A method for processing data of power response performance of vehicle uniform speed reacceleration

Abstract

The application discloses a kind of vehicle uniform speed reacceleration power response performance data processing methods.Process is: obtaining the test data of uniform speed reacceleration, filtering processing is carried out to test data, the final effective data is obtained by screening after filtering data, and test performance index is obtained based on the final effective data;The screening includes splitting data, time screening, full throttle screening, starting speed screening, bend data elimination, stable speed screening and stable opening degree screening.The application can enable user to select a group of test data for carrying out automobile performance evaluation according to the key performance index in the at least one group of test data, so as to avoid the user to import and tedious selection of one group of data in turn, and also can generate automobile performance evaluation report according to the selected test data, without manual data pasting and arrangement, so as to effectively improve the processing efficiency of automobile test data.

Description

Technical Field

[0001] This invention belongs to the field of data processing technology, specifically relating to a method for processing data on the dynamic response performance of a vehicle at constant speed and then accelerating. Background Technology

[0002] Multiple tests are often conducted on the same vehicle model under the same operating conditions, but only the data that meets the requirements is typically selected for vehicle performance evaluation. Data selection requires engineers to sequentially select, import, and retrieve data, and the key indicators obtained after retrieval have nowhere to be stored, often requiring recording on another medium (such as pen and paper). This method is extremely tiring for staff when processing large amounts of data and is prone to errors, leading to reduced work efficiency. Existing data processing methods involve manually processing key indicators and filling them into an Excel spreadsheet; selecting the corresponding variable names for the data needed for evaluation and pasting this data into the spreadsheet is inefficient. Manually organizing and formatting the data in the spreadsheet before pasting it into a document to create an evaluation report is also inefficient. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the aforementioned background technology and provide a method for processing vehicle constant speed re-acceleration dynamic response performance data.

[0004] The technical solution adopted in this invention is: a method for processing vehicle constant speed re-acceleration power response performance data, which involves acquiring test data of constant speed re-acceleration, filtering the test data, selecting the filtered data to obtain the final effective data, and calculating the test performance index based on the final effective data; the selection includes splitting data, time selection, full throttle selection, starting speed selection, cornering data removal, stable speed selection, and stable opening degree selection.

[0005] Furthermore, the data splitting involves removing data with a 0% accelerator pedal opening from the filtered data, and then splitting the remaining discontinuous data into multiple data groups.

[0006] Furthermore, the time filtering involves removing data from the split data that is shorter than the test condition time.

[0007] Furthermore, the full throttle screening involves selecting data from the time-filtered data where the maximum throttle pedal opening percentage is greater than a set value to form the stabledate1 data group.

[0008] Furthermore, the starting speed screening process is as follows: select the starting time of each stabledate1 data as t1, set t2 = t1 + 0.5s, determine the speed corresponding to each data at time t2, if the speed is not zero, it indicates that the stabledate1 data is valid, and combine all valid stabledate1 data into a stabledate2 data group.

[0009] Furthermore, the process of removing curve data is as follows: select the moment of maximum acceleration in each stabledate2 data as t3, and extract the data between time t1 and time t3 in each stabledate2 data to form a stabledate3 data group;

[0010] Select the accelerator pedal opening at time t3 in each stabledate3 data and denote it as maxped. Determine whether maxped is greater than the threshold. If it is, it means that the stabledate3 data is valid. Combine all valid stabledate3 data into a stabledate4 data group.

[0011] Furthermore, the process of stable opening degree screening is as follows:

[0012] The inflection point of the throttle opening change when the stabledate4 data group accelerates at a constant speed and then accelerates at full throttle is recorded as t4. Data between time t1 and time t4 in each stabledate4 data group are extracted to form the stabledate5 data group.

[0013] In the stabledate5 data group, find each data segment with a duration of T2 in each stabledate5 data, and the variance of the accelerator pedal opening percentage in the data segment is less than the opening set value to obtain the T2 data segment group. In the T2 data segment group, find the smallest start time among all data and record it as t5 and the largest end time among all data and record it as t6. Extract the data between time t5 and time t6 in each stabledate5 data to obtain the stabledate6 data group.

[0014] Furthermore, the process of selecting stable vehicle speed is as follows:

[0015] In the stabledate6 data set, find a data segment of duration T3 within each stabledate6 data set, where the variance of the vehicle speed in the data segment is less than the set vehicle speed value, thus obtaining the T3 data segment group. In the T3 data segment group, find the smallest start time among all data and denote it as time t7, and the largest end time among all data and denote it as time t8. Extract the data between time t7 and time t8 from each stabledate6 data set to obtain the stabledate7 data set.

[0016] Furthermore, the test performance metrics include re-acceleration response hysteresis, calculated using the following formula.

[0017] RRD = (S1 + S2 - S3) / Ramax

[0018] S1 = 0.5 * (t9 - t4) * Ramax

[0019] S2=(t3-t9)*Ramax

[0020]

[0021] Where RRD represents the re-acceleration response hysteresis; S1 represents the ideal acceleration area from the initial moment of re-acceleration to the initial moment of maximum throttle; S2 represents the ideal acceleration area from the initial moment of maximum throttle to the moment of maximum acceleration; S3 represents the actual acceleration area from the initial moment of re-acceleration to the moment of maximum acceleration; t4 represents the initial moment of re-acceleration throttle opening; t9 represents the moment of re-acceleration target throttle inflection point opening; t3 represents the moment of maximum re-acceleration; a represents the actual acceleration; and Ramax represents the maximum re-acceleration of the vehicle.

[0022] Furthermore, before filtering the test data, the identification instructions corresponding to the test data Excel matrix are first identified, and then the original data is imported. The identification instructions include the test time header, the accelerator pedal opening percentage header, the vehicle acceleration header, and the vehicle speed header; the original data includes the test time data, accelerator pedal opening percentage data, vehicle acceleration data, and vehicle speed data corresponding to the identification instructions.

[0023] The data processing method of the present invention allows users to intuitively select a set of test data for vehicle performance evaluation from at least one set of test data based on the key performance indicators, thereby avoiding the tedious process of importing and selecting data set by set. At the same time, it can generate a vehicle performance evaluation report based on the selected test data without the need for manual data pasting and organization, thus effectively improving the processing efficiency of vehicle test data.

[0024] The data processing of this invention covers all evaluation indicators of dynamic performance related to Ramax and RRD, which can better reflect the driving dynamic performance of the test vehicle. Attached Figure Description

[0025] Figure 1 This is a flowchart of the data processing in this invention.

[0026] Figure 2 The curves showing the change of acceleration and accelerator pedal travel over time during re-acceleration according to the present invention. Detailed Implementation

[0027] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings. It should be noted that these descriptions are for the purpose of aiding understanding the present invention, but do not constitute a limitation thereof. Furthermore, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0028] like Figure 1 As shown, this invention provides a method for processing vehicle constant speed re-acceleration dynamic response performance data. In actual testing, the state variables of acceleration and vehicle speed are random and cannot be measured precisely. Therefore, the measured acceleration and vehicle speed need to be filtered. Then, the test time is used for data filtering, removing data without pedal travel and filtering out data that is shorter than the test duration. The starting segment of each data segment to the maximum acceleration segment is extracted, and then filtered using throttle opening drop, variance for stable throttle opening, accurate, fast and stable pressing of the accelerator pedal to the target opening, and starting speed, to obtain the final effective data. The effective data is calculated to generate RPA and RRD test performance indicators, and a test report is generated based on the test performance indicators to evaluate the test results.

[0029] Example

[0030] Vbox exports an Excel matrix of test data, identifies the corresponding recognition commands within the matrix, and filters and imports rawdate data. The recognition commands include at least the test time header, accelerator pedal opening percentage header, vehicle acceleration header, and vehicle speedometer header. The rawdate data includes the test time data, accelerator pedal opening percentage data, vehicle acceleration data, and vehicle speed data corresponding to the recognition commands. The time interval between each adjacent point in the exported Vbox test data Excel matrix is ​​0.01 seconds.

[0031] The vehicle acceleration and speed data from the rawdate data are processed using Kalman filtering to obtain the filtereddate data.

[0032] Since the test conditions were all completed with the accelerator pedal depressed, the `filterdate` data was filtered to remove data where the accelerator pedal opening was 0%, and the remaining discontinuous data was split into `tempdate` data groups. Here, `tempdate` data represents the data segment with an accelerator pedal signal, starting from 0% and increasing, and ending with the accelerator pedal opening decreasing back to 0%.

[0033] Since a complete test condition requires at least a certain period of time to complete, tempdate data with a duration less than T1 is removed from the tempdate data group to obtain the effdate data group. The duration T1 is a set value.

[0034] Because the vehicle needs to be fully depressed to accelerate at a constant speed during the test, data with a maximum accelerator pedal opening percentage greater than 98% are selected as valid data from the effdate data set (allowing for a 2% error during driver operation). All valid teffdate data are combined into the stabledate1 data set.

[0035] Because the HEV model had situations where the vehicle was stationary and the driver was pressing the accelerator to charge the battery during the test, the starting time of each stabledate1 data was selected as t1, and each t2 = t1 + 0.5s. The vehicle speed at time t2 of each stabledate1 data was selected. If the vehicle speed was not zero, it indicated that the stabledate1 data was valid. All valid stabledate1 data were combined into a stabledate2 data group.

[0036] Select the moment of maximum acceleration for each stabledate2 data point as t3, and extract the data within the time intervals t1 and t3 of each stabledate2 data point to form the stabledate3 data group.

[0037] The maximum acceleration moment for each stabledate3 data point is denoted as t3, and the corresponding accelerator pedal opening at each t3 moment is denoted as maxped. A condition is set that maxped > 98%. If this condition is met, it indicates that the maximum acceleration occurred at full accelerator pedal opening, which meets the requirements. This check avoids interference from vehicle turning data. All valid stabledate3 data points are grouped into a stabledate4 data set.

[0038] The inflection point of the throttle opening change when the stabledate4 data set accelerates at a constant speed and then accelerates to full throttle is recorded as t4. Data from time t1 and time t4 in each stabledate4 data set are extracted to form the stabledate5 data set.

[0039] Because the accelerator pedal opening should be kept stable before the full throttle condition during the actual test, the following operations should be performed.

[0040] Within the stabledate5 data set, find a data segment of duration T2 within each stabledate5 data point, where the variance of the accelerator pedal opening percentage is less than stablejudge1. This results in the T2 data segment group. Within the T2 data segment group, identify the minimum start time (t5) and the maximum end time (t6). Extract the data from time t5 to time t6 from each stabledate5 data point to obtain the stabledate6 data set. stablejudge1 and duration T2 are set values.

[0041] Because the vehicle speed should be kept stable during the actual test, the following operations should be performed.

[0042] Within the stabledate6 data set, find a data segment of duration T3 within each stabledate6 data set, where the vehicle speed variance of the data segment is less than stablejudge2, thus obtaining the T3 data segment group. Within the T3 data segment group, identify the smallest start time (t7) and the largest end time (t8) among all data points. Extract the data from time t7 to time t8 from each stabledate2 data set, resulting in the stabledate7 data set. stablejudge2 and duration T3 are set values.

[0043] Select the data corresponding to each stabledate7 data from the stabledate4 data group to form the stabledate8 data group.

[0044] The maximum acceleration of each stabledate8 data point in the stabledate8 dataset is denoted as Ramax. The time of the first target throttle inflection point when the throttle opening is fully depressed in each stabledate8 data point is denoted as t9. For example... Figure 2 As shown, the re-acceleration response hysteresis RRD is calculated based on t3, t4, t9, and Ramax.

[0045] RRD = (S1 + S2 - S3) / Ramax

[0046] S1 = 0.5 * (t9 - t4) * Ramax

[0047] S2=(t3-t9)*Ramax

[0048]

[0049] Where RRD represents the re-acceleration response hysteresis; S1 represents the ideal acceleration area from the initial moment of re-acceleration to the initial moment of maximum throttle; S2 represents the ideal acceleration area from the initial moment of maximum throttle to the moment of maximum acceleration; S3 represents the actual acceleration area from the initial moment of re-acceleration to the moment of maximum acceleration; t4 represents the initial moment of re-acceleration throttle opening; t9 represents the moment of re-acceleration target throttle inflection point opening; t3 represents the moment of maximum re-acceleration; a represents the actual acceleration; and Ramax represents the maximum re-acceleration of the vehicle.

[0050] The names stabledate1 to stabledate9 mentioned above are merely a naming convention for the arrays and have no special meaning.

[0051] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Contents not described in detail in this specification belong to prior art known to those skilled in the art.

Claims

1. A method for processing vehicle constant speed re-acceleration dynamic response performance data, characterized in that: The test data of constant speed and then acceleration is obtained, the test data is filtered, the filtered data is screened to obtain the final effective data, and the test performance index is calculated based on the final effective data; the screening includes splitting data, time screening, full throttle screening, starting speed screening, cornering data removal, stable speed screening, and stable opening degree screening. The performance metrics tested include re-acceleration response hysteresis, which is calculated using the following formula: RRD = (S1 + S2 - S3) / Ramax; S1 = 0.5 * (t9 - t4) * Ramax; S2 = (t3 - t9) * Ramax; Where RRD is the re-acceleration response hysteresis; S1 is the ideal acceleration area from the start of re-acceleration throttle to the start of maximum throttle; S2 is the ideal acceleration area from the start of maximum throttle to the moment of maximum acceleration; S3 is the actual acceleration area from the start of re-acceleration throttle to the moment of maximum acceleration; t4 is the start of re-acceleration throttle opening; t9 is the start of maximum re-acceleration throttle; t3 is the moment of maximum re-acceleration; a is the actual acceleration; and Ramax is the maximum re-acceleration acceleration of the vehicle.

2. The vehicle constant speed re-acceleration dynamic response performance data processing method according to claim 1, characterized in that: The data splitting involves removing data with a 0% accelerator pedal opening from the filtered data, and then splitting the remaining discontinuous data into multiple data groups.

3. The method for processing vehicle constant speed re-acceleration dynamic response performance data according to claim 1, characterized in that: The time filtering involves removing data from the split data that is shorter than the test condition time.

4. The method for processing vehicle constant speed re-acceleration dynamic response performance data according to claim 1, characterized in that: The full throttle screening involves selecting data from the time-filtered data where the maximum throttle pedal opening percentage is greater than a set value to form the stabledate1 data group.

5. The vehicle constant speed re-acceleration dynamic response performance data processing method according to claim 4, characterized in that: The starting speed screening process is as follows: select the starting time of each stabledate1 data as t1, set t2 = t1 + 0.5s, determine the speed corresponding to each data at time t2, if the speed is not zero, it indicates that the stabledate1 data is valid, and combine all valid stabledate1 data into stabledate2 data group.

6. The vehicle constant speed re-acceleration dynamic response performance data processing method according to claim 5, characterized in that: The process of removing curve data is as follows: select the time of maximum acceleration during re-acceleration in each stabledate2 data set and record it as t3. Extract the data between time t1 and time t3 in each stabledate2 data set to form a stabledate3 data group. Select the accelerator pedal opening at time t3 in each stabledate3 data and denote it as maxped. Determine whether maxped is greater than the threshold. If it is, it means that the stabledate3 data is valid. Combine all valid stabledate3 data into a stabledate4 data group.

7. The method for processing vehicle constant speed re-acceleration dynamic response performance data according to claim 6, characterized in that: The process of screening for stable opening is as follows: The starting time of the acceleration after the stabledate4 data group is at a constant speed and then the throttle is fully depressed is recorded as t4. The data between time t1 and time t4 in each stabledate4 data group are extracted to form the stabledate5 data group. In the stabledate5 data group, find each data segment with a duration of T2 in each stabledate5 data, and the variance of the accelerator pedal opening percentage in the data segment is less than the opening set value to obtain the T2 data segment group. In the T2 data segment group, find the smallest start time among all data and record it as t5 and the largest end time among all data and record it as t6. Extract the data between time t5 and time t6 in each stabledate5 data to obtain the stabledate6 data group.

8. The method for processing vehicle constant speed re-acceleration dynamic response performance data according to claim 7, characterized in that: The process of screening for stable vehicle speed is as follows: In the stabledate6 data group, find a data segment with a duration of T3 in each stabledate6 data, and the speed variance of the data segment is less than the speed setting value to obtain the T3 data segment group. In the T3 data segment group, find the smallest start time among all data and record it as time t7, and the largest end time and record it as time t8. Extract the data between time t7 and time t8 in each stabledate6 data to obtain the stabledate7 data group.

9. The method for processing vehicle constant speed re-acceleration dynamic response performance data according to claim 1, characterized in that: Before filtering the test data, the identification instructions corresponding to the test data Excel matrix are first identified, and then the original data is imported. The identification instructions include the test time header, the accelerator pedal opening percentage header, the vehicle acceleration header, and the vehicle speed header. The original data includes the test time data, accelerator pedal opening percentage data, vehicle acceleration data, and vehicle speed data corresponding to the identification instructions.

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