage corresponding to each short-stroke segment;

[0013] Statistical calculations were performed on the fatigue damage information obtained for each unit mileage, and the damage value at the 95th percentile was selected as the fatigue damage value per unit mileage for the typical area of ​​this group.

[0014] Obtain typical regional cyclic operating conditions;

[0015] The fatigue damage value per unit mileage based on cyclic operating conditions is obtained from the data of each short-stroke segment.

[0016] The cycle coefficient is obtained based on the fatigue damage value per unit mileage under the cyclic operating conditions and the fatigue damage value per unit mileage in a typical region.

[0017] The final cycle condition of the typical region data is obtained based on the cycle coefficient and the cycle condition of the typical region.

[0018] Optionally, the basic operating data includes vehicle speed information, motor torque information, motor speed information, operating time information, operating mileage information, acceleration information, and GPS signal.

[0019] Optionally, the basic operating data of each vehicle is divided to obtain at least one set of typical area data, each set of typical area data including at least one short-trip segment data, including:

[0020] The basic operating data of each vehicle is divided to obtain the data of each short-trip segment of the vehicle;

[0021] Each short-travel segment data is identified, and a typical region label is assigned to each short-travel segment data. Short-travel segments data with the same typical region label constitute the typical region data.

[0022] Optionally, obtaining the fatigue damage information per unit mileage corresponding to each short-stroke segment data based on each short-stroke segment data includes:

[0023] Obtain the driving mileage data, speed data, and torque data from the short-stroke segment data;

[0024] Construct failure mode models for each rotating component in the electric drive system;

[0025] Fatigue damage analysis is conducted based on the Miner linear damage principle. The fatigue damage information per unit mileage corresponding to the short-stroke segment data is obtained based on the mileage data, speed data, and torque data.

[0026] Optionally, obtaining typical regional cyclic operating conditions includes:

[0027] Extract feature parameter data from each short-stroke segment data;

[0028] Cluster analysis is performed on the characteristic parameter data in each short-stroke segment data, and the characteristic parameter data in each short-stroke segment data closest to the cluster center is selected for recombination to obtain the typical regional cyclic operating conditions.

[0029] Optionally, the cycle coefficient is obtained by using the following formula: based on the fatigue damage value per unit mileage under cyclic operating conditions and the fatigue damage value per unit mileage in a typical region.

[0030] Cyclic coefficient k = fatigue damage value per unit mileage in a typical region / fatigue damage value per unit mileage based on cyclic operating conditions.

[0031] Optionally, the step of identifying each short-travel segment data and assigning a typical region label to each short-travel segment data, wherein the typical region data comprises short-travel segment data with the same typical region label, includes:

[0032] Perform the following operations on each short-stroke segment data:

[0033] Acquire GPS signals from each short-range data segment;

[0034] The location of the short-range data segment is obtained based on GPS signals.

[0035] Obtain a typical region comparison table, which includes preset region locations and typical region labels corresponding to preset typical regions;

[0036] Obtain the typical region label corresponding to the preset region location that is the same as the region where the short-trip data is located.

[0037] Optionally, the basic operating data for each vehicle is divided to obtain data for each short-trip segment of the vehicle, including:

[0038] Obtain vehicle startup information at the first point in time;

[0039] Obtain the power-off information of the vehicle after startup at a second time point following the first time point;

[0040] Obtain the vehicle's status information between the first and second time points;

[0041] Based on the status information, determine whether the vehicle includes at least one idling segment and one moving segment between the first time point and the second time point. If so, obtain the basic operating data between the first time point and the second time point as a short-stroke segment data.

[0042] A complete kinematic segment is defined, including an idle segment and a motion segment. Typically, the algorithm selects the data between two idle points as the kinematic segment. A complete kinematic segment should include small segments of idling, acceleration, constant speed, and deceleration. The high-voltage power signal must remain active throughout the short-stroke continuous process.

[0043] Optionally, after extracting the feature parameter data from each short-stroke segment, obtaining the typical regional cyclic operating condition further includes:

[0044] Principal component analysis is performed on the feature parameter data of each extracted short-stroke segment data to obtain the feature parameter data of each short-stroke segment data after filtering by principal component analysis.

[0045] This application also provides a device for constructing reliable cyclic operating conditions of an electric drive system based on user big data, the device comprising:

[0046] A basic operation data acquisition module is used to acquire the basic operation data of each vehicle in the area to be constructed.

[0047] The segmentation module is used to segment the basic operating data of each vehicle to obtain at least one set of typical area data, and each set of typical area data includes at least one short-trip segment data.

[0048] A single typical region operation module is provided, which is used to operate on each group of typical region data separately. The single typical region operation module includes the following modules:

[0049] A fatigue damage information acquisition module for unit mileage is used to acquire fatigue damage information for unit mileage corresponding to each short-distance segment based on the data of each short-distance segment.

[0050] The fatigue damage value acquisition module is used to perform statistical calculations on the acquired fatigue damage information per unit mileage, and select the 95th percentile damage value as the fatigue damage value per unit mileage for the typical area of ​​the group.

[0051] A typical area cyclic operating condition acquisition module is used to acquire typical area cyclic operating conditions.

[0052] The fatigue damage value acquisition module based on cyclic operating conditions is used to obtain the cycle coefficient based on the fatigue damage value per unit mileage under cyclic operating conditions and the fatigue damage value per unit mileage in a typical area.

[0053] The cyclic coefficient acquisition module is used to acquire the cyclic coefficient based on the fatigue damage value per unit mileage under the cyclic working condition and the fatigue damage value per unit mileage in a typical area.

[0054] The final cycle condition acquisition module is used to acquire the final cycle condition of the typical region data based on the cycle coefficient and the typical region cycle condition.

[0055] Beneficial effects

[0056] This application presents a method for constructing reliable cyclic operating conditions for electric drive systems based on user big data. The method categorizes vehicle driving conditions according to the characteristics of typical urban road conditions in my country, obtaining short-trip segment databases for four typical regions: urban areas, mountainous areas, highways, and suburbs. By performing hypothesis testing on the fatigue damage per unit mileage for short-trip segments in each typical region, the 95th percentile fatigue damage value per unit mileage is taken as the target fatigue damage value for the typical region. This value is then fused with the fatigue damage value per unit mileage of the constructed typical region cyclic operating conditions to obtain the cyclic coefficient, thereby accurately constructing reliable cyclic operating conditions representing the damage level of 95% of users. Attached Figure Description

[0057] Figure 1 This is a flowchart illustrating a method for constructing a reliability cyclic operating condition for an electric drive system based on user big data, according to an embodiment of this application.

[0058] Figure 2 This is a schematic diagram of an electronic device capable of implementing the user big data-based electric drive system reliability cyclic operating condition construction method of an embodiment of this application.

[0059] Figure 3 This is a schematic diagram of short-stroke segment division according to an embodiment of this application.

[0060] Figure 4 This is a schematic diagram of the SN curve (stress-life curve) of an embodiment of this application. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be described in more detail below with reference to the accompanying drawings. 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 some, but not all, embodiments of this application. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. The embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0062] Figure 1 This is a flowchart illustrating a method for constructing a reliability cyclic operating condition for an electric drive system based on user big data, according to an embodiment of this application.

[0063] like Figure 1 The method for constructing reliability cyclic operating conditions for electric drive systems based on user big data, as shown, includes:

[0064] Step 1: Obtain basic operational data for each vehicle in the area to be constructed;

[0065] Step 2: Divide the basic operating data of each vehicle to obtain at least one set of typical area data. Each set of typical area data includes at least one short-trip segment data.

[0066] Perform the following operations on each set of typical region data:

[0067] Step 3: Obtain the fatigue damage information per unit mileage corresponding to each short-stroke segment based on the data of each short-stroke segment;

[0068] Step 4: Perform statistical calculations on the obtained fatigue damage information per unit mileage, and select the 95th percentile damage value as the fatigue damage value per unit mileage for the typical area of ​​this group.

[0069] Step 5: Obtain typical regional cyclic operating conditions;

[0070] Step 6: Obtain the fatigue damage value per unit mileage based on cyclic operating conditions based on the data of each short-stroke segment;

[0071] Step 7: Obtain the cycle coefficient based on the fatigue damage value per unit mileage under cyclic operating conditions and the fatigue damage value per unit mileage in typical areas;

[0072] Step 8: Obtain the final cycle condition of the typical region data based on the cycle coefficient and the cycle condition of the typical region.

[0073] This application presents a method for constructing reliable cyclic operating conditions for electric drive systems based on user big data. The method categorizes vehicle driving conditions according to the characteristics of typical urban road conditions in my country, obtaining short-trip segment databases for four typical regions: urban areas, mountainous areas, highways, and suburbs. By performing hypothesis testing on the fatigue damage per unit mileage for short-trip segments in each typical region, the 95th percentile fatigue damage value per unit mileage is taken as the target fatigue damage value for the typical region. This value is then fused with the fatigue damage value per unit mileage of the constructed typical region cyclic operating conditions to obtain the cyclic coefficient, thereby accurately constructing reliable cyclic operating conditions representing the damage level of 95% of users.

[0074] In this embodiment, the basic operating data includes vehicle speed information, motor torque information, motor speed information, operating time information, operating mileage information, acceleration information, and GPS signal.

[0075] In this embodiment, the basic operating data of each vehicle is divided to obtain at least one set of typical area data. Each set of typical area data includes at least one short-trip segment data, including:

[0076] The basic operating data of each vehicle is divided to obtain the data of each short-trip segment of the vehicle;

[0077] Each short-travel segment data is identified, and a typical region label is assigned to each short-travel segment data. Short-travel segments data with the same typical region label constitute the typical region data.

[0078] In this embodiment, obtaining the fatigue damage information per unit mileage corresponding to each short-stroke segment data based on each short-stroke segment data includes:

[0079] Obtain the driving mileage data, speed data, and motor torque data from the short-stroke segment data;

[0080] Construct failure mode models for each rotating component in the electric drive system;

[0081] Fatigue damage analysis is conducted based on the Miner linear damage principle. The fatigue damage information per unit mileage corresponding to the short-stroke segment data is obtained based on the mileage data, speed data, and torque data.

[0082] In this embodiment, obtaining typical regional cyclic operating conditions includes:

[0083] Extract feature parameter data from each short-stroke segment data;

[0084] Cluster analysis is performed on the characteristic parameter data in each short-stroke segment data, and the characteristic parameter data in each short-stroke segment data closest to the cluster center is selected for recombination to obtain the typical regional cyclic operating conditions.

[0085] In this embodiment, the cycle coefficient is obtained by the following formula based on the fatigue damage value per unit mileage under cyclic operating conditions and the fatigue damage value per unit mileage in a typical region:

[0086] Cyclic coefficient k = fatigue damage value per unit mileage in a typical region / fatigue damage value per unit mileage based on cyclic operating conditions.

[0087] In this embodiment, the step of identifying each short-travel segment data and assigning a typical region label to each short-travel segment data, wherein the typical region data comprises short-travel segment data with the same typical region label, includes:

[0088] Perform the following operations on each short-stroke segment data:

[0089] Acquire GPS signals from each short-range data segment;

[0090] The location of the short-range data segment is obtained based on GPS signals.

[0091] Obtain a typical region comparison table, which includes preset region locations and typical region labels corresponding to preset typical regions;

[0092] Obtain the typical region label corresponding to the preset region location that is the same as the region where the short-trip data is located.

[0093] In this embodiment, the basic operating data of each vehicle is divided to obtain the data of each short-trip segment of the vehicle, including:

[0094] Obtain vehicle startup information at the first point in time;

[0095] Obtain the power-off information of the vehicle after startup at a second time point following the first time point;

[0096] Obtain the vehicle's status information between the first and second time points;

[0097] Based on the status information, determine whether the vehicle includes at least one idling segment and one moving segment between the first time point and the second time point. If so, obtain the basic operating data between the first time point and the second time point as a short-stroke segment data.

[0098] Understandably, a kinematic segment is the basic unit for constructing driving conditions, referring to the state changes a vehicle undergoes from one idle speed to the next. See also Figure 3 In this embodiment, in addition to using a vehicle speed of 0 as a determination condition, the start and end points of the short journey are also determined by the high-voltage power-on signal. The high-voltage power-on signal is continuously valid throughout the short journey. When the high-voltage power-on signal is identified as 0 and the speed is 0, this is used as the determination condition for the end point of the short journey.

[0099] A complete kinematic segment is defined, including an idle segment and a motion segment. Typically, the algorithm selects the data between two idle points as the kinematic segment. A complete kinematic segment should include small segments of idling, acceleration, constant speed, and deceleration. The high-voltage power signal must remain active throughout the short-stroke continuous process.

[0100] In this embodiment, after extracting the feature parameter data from each short-stroke segment, obtaining the typical regional cyclic operating condition further includes:

[0101] Principal component analysis is performed on the feature parameter data of each extracted short-stroke segment data to obtain the feature parameter data of each short-stroke segment data after filtering by principal component analysis.

[0102] Specifically, firstly, characteristic parameters (e.g., average speed, average torque, maximum torque, maximum speed, maximum acceleration, average acceleration, etc.) are calculated for each short stroke. Then, principal component analysis is used to perform nonlinear dimensionality reduction on the characteristic parameters. The aim is to retain the main features while reducing computational costs and the complexity of the problem.

[0103] The following examples further illustrate this application; it is understood that these examples do not constitute any limitation on this application.

[0104] Step 1: Obtain basic operating data for each vehicle in the area to be constructed. For example, obtain basic operating data for each vehicle in Beijing during normal driving. Understandably, each vehicle can upload its basic operating data during normal driving to the cloud, and we only need to obtain this data from the cloud.

[0105] Step 2: Divide the basic operating data of each vehicle to obtain at least one set of typical area data. Each set of typical area data includes at least one short-trip segment data. Specifically, divide the basic operating data of each vehicle to obtain the short-trip segment data of that vehicle.

[0106] Each short-travel segment data is identified, and a typical region label is assigned to each short-travel segment data. Short-travel segments data with the same typical region label constitute the typical region data.

[0107] In this embodiment, the following principles are used for segmentation:

[0108] A complete kinematic segment is defined, including an idle segment and a motion segment. Typically, the algorithm selects the data between two idle points as the kinematic segment. A complete kinematic segment should include small segments of idling, acceleration, constant speed, and deceleration. The high-voltage power signal must remain active throughout the short-stroke continuous process.

[0109] In this embodiment, it is assumed that vehicle A is divided into 100 short-trip data segments and vehicle B is divided into 100 short-trip data segments. All 100 short-trip data segments of vehicle A need to be identified to determine which typical region each short-trip data segment belongs to. Similarly, all 100 short-trip data segments of vehicle B need to be identified to determine which typical region each short-trip data segment belongs to.

[0110] In this embodiment, typical areas are identified using GPS. For example, if the GPS information transmitted by a short-trip data segment of vehicle A is determined by map search to be located in a mountainous area of ​​Beijing, then the short-trip data segment is considered to be typical data of a mountainous area.

[0111] In this embodiment, typical user roads are divided into four types: main urban area, expressway, mountainous area, and suburbs (including national highways, provincial highways, and rural roads). 4) The ring expressway or ring road of a typical city is used as the boundary line between the city and the suburbs, and then intersected with the national road database to obtain the main urban area road database and the suburban road database; the expressway road database is a collection of expressway sections of roads within the province, including suburban expressways and ring expressways; the administrative classification of ordinary highways uses G, S, X, Y, C, and Z to distinguish national highways, provincial highways, county roads, township roads, village roads, and special highways, mainly covering the suburbs except for expressways and other mountainous roads. Mountainous roads are obtained by using DEM data and obtaining the area of ​​mountainous areas in typical cities in ArcGIS software, thus obtaining the mountainous road database; the GPS constraint set is obtained through ArcGIS software as a condition for short-distance filtering, thereby realizing the identification and classification of short-distance segments in typical areas.

[0112] Once the data for each short-trip segment of each vehicle is obtained, and each short-trip segment is divided into typical regions, then the typical region data for each group can be obtained. For example, in this embodiment, the typical region data includes four groups: typical region data for the main urban area, typical region data for highways, typical region data for mountainous areas, and typical region data for suburbs.

[0113] Perform the following operations for each set of typical region data:

[0114] Step 3: Obtain fatigue damage information per unit mileage for each short-stroke segment based on the data of each short-stroke segment. Specifically, extract the rotational speed data and motor torque information from each short-stroke segment data of each typical region, construct a failure mode model for rotating components such as shafts and gears, conduct fatigue damage analysis based on the Miner linear damage principle, and simultaneously extract the mileage of the short-stroke segment to obtain the fatigue damage value per unit mileage.

[0115] For example, the torque and speed of each short stroke are used as inputs, and the number of cycles for each torque range is obtained by using a rotating rainflow counter. Then, based on the linear damage principle, the fatigue damage value for each short stroke is obtained.

[0116] In this embodiment, the fatigue damage per unit time is obtained using the following formula:

[0117] in,

[0118] n i Torque Ti The number of rotations, N i For torque T i Fatigue life at time, k is the SN curve ( Figure 4 The power exponent of the curve shown.

[0119] Step 4: Perform statistical calculations on the obtained fatigue damage information per unit mileage, and select the 95th percentile damage value as the fatigue damage value per unit mileage for the typical area of ​​this group.

[0120] Step 5: Obtain typical regional cyclic operating conditions. Specifically, the following method is used to obtain typical regional cyclic operating conditions:

[0121] Feature parameter data are extracted from each short-stroke segment. In this embodiment, the vehicle operation feature parameters most relevant to the reliability cycle are selected, mainly including 5 speed parameters, 5 torque parameters, 8 acceleration parameters, 5 time parameters, and 1 mileage parameter, for a total of 24 feature parameters. Specifically, in this embodiment, the 5 speed parameters include: average speed, operating speed, minimum speed, maximum speed, and speed standard deviation; the 5 torque parameters include: average positive torque, average negative torque, maximum positive torque, maximum negative torque, and torque standard deviation; the 8 acceleration parameters include: maximum acceleration, maximum deceleration, average acceleration during acceleration, average deceleration during deceleration, positive acceleration standard deviation, negative acceleration standard deviation, acceleration standard deviation, and relative positive acceleration; the 5 time parameters include: acceleration time, deceleration time, constant speed time, positive torque time, and negative torque time; and the 1 mileage parameter includes: mileage traveled.

[0122] In this embodiment, after obtaining the aforementioned feature parameter data from each short-stroke segment, 8) Principal Component Analysis (PCA) is used for dimensionality reduction. Using 0.85 as the variance contribution rate threshold, the first few principal components satisfying 0.85 are selected to replace all principal components, reducing subsequent computational load, which is also the purpose of dimensionality reduction. Specifically, in this embodiment, principal component analysis reduces the original 24-dimensional feature parameter matrix to 5 dimensions (assuming a contribution rate of 0.85 is 5-dimensional), reducing computational load.

[0123] Cluster analysis was performed on the feature parameter data of each short-stroke segment, and the feature parameter data of the short-stroke segment data closest to the cluster center were selected for recombination to obtain the typical regional cyclic operating conditions. K-means cluster analysis was used to classify the short-stroke segments in each typical region. Specifically, the data closest to the cluster center in each cluster was obtained (this data represents the representative data of that feature).

[0124] Step 6: Obtain the fatigue damage value per unit mileage based on the cyclic operating conditions based on the data of each short-stroke segment. Specifically, use the fatigue damage formula per unit mileage mentioned above to perform fatigue damage calculation per unit mileage based on the cyclic operating conditions for the constructed cyclic operating conditions.

[0125] Step 7: Obtain the cycle coefficient based on the fatigue damage value per unit mileage under cyclic operating conditions and the fatigue damage value per unit mileage in a typical area. Specifically, the cycle coefficient k = fatigue damage value per unit mileage in a typical area / fatigue damage value per unit mileage under cyclic operating conditions.

[0126] Step 8: Obtain the final cycle condition based on the cycle coefficient and the cycle condition of typical areas. Specifically, multiply the cycle condition of typical areas constructed in Step 5 by the corresponding coefficient k to obtain the final cycle condition representing the road characteristics of each typical area.

[0127] This application also provides a device for constructing reliable cyclic operating conditions for electric drive systems based on user big data. This device includes a basic operating data acquisition module, a partitioning module, and a single-group typical area operation module. The single-group typical area operation module includes a module for acquiring fatigue damage values ​​per unit mileage based on cyclic operating conditions, a module for acquiring fatigue damage information per unit mileage, a fatigue damage value acquisition module, a typical area cyclic operating condition acquisition module, a cyclic coefficient acquisition module, and a final cyclic operating condition acquisition module.

[0128] The basic operation data acquisition module is used to acquire the basic operation data of each vehicle in the area to be constructed;

[0129] The segmentation module is used to segment the basic operating data of each vehicle to obtain at least one set of typical area data, and each set of typical area data includes at least one short-trip segment data.

[0130] The single-group typical region operation module is used to operate on each group of typical region data separately. The single-group typical region operation module includes the following modules:

[0131] The fatigue damage information acquisition module per unit mileage is used to acquire fatigue damage information per unit mileage corresponding to each short-stroke segment based on the data of each short-stroke segment.

[0132] The fatigue damage value acquisition module is used to perform statistical calculations on the acquired fatigue damage information per unit mileage, and selects the 95th percentile damage value as the fatigue damage value per unit mileage for the typical area of ​​this group.

[0133] The typical area cyclic operating condition acquisition module is used to acquire typical area cyclic operating conditions.

[0134] The fatigue damage value acquisition module based on cyclic operating conditions per unit mileage is used to obtain the cycle coefficient based on the fatigue damage value per unit mileage based on cyclic operating conditions and the fatigue damage value per unit mileage in a typical area.

[0135] The cycle coefficient acquisition module is used to acquire the cycle coefficient based on the fatigue damage value per unit mileage under the cyclic working condition and the fatigue damage value per unit mileage in a typical area.

[0136] The final cycle condition acquisition module is used to acquire the final cycle condition of the typical region data based on the cycle coefficient and the typical region cycle condition.

[0137] It is understandable that the above description of the method also applies to the description of the apparatus.

[0138] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the computer program, it implements the above-described method for constructing reliable cyclic operating conditions for an electric drive system based on user big data.

[0139] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the implementation of the above-described method for constructing reliable cyclic operating conditions for electric drive systems based on user big data.

[0140] Figure 2 This is an exemplary structural diagram of an electronic device capable of implementing the user big data-based electric drive system reliability cyclic operating condition construction method provided in one embodiment of this application.

[0141] like Figure 2 As shown, the electronic device includes an input device 501, an input interface 502, a central processing unit 503, a memory 504, an output interface 505, and an output device 506. The input interface 502, central processing unit 503, memory 504, and output interface 505 are interconnected via a bus 507. The input device 501 and output device 506 are connected to the bus 507 via the input interface 502 and output interface 505, respectively, and thus connected to other components of the electronic device. Specifically, the input device 504 receives input information from the outside and transmits it to the central processing unit 503 via the input interface 502. The central processing unit 503 processes the input information based on computer-executable instructions stored in the memory 504 to generate output information, temporarily or permanently storing the output information in the memory 504, and then transmitting the output information to the output device 506 via the output interface 505. The output device 506 outputs the output information to the outside of the electronic device for user use.

[0142] In other words, Figure 2 The illustrated electronic device may also be implemented as including: a memory storing computer-executable instructions; and one or more processors, which can be coupled when executing the computer-executable instructions. Figure 1 This describes a method for constructing reliability cyclic operating conditions for electric drive systems based on user big data.

[0143] In one embodiment, Figure 2 The electronic device shown can be implemented as including: a memory 504 configured to store executable program code; and one or more processors 503 configured to run the executable program code stored in the memory 504 to execute the method for constructing reliable cyclic operating conditions of electric drive systems based on user big data in the above embodiments.

[0144] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0145] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0146] Computer-readable media include both permanent and non-permanent, removable and non-removable media, and information storage can be achieved by any method or technology. Information can be computer-readable instructions, data structures, program modules, 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 technologies, CD-ROM, DVD or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0147] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can 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, etc.) containing computer-usable program code.

[0148] Furthermore, it is clear that the word "comprising" does not exclude other units or steps. Multiple units, modules, or devices recited in the apparatus claims may also be implemented by a single unit or overall apparatus via software or hardware.

[0149] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code, which includes one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutively marked blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or the overall flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0150] In this embodiment, the processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0151] Memory can be used to store computer programs and / or modules. The processor implements various functions of the device / terminal equipment by running or executing the computer programs and / or modules stored in the memory, and by accessing data stored in the memory. Memory can mainly include a program storage area and a data storage area. The program storage area can store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area can store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). In addition, memory can include high-speed random access memory, and can also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0152] In this embodiment, if the modules / units integrated into the device / terminal equipment are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. Although this application discloses preferred embodiments as described above, it is not intended to limit this application. Any person skilled in the art can make possible changes and modifications without departing from the spirit and scope of this application. Therefore, the scope of protection of this application should be determined by the scope defined in the claims of this application.

[0153] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can 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, etc.) containing computer-usable program code.

[0154] Furthermore, it is clear that the word "comprising" does not exclude other units or steps. Multiple units, modules, or devices recited in the apparatus claims may also be implemented by a single unit or overall apparatus via software or hardware.

[0155] Although the present invention has been described in detail above with general descriptions and specific embodiments, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the spirit of the present invention fall within the scope of protection claimed by the present invention.

Claims

1. A method for constructing reliability cyclic operating conditions for electric drive systems based on user big data, characterized in that, The method for constructing reliable cyclic operating conditions for electric drive systems based on user big data includes: Obtain basic operational data for each vehicle in the area to be constructed; The basic operating data of each vehicle is divided to obtain at least one set of typical area data, and each set of typical area data includes at least one short-trip segment data. Perform the following operations on each set of typical region data: Based on the data of each short-stroke segment, obtain the fatigue damage information per unit mileage corresponding to each short-stroke segment; Statistical calculations were performed on the fatigue damage information obtained for each unit mileage, and the damage value at the 95th percentile was selected as the fatigue damage value per unit mileage for the typical area of ​​this group. Obtain typical regional cyclic operating conditions; The fatigue damage value per unit mileage based on cyclic operating conditions is obtained from the data of each short-stroke segment. The cycle coefficient is obtained based on the fatigue damage value per unit mileage under the cyclic operating conditions and the fatigue damage value per unit mileage in a typical region. The final cycle condition of the typical region data is obtained based on the cycle coefficient and the cycle condition of the typical region. The basic operating data includes vehicle speed information, motor torque information, motor speed information, operating time information, operating mileage information, acceleration information, and GPS signal; The basic operating data of each vehicle is divided to obtain at least one set of typical area data. Each set of typical area data includes at least one short-trip segment data, including: The basic operating data of each vehicle is divided to obtain the data of each short-trip segment of the vehicle; Each short-travel segment data is identified, and each short-travel segment data is assigned a typical region label. Short-travel segment data with the same typical region label constitute the typical region data. The step of obtaining the fatigue damage information per unit mileage corresponding to each short-stroke segment data based on each short-stroke segment data includes: Obtain the driving mileage data, speed data, and torque data from the short-stroke segment data; Construct failure mode models for each rotating component in the electric drive system; Fatigue damage analysis is conducted based on the Miner linear damage principle. The fatigue damage information per unit mileage corresponding to the short-stroke segment data is obtained based on the mileage data, speed data, and torque data.

2. The method for constructing a reliability cyclic operating condition for an electric drive system based on user big data as described in claim 1, characterized in that, The acquisition of typical regional cyclic operating conditions includes: Extract feature parameter data from each short-stroke segment data; Cluster analysis is performed on the characteristic parameter data in each short-stroke segment data, and the characteristic parameter data in each short-stroke segment data closest to the cluster center is selected for recombination to obtain the typical regional cyclic operating conditions.

3. The method for constructing reliability cyclic operating conditions for electric drive systems based on user big data as described in claim 2, characterized in that, The cycle coefficient, obtained based on the fatigue damage value per unit mileage under cyclic operating conditions and the fatigue damage value per unit mileage in a typical region, is obtained using the following formula: Cyclic coefficient k = fatigue damage value per unit mileage in a typical region / fatigue damage value per unit mileage based on cyclic operating conditions.

4. The method for constructing a reliability cyclic operating condition for an electric drive system based on user big data as described in claim 1, characterized in that, The process of identifying each short-travel segment data and assigning a typical region label to each short-travel segment data, wherein the typical region data comprises short-travel segment data with the same typical region label, includes: Perform the following operations on each short-stroke segment data: Acquire GPS signals from each short-range data segment; The location of the short-range data segment is obtained based on GPS signals. Obtain a typical region comparison table, which includes preset region locations and typical region labels corresponding to preset typical regions; Obtain the typical region label corresponding to the preset region location that is the same as the region where the short-trip data is located.

5. The method for constructing reliability cyclic operating conditions for electric drive systems based on user big data as described in claim 4, characterized in that, The basic operating data for each vehicle is divided to obtain data for each short-trip segment of that vehicle, including: Obtain vehicle startup information at the first point in time; Obtain the power-off information of the vehicle after startup at a second time point following the first time point; Obtain the vehicle's status information between the first and second time points; Based on the status information, determine whether the vehicle includes at least one idling segment and one moving segment between the first time point and the second time point. If so, obtain the basic operating data between the first time point and the second time point as a short-stroke segment data.

6. The method for constructing reliability cyclic operating conditions for electric drive systems based on user big data as described in claim 2, characterized in that, After extracting the feature parameter data from each short-stroke segment, the acquisition of typical regional cyclic operating conditions further includes: Principal component analysis is performed on the feature parameter data of each extracted short-stroke segment data to obtain the feature parameter data of each short-stroke segment data after filtering by principal component analysis.

7. A device for constructing reliability cyclic operating conditions for an electric drive system based on user big data, used in the method for constructing reliability cyclic operating conditions for an electric drive system based on user big data as described in any one of claims 1 to 6, characterized in that, The device for constructing reliable cyclic operating conditions of electric drive systems based on user big data includes: A basic operation data acquisition module is used to acquire the basic operation data of each vehicle in the area to be constructed. The segmentation module is used to segment the basic operating data of each vehicle to obtain at least one set of typical area data, and each set of typical area data includes at least one short-trip segment data. A single typical region operation module is provided, which is used to operate on each group of typical region data separately. The single typical region operation module includes the following modules: A fatigue damage information acquisition module for unit mileage is used to acquire fatigue damage information for unit mileage corresponding to each short-distance segment based on the data of each short-distance segment. The fatigue damage value acquisition module is used to perform statistical calculations on the acquired fatigue damage information per unit mileage, and select the 95th percentile damage value as the fatigue damage value per unit mileage for the typical area of ​​the group. A typical area cyclic operating condition acquisition module is used to acquire typical area cyclic operating conditions. The fatigue damage value acquisition module based on cyclic operating conditions is used to obtain the cycle coefficient based on the fatigue damage value per unit mileage under cyclic operating conditions and the fatigue damage value per unit mileage in a typical area. The cyclic coefficient acquisition module is used to acquire the cyclic coefficient based on the fatigue damage value per unit mileage under the cyclic working condition and the fatigue damage value per unit mileage in a typical area. The final cycle condition acquisition module is used to acquire the final cycle condition of the typical region data based on the cycle coefficient and the typical region cycle condition.

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