A multi-rate data separation processing method based on WLTC cycle

Abstract

The application discloses a kind of multi-rate data separation processing methods based on WLTC cycle, it is related to new energy vehicle test and performance evaluation field. Including: S1.test data reading and multi-source time series construction;S2.through high-speed driving, the identification of the end time of the fourth phase of WLTC is completed to three-section type physical characteristics of continuous high speed and parking zero;S3.according to the end time of the fourth phase identified, the theoretical boundary point of adjacent phase is calculated in combination with the theoretical length of four stages, and accurate positioning is completed through the detection of actual parking start;S4.calculate the power data and other data of each stage from electrical and mechanical double path;S5.generate chart.The scheme is based on the physical law of vehicle operation, using the three-section characteristics that must appear at the end of the fourth phase of WLTC cycle, combined with the theoretical length of each stage, the boundary point of adjacent stage is reversely calculated, and the boundary is fine-tuned combined with the parking start process.The dependence on data sampling integrity and synchronization accuracy is reduced, and the applicability in actual test is improved.

Description

Technical Field

[0001] This invention relates to the field of new energy vehicle testing and performance evaluation, and in particular to a multi-rate data separation and processing method based on WLTC cycles. Background Technology

[0002] In the energy efficiency assessment of light-duty vehicles, the WLTC (Worldwide Harmonized Light Vehicles Test Cycle) is an internationally recognized standard test procedure. Currently, the WLTC test procedure typically only outputs the combined fuel consumption and electricity consumption for the entire cycle, a single indicator that has significant limitations: Unable to reveal the energy consumption distribution characteristics of key systems: Unable to quantify the energy consumption contribution of the motor system (MCU), generator system (GCU), and engine system (EMS) in the four characteristic sub-stages of WLTC (low speed, medium speed, high speed, and ultra-high speed), thus restricting the refined analysis and optimization of the vehicle's energy consumption bottleneck; Lack of multi-parameter collaborative analysis capability: The sampling rate of energy consumption-related parameters (such as voltage, current, torque, and speed) is inconsistent with the speed signal, making it difficult to achieve stage alignment of multi-source heterogeneous frequency data.

[0003] Currently, the mainstream methods for segmenting vehicle speed data include: 1. Template matching method: Using the standard operating condition speed curve as a template, the least squares error between the measured data and the template is calculated through a sliding window to find the most similar segment. This method requires complete and highly accurate synchronized data, which is difficult to address the aforementioned problems.

[0004] 2. Simple segmentation method based on deceleration events: This method divides the cycle into stages by detecting when the vehicle speed drops to 0 km / h or a low-speed threshold. However, it cannot distinguish between similar deceleration events (e.g., both the low-speed and high-speed segments in WLTC involve deceleration, leading to potential misjudgments); and it does not incorporate a fixed cycle duration, making it impossible to accurately pinpoint the start and end points of each sub-stage.

[0005] 3. Fixed-duration interpolation method: Based on the theoretical duration of each stage of WLTC (e.g., 433 seconds for the ultra-high-speed segment), missing data is interpolated and completed. It cannot actively locate the cycle start point and requires external synchronization signals; its dynamic adaptability is poor, and if actual driving deviates from standard operating conditions, the interpolation results will be distorted.

[0006] Therefore, existing technologies are insufficient to support refined energy consumption calculations based on WLTC multi-stage data that integrates multi-source frequency data, which restricts the in-depth utilization of test data and the accurate assessment of vehicle energy efficiency. Summary of the Invention

[0007] The technical problem this invention aims to solve is: to accurately identify the four-stage boundary of WLTC, address the time synchronization challenge of sensor data at different sampling rates, avoid information loss caused by interpolation or downsampling, and improve robustness. This invention provides a multi-rate data separation and processing method based on the WLTC cycle, achieving multi-stage energy consumption analysis based on physical characteristics and time logic. The technical means include: Three-stage physical feature recognition: By detecting typical physical features of "ultra-high speed → sustained high speed → deceleration and stopping" in the vehicle speed time series, the end time of WLTC Phase 4 can be accurately located. Fixed-duration reverse calculation and boundary correction: Based on the identified end time of the fourth stage, the theoretical boundary points of adjacent stages are calculated in reverse by combining the theoretical duration of each stage of WLTC. Near each theoretical boundary point, the actual parking and starting process is detected to accurately locate and offset the stage boundary (the starting point is moved forward by 0.1 seconds and the parking point is moved backward by 0.1 seconds) to ensure that the boundary completely covers the parking and starting transient. Dynamic alignment of multi-source data: Based on the speed stage division results, different frequency parameters (such as current, voltage, torque, and speed) are automatically associated with the corresponding stage through time matching, realizing direct joint analysis of data with different sampling rates without forced resampling or interpolation, and preserving the original data characteristics.

[0008] The technical solution adopted by this invention to solve its technical problem is: a multi-rate data separation and processing method based on WLTC cycle, comprising the following steps: S1.WLTC Test Data Reading and Multi-Source Time Series Construction: Reads raw test data containing different parameters (voltage, current, vehicle speed, torque, rotational speed, power, etc., belonging to electrical and mechanical categories) and their respective timestamps. The sampling frequency is calculated by analyzing the sampling interval of each parameter's time series.

[0009] S2. Identification of the end time of WLTC Phase 4: Detect the typical three-stage physical characteristics of "ultra-high speed driving → continuous high speed → deceleration and stopping" in the vehicle speed time series to accurately locate the end time of WLTC Phase 4 (ultra-high speed segment), that is, the starting point of stopping.

[0010] S3. Precise positioning of stage boundaries based on theoretical duration and parking / starting process: Using the identified end time of the fourth stage as a benchmark, and combining the theoretical duration of the four stages of WLTC (589 s for low speed, 433 s for medium speed, 455 s for high speed, and 323 s for ultra-high speed), the theoretical boundary points of each adjacent stage are calculated in reverse. Then, the actual parking segment is searched near each theoretical boundary point. The stage boundaries are corrected by detecting the "parking start point" and "starting point". The starting point is shifted forward by 0.1 seconds (before starting) and the ending point is shifted backward by 0.1 seconds (after parking) to fully cover the parking / starting transient process. Finally, the zero point of time is redefined as 1800 seconds before the end point of the fourth stage to achieve time benchmark unification.

[0011] S4. Dual-path power calculation: Power calculation is performed for each stage from both the electrical path (voltage, current, etc.) and the mechanical path (torque, speed, etc.), and the cumulative energy consumption (kWh) of each stage is obtained by using the trapezoidal integral method. At the same time, positive power (driving) and negative power (braking) are distinguished to achieve comparison and cross-verification of the two paths.

[0012] S5. Multi-dimensional collaborative visualization and report generation: Automatically generates a series of comparative analysis charts to intuitively display the analysis results.

[0013] The technical approach of this solution is "physical feature recognition and calculation," which does not rely on matching the entire theoretical operating condition waveform. Its core principle is the application of the physical laws of vehicle operation. First, it identifies feature points with clear physical significance in the vehicle speed time series—namely, the three-stage physical feature of "ultra-high speed driving → sustained high speed → deceleration and stopping" that inevitably occurs at the end of the fourth stage of WLTC, reducing the misjudgment rate in high-noise environments. Then, based on this feature and combined with the fixed duration of the cycle, it reverse-calculates the theoretical boundary point, and then precisely corrects and adjusts the stage boundary by detecting the actual stopping and starting process. Thus, while preserving the key physical features completely, it can accurately locate the start and end points of the stage. Even when local data is missing, it can still complete the stage based on the theoretical duration, ensuring the basic continuity of the analysis. This method does not require forced resampling of data from different frequencies, and achieves natural alignment of multi-source data through time matching, reducing the stringent requirements for data integrity and sampling synchronization, and significantly improving the applicability and accuracy of analysis in actual testing.

[0014] Based on the above technical solution, in step S3, the start and end times of each stage are identified and adjusted according to the vehicle speed time series data. The nearest neighbor matching algorithm is used to find the closest discrete sampling point in the time series of low sampling rate parameters (such as current and voltage), so as to achieve accurate synchronization of high and low sampling rate data in WLTC stage division.

[0015] This alignment method avoids information loss caused by forced resampling and provides a reliable data segmentation basis for subsequent dual-path joint analysis (such as voltage, current and power calculation).

[0016] Based on the above technical solution, in step S3, the determination of the start and end points of each stage is divided into two steps, namely: Rough calculation: Based on the identified end time of the fourth stage, and combined with the theoretical duration of the four stages of WLTC and the sampling frequency of each parameter, the approximate boundary line (theoretical boundary point) between adjacent stages is first calculated. Precise calculation: Near each theoretical dividing point, by combining the vehicle's starting and stopping process with an offset adjustment of ±0.1 seconds, the precise start and end index of each stage is obtained.

[0017] The start and end line recognition logic of this solution is directly derived from the inevitable physical behavior of the vehicle under WLTC conditions. It does not rely on precise waveform matching of the theoretical speed curve, so it has strong resistance to data loss and noise and can better adapt to dynamic scenarios in actual testing.

[0018] Based on the above technical solution, in step S1, all parameter data are collected through the vehicle's OBD interface. Torque, speed, voltage, and current all originate from the same vehicle bus system and have a unified time reference but different sampling rates.

[0019] This dual-path verification scheme verifies the physical consistency of the power balance relationship during vehicle energy conversion by comparing the differences between mechanical power (calculated based on torque and speed) and electrical power (calculated based on voltage and current). This represents a shift in technical approach from "multi-source synchronization" to "single-source verification" and from "data alignment" to "energy flow analysis."

[0020] Based on the above technical solution, the analysis charts in step S5 include: energy consumption charts of electrical and mechanical paths at each stage, positive and negative cumulative power charts, and stage-annotated curves showing the changes of key parameters over time.

[0021] The beneficial effects of this invention are: 1. This invention achieves high-precision alignment of multi-source data throughout the entire WLTC cycle by combining physical feature recognition with time extrapolation and a nearest neighbor matching algorithm. It effectively solves the inherent timestamp offset problem between different sensor data streams in traditional technologies, ensuring that electrical responses and mechanical actions accurately correspond when analyzing specific stages, thereby significantly improving the accuracy and reliability of WLTC stage performance consumption calculations. 2. This invention utilizes two independent sets of electrical and mechanical data to calculate power in parallel at each stage of WLTC, forming a complete dual-path power calculation. By comparing P... UI With PTn The curve changes can effectively identify and locate sensor drift problems; 3. This invention performs independent energy flow calculations according to the four stages (low speed, medium speed, high speed, and ultra-high speed) defined by WLTC, thereby realizing refined energy consumption monitoring of vehicles under different operating conditions. Attached Figure Description

[0022] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0023] Figure 1 This is a flowchart illustrating the overall steps of the preferred embodiment of the present invention.

[0024] Figure 2 This is a flowchart illustrating the steps of reading WLTC test data and constructing a benchmark time series in the preferred embodiment of the present invention.

[0025] Figure 3 This is a flowchart illustrating the steps for accurately locating the end time of the fourth stage of WLTC in the optimal embodiment of the present invention.

[0026] Figure 4 This is a flowchart illustrating the steps for calculating the start and end points of the four stages of WLTC in the optimal embodiment of the present invention.

[0027] Figure 5 This is a flowchart of the steps for WLTC end-to-end dual-path power calculation and report generation in the optimal embodiment of the present invention.

[0028] Figure 6 This is the detection diagram of the fourth stage end point of the present invention.

[0029] Figure 7 This is a comparison chart of WLTC vehicle speed, voltage, and current timing generated by this invention.

[0030] Figure 8 This is a time-series comparison chart of WLTC vehicle speed, torque, and RPM generated by this invention.

[0031] Figure 9 This is a comparison diagram of the staged dual-power calculation method generated by the present invention.

[0032] Figure 10 This is a structured statistical table of WLTC energy consumption generated by the present invention. Detailed Implementation

[0033] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0034] Since the data structures of GCU and EMS are similar to those of MCU, the data separation processing method for GCU and EMS in the WLTC loop is the same as that for MCU.

[0035] The following examples use MCU data as an example to illustrate the data separation and processing method.

[0036] Example 1: like Figures 1-5 The image shows an embodiment of the present invention, a multi-rate data separation processing method based on WLTC cycles, comprising the following steps: S1. WLTC Test Data Reading and Multi-Source Benchmark Time Series Construction: S11. Read the raw data from the data file collected by the vehicle's OBD interface, and extract the parameters (voltage U, current I, vehicle speed v, torque T, speed n, power P) and their respective timestamps according to the specified column index.

[0037] S12. Perform differential calculation on the timestamp sequence of each parameter to obtain the sampling interval; after removing outliers (zero or negative intervals), use the median as the effective sampling interval, and then calculate the sampling frequency (Hz).

[0038] In this embodiment, the sampling frequency for vehicle speed, torque, and rotational speed is 50 Hz, and the sampling frequency for voltage and current is 10 Hz.

[0039] S13. A uniform 0.1-second time offset is reserved for subsequent boundary adjustments. This offset will be converted into the corresponding number of sampling points according to the sampling frequency of each parameter (e.g., 50 Hz data corresponds to 5 points, and 10 Hz data corresponds to 1 point).

[0040] Precise location of the end time of S2.WLTC Phase 4 (Ultra-high-speed phase): In this embodiment, the sampling frequency for vehicle speed, torque, and rotational speed is 50 Hz, and the sampling frequency for voltage, current, and power is 10 Hz.

[0041] S21. Perform smoothing filtering (such as 5-point moving average) on the original vehicle speed time series to reduce noise interference.

[0042] Specifically, the "5-point moving average" uses a sliding window with a width of 5 data points to continuously calculate the average value of the data within the window, and then uses this average value to replace the original value at the center point of the window. The goal is to obtain a cleaner and smoother vehicle speed curve, thereby making physical feature recognition based on the vehicle speed curve (such as finding parking points and starting points) more accurate and robust.

[0043] S22. Set detection parameters: ultra-high speed threshold 100 km / h, continuous high speed threshold 80 km / h, minimum continuous high speed duration 15 seconds, parking threshold 0.5 km / h, minimum parking duration 5 seconds. Count back 1800 seconds from the end of the data to identify all parking segments that meet the parking conditions (vehicle speed continuously below 0.5 km / h and duration ≥ 5 seconds).

[0044] S23. For each parking segment, trace back in reverse the continuous high-speed segment (vehicle speed ≥ 80 km / h and duration ≥ 15 seconds), and then look forward to find the first ultra-high-speed point (vehicle speed ≥ 100 km / h).

[0045] S24. Verify the time sequence of the three key points (time of ultra-high speed point < time of end of continuous high speed point < time of start of parking point) and the deceleration trend. If it conforms to the complete physical process of "ultra-high speed driving → continuous high speed → deceleration and parking", then confirm the parking start point as the end time of the fourth stage. Figure 6 .

[0046] S25. Shift the determined end time backward by 0.1 seconds (i.e., one sampling interval corresponding to the lowest sampling rate, which is equivalent to 5 points for 50 Hz vehicle speed data) to ensure that the current / voltage sampling time does not advance the actual end point when aligning with the low sampling rate data.

[0047] S3.WLTC Four-Stage Start and End Line Calculation: S31. Estimation of stage boundary time based on theoretical duration: S311. Using the end time of the fourth stage obtained in step 2 as a benchmark, and utilizing the theoretical durations of each stage of WLTC (589 seconds for the low-speed stage, 433 seconds for the medium-speed stage, 455 seconds for the high-speed stage, and 323 seconds for the ultra-high-speed stage), the theoretical boundary points of each adjacent stage are calculated in reverse: Phase 3-4 boundary = End of Phase 4 – Duration of the ultra-high-speed section; Phase 2-3 boundary = Phase 3-4 boundary – High-speed segment duration; Phase 1-2 boundary = Phase 2-3 boundary – duration of the mid-speed segment; S312. In the vehicle speed time series, the indexes closest to the three theoretical boundary times mentioned above are obtained through nearest neighbor search, and used as reference centers for subsequent precise searches.

[0048] S32. Accurate identification of stage boundaries based on parking start point: S321. Set the search parameters: parking threshold (0.1 km / h), starting threshold (0.1 km / h), search window (30 seconds before and after the theoretical dividing point), and minimum parking duration (3 seconds), and convert them into the corresponding number of points according to the vehicle speed sampling rate (the number of data collection points, i.e., the number of discrete sampling point indices).

[0049] It should be noted that the purpose of the conversion is to achieve a precise mapping of time conditions; to convert the time constraints (seconds) of the physical world into the index constraints (number of points) of the data world, so that the search algorithm can accurately understand that "lasting 3 seconds" is equal to the number of consecutive data points checked.

[0050] S322. Within the window near the theoretical boundary point of stage 3-4, search for the first continuous segment that meets the parking conditions (vehicle speed continuously below the parking threshold and duration ≥ 3 seconds), and record its start index (parking begins) and end index (parking ends).

[0051] The third stage end point: trace back from the starting point of the parking section to find the last point where the vehicle speed is still higher than the starting threshold (i.e., the instant before the vehicle decelerates to a stop), and use it as the third stage end point (the initial boundary without offset adjustment). The starting point of the fourth stage: trace back from the end of the parking section to find the first point where the vehicle speed exceeds the starting threshold (i.e. the moment when the vehicle starts to start), and use it as the starting point of the fourth stage (the initial boundary without offset adjustment). If no parking segment is found, the theoretical boundary point index is used directly as the boundary.

[0052] It should be noted that "if no parking segment is found, the theoretical boundary point index is directly used as the boundary" is a local fault-tolerant mechanism in the stage boundary accurate identification step. This mechanism occurs after the theoretical boundary point has been obtained and during the parking segment search. If no actual parking segment is detected in the search window, it temporarily reverts to the theoretical boundary point index as the boundary, and will still undergo ±0.1 seconds of offset adjustment afterward.

[0053] S323. Similarly, search near the theoretical boundary point of stage 2-3 to determine the end point of the second stage and the start point of the third stage.

[0054] S324. Search near the theoretical boundary point of stage 1-2 to determine the end point of the first stage and the start point of the second stage.

[0055] S325. Determine the starting point of the first stage: Within a 30-second range before and after the start of the test, find the first point (in chronological order) where the vehicle speed exceeds the starting threshold, and use it as the starting point of the first stage.

[0056] It should be noted that the reasons for retrieving stopping and starting times in the WLTC continuous operating cycle are as follows: First, the "stopping segment" is used as a high-precision positioning anchor point. By locking the lowest vehicle speed near the stage boundary through a low threshold of 0.1 km / h, the deceleration end point and acceleration start point are accurately separated, achieving a boundary division that is closer to physical reality than the theoretical time. Second, it is to deal with real-world stopping that may occur in actual testing (such as road condition interference, operation delays, etc.), avoiding incorrectly including stopping periods in the driving stage, and ensuring the accuracy of subsequent energy consumption calculations.

[0057] S33. Time offset adjustment at stage boundaries (±0.1 seconds): S331. Based on the sampling frequency of the high sampling rate data (vehicle speed), calculate the number of points corresponding to 0.1 seconds (rounded to the nearest integer).

[0058] S332. Shift the starting point of each stage forward by 0.1 seconds (i.e., 0.1 seconds before starting), ensuring that the index after the shift is ≥1 and does not overlap with the ending point of the previous stage.

[0059] In this embodiment, if the first stage ends at index 100 (number of sampling points), then the second stage must start from 101 (i.e., 100+1).

[0060] S333. Offset the end point of each stage by 0.1 seconds (i.e., 0.1 seconds after stopping), ensuring that the index after the offset is less than or equal to the end of the data and not greater than the start point of the next stage - 1.

[0061] S334. Obtain the precise index range for four stages of high sampling rate data (vehicle speed, torque, and RPM).

[0062] S34. Time zero point uniform adjustment: S341. Using the original time corresponding to the end time of the fourth stage determined in step S2 as the benchmark, advance the theoretical total duration of WLTC by 1800 seconds, and define the calculation result as the new time zero point.

[0063] S342. Subtract the zero offset from the timestamps of all high sampling rate data to obtain the timeline based on the new zero point.

[0064] S343. Subtract the same offset from the timestamps of all low sampling rate data (current, voltage) to unify the time axis of all parameters. At this time, the end point of the fourth stage corresponds to 1800 seconds on the new time axis.

[0065] Setting a unified timeline ensures that the theoretical start and end times of each stage naturally fall into their corresponding positions, avoiding timing misalignments caused by device startup delays or storage delays.

[0066] S35. Index Mapping for Low Sampling Rate Data Phase: S351. For each stage, based on the start and end times of the high sampling rate stage, find the nearest index in the low sampling rate time axis through nearest neighbor search.

[0067] S352. Correct the out-of-bounds errors of the found indexes to ensure that the starting index is less than or equal to the ending index and is within the data range, thereby obtaining the index range of each stage of the low sampling rate data and achieving time alignment of data with different sampling rates.

[0068] This embodiment achieves precise alignment of data with different sampling rates in WLTC testing through time base unification and dynamic index mapping, ensuring the comparability between parameters and providing a reliable data foundation for subsequent vehicle performance analysis.

[0069] S4.WLTC Power Calculation for the Entire Dual Path: Taking motor system (MCU) data as an example: S41. First Path (DC-Side Power Calculation): Calculate the instantaneous power P based on voltage U and current I. UI =U*I. Positive current values ​​correspond to discharge (driving), and negative current values ​​correspond to charging (regenerative braking).

[0070] S42. Second Path (Mechanical Power Calculation): Calculate the instantaneous mechanical power P based on the torque T and rotational speed n. Tn =T*(2π*n / 60).

[0071] S43. Refined integral calculation of energy consumption at each stage of WLTC: S431. Regarding the calculated P UI and P Tn Numerical integration (trapezoidal method) is performed on each of the four divided stages to calculate the results within each stage: Positive cumulative power: Integrating the power > 0 portion, characterizing the stage drive energy consumption; Negative cumulative work: Integrating the portion where power is less than 0, characterizing the energy recovered by regenerative braking in a given stage; Net cumulative work: the algebraic sum of positive and negative work, representing the net energy consumption of a stage.

[0072] S432. Convert the integral result (unit: joule J) to the commonly used energy unit kilowatt-hour (kWh).

[0073] S5. Multi-dimensional collaborative visualization and structured report generation: S51. In the original vehicle speed-time curve, generate several features in the automatic identification process of the end point of WLTC Phase 4, including the identified ultra-high speed segment, deceleration and stopping segment, ultra-high speed point, continuous high speed end point and stopping end point.

[0074] S52. Generate a WLTC speed, voltage, and current timing comparison chart, such as... Figure 7 (Vehicle speed sampling frequency is 50 Hz, voltage and current sampling frequency is 10 Hz): The diagram features a three-row subplot layout: the first row represents the vehicle speed-time curve, the second row represents the voltage-time curve, and the third row represents the current-time curve; different colored semi-transparent backgrounds are used to clearly identify the four stages of WLTC.

[0075] S53. Generate a time-series comparison chart of vehicle speed, torque, and engine speed, such as... Figure 8 (All three samples are taken at a frequency of 50 Hz): The diagram features a three-row subplot layout: the first row is the vehicle speed-time curve, the second row is the torque-time curve, and the third row is the engine speed-time curve; different colored semi-transparent backgrounds are used to clearly identify the four stages of WLTC.

[0076] S54. Comparison chart of staged dual-power calculation methods: as shown in the figure. Figure 5 (Torque, speed, and power sampling frequency is 50Hz; voltage, current, and power sampling frequency is 10Hz; vehicle speed sampling frequency is 50Hz). A 2×2 subplot layout is used to display the power P calculated using voltage and current at four speed stages: low speed, medium speed, high speed, and ultra-high speed. UI Power P calculated using torque and speed Tn And the vehicle speed-time curve. A comparative display of P during this phase. UI With P Tn The degree of curve matching.

[0077] S55. Generate a structured statistical table of WLTC energy consumption: such as Figure 9 The four stages and the duration of the entire cycle, P, are clearly listed in tabular form. UI and P Tn Positive work, negative work, and net work (unit: kWh) under two calculation methods.

[0078] Example 2: Reference Figure 1 , Figure 3 A multi-rate data separation processing method based on WLTC cycle, in addition to the method in Embodiment 1, if the three-stage physical feature detection of the end time of the fourth stage (main scheme) fails in step S204, an alternative scheme is adopted.

[0079] Alternative Option 1: Locate the last high-speed point and then search backwards for the first point that meets the parking conditions; Alternative Option 2: If Alternative Option 1 also fails, the end time will be calculated from the start of the test based on the theoretical total duration of 1800 seconds in WLTC.

[0080] The design of hierarchical alternative schemes (main scheme → alternative scheme one → alternative scheme two) significantly improves the robustness, accuracy and engineering practicality of the system by utilizing a multi-level fault-tolerant mechanism.

[0081] Main approach: Highest accuracy, but relies on complete physical features; Alternative Option 1: Medium accuracy, relying only on key points; Alternative Option 2: Lowest accuracy, but avoids complete failure.

[0082] This solution employs a three-tiered progressive strategy to achieve a complete fault-tolerant chain of "high precision → medium precision → safety net function," where the threshold for activating alternative solutions is set according to actual needs.

[0083] Based on the above-described preferred embodiments of the present invention, and through the foregoing description, those skilled in the art can make various changes and modifications without departing from the inventive concept. The technical scope of this invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. A multi-rate data separation processing method based on WLTC cycle, characterized in that, Includes the following steps: S1. WLTC Test Data Reading and Multi-Source Time Series Construction: Reading raw time series parameters with timestamps from electrical and mechanical aspects; S2.WLTC Fourth Stage End Time Identification: Detect the typical three-stage physical characteristics consisting of ultra-high speed driving - continuous high speed - deceleration and stopping in the vehicle speed time series in the parameters to accurately identify the end time of the fourth stage; S3. Precise positioning based on theoretical duration and stage boundaries of the parking and starting process: Stage boundary time estimation based on theoretical duration: Based on the end time of the fourth stage identified by the three-stage physical characteristics, the theoretical boundary point of each adjacent stage is calculated in reverse by combining the theoretical duration of the four stages. Accurate identification of stage boundaries based on parking start point: Then, by detecting the actual vehicle starting and stopping process, the true start and end times of the stage are accurately located; S4. Dual-path power calculation: For each stage, calculate the instantaneous power from both the electrical path and the mechanical path. S5. Multi-dimensional collaborative visualization and report generation: Generate comparative analysis charts to display analysis results.

2. The multi-rate data separation processing method based on WLTC cycle according to claim 1, characterized in that, In step S2, the three-stage physical feature detection of the end time of the fourth stage is used as the primary solution. If the primary solution fails, an alternative solution is adopted, which includes: Alternative Option 1: Locate the last ultra-high-speed point and then find the first point that meets the parking conditions as the end time of the fourth stage; Alternative Plan 2: If Alternative Plan 1 also fails, the end time of the fourth phase will be calculated from the starting point of the test based on the theoretical total duration of WLTC.

3. The multi-rate data separation processing method based on WLTC cycle according to claim 1, characterized in that, In step S2, the three-segment physical feature detection method is as follows: starting from the end of the vehicle speed time series, find the parking start point that meets the parking conditions; Then, trace back from that point to find the continuous high-speed segment; Search further to find the ultra-high speed point.

4. The multi-rate data separation processing method based on WLTC cycle according to claim 1, characterized in that, In step S3, based on the end time of the fourth stage identified by the vehicle speed time series, the nearest neighbor matching algorithm is used to obtain the end time of the fourth stage of other parameters, thereby completing the parameter data alignment.

5. The multi-rate data separation processing method based on WLTC cycle according to claim 1, characterized in that, In step S3, the method for accurately identifying the stage boundary based on the parking start point is as follows: The actual parking segment is searched near each theoretical boundary point. The stage boundary is accurately corrected by detecting the parking start point and the starting point. The start point of each stage is shifted forward and the end point is shifted backward to fully cover the parking and starting transient process.

6. The multi-rate data separation processing method based on WLTC cycle according to claim 1, characterized in that, In step S1, the parameters include, but are not limited to, voltage, current, vehicle speed, torque, rotational speed, and power.

7. The multi-rate data separation processing method based on WLTC cycle according to claim 1, characterized in that, In step S3, data alignment at different sampling frequencies is achieved through time base unification and dynamic index mapping.

8. The multi-rate data separation processing method based on WLTC cycle according to claim 7, characterized in that, The method for unifying the time base is as follows: redefine the zero point of time as 1800 seconds before the end of the fourth stage.

9. The multi-rate data separation processing method based on WLTC cycle according to claim 1, characterized in that, In step S4, the energy consumption of each stage of WLTC is calculated using the trapezoidal integral method, while distinguishing between positive and negative work.

10. The multi-rate data separation processing method based on WLTC cycle according to claim 1, characterized in that, In step S1, all parameter data are collected through the vehicle's OBD interface.

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