A development method of vehicle speed-slope compound test working condition cycle

Abstract

The application provides a kind of vehicle speed-slope composite test working condition cycle development method, comprising the following steps: S1, processing and screening to vehicle actual road data and feature calculation, by establishing the working condition segment library of division speed range, distinguish the driving scene of vehicle differentiation;S2, establish the rules that can keep and the consistency of the length characteristics of working condition segment library, and meet the standard of subsequent segment combination process, merge speed zone range, complete the design of working condition interval and interval cycle structure;S3, by distance verification, select the motion segment that meets the design result for high-speed working condition interval.The application has beneficial effects: establish the working condition segment library of division speed range and design working condition interval according to the working condition characteristics of each library, so that the test working condition can truly and effectively reflect the time distribution of different working condition scenarios and the actual road speed-slope situation represented by each scenario, thereby realizing the differentiation of vehicle differentiation driving scene.

Description

Technical Field

[0001] This invention belongs to the field of transportation, and in particular relates to a development method for a vehicle speed-gradient composite test cycle. Background Technology

[0002] Vehicle testing conditions are a crucial and fundamental technology in the automotive industry. They form the basis for testing specifications and limits that evaluate vehicle performance, such as emissions and energy consumption, and also serve as the benchmark for vehicle technical calibration and optimization.

[0003] During actual road driving, gradient has a significant impact on energy consumption, emissions, and other performance characteristics. Currently, the fuel consumption and emissions tests conducted on laboratory wheel turrets in my country use speed cycles, which are simply speed-time curves. These results fail to reflect the impact of gradient on vehicle performance. It is necessary to incorporate gradient cycles that change with speed over time, developing test conditions that include both speed and gradient information to achieve a more comprehensive evaluation of vehicle performance.

[0004] However, the correlation between real-time gradient and speed information collected by vehicles under diverse driving scenarios is low. Most existing gradient cycle development processes are continuations of single-speed cycle development techniques, failing to address the complex coupling between speed, gradient, and time data. While the output conditions include speed and gradient information, they cannot effectively reflect the combined speed-gradient characteristics of the road. Summary of the Invention

[0005] In view of this, the present invention aims to propose a method for developing a vehicle speed-gradient composite test cycle to address the problem that existing test cycles cannot reflect the impact of road gradient on vehicle performance. By processing and analyzing data collected from actual vehicle driving on roads, a library of test cycle segments reflecting speed-gradient road characteristics is established. Based on this, the speed-gradient composite test cycle can realistically and effectively reflect the combined impact of speed and gradient on vehicle performance. Using this as a test cycle, a more comprehensive and accurate evaluation of vehicle fuel consumption, emissions, and other performance characteristics can be achieved through laboratory wheel rotation tests.

[0006] To achieve the above objectives, the technical solution of the present invention is implemented as follows:

[0007] A method for developing a vehicle speed-gradient composite test cycle includes the following steps:

[0008] S1. Process, filter, and perform feature calculations on actual road data of vehicles. By establishing a working condition segment library with different speed ranges, differentiate the driving scenarios of vehicles.

[0009] S2. Establish rules that can maintain consistency with the duration characteristics of the working condition segment library and meet the standardization of subsequent segment combination processes, merge speed ranges, and complete the design of the working condition interval and interval loop structure.

[0010] S3. Through distance verification, select motion segments that meet the design results for high-speed operating conditions;

[0011] S4. Through speed-gradient composite feature verification, select motion segments that meet the design results for non-high-speed working conditions and combine them to establish a set of alternative cycles for working conditions.

[0012] S5. Select the optimal combination of cycles across intervals through the composite chi-square test and construct the test condition cycle.

[0013] Furthermore, the establishment of the working condition segment library for the speed range mentioned in step S1 includes the following steps:

[0014] A1. Perform frequency transformation on the collected data to form second-by-second speed-gradient data; segment the second-by-second data to establish a motion and idling segment library: the time when the speed exceeds 1km / h is taken as the starting point and the time when it decreases to 1km / h is taken as the ending point, which is defined as a motion segment; from the end point of the motion segment until the speed exceeds 1km / h again, it is defined as the idling segment corresponding to that motion segment.

[0015] A2. Supplement, optimize, and filter the fragment library established in A1 to establish a working condition fragment library;

[0016] A3. Divide the working condition segment library established in A2 into different speed ranges to reflect the speed-gradient working condition characteristics under different scenarios.

[0017] Furthermore, in step A2, the establishment of the working condition segment library includes the following steps:

[0018] A21. Based on the vehicle speed v per second of the motion segment i Calculate the corresponding acceleration a per second (km / h). i (m / s 2 The calculation formula is as follows:

[0019] a i =(v i+1 -v i-1 ) / 7.2

[0020] A22. Segment selection based on speed requirements: If the maximum speed v of a motion segment... max At speeds of <5km / h or >130km / h, the maximum acceleration a max >4.5m / s 2or minimum deceleration a min <-4.5m / s 2 If so, then the motion segment and its corresponding idle segment will be removed;

[0021] A23. For the selected motion segments, a robust local weighting method is used to filter and smooth the second-by-second slope (%) to reduce the impact of interference signals on the slope.

[0022] Segments are filtered based on slope requirements: if the maximum slope s of a motion segment... max >15% or minimum slope min <-15%; if the number of seconds with a slope of 0 in a segment accounts for more than 5% of the segment duration, then the motion segment and its corresponding idle segment will be removed.

[0023] A24. Based on the second-by-second slope S of the filtered motion segments i (%) Calculate the rate of change of slope per second ΔS i (%), the calculation formula is as follows:

[0024] ΔS i =(S i+1 -S i-1 ) / 2.

[0025] Furthermore, in step A3, the establishment of the working condition segment library for different speed ranges includes the following steps:

[0026] A31. For the motion segments filtered by A2, calculate their velocity and slope characteristic parameters:

[0027] Average speed v m (km / h): The average speed of all sampling points in the running segment;

[0028] Exercise duration l sm (s): The total duration in seconds of the running segment;

[0029] Idle time l si (s): The total duration in seconds of the corresponding idle segment;

[0030] Acceleration time l + sm (s): Acceleration a in the segment ≥ 0.1 m / s² 2 The number of sampling points in seconds;

[0031] Average acceleration a + m (m / s 2 ): The average acceleration of all acceleration sampling points in the segment;

[0032] deceleration time l －sm (s): In the segment, a ≤ -0.1 m / s 2 The number of sampling points in seconds;

[0033] Average deceleration a － m (m / s 2 ): The average acceleration of all deceleration sampling points in the segment;

[0034] Uphill ratio p up (%): The ratio of the number of seconds to the duration of motion at sampling points with a slope S≥0.1;

[0035] Average uphill slope s + m (%): The average slope of all uphill time sampling points in the segment;

[0036] downhill ratio p down (%): The ratio of the number of seconds to the duration of motion for sampling points with a slope S≤-0.1;

[0037] Average downhill slope s － m (%): The average slope of all uphill time sampling points in the segment;

[0038] A32, Average vehicle speed v m Motion segments with speeds ≥ i*5km / h and < (i+1)*5km / h (i = 0, 1, 2…20) and their corresponding idle speed segments are classified into the i-th speed range segment library.

[0039] A33. For each range library i formed by the partitioning in A32, according to N in the library... i The overall operating condition characteristics corresponding to the feature parameters of each segment are calculated using the following formula:

[0040] Average exercise duration (s):

[0041] Average vehicle speed (km / h):

[0042] Average acceleration (m / s²) 2 ):

[0043] Average deceleration (m / s) 2 ):

[0044] Uphill percentage (%):

[0045] Average uphill slope (%):

[0046] Downhill percentage (%):

[0047] Average uphill slope (%):

[0048] Furthermore, the design of the operating condition interval and interval cycle structure in step S2 includes the following steps:

[0049] B1: Based on the total duration L of motion segments in the velocity range database formed in S1 i (s) and the total duration of all idling segments Idlet(s), calculate the time proportion of each speed range and idling condition;

[0050] B2. Based on the proportions obtained in B1 and the cycle duration of the operating conditions, calculate the lv values ​​for each speed range. i (s) and the cycle duration of idling condition Idlec(s); combined with the average duration L0 of the motion segment i Calculate the number of initial motion segments N0 contained in the range loop;

[0051] B3. Establish rules to merge speed ranges, forming k working condition intervals, and cumulatively calculate the number of motion segments Nc that the j-th (j=1,2…k) interval cycle should contain. j and loop duration lc0 j Accumulated Nc j Calculate the total number of motion segments NC in the loop, and the total number of idle segments NC+1;

[0052] B4. Establish a working condition interval segment library, and calculate the overall working condition characteristics of each interval segment library based on the characteristic parameters of the segments in the library.

[0053] Furthermore, in step B3, the specific rules are as follows:

[0054] Based on N0 of each speed range from low to high, determine whether to merge adjacent speed ranges;

[0055] For speeds below 30 km / h, if 1.75 ≤ N0 ≤ 3, then this speed range is defined as a working condition interval, and Nc is specified. j =2; if N0>3, then Nc is defined as 2. j =4;

[0056] For speeds above 30 km / h, if 1.75 ≤ N0 ≤ 3.5, then this speed range is defined as a working condition interval, and Nc is specified. j =2; if N0>3.5, then Nc is defined as 2. j =4;

[0057] If 0.5 ≤ N0 < 1.75, then this speed range is successively merged with subsequent ranges until the cumulative N0 value of the merged range is > 1.75. The merged range is then defined as a single operating condition interval, and Nc is specified. j =2;

[0058] If N0 < 0.5, then this speed range is merged with all subsequent ranges to determine the high-speed operating range (j = k), and Nc is specified. k =1.

[0059] Furthermore, the selection of high-speed operating condition motion segments that conform to the design results in step S3 includes the following steps:

[0060] C1. Define the operating condition interval with the highest average vehicle speed in S2 as the high-speed operating condition interval. Select the sample with the smallest dimensionless distance between the feature parameters and the overall operating condition features of the interval segment library as the operating condition cycle of the high-speed interval.

[0061] C2. Based on the duration of the selected segment and the duration of the idling condition, adjust the duration of the cycle for other operating condition intervals.

[0062] Furthermore, the establishment of the candidate cycle set for the non-high-speed operating condition range in step S4 includes the following steps:

[0063] D1. Form a set of two-segment combinations that meet the requirements;

[0064] D2. Form a set of four segments that meet the requirements;

[0065] The formation of the set of two segments that meet the requirements in step D1 includes the following steps:

[0066] D11: For each non-high-speed operating interval, samples in the interval segment library are randomly selected after being combined in pairs to form a large number of two-segment combinations.

[0067] D12: Calculate the velocity-slope composite characteristic parameters of each combined sample, and select the set of two combined samples whose consistency with the overall working condition characteristics of the interval reservoir meets the requirements.

[0068] D13: For a work condition interval that is specified in B2 as containing two motion segments, select two combination samples from the combination set in D12 whose duration and interval work condition cycle duration match the requirements, and establish a candidate cycle set for the work condition interval.

[0069] The formation of the four-segment combination set that meets the requirements in step D2 includes the following steps:

[0070] D21: For the working condition interval that is specified in B2 as containing 4 motion segments, the samples in the combination set of D12 are combined in pairs again to form a large number of four-segment combinations.

[0071] D22: For a work condition interval that is specified in B2 as containing 4 motion segments, select a four-segment combination sample whose duration and interval work condition cycle duration meet the requirements from the combination set in D21, and establish a candidate cycle set for that work condition interval.

[0072] Furthermore, the construction of the test condition cycle in step S5 includes the following steps:

[0073] E1: Samples are drawn from the candidate operating condition cycle set of each non-high-speed operating condition range and randomly combined across the range to form a set of several non-high-speed operating condition cycles.

[0074] E2: Calculate the second-by-second simultaneous distribution of velocity (v)-acceleration (a) and slope (s)-slope change rate (Δs) for each cycle sample in the set, as well as the overall VA and S-ΔS distribution of the non-high-speed working condition motion segment library; based on this, perform a composite chi-square test of velocity and slope: select the cycle sample with the smallest chi-square value as the working condition cycle in the non-high-speed range.

[0075] E3: Build speed-slope cycle.

[0076] Furthermore, the construction of the speed-gradient cycle in step E3 includes the following steps:

[0077] E31: Arrange the motion segments contained in the E2 cycle in order of speed from low to high, and add idling segments of fixed duration between them to construct a test condition cycle in the non-high speed range;

[0078] E32: After the non-high-speed interval cycle, add the high-speed condition segment from S3 and a fixed-duration idle segment to construct the speed-gradient composite test condition cycle.

[0079] E33: Output operating cycle, calculate and verify operating characteristics.

[0080] Compared with existing technologies, the development method of a vehicle speed-gradient composite test cycle described in this invention has the following advantages:

[0081] (1) The development method of a vehicle speed-gradient composite test cycle described in this invention establishes a test segment library with different speed ranges and designs test intervals based on the test characteristics of each library, so that the test conditions can truly and effectively reflect the time distribution of different test scenarios and the actual road speed-gradient conditions represented by each scenario, thereby realizing the differentiation of vehicle driving scenarios.

[0082] (2) The development method for a vehicle speed-gradient composite test cycle described in this invention has high operational complexity and resource consumption in the random combination process of segments. To address this, in the test cycle design step, a reasonable speed range merging rule is formulated. Under the premise of maintaining high consistency with the duration characteristics of the test cycle segment library, the number of motion segments included in the high-speed interval cycle is specified as 1, and the number of segments in other intervals is specified as 2 or 4. This standardizes the above random combination process into a pairwise combination of 1 or 2 times, thereby improving the efficiency and stability of test cycle development.

[0083] (3) The development method of the vehicle speed-gradient composite test cycle described in this invention has a low correlation between real-time speed and gradient data. In order to solve the coupling problem between them, a speed-gradient composite feature verification process based on the pairwise combination of the above segments is added in the step of establishing the alternative cycle set of the test interval, so as to ensure that the interval cycle can meet the consistency requirements of the speed-gradient composite test feature. Attached Figure Description

[0084] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0085] Figure 1 This is a schematic diagram illustrating the process of establishing a segment library of test conditions for different speed intervals in a development method for a vehicle speed-gradient composite test cycle according to an embodiment of the present invention.

[0086] Figure 2 This is a schematic diagram of the design process of the working condition interval and the interval cycle structure of the development method of the vehicle speed-gradient composite test working condition cycle according to an embodiment of the present invention.

[0087] Figure 3 This is a schematic diagram of the speed range merging process of a development method for a vehicle speed-gradient composite test cycle according to an embodiment of the present invention;

[0088] Figure 4 This is a schematic diagram of a speed-gradient cycle example in the high-speed operating range of a vehicle speed-gradient composite test cycle development method according to an embodiment of the present invention.

[0089] Figure 5 This is a schematic diagram of the construction process of the speed-gradient test cycle of the development method of the vehicle speed-gradient composite test cycle according to an embodiment of the present invention.

[0090] Figure 6This is a schematic diagram of a vehicle speed-gradient composite test cycle example, which is a development method for a vehicle speed-gradient composite test cycle according to an embodiment of the present invention. Detailed Implementation

[0091] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0092] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0093] Definitions:

[0094] Local weighting method: Taking a point x as the center, extract a data segment of length frac before and after it. Perform a weighted linear regression on this segment using a weight function w. Let (x,y^)(x,\hat{y})(x,y) be the center value of this regression line, where y^\hat{y}y^ represents the corresponding value of the fitted curve. For all n data points, n weighted regression lines can be drawn. The line connecting the center values ​​of each regression line forms the Lowess curve for this data segment.

[0095] Dimensionless transformation: By replacing some or all of the units in an equation involving physical quantities with a suitable variable, the purpose of simplifying experiments or calculations can be achieved.

[0096] Chi-square test: The degree of deviation between the actual observed value and the theoretical inferred value of a statistical sample. The degree of deviation between the actual observed value and the theoretical inferred value determines the size of the chi-square value. If the chi-square value is larger, the deviation between the two is greater; conversely, the deviation between the two is smaller. If the two values ​​are completely equal, the chi-square value is 0, indicating that the theoretical value is completely consistent.

[0097] like Figures 1 to 6 As shown, a method for developing a vehicle speed-gradient composite test cycle includes the following steps:

[0098] S1. Process, filter, and perform feature calculations on actual road data of vehicles. By establishing a working condition segment library with different speed ranges, differentiate the driving scenarios of vehicles.

[0099] S2. Establish rules that can maintain the consistency of the duration characteristics of the working condition segment library and meet the standardization of the subsequent segment combination process, merge the speed zone range, and complete the design of the working condition interval and interval loop structure.

[0100] S3. Through distance verification, select motion segments that meet the design results for high-speed operating conditions;

[0101] S4. Through speed-gradient composite feature verification, select motion segments that meet the design results for non-high-speed working conditions and combine them to establish a set of alternative cycles for working conditions.

[0102] S5. Select the optimal combination of cycles across intervals through the composite chi-square test and construct the test condition cycle.

[0103] The speed-gradient composite vehicle test cycle developed according to the method provided by this invention can more effectively reflect the speed-gradient conditions of vehicles during actual road driving. Using this as a test cycle, a more comprehensive and accurate evaluation of vehicle performance, such as fuel consumption and emissions, can be achieved through laboratory wheel rotation tests, thereby providing technical support for government standard setting, enterprise vehicle product development, and test design.

[0104] Another objective of this invention is to propose a method for developing a vehicle speed-gradient composite test cycle: establishing a cycle library of test segments for different speed ranges to improve the representativeness of the cycle for speed-gradient characteristics under various test scenarios; formulating speed range merging rules to improve the efficiency and stability of the construction; and designing an interval cycle structure based on segment combinations to meet the consistency requirements between the cycle and the speed-gradient composite test characteristics.

[0105] The operation of establishing the working condition segment library with different speed ranges in step S1 includes frequency transformation and segmentation of the collected data, supplementation, optimization and screening of the segment library, as well as further feature calculation and speed range division.

[0106] In this embodiment, the following steps are included:

[0107] A1. Perform frequency transformation and segmentation on the actual road data collected from vehicles to establish a library of motion and idling segments;

[0108] The test cycle is characterized by speed (km / h) and gradient (%) data that change every second. In order to meet the requirements of the test cycle construction, the real-time data collected by the vehicle on the actual road needs to be frequency transformed to form speed-gradient data corresponding to each second.

[0109] The test cycle is constructed by combining segments. To this end, the aforementioned second-by-second data is segmented: the time (in seconds) when the speed exceeds 1 km / h is taken as the starting point, and the time when the speed decreases to 1 km / h is taken as the ending point, defined as one motion segment; the period from the end of the motion segment until the speed exceeds 1 km / h again is defined as the corresponding idle segment. Furthermore, the duration of each motion segment should be ≥5s and ≤3600s, and the duration of the idle segment should be ≤200s; otherwise, the segment is discarded.

[0110] A2. Supplement, optimize, and filter the fragment library to establish a working condition fragment library;

[0111] Based on the vehicle speed v per second of the motion segment i (km / h) Calculate the corresponding acceleration per second a i (m / s 2 The calculation formula is as follows:

[0112] a i =(v i+1 -v i-1 ) / 7.2

[0113] Segments are selected based on speed requirements: if the maximum speed v of the motion segment... max At speeds of <5km / h or >130km / h, the maximum acceleration a max >4.5m / s 2 or minimum deceleration a min <-4.5m / s 2 If so, then the motion segment and its corresponding idle segment will be removed.

[0114] For the selected motion segments, a robust local weighting method (rlowess) is used to filter and smooth the second-by-second slope (%) to reduce the impact of interference signals on the slope.

[0115] Segments are filtered based on slope requirements: if the maximum slope s of a motion segment... max >15% or minimum slope min <-15%; if the number of seconds with a slope of 0 in a segment accounts for more than 5% of the segment duration, then the motion segment and its corresponding idle segment will be removed.

[0116] Based on the second-by-second slope S of the selected motion segments i (%) Calculate the rate of change of slope per second ΔS i (%), the calculation formula is as follows:

[0117] ΔS i =(S i+1 -S i-1 ) / 2

[0118] A3. Calculate the speed and slope characteristic parameters for each segment:

[0119] Average speed v m (km / h): The average speed of all sampling points in the running segment;

[0120] Exercise duration l sm (s): The total duration in seconds of the running segment;

[0121] Idle time l si (s): The total duration in seconds of the corresponding idle segment;

[0122] Acceleration time l + sm (s): Acceleration a in the segment ≥ 0.1 m / s² 2 The number of sampling points in seconds;

[0123] Average acceleration a + m (m / s 2 ): The average acceleration of all acceleration sampling points in the segment;

[0124] deceleration time l － sm (s): In the segment, a ≤ -0.1 m / s 2 The number of sampling points in seconds;

[0125] Average deceleration a － m (m / s 2 ): The average acceleration of all deceleration sampling points in the segment;

[0126] Uphill ratio p up (%): The ratio of the number of seconds to the duration of motion at sampling points with a slope S≥0.1;

[0127] Average uphill slope s + m (%): The average slope of all uphill time sampling points in the segment;

[0128] downhill ratio p down (%): The ratio of the number of seconds to the duration of motion for sampling points with a slope S≤-0.1;

[0129] Average downhill slope s － m (%): The average slope of all uphill time sampling points in the segment.

[0130] Average vehicle speed v m Motion segments ≥ i*5km / h and < (i+1)*5km / h (i=0,1,2……20) and their corresponding idle speed segments are classified into the i-th speed range segment library. For each range library i, based on N in the library... i The overall operating condition characteristics corresponding to the feature parameters of each segment are calculated using the following formula:

[0131] Average exercise duration (s):

[0132] Average vehicle speed (km / h):

[0133] Average acceleration (m / s²) 2 ):

[0134] Average deceleration (m / s) 2 ):

[0135] Uphill percentage (%):

[0136] Average uphill slope (%):

[0137] Downhill percentage (%):

[0138] Average uphill slope (%):

[0139] The design of the operating condition interval and interval cycle structure in step S2 includes: calculating the cycle duration of each speed range and idling condition and the number of initial segments it contains; merging adjacent speed ranges to form operating condition intervals; and calculating the overall operating condition characteristics of the segment library of each operating condition interval.

[0140] In this embodiment, the following steps are included:

[0141] B1: Calculate the time ratio of each speed range and the idling condition based on the total duration of motion segments in each speed range library and the total duration of all idling segments.

[0142] B2. Based on the proportions obtained in B1 and the cycle duration of the operating conditions, calculate the cycle duration of each speed range and the idling condition; and based on the average duration of the motion segments, calculate the number of initial motion segments included in the range cycle.

[0143] In this embodiment, the output cycle duration is set to 3600s. The total duration L of the motion segments included in each speed range is calculated. i (s) and the total duration Idlet(s) of all idling segments in the library, calculate lv for each speed range. i (s) Cycle duration and idle condition cycle duration Idlec(s); the calculation formula is as follows:

[0144] lv i =3600×L i / (Idlet+∑ i＝1…20 L i )

[0145] Idlec=3600×Idlet / (Idlet+∑ i＝1…20 L i )

[0146] Use loop duration lv i Divide by the average movement time L0 iThis yields the number of original motion segments N0 that the loop should contain within that range;

[0147] B3. Merge adjacent speed ranges to form k operating condition intervals, and cumulatively calculate the number of motion segments Nc that the j-th (j=1,2…k) interval cycle should contain. j and loop duration lc0 j .

[0148] In the subsequent construction process, samples need to be randomly combined from the motion segment library of each working condition range formed in this step to form a large number of Nc j The set of fragment combinations of each sample serves as the basis for constructing the working conditions. The above random combination process is highly complex, consuming significant time and computer memory, and its complexity increases with Nc. j The growth rate is exponential.

[0149] To this end, rules were established to determine whether to merge adjacent speed ranges based on N0 for each speed range, from low to high. While maintaining a high degree of consistency with the initial number of segments N0, the Nc of each merged operating condition interval was... j The value is limited to 1, 2, or 4. This simplifies the above random combination process into one or two pairwise combinations, improving the efficiency and stability of working condition development.

[0150] For speeds below 30 km / h, if 1.75 ≤ N0 ≤ 3, then this speed range is defined as a working condition interval, and Nc is specified. j =2; if N0>3, then Nc is defined as 2. j =4;

[0151] For speeds above 30 km / h, if 1.75 ≤ N0 ≤ 3.5, then this speed range is defined as a working condition interval, and Nc is specified. j =2; if N0>3.5, then Nc is defined as 2. j =4;

[0152] If 0.5 ≤ N0 < 1.75, then this speed range is merged with subsequent ranges sequentially until the cumulative N0 value of the merged ranges > 1.75. The merged range is then defined as a single operating condition interval, and Nc is specified. j =2;

[0153] If N0 < 0.5, then this speed range is merged with all subsequent ranges to determine the high-speed operating range (j = k), and Nc is specified. k =1.

[0154] Furthermore, accumulate Nc j The total number of motion segments in the calculation cycle is NC, and the total number of idle segments is NC+1.

[0155] B4. Establish a working condition interval segment library, and calculate the overall working condition characteristics of each interval segment library based on the characteristic parameters of the segments in the library.

[0156] The selection of motion segments in the high-speed operating condition interval that conforms to the design results in step S3 includes: selecting the motion segment with the highest degree of conformity to the operating condition characteristics in the interval segment library as the loop of the high-speed operating condition interval; and adjusting the operating condition loop duration of other operating condition intervals according to the running duration of the selected segment.

[0157] In this embodiment, the following steps are included:

[0158] C1. The high-speed operating condition interval contains a motion segment. In order to ensure that the high-speed operating condition cycle can effectively reflect the speed-gradient situation of the vehicle when driving at high speed on actual roads, it is necessary to select the motion segment sample with the highest consistency between the operating condition characteristics and the high-speed interval segment library as the high-speed operating condition cycle.

[0159] The dimensionless distance D between the sample and the working condition database is calculated as a quantitative indicator for evaluating the degree of fit. The calculation formula is as follows:

[0160]

[0161] The motion segment with the smallest D value is selected as the operating cycle in the high-speed range.

[0162] C2, based on the runtime of the high-speed segment (lc) h (s) Adjust the cycle duration for other operating conditions, calculated using the following formula:

[0163] Operating condition interval j cycle duration (s): lc j =lc0 j ×(3600-lc h -Idlet) / ∑ j＝1…k-1 lc0 j

[0164] Divide the idle cycle duration (Idlec) by the total number of idle segments (NC+1) and round it to obtain the fixed duration of the idle segments that make up the cycle.

[0165] The establishment of the non-high-speed operating condition range candidate cycle set in step S4 includes: for Nc j The operating intervals of 2 form a set of two segments that meet the requirements; for Nc j The operating condition interval of 4 forms a set of four segments that meet the requirements.

[0166] In order to ensure that the loops can effectively reflect the actual road speed-gradient conditions represented by the working conditions of each section, a candidate loop set is generated by selecting samples that simultaneously meet the speed-gradient composite working condition feature matching requirements from the above set through composite feature verification.

[0167] In this embodiment, the following steps are included:

[0168] D1. Form a set of two-segment combinations that meet the requirements;

[0169] Using MATLAB's `combntns` function to calculate N from the operating condition interval library `j` j The segments form a total of (N) j *(N j The complete set of -1) / 2) two-segment combination samples is further obtained by applying the randperm function to randomly sample min(1500000, N) samples from the set. j *(N j -1) / 20) combination samples; calculate the deviation between the seven characteristic parameters of speed and slope of each sample and the overall working condition characteristics of the corresponding interval reservoir, as a quantitative indicator for evaluating the consistency, screen out the combinations in which the deviation of each parameter is within 8%, and accumulate the combination time.

[0170] The number of motion segments that should be included, Nc j For a working interval of 2, select the duration and the cycle duration of the working interval (lc). j Two segments with a deviation within 5% are combined to form a candidate cycle set for the operating condition range.

[0171] D2. Form a set of four segments that meet the requirements.

[0172] The number of motion segments that should be included, Nc j For a work condition interval of 4, based on the set of two-segment combination samples that meet the above feature matching requirements, further random pairwise combinations are performed to form a large number of four-segment combinations, and the combination duration is calculated by summing these combinations. The duration and work condition interval cycle time (lc) are then selected. j Four-segment combination samples with deviations within 5% form the alternative operating condition cycle set for this interval.

[0173] The construction of the speed-gradient composite test cycle in step S5 includes: forming a set of non-high-speed test cycles by random combination across intervals; selecting the optimal non-high-speed test cycle by a composite chi-square test of speed and gradient; constructing and verifying the speed-gradient composite test cycle.

[0174] This embodiment includes the following steps:

[0175] E1: Samples are drawn from the candidate operating condition cycle set of each non-high-speed operating condition range and randomly combined across the range to form a set of several non-high-speed operating condition cycles.

[0176] E2: For each cyclic sample in the set, calculate its position within each span of [5km / h-0.5m / s]. 2 The ratio of the number of seconds in the velocity (v) - acceleration (a) interval to its total duration is the combined velocity distribution va for that cycle. Similarly, calculate the overall distribution VA for all motion segments under non-high-speed conditions.

[0177] For each cycle sample in the set, calculate the ratio of the number of seconds in each interval of slope(s) - slope change rate(Δs) with a span of [1%-0.2%] to its total duration; this is the simultaneous slope distribution sΔs for that cycle. Similarly, calculate the overall distribution SΔS for all non-high-speed motion segments.

[0178] Based on the above working condition interval distribution, the chi-square values ​​of the velocity and slope distributions for each cycle sample are calculated using the following formulas:

[0179]

[0180] (i=1,2,3…22, j=1,2,3…18)

[0181]

[0182] (i=1,2,3…28, j=1,2,3…22)

[0183] The sample with the smallest sum of the above chi-square values ​​is selected as the operating cycle in the non-high-speed range.

[0184] E3: Constructing a speed-gradient test cycle: Arrange the motion segments included in the above cycle from low to high speed, and add fixed-duration idling segments at the beginning and end and between segments to form a speed-gradient test cycle in the non-high-speed range. Furthermore, add a high-speed test segment and a fixed-duration idling segment after the cycle to complete the construction of the vehicle speed-gradient composite test cycle.

[0185] Example 1

[0186] The invention method will be further described in detail below with reference to the accompanying drawings.

[0187] (1) Data acquisition and preprocessing

[0188] A fleet of vehicles was established in a typical city, and real-time speed-gradient data was collected through long-term free-roaming on actual roads, serving as the basis for constructing operating conditions. The data collection frequency was 4 Hz, meaning four sets of speed-gradient data were collected per second, varying synchronously over time. The arithmetic mean of the four sets of data was calculated as the speed-gradient data for that second. The above second-by-second data was segmented to form motion and idling speed library segments; these segments were further supplemented, optimized, and filtered to establish an operating condition segment library. The processing flow is as follows: Figure 1 As shown.

[0189] In this embodiment, the data is shown in Table 1:

[0190] Table 1: Data Acquisition and Preprocessing Status

[0191]

[0192] (2) Speed ​​range division

[0193] Based on the average vehicle speed, the motion segments and their corresponding idle speed segments are divided into different speed range segment libraries, and the overall operating condition characteristics of each library are calculated. The calculation results of this embodiment are shown in Table 2:

[0194] Table 2: Overall Operating Characteristics of Segment Libraries in Each Speed ​​Range

[0195]

[0196] (3) Operating condition range design

[0197] In this embodiment, the design process of the working condition interval and the interval cycle structure is as follows: Figure 2 As shown: Calculate the cycle duration and number of initial segments for each speed range and idling condition, and merge adjacent speed ranges into a condition interval according to rules. The merging process is as follows: Figure 3 As shown.

[0198] The interval design process and results of this embodiment are shown in Table 3: The speed ranges of 40-45 km / h and 45-50 km / h, as well as the speed range above 50 km / h, were merged, as shown in the bold rows in the table. This resulted in 10 operating condition intervals. A segment library of operating condition intervals was established, and the overall operating condition characteristics of the intervals were calculated based on the characteristic parameters of the segment samples in the library.

[0199] Table 3: Design Process and Results of Operating Condition Ranges

[0200]

[0201] The operating cycle contains a total of 29 motion segments and a total of 30 idle segments.

[0202] (4) Optimal motion segment selection in high-speed operating conditions

[0203] In this embodiment, the high-speed operating condition interval represents a speed range of 50–105 km / h. The sample with the smallest dimensionless distance between its speed and the eight feature parameters in the operating condition interval library is selected from the interval motion segment library as the cycle of the high-speed operating condition interval. The speed-gradient cycle of the segment is as follows: Figure 4 As shown in Table 4, the characteristic parameters of the segment and the high-speed operating condition are as follows:

[0204] Table 4. Characteristics of high-speed operating conditions

[0205]

[0206] The operating cycle duration of the other 9 operating condition intervals was adjusted according to the duration of the selected segment. The results after adjustment are shown in column 8 of Table 3. The fixed duration of the idling segment constituting the cycle is 29s.

[0207] (5) Establishment of alternative cycle set for non-high-speed operating range

[0208] Based on the above design results, for the four operating ranges of 0–5, 30–35, 35–40, and 40–50 km / h, samples from the motion segment library are randomly selected after pairwise combinations to form a large number of two-segment combinations. Combination samples that meet the requirements are selected through feature and duration verification to form a candidate operating range cycle set.

[0209] For the five operating conditions of 10–15, 15–20, 20–25, 25–30 and 5–10 km / h, based on the two-segment combination sample set whose corresponding feature matching degree meets the requirements, pairwise random combinations are performed again to form a large number of four-segment combinations; further, combination samples that meet the duration requirements are selected to form a cycle set of candidate operating conditions for the interval.

[0210] The number of samples in the candidate cycle set for each non-high-speed operating condition range is shown in column 9 of Table 3.

[0211] (6) Cross-interval cyclic random combination and composite chi-square test

[0212] Samples were randomly drawn from nine candidate cycle sets of non-high-speed driving conditions and combined across intervals to form several candidate cycle sets of non-high-speed driving conditions (0–50 km / h). The simultaneous velocity distribution va and the simultaneous gradient distribution sΔs of each cycle sample in the set were calculated.

[0213] The overall VA distribution of the motion segment library in the 0–50 km / h driving range was calculated. The results are shown in Table 5.

[0214] Table 5 Speed ​​distribution in the 0–50 km / h operating range

[0215]

[0216] The overall distribution SΔS of the motion segment library in the 0–50 km / h driving range is calculated, and the results are shown in Table 6:

[0217] Table 6. Gradient distribution in the 0–50 km / h driving range

[0218]

[0219] Based on the overall VA and SΔS distributions mentioned above, a composite chi-square test is performed on the cyclic sample set, and the sample with the smallest chi-square value is selected as the operating cycle in the non-high-speed range.

[0220] (7) Construction and verification of speed-gradient driving cycle

[0221] Arrange the 28 motion segments contained in the above cycle in ascending order of speed. Add 29-second idling segments at the beginning, end, and between segments. Add a high-speed driving segment and an idling segment after the cycle to complete the construction of the vehicle speed-gradient composite test cycle. The process from selecting and combining segments in each interval to constructing the cycle is as follows: Figure 5 As shown.

[0222] The vehicle speed-gradient composite test cycle example constructed in this embodiment is characterized by a curve showing the change of speed (km / h)-gradient (%) over time (s), lasting 3616s and consisting of 10 test intervals, including 29 motion segments and 30 idle segments, as shown below. Figure 6 As shown.

[0223] The key feature parameters of the calculated sample were compared and verified with the working condition features collected by the fleet from the actual road database. The results are shown in Table 7.

[0224] Table 7. Characteristics of Operating Condition Examples

[0225]

[0226] The deviation of each parameter in the table is within 5%, indicating that the working condition example constructed in this instance matches the actual road data collected by the vehicle well, and can effectively reflect the speed-gradient situation of the vehicle when driving on actual roads. This verifies the rationality and operability of the vehicle speed-gradient composite test cycle working condition development method provided by this invention.

[0227] Those skilled in the art will recognize that the units and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0228] In the several embodiments provided in this application, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the division of units described above is merely a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. The aforementioned units may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of the present invention according to actual needs.

[0229] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

[0230] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for developing a cyclic testing process for vehicle speed-gradient composite test conditions, characterized in that: Includes the following steps: S1. Process, filter, and perform feature calculations on actual road data of vehicles. By establishing a working condition segment library with different speed ranges, differentiate the driving scenarios of vehicles. S2. Establish rules that can maintain consistency with the duration characteristics of the working condition segment library and meet the standardization of subsequent segment combination processes, merge speed ranges, and complete the design of the working condition interval and interval loop structure. S3. Through distance verification, select motion segments that meet the design results for high-speed operating conditions; S4. Through speed-gradient composite feature verification, select motion segments that meet the design results for non-high-speed working conditions and combine them to establish a set of alternative cycles for working conditions. S5. Select the optimal combination of cycles across intervals through the composite chi-square test and construct the test condition cycle; The design of the operating condition interval and interval cycle structure in step S2 includes the following steps: B1: Based on the total duration L of motion segments in the velocity range database formed in S1 i (s) and the total duration of all idling segments Idlet(s), calculate the time proportion of each speed range and idling condition; B2. Based on the proportions obtained in B1 and the cycle duration of the operating conditions, calculate the lv values ​​for each speed range. i (s) and the cycle duration of idling condition Idlec(s); combined with the average duration L0 of the motion segment i Calculate the number of initial motion segments N0 contained in the range loop; B3. Establish rules to merge speed ranges, forming k working condition intervals, and cumulatively calculate the number of motion segments Nc that the j-th (j=1,2…k) interval cycle should contain. j and loop duration lc0 j Accumulated Nc j Calculate the total number of motion segments NC in the loop, and the total number of idle segments NC+1; B4. Establish a working condition interval segment library, and calculate the overall working condition characteristics of each interval segment library based on the characteristic parameters of the segments in the library; The construction of the test cycle in step S5 includes the following steps: E1: Samples are drawn from the candidate operating condition cycle set of each non-high-speed operating condition range and randomly combined across the range to form a set of several non-high-speed operating condition cycles. E2: Calculate the second-by-second simultaneous distribution of velocity (v)-acceleration (a) and slope (s)-slope change rate (Δs) for each cycle sample in the set, as well as the overall VA and S-ΔS distribution of the non-high-speed working condition motion segment library; based on this, perform a composite chi-square test of velocity and slope: select the cycle sample with the smallest chi-square value as the working condition cycle in the non-high-speed range. E3: Build speed-slope cycle.

2. The vehicle speed-gradient composite test condition cycle development method according to claim 1, characterized in that: The establishment of the working condition segment library for the speed range mentioned in step S1 includes the following steps: A1. Perform frequency transformation on the collected data to form second-by-second speed-gradient data; segment the second-by-second data to establish a motion and idling segment library: the time when the speed exceeds 1km / h is taken as the starting point and the time when it decreases to 1km / h is taken as the ending point, which is defined as a motion segment; from the end point of the motion segment until the speed exceeds 1km / h again, it is defined as the idling segment corresponding to that motion segment. A2. Supplement, optimize, and filter the fragment library established in A1 to establish a working condition fragment library; A3. Divide the working condition segment library established in A2 into different speed ranges to reflect the speed-gradient working condition characteristics under different scenarios.

3. The vehicle speed-gradient composite test condition cycle development method according to claim 2, characterized in that: In step A2, the establishment of the working condition segment library includes the following steps: A21. Based on the vehicle speed v per second of the motion segment i Calculate the corresponding acceleration a per second (km / h). i (m / s 2 The calculation formula is as follows: and i =(in i+1 -v i-1 ) / 7.2 A22. Segment selection based on speed requirements: If the maximum speed v of a motion segment... max At speeds of <5km / h or >130km / h, the maximum acceleration a max >4.5m / s 2 or minimum deceleration a min <-4.5m / s 2 If so, then the motion segment and its corresponding idle segment will be removed; A23. For the selected motion segments, a robust local weighting method is used to filter and smooth the second-by-second slope (%) to reduce the impact of interference signals on the slope. Segments are filtered based on slope requirements: if the maximum slope s of a motion segment... max >15% or minimum slope min <-15%; if the number of seconds with a slope of 0 in a segment accounts for more than 5% of the segment duration, then the motion segment and its corresponding idle segment will be removed. A24. Based on the second-by-second slope S of the filtered motion segments i (%) Calculate the rate of change of slope per second ΔS i (%), the calculation formula is as follows: ΔS i =(S i+1 -S i-1 ) / 2。 4. The vehicle speed-gradient composite test condition cycle development method according to claim 2, characterized in that: In step A3, the establishment of the working condition segment library for different speed ranges includes the following steps: A31. For the motion segments filtered by A2, calculate their velocity and slope characteristic parameters: Average speed v m (km / h): The average speed of all sampling points in the running segment; Exercise duration l sm (s): The total duration in seconds of the running segment; Idle time l si (s): The total duration in seconds of the corresponding idle segment; Acceleration time l + sm (s): Acceleration a in the segment ≥ 0.1 m / s² 2 The number of sampling points in seconds; Average acceleration a + m (m / s 2 ): The average acceleration of all acceleration sampling points in the segment; deceleration time l － sm (s): In the segment, a ≤ -0.1 m / s 2 The number of sampling points in seconds; Average deceleration a － m (m / s 2 ): The average acceleration of all deceleration sampling points in the segment; Uphill ratio p up (%): The ratio of the number of seconds to the duration of motion at sampling points with a slope S≥0.1; Average uphill slope s + m (%): The average slope of all uphill time sampling points in the segment; downhill ratio p down (%): The ratio of the number of seconds to the duration of motion for sampling points with a slope S≤-0.1; Average downhill slope s － m (%): The average slope of all uphill time sampling points in the segment; A32, Average vehicle speed v m Motion segments with speeds ≥ i*5km / h and < (i+1)*5km / h (i = 0, 1, 2…20) and their corresponding idle speed segments are classified into the i-th speed range segment library. A33. For each range library i formed by the partitioning in A32, according to N in the library... i The overall operating condition characteristics corresponding to the feature parameters of each segment are calculated using the following formula: Average exercise duration (s): Average vehicle speed (km / h): Average acceleration (m / s²) 2 ): Average deceleration (m / s) 2 ): Uphill percentage (%): Average uphill slope (%): Downhill percentage (%): Average uphill slope (%):

5. The development method of a vehicle speed-gradient composite test cycle according to claim 1, characterized in that: In step B3, the specific rules are as follows: Based on N0 of each speed range from low to high, determine whether to merge adjacent speed ranges; For speeds below 30 km / h, if 1.75 ≤ N0 ≤ 3, then this speed range is defined as a working condition interval, and Nc is specified. j =2; if N0>3, then Nc is defined as 2. j =4; For speeds above 30 km / h, if 1.75 ≤ N0 ≤ 3.5, then this speed range is defined as a working condition interval, and Nc is specified. j =2; if N0>3.5, then Nc is defined as 2. j =4; If 0.5 ≤ N0 < 1.75, then this speed range is successively merged with subsequent ranges until the cumulative N0 value of the merged range is > 1.

75. The merged range is then defined as a single operating condition interval, and Nc is specified. j =2; If N0 < 0.5, then this speed range is merged with all subsequent ranges to determine the high-speed operating range (j = k), and Nc is specified. k =1; The above rules ensure that, while maintaining a high degree of consistency with the initial number of segments N0, the Nc of each merged interval will be... j By limiting the process to three values, 1, 2, or 4, the random combination process of subsequent interval segments is standardized into one or two pairwise combination operations, thereby improving the efficiency and stability of working condition development.

6. The development method of a vehicle speed-gradient composite test cycle according to claim 1, characterized in that: The selection of high-speed operating condition motion segments that conform to the design results in step S3 includes the following steps: C1. Define the operating condition interval with the highest average vehicle speed in S2 as the high-speed operating condition interval. Select the sample with the smallest dimensionless distance between the feature parameters and the overall operating condition features of the interval segment library as the operating condition cycle of the high-speed interval. C2. Based on the duration of the selected segment and the duration of the idling condition, adjust the duration of the cycle for other operating condition intervals.

7. The development method of the vehicle speed-gradient composite test cycle according to claim 1, characterized in that: The establishment of the candidate cycle set for the non-high-speed operating condition range in step S4 includes the following steps: D1. Form a set of two-segment combinations that meet the requirements; D2. Form a set of four segments that meet the requirements; The formation of the set of two segments that meet the requirements in step D1 includes the following steps: D11: For each non-high-speed operating interval, samples in the interval segment library are randomly selected after being combined in pairs to form a large number of two-segment combinations. D12: Calculate the velocity-slope composite characteristic parameters of each combined sample, and select the set of two combined samples whose consistency with the overall working condition characteristics of the interval reservoir meets the requirements. D13: For a work condition interval that is specified in B2 as containing two motion segments, select two combination samples from the combination set in D12 whose duration and interval work condition cycle duration match the requirements, and establish a candidate cycle set for that work condition interval. The formation of the four-segment combination set that meets the requirements in step D2 includes the following steps: D21: For the working condition interval that is specified in B2 as containing 4 motion segments, the samples in the combination set of D12 are combined in pairs again to form a large number of four-segment combinations. D22: For a work condition interval that is specified in B2 as containing 4 motion segments, select a four-segment combination sample whose duration and interval work condition cycle duration meet the requirements from the combination set in D21, and establish a candidate cycle set for that work condition interval.

8. The development method of a vehicle speed-gradient composite test cycle according to claim 1, characterized in that, The construction of the speed-gradient cycle in step E3 includes the following steps: E31: Arrange the motion segments contained in the E2 cycle in order of speed from low to high, and add idling segments of fixed duration between them to construct a test condition cycle in the non-high speed range; E32: After the non-high-speed interval cycle, add the high-speed condition segment from S3 and a fixed-duration idle segment to construct the speed-gradient composite test condition cycle. E33: Output operating cycle, calculate and verify operating characteristics.

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