A working condition reproduction method for regional scene adaptability

Abstract

The application provides a working condition reproduction method for regional scene adaptability, relates to the technical field of vehicle engineering, and comprises the following steps: constructing a multi-dimensional second regional working condition database based on first regional working condition data; the first regional working condition data is data of an environment in which a target object is located; performing clustering and screening based on first regional test data and the second regional working condition database to obtain a target second regional working condition database; the first regional test data is data obtained by performing vehicle testing based on the first regional working condition data; performing vehicle testing based on the target second regional working condition database to obtain second regional test data; performing standard verification on the second regional test data to obtain a verification result; and the standard verification is performed based on a verification standard of the target object. The application solves the problem that there is a lack of working condition reproduction technology for target object working conditions in the existing vehicle engineering field by using the above method.

Description

Technical Field

[0001] This invention relates to the field of vehicle engineering technology, and in particular to a method for reproducing working conditions that is adaptable to regional scenarios. Background Technology

[0002] Due to significant differences in regional climate, altitude, road conditions, and driving habits, the distribution of testing environments in different regions varies considerably from that in existing databases. Current testing methods typically collect vehicle driving data only by selecting points or routes in representative areas of the region, making it difficult to accurately reflect the diverse needs of special operating conditions (such as continuous uphill climbing, heavy air conditioning loads, and multiple scenario combinations). Furthermore, the vehicle driving database lacks precise benchmarking of environmental parameters and sufficient coverage of multi-environment adaptability operating conditions, as well as a systematic data screening and operating condition construction approach that directly benchmarks against standards in other regions.

[0003] When conducting adaptive development in different regions, a large amount of real-vehicle data collection in the region is usually required. This is not only costly and time-consuming, but also often limited by the standards, resources, and market conditions of the target region, making it difficult to support the development of solutions in a large-scale and efficient manner. Simply borrowing existing data from the region also has limited guiding value for the accuracy of development and energy consumption and emission calibration due to inconsistencies in environmental and operating condition distribution.

[0004] In addition, existing operating condition development methods often focus on single characteristic parameters such as vehicle speed, lacking intelligent processing methods such as structured cluster analysis, target matching and screening, and multi-environment fusion. This makes it difficult to easily obtain equivalent operating conditions that are highly consistent with the target market in the region, resulting in significant shortcomings in energy consumption / emission optimization and bench verification. Summary of the Invention

[0005] This application provides a working condition reproduction method that is adaptable to regional scenarios, in order to solve the problem that there is a lack of working condition reproduction technology for target objects in the existing field of vehicle engineering.

[0006] The method includes:

[0007] A multi-dimensional second-region operating condition database is constructed based on the operating condition data of the first region; the operating condition data of the first region is the data of the environment in which the target object is located.

[0008] Clustering and filtering are performed based on the test data from the first region and the operating condition database from the second region to obtain the target operating condition database for the second region; the test data from the first region is the data obtained from vehicle testing based on the operating condition data from the first region.

[0009] Vehicle testing is performed based on the target second region operating condition database to obtain second region test data;

[0010] The test data from the second region is subjected to standard verification to obtain verification results; the standard verification is based on the verification standard of the target object.

[0011] The steps for constructing a multi-dimensional second-region operating condition database based on the first-region operating condition data include:

[0012] Obtain the first area operating condition data of the target object, and set the environmental characteristic parameters corresponding to the first area operating condition data as a comparison sequence;

[0013] Based on all driving data chains of vehicles in the second region, the corresponding environmental feature parameters in the operating condition data of the second region are extracted as reference sequences, and the reference sequences are dimensionless. The comparison sequences and the reference sequences all correspond to the environmental feature parameters of the same type of driving data chain.

[0014] The second regional operating condition database is obtained by filtering the reference sequence and the comparison sequence.

[0015] Preferably, the step of constructing a multi-dimensional second-region operating condition database based on the first-region operating condition data further includes:

[0016] The basic database is constructed by filtering based on the grey relational analysis method, the reference sequence, and the comparison sequence;

[0017] The basic database is filtered to obtain the second regional operating condition database; the database filtering includes filtering the basic database according to preset driving condition parameters.

[0018] Preferably, the step of filtering based on the grey relational analysis method, the reference sequence, and the comparison sequence, and constructing the basic database includes:

[0019] The grey relational analysis method was used to calculate the grey relational degree between the reference sequence and the comparison sequence.

[0020] The driving data chains are filtered according to a preset ratio based on the gray relational degree ranking, and the basic database is constructed.

[0021] Preferably, the formulas for the reference sequence and the comparison sequence include:

[0022] ;

[0023] ;

[0024] Where X0 is the reference sequence, X iFor comparison, i = 1, 2, 3, ..., n, k = 1, 2, 3, ..., 8, where n is the total number of driving data links in the second region environment;

[0025] The formulas for dimensionless processing include:

[0026] ;

[0027] in, These are the dimensionless environmental characteristic parameters;

[0028] The formulas for calculating the degree of correlation include:

[0029] ;

[0030] ;

[0031] in, Let be the correlation coefficient corresponding to the k-th environmental feature parameter of the i-th driving data link. This refers to the k-th environmental feature parameter of the dimensionless reference sequence. To compare the k-th environmental feature parameter of the i-th driving data link in the dimensionless comparison sequence, The resolution coefficient, Let represent the grey relational degree of the i-th driving data chain.

[0032] Preferably, the environmental characteristic parameters of the comparison series and the reference series include average temperature, 95% maximum temperature in summer, 5% minimum temperature in winter, average altitude, 95% maximum altitude, 5% minimum altitude, average humidity in summer, and average humidity in winter.

[0033] Preferably, the step of clustering and filtering based on the test data of the first region and the operating condition database of the second region includes:

[0034] Based on the vehicle speed-time curve of the test data in the first region, the test data in the first region is divided into several short-stroke segments in the first region, and the first region working condition characteristic parameters of the short-stroke segments in the first region are extracted.

[0035] The fuzzy C-means clustering method is used to cluster the short-stroke segments of the first region to obtain the low-speed, medium-speed and high-speed working condition cluster centers and the characteristic parameter distribution of the working condition cluster centers;

[0036] The second region operating condition database is divided into short-stroke segments of the second region, and the second region operating condition feature parameters of the short-stroke segments of the second region are extracted;

[0037] The fuzzy C-means clustering method is used to cluster the operating condition feature parameters of the second region with the operating condition cluster center as the center to obtain the target second region operating condition database.

[0038] Preferably, the step of conducting vehicle testing based on the target second area operating condition database includes:

[0039] Based on the target second region working condition database, and using short-stroke segments and random combination methods, an equivalent working condition sequence for the second region is generated.

[0040] The second region's equivalent operating condition sequence is adjusted to meet preset conditions to obtain the target equivalent operating condition. Meeting the preset conditions includes that the relative deviations of the average vehicle speed, speed standard deviation, acceleration / deceleration statistical characteristics, and acceleration / deceleration / uniform / idle speed ratio parameters in the second region's equivalent operating condition sequence are all no greater than 10%, and that the speed and acceleration distributions are... The divergence is minimal;

[0041] Based on the target equivalent operating conditions, the vehicle energy consumption or emission simulation platform is invoked to output simulated fuel consumption and emission data for the target vehicle model second by second.

[0042] Based on the simulation output for high energy consumption or high emission characteristic sections during acceleration, braking, and idling, the vehicle power system control parameters are adjusted to obtain the test data for the second region.

[0043] Preferably, the step of performing standard verification on the test data of the second region includes:

[0044] Based on the working condition database of the second target area, the working condition is expanded to obtain expanded working condition data, which includes slope working condition, high temperature cooling working condition, high cold heating working condition, compound harsh working condition and standard working condition of the first target area.

[0045] The second area test data is verified based on the extended operating condition data and the first area test requirements to obtain the verification results.

[0046] The preferred formula for cluster screening is as follows:

[0047] ;

[0048] ;

[0049] ;

[0050] ;

[0051] ;

[0052] in, The function is used to represent the membership degree of the m-th sample to the j-th cluster, where C is the number of clusters, J is the objective function, and z is the fuzzy index. For the m-th sample, For the j-th cluster center, The distance between the sample and the cluster center. Let be the membership degree of the m-th sample to the j-th class after the t-th iteration. This serves as the basis for judgment.

[0053] Preferably, the formula for vehicle testing is as follows:

[0054] ;

[0055] ;

[0056] in, This is a relative deviation. For the h-th operating condition characteristic parameter in the first region, For the h-th operating condition characteristic parameter in the second region, For the true distribution, For approximate distribution, For the divergence deviation of velocity and acceleration distributions, For velocity variables, It is acceleration.

[0057] As described above, this application provides a method for reproducing operating conditions adaptable to regional scenarios. The method includes constructing a multi-dimensional second regional operating condition database based on first regional operating condition data; the first regional operating condition data is data about the environment in which the target object is located; performing clustering and filtering based on the first regional test data and the second regional operating condition database to obtain a target second regional operating condition database; the first regional test data is data obtained from vehicle testing based on the first regional operating condition data; performing vehicle testing based on the target second regional operating condition database to obtain second regional test data; and performing standard verification on the second regional test data to obtain verification results; the standard verification is based on the verification standard of the target object. This application solves the problem of the lack of operating condition reproduction technology for target objects in the existing field of vehicle engineering through the above method. Attached Figure Description

[0058] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1This is a flowchart of a working condition reproduction method adapted to regional scenarios according to this application;

[0060] Figure 2 This is a flowchart illustrating the construction of a second regional working condition database in a working condition reproduction method adapted to regional scenarios according to this application.

[0061] Figure 3 This is a flowchart of clustering and filtering in a working condition reproduction method adapted to regional scenarios according to this application;

[0062] Figure 4 This is a flowchart of vehicle testing in a working condition reproduction method for regional scene adaptability according to this application;

[0063] Figure 5 This is a flowchart illustrating the standard verification process in a regional scenario-adaptive working condition reproduction method of this application.

[0064] Figure 6 This is a high-speed velocity-acceleration distribution diagram;

[0065] Figure 7 This is a speed-gradient-time graph. Detailed Implementation

[0066] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0067] It should be noted that the brief descriptions of terms in this application are only for the convenience of understanding the embodiments described below, and are not intended to limit the embodiments of this application. Unless otherwise stated, these terms should be understood in their ordinary and common meaning.

[0068] It should be noted that, in this application, the terms "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0069] Figure 1 This is a flowchart of a working condition reproduction method adapted to regional scenarios according to this application.

[0070] See Figure 1As can be seen, this embodiment provides a working condition reproduction method that is adaptable to regional scenarios, the method including:

[0071] S100, construct a multi-dimensional second-region operating condition database based on the first-region operating condition data; the first-region operating condition data is the data of the environment in which the target object is located.

[0072] Specifically, in this embodiment, environmental parameters of the target first region are acquired, including average temperature, 95% maximum temperature in summer, 5% minimum temperature in winter, average altitude, 95% maximum altitude, 5% minimum altitude, and average humidity in summer and winter, forming a test environment parameter set; based on the annual driving data chain of vehicles in the second region, corresponding environmental feature parameters are extracted as a comparison sequence, and the parameters are dimensionless; the grey relational analysis method is used to calculate the correlation degree between each driving data chain and the reference sequence, and the top 10% of typical driving data chains with the highest correlation degree are selected to establish the core basic database of the second region; based on the second-by-second data such as actual driving segments, air conditioning temperature and opening degree, a second region operating condition database containing multi-dimensional driving conditions is formed.

[0073] Figure 2 This is a flowchart illustrating the construction of a second regional working condition database in a working condition reproduction method adapted to regional scenarios according to this application.

[0074] See Figure 2 It can be seen that, further, in some embodiments, the step of constructing a multi-dimensional second-region operating condition database based on the first-region operating condition data includes:

[0075] S110, acquire the first area working condition data of the target object, and set the environmental characteristic parameters corresponding to the first area working condition data as a comparison sequence.

[0076] Specifically, in this embodiment, based on the climate, geography and other environmental parameters of the first region, the following environmental characteristic parameters are determined as a comparison sequence of the test environment: (1) average temperature, (2) 95% maximum temperature in summer, (3) 5% minimum temperature in winter, (4) average altitude, (5) 95% maximum altitude, (6) 5% minimum altitude, (7) average humidity in summer, and (8) average humidity in winter and summer.

[0077] The step of constructing a multi-dimensional second-region operating condition database based on the first-region operating condition data further includes:

[0078] S120, based on all driving data chains of vehicles in the second region, extract the corresponding environmental feature parameters from the operating condition data of the second region as a reference sequence, and perform dimensionless processing on the reference sequence; the comparison sequence and the reference sequence both correspond to the environmental feature parameters of the same type of driving data chain.

[0079] S130, Based on the grey relational analysis method, the reference sequence and the comparison sequence, a basic database is constructed.

[0080] Specifically, in this embodiment, the environmental characteristic parameters of the second region are extracted as reference sequences from the annual driving data chains of each vehicle within the second region (driving data chain: all driving data of a single vehicle in a single year), and dimensionless transformation is performed. The typical driving data chain of the second region environment is determined using the grey relational analysis method. The grey relational analysis process is shown in the following formula:

[0081] (1)

[0082] (2)

[0083] Where X0 is the reference sequence, X i For comparison, i = 1, 2, 3, ..., n, k = 1, 2, 3, ..., 8, where n is the total number of driving data links in the second region environment.

[0084] Dimensionless processing is performed, and the environmental characteristic parameters after dimensionless processing are obtained. As shown in the following formula:

[0085] (3)

[0086] The grey relational coefficient is calculated as shown in Equation (4), and the grey relational degree of each driving data chain is calculated as shown in Equation (5):

[0087] (4)

[0088] (5)

[0089] in, Let be the correlation coefficient corresponding to the k-th environmental feature parameter of the i-th driving data link. This refers to the k-th environmental feature parameter of the dimensionless reference sequence. To compare the k-th environmental feature parameter of the i-th driving data link in the dimensionless comparison sequence, The resolution coefficient, Let represent the grey relational degree of the i-th driving data chain.

[0090] The step of constructing a multi-dimensional second-region operating condition database based on the first-region operating condition data further includes:

[0091] S140, perform database filtering on the basic database to obtain the second regional operating condition database; the database filtering includes filtering the basic database according to preset driving condition parameters.

[0092] Specifically, in this embodiment, the selected typical driving data chain is extracted as a basic database, which includes the vehicle speed per second, driving gradient, air conditioning cooling temperature and opening degree, air conditioning heating temperature and opening degree, etc.

[0093] The method further includes:

[0094] S200, clustering and filtering are performed based on the test data of the first region and the operating condition database of the second region to obtain the target operating condition database of the second region; the test data of the first region is the data obtained by vehicle testing based on the operating condition data of the first region.

[0095] Specifically, in this embodiment, based on the vehicle speed-time curve of the test data in the first region, the test data is divided into short-stroke segments, and operating condition characteristic parameters such as average speed, speed standard deviation, average acceleration, acceleration standard deviation, average deceleration, deceleration standard deviation, acceleration ratio, deceleration ratio, constant speed ratio, and idle speed ratio are extracted. The fuzzy C-means (FCM) clustering method is used to cluster the segments in the first region to obtain the cluster centers and characteristic parameter distributions of the three operating conditions: low speed, medium speed, and high speed. The basic database of the second region is divided into short-stroke segments, the above operating condition characteristic parameters are extracted, and the FCM method is used to cluster the segments with the cluster centers of the first region as the center to obtain sample segments with a membership degree greater than or equal to 0.7, which are then included in the corresponding subsets to construct the target second region operating condition database of the three categories in the second region.

[0096] Figure 3 This is a flowchart of clustering and screening in a working condition reproduction method adapted to regional scenarios proposed in this application.

[0097] See Figure 3 It can be seen that, further, in some embodiments, the step of clustering and screening based on the test data of the first region and the operating condition database of the second region includes:

[0098] S210, based on the vehicle speed-time curve of the first area test data, the first area test data is divided into several first area short-stroke segments, and the first area working condition characteristic parameters of the first area short-stroke segments are extracted.

[0099] S220, the fuzzy C-means clustering method is used to cluster the short-stroke segments of the first region to obtain the low-speed, medium-speed and high-speed working condition cluster centers and the characteristic parameter distribution of the working condition cluster centers;

[0100] S230, the second region working condition database is divided into second region short-stroke segments, and the second region working condition feature parameters of the second region short-stroke segments are extracted;

[0101] S240, using the fuzzy C-means clustering method with the operating condition cluster center as the center, the operating condition feature parameters of the second region are clustered to obtain the target second region operating condition database.

[0102] Specifically, in this embodiment, the vehicle speed-time curve of the test data in the first region is extracted, divided into short-stroke segments, and operating condition characteristic parameters such as average speed, speed standard deviation, average acceleration, acceleration standard deviation, average deceleration, deceleration standard deviation, acceleration ratio, deceleration ratio, constant speed ratio, and idle speed ratio are calculated, as shown in Table 1, and a test database for the first region is created.

[0103] Table 1

[0104]

[0105] First, the operating condition characteristic parameters of the first region test database are dimensionless. Using the FCM fuzzy clustering method, the first region test database is divided into low-speed, medium-speed, and high-speed databases, and the cluster centers corresponding to the three types of databases in the first region are recorded. At the same time, the aforementioned characteristic parameters of the three types of databases in the first region, the velocity-acceleration distribution of the three types of databases in the first region, and the velocity-acceleration distribution of the overall database in the first region are calculated.

[0106] The main idea of ​​FCM (Fuzzy Clustering) is that each sample point can belong to multiple clusters, and there is a "membership degree" between each sample and the cluster center to represent the degree of its belonging. The main calculation process is as follows:

[0107] (1) Each sample has a certain "membership degree" to each cluster center, rather than belonging to only one cluster center. The membership degree is denoted as... Let represent the membership degree of the m-th sample to the j-th cluster, and C be the cluster number with a value of 3, satisfying:

[0108] (6)

[0109] (2) Calculate cluster centers: Based on the initial membership of all samples, calculate the position of each cluster center. The closer the points are, the higher their weight. The objective function J is shown in Equation (7). The cluster centers are calculated by weighting the membership of all samples as shown in Equation (8).

[0110] (7)

[0111] (8)

[0112] J is the objective function, and z is the fuzzy index. For the m-th sample, For the j-th cluster center, This represents the distance between the sample and the cluster center.

[0113] (3) Update membership: Based on the calculated cluster centers, update the membership of each sample to each cluster using formula (9):

[0114] (9)

[0115] (4) Repeat (2) and (3) until the convergence criterion (10) is satisfied:

[0116] (10)

[0117] Let be the membership degree of the m-th sample to the j-th class after the t-th iteration. This serves as the basis for judgment.

[0118] The driving data in the second region's basic database is divided into short-stroke segments, and operating condition characteristic parameters are calculated for each short-stroke segment.

[0119] Using the FCM fuzzy clustering method, the cluster centers corresponding to the three types of databases in the first region are used as the cluster centers for this clustering. The membership degree of each short-range segment sample in the three types of cluster centers is calculated. Segments with a membership degree ≥ 0.7 are retained and included in the subsets of each cluster type to form the three types of core databases in the second region. The membership degree of each segment is shown in Table 2.

[0120] Table 2

[0121]

[0122] The method further includes:

[0123] S300, based on the target second area working condition database, vehicle testing is performed to obtain second area test data.

[0124] Specifically, in this embodiment, based on the second region operating condition database of three types of targets, a short-stroke segment + random combination method is used to generate the second region equivalent target operating condition sequence; the equivalent operating condition sequence is controlled to satisfy the following: the relative deviation (MAPE) of parameters such as average vehicle speed, speed standard deviation, acceleration and deceleration statistical characteristics, and acceleration / deceleration / uniform / idle speed ratio is not greater than 10%, and the speed-acceleration distribution is... Minimize divergence; based on the developed equivalent operating conditions, call the vehicle energy consumption / emission simulation platform to output simulated fuel consumption and emission data of the target model second by second; according to the simulation output, adjust the vehicle power system control parameters for high energy consumption / high emission characteristic sections such as acceleration, braking, and idling to achieve optimized calibration of fuel consumption and emissions.

[0125] Figure 4This is a flowchart of vehicle testing in a working condition reproduction method adapted to regional scenarios according to this application.

[0126] See Figure 4 It is understood that, furthermore, in some embodiments, the step of conducting vehicle testing based on the target second area operating condition database includes:

[0127] S310, Based on the target second region working condition database, and using short-stroke segments and random combination methods, generate the second region equivalent working condition sequence;

[0128] S320, adjust the second region equivalent operating condition sequence to meet preset conditions to obtain the target equivalent operating condition; meeting the preset conditions includes that the relative deviations of the average vehicle speed, speed standard deviation, acceleration / deceleration statistical characteristics, and acceleration / deceleration / uniform / idle speed ratio parameters in the second region equivalent operating condition sequence are all no greater than 10%, and the speed and acceleration distributions are... The divergence is minimal;

[0129] S330, based on the target equivalent operating condition, call the vehicle energy consumption or emission simulation platform to output simulated fuel consumption and emission data of the target vehicle every second;

[0130] S340, based on the simulation output for high energy consumption or high emission characteristic sections of acceleration, braking, and idling, adjust the vehicle power system control parameters to obtain the test data for the second region.

[0131] Specifically, in this embodiment, based on the constructed database of three types of target working conditions in the second region, the working condition feature parameters of all short-stroke segments are extracted, and the short-stroke + random combination method is used to construct the equivalent target working condition sequence of the second region, with the duration controlled at around 1800s.

[0132] Collaborative matching of key characteristic parameters such as average vehicle speed, speed standard deviation, average acceleration / deceleration, acceleration / deceleration deviation, and acceleration / deceleration / uniform / idle ratio is performed. This ensures that the relative deviations between the low-speed, medium-speed, high-speed, and overall characteristic parameters of the developed second-region operating condition curves and the characteristic parameters of the three types of databases and the overall database of the first region in section 2.1 are less than 10%, and the speed-acceleration distribution... The formula for calculating the relative deviation MAPE, which has the smallest divergence deviation, is shown in (11). The formula for divergence calculation is shown in equation (12), and the schematic diagram of high-speed velocity-acceleration distribution is shown in the figure. Figure 6 As shown.

[0133] (11)

[0134] in, For the h-th operating condition characteristic parameter in the first region, This refers to the h-th operating condition characteristic parameter in the second region.

[0135] (12)

[0136] in, It is the true distribution, that is, the velocity-acceleration distribution of the first region database. It is an approximate distribution, representing the velocity-acceleration distribution of the truck. The smaller the divergence value, the more similar the two distributions are; the larger the value, the greater the difference. When the two distributions are completely identical, The divergence is 0. For velocity variables, It is acceleration.

[0137] Based on the operating condition curves of the second region obtained through development, the vehicle model of the first region developed in this study was used to conduct second-by-second simulation tests using a vehicle energy consumption / emission simulation platform, and the time series data of vehicle fuel consumption and emissions under this operating condition were output.

[0138] Based on the second-by-second fuel consumption and emission data output from the operating condition simulation, the high fuel consumption operating points and high emission characteristic periods are analyzed. Calibration methods (such as fine-tuning the parameters of vehicle control strategies during acceleration, regenerative braking, and idle speed control) are used to optimize the vehicle's power distribution, energy management, and control logic of key components, thereby achieving a reduction in overall fuel consumption and emission levels.

[0139] The method further includes:

[0140] S400, standard verification is performed on the test data of the second region to obtain the verification result; the standard verification is based on the verification standard of the target object.

[0141] Specifically, in this embodiment, special operating condition curves and composite operating condition test scenarios are expanded and constructed by combining typical extreme operating conditions such as slope, high temperature and low temperature, air conditioning load and multiple environment combinations; according to the target area standard, the corresponding standard operating conditions are loaded, and bench verification is carried out based on bench / wheel hub test to ensure that all key indicators meet the target operating condition standard requirements.

[0142] Figure 5 This is a flowchart of the standard verification process in a working condition reproduction method adapted to regional scenarios proposed in this application.

[0143] See Figure 5 It can be seen that, further, in some embodiments, the step of performing standard verification on the test data of the second region includes:

[0144] S410, Based on the target second area working condition database, the working condition is expanded to obtain expanded working condition data;

[0145] S420, the second area test data is verified based on the extended operating condition data and the first area test requirements to obtain the verification result.

[0146] Specifically, in this embodiment, based on the aforementioned working condition development, the testing and analysis are further expanded to include slope scenarios, high temperature and low temperature scenarios, air conditioning load scenarios, and combined extreme scenarios, and are constructed respectively:

[0147] Slope conditions:

[0148] Based on the operating conditions developed for S310, the corresponding gradient in the actual driving data of this operating condition is added to form a speed-gradient-time operating condition. The average ambient temperature of the first region is added as a driving scenario. The operating conditions are as follows: Figure 7 As shown; based on the working conditions developed by S310, the average gradients of 3%, 6% and 9% are increased to form more intense speed-gradient-time working conditions, and the average ambient temperature of the first area is increased as a driving scenario.

[0149] High-temperature refrigeration conditions:

[0150] Based on the operating conditions developed by S310, the first area's ambient temperature is set at 95% of the annual average maximum temperature and summer average humidity as a driving scenario under extreme high-temperature conditions in summer. The air conditioning cooling temperature and opening degree of the first 600 seconds are set to 95% of the air conditioning cooling temperature and opening degree of the second area's core database, and the air conditioning cooling temperature and opening degree of the last 1200 seconds are set to 50% of the air conditioning cooling temperature and opening degree of the second area's core database.

[0151] High-altitude heating conditions:

[0152] Based on the operating conditions developed by S310, the first area's ambient temperature is set at 5% of the annual minimum temperature in winter, and the average humidity in winter is set as a driving scenario under extreme high temperature conditions in winter. The air conditioning heating temperature and opening degree in the first 600 seconds are set to 5% of the air conditioning cooling temperature and 95% of the opening degree in the core database of the second area, and in the last 1200 seconds, they are set to 50% of the air conditioning heating temperature and opening degree in the core database of the second area.

[0153] Complex and harsh working conditions:

[0154] By combining typical operating conditions such as slope, temperature, and air conditioning, multi-dimensional composite operating conditions are formed for extreme actual driving scenarios, such as the 9% slope climbing high temperature cooling operating condition.

[0155] Standard operating conditions for the first target area:

[0156] Strictly adhere to the requirements of the target area and conduct bench (drum) tests under the corresponding standard operating conditions. All tests should be conducted in accordance with the control boundary conditions specified in Zone 1, including ambient temperature, humidity, tire pressure, fuel type, and battery SOC.

[0157] Based on the aforementioned operating condition curves, vehicle dynamics simulation tools are used to simulate the target vehicle's power performance, hill-climbing ability, and acceleration response under various operating conditions. Based on the simulation results, the power output distribution strategy, thermal management strategy, and air conditioning load system control strategy are calibrated. At the same time, the target vehicle's first-region standard operating condition test conditions should be verified on a rotary test bench. The test results should be compared with the limits and technical requirements of the target first-region standard operating condition. The fuel consumption / electricity consumption limits, exhaust emission limits, and power and acceleration response indicators should all meet the target market operating condition standards.

[0158] This embodiment has the following advantages:

[0159] By intelligently filtering and processing relevant data from the driving database of the target first region based on the environmental characteristic parameters of the first region, the equivalent vehicle operating conditions that meet the requirements of the first region are efficiently constructed, thereby improving the adaptability of vehicle models in this region to the target region market.

[0160] For ease of explanation, the above description has been provided in conjunction with specific embodiments. However, the discussion in some embodiments is not intended to be exhaustive or to limit the embodiments to the specific forms disclosed above. Various modifications and variations can be obtained based on the above teachings. The selection and description of the above embodiments are for the purpose of better explaining the contents of this disclosure, thereby enabling those skilled in the art to better utilize the embodiments.

Claims

1. A method for reproducing working conditions adaptable to regional scenarios, characterized in that, The method includes: A multi-dimensional second-region operating condition database is constructed based on the operating condition data of the first region; the operating condition data of the first region is the data of the environment in which the target object is located. Clustering and filtering are performed based on the test data from the first region and the operating condition database from the second region to obtain the target operating condition database for the second region; the test data from the first region is the data obtained from vehicle testing based on the operating condition data from the first region. Vehicle energy consumption and emissions tests are conducted based on the target second region operating condition database to obtain second region test data. The test data from the second region is subjected to standard verification to obtain verification results; the standard verification is based on the verification standard of the target object. The steps for constructing a multi-dimensional second-region operating condition database based on the first-region operating condition data include: Obtain the first area operating condition data of the target object, and set the environmental characteristic parameters corresponding to the first area operating condition data as a comparison sequence; Based on all driving data chains of vehicles in the second region, the corresponding environmental feature parameters in the operating condition data of the second region are extracted as reference sequences, and the reference sequences are dimensionless. The comparison sequences and the reference sequences all correspond to the environmental feature parameters of the same type of driving data chain. Based on the correlation between the reference sequence and the comparison sequence, typical driving data chains are selected from all driving data chains of vehicles in the second region to obtain a basic database. Database filtering is then performed on the basic database to obtain the operating condition database of the second region. The step of clustering and filtering based on the test data of the first region and the operating condition database of the second region includes: Based on the vehicle speed-time curve of the test data in the first region, the test data in the first region is divided into several short-stroke segments in the first region, and the first region working condition characteristic parameters of the short-stroke segments in the first region are extracted. Clustering algorithms are used to cluster short-stroke segments in the first region to obtain low-speed, medium-speed, and high-speed operating condition cluster centers and the characteristic parameter distribution of the operating condition cluster centers; The second region operating condition database is divided into short-stroke segments of the second region, and the second region operating condition feature parameters of the short-stroke segments of the second region are extracted; The clustering algorithm is used to cluster the working condition feature parameters of the second region with the working condition cluster center as the center, and the fragments with the membership degree higher than the threshold are retained to obtain the working condition database of the target second region.

2. The method for reproducing working conditions adaptable to regional scenarios according to claim 1, characterized in that, The step of constructing a multi-dimensional second-region operating condition database based on the first-region operating condition data further includes: The basic database is constructed by filtering based on the grey relational analysis method, the reference sequence, and the comparison sequence; The basic database is filtered to obtain the second regional operating condition database; the database filtering includes filtering the basic database according to preset driving condition parameters.

3. The method for reproducing working conditions adaptable to regional scenarios according to claim 2, characterized in that, The steps of filtering based on the grey relational analysis method, the reference sequence, and the comparison sequence, and constructing the basic database include: The grey relational analysis method was used to calculate the grey relational degree between the reference sequence and the comparison sequence. The driving data chains are filtered according to a preset ratio based on the gray relational degree ranking, and the basic database is constructed.

4. The method for reproducing working conditions adaptable to regional scenarios according to claim 3, characterized in that, The formulas for the reference sequence and the comparison sequence include: ； ； Where X0 is the reference sequence, X i For comparison, i = 1, 2, 3, ..., n, k = 1, 2, 3, ..., 8, where n is the total number of driving data links in the second region environment; The formulas for dimensionless processing include: ； in, These are the dimensionless environmental characteristic parameters; The formulas for calculating the degree of correlation include: ； ； in, Let be the correlation coefficient corresponding to the k-th environmental feature parameter of the i-th driving data link. This refers to the k-th environmental feature parameter of the dimensionless reference sequence. To compare the k-th environmental feature parameter of the i-th driving data link in the dimensionless comparison sequence, The resolution coefficient, Let represent the grey relational degree of the i-th driving data chain.

5. The method for reproducing working conditions adaptable to regional scenarios according to claim 2, characterized in that, The environmental characteristic parameters of the comparison series and the reference series include average temperature, 95% maximum temperature in summer, 5% minimum temperature in winter, average altitude, 95% maximum altitude, 5% minimum altitude, average humidity in summer, and average humidity in winter.

6. The method for reproducing working conditions adaptable to regional scenarios according to claim 1, characterized in that, The steps for conducting vehicle testing based on the target second region operating condition database include: Based on the target second region working condition database, and using short-stroke segments and random combination methods, an equivalent working condition sequence for the second region is generated. The second region equivalent working condition sequence is adjusted to meet preset conditions to obtain the target equivalent working condition; meeting the preset conditions includes that the relative deviations of the average vehicle speed, speed standard deviation, acceleration and deceleration statistical characteristics, and acceleration / deceleration / uniform / idle speed ratio parameters in the second region equivalent working condition sequence are all no greater than 10%, and the KL divergence of the speed and acceleration distribution is minimized. Based on the target equivalent operating conditions, the vehicle energy consumption or emission simulation platform is invoked to output simulated fuel consumption and emission data for the target vehicle model second by second. Based on the simulation output for high energy consumption or high emission characteristic sections during acceleration, braking, and idling, the vehicle power system control parameters are adjusted to obtain the test data for the second region.

7. The method for reproducing working conditions adaptable to regional scenarios according to claim 1, characterized in that, The steps for standard verification of the test data in the second region include: Based on the working condition database of the second target area, the working condition is expanded to obtain expanded working condition data, which includes slope working condition, high temperature cooling working condition, high cold heating working condition, compound harsh working condition and standard working condition of the first target area. The second area test data is verified based on the extended operating condition data and the first area test requirements to obtain the verification results.

8. The method for reproducing working conditions adaptable to regional scenarios according to claim 1, characterized in that, The formula for cluster screening is as follows: ； ； ； ； ； in, The function is used to represent the membership degree of the m-th sample to the j-th cluster, where C is the number of clusters, J is the objective function, and z is the fuzzy index. For the m-th sample, For the j-th cluster center, The distance between the sample and the cluster center. Let be the membership degree of the m-th sample to the j-th class after the t-th iteration. This serves as the basis for judgment.

9. A method for reproducing working conditions adaptable to regional scenarios according to claim 6, characterized in that, The formula for vehicle testing is as follows: ； ； in, This is a relative deviation. For the h-th operating condition characteristic parameter in the first region, For the h-th operating condition characteristic parameter in the second region, For the true distribution, For approximate distribution, For the divergence deviation of velocity and acceleration distributions, For velocity variables, It is acceleration.

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