Accelerator pedal signal filtering method, device, equipment, medium and program product

Abstract

The invention relates to an accelerator pedal signal filtering method, device and equipment, a medium and a program product, and relates to the technical field of electric automobiles. The method comprises the following steps: acquiring a first sampling sequence corresponding to a vertical acceleration signal of the electric vehicle and a second sampling sequence corresponding to an accelerator pedal signal; determining an acceleration amplitude sequence corresponding to the first sampling sequence and a pedal signal change rate sequence corresponding to the second sampling sequence; based on the acceleration amplitude sequence and the pedal signal change rate sequence, determining a Pearson correlation coefficient representing the correlation of the two signals; based on the Pearson correlation coefficient, whether the accelerator pedal signal is interfered by vehicle body vibration or not is judged; and under the condition that interference is determined, anti-vibration filtering processing is carried out on the second sampling sequence, the sampling sequence after filtering processing is obtained, and by implementing targeted anti-vibration filtering processing, the reflection precision and the reduction degree of the pedal signal to the real intention of a driver are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle technology, and specifically to a filtering method, apparatus, device, medium, and program product for accelerator pedal signals. Background Technology

[0002] With the adjustment of the global energy structure, electric vehicles, as the core carrier of clean energy transportation, have become a strategic direction for the transformation of the automotive industry. Compared with traditional fuel vehicles, electric vehicles significantly reduce dependence on fossil fuels due to their advantages of low emissions, high energy efficiency, and intelligent technology, and have promoted the development of technologies such as vehicle-to-everything (V2X), autonomous driving, and distributed energy storage. Meanwhile, the drive motor of an electric vehicle, with its instantaneous torque response, can output constant peak torque in the low-speed range, a characteristic that makes its acceleration performance significantly better than that of traditional fuel vehicles. However, this characteristic places more stringent requirements on the real-time performance and acquisition accuracy of the accelerator pedal signal. During the operation of an electric vehicle, if the vehicle body experiences severe bumps due to rough road conditions, the strong vibration interference will cause deviations in the accelerator pedal signal, failing to accurately reflect the driver's true operating intentions. Therefore, real-time processing and compensation of the accelerator pedal signal are necessary to eliminate the impact of vibration interference and ensure that the vehicle's power response matches the driver's operating intentions, thereby improving driving safety and ride smoothness.

[0003] In related technologies, first-order low-pass filtering or stepped calibration filtering is typically used to filter the accelerator pedal input signal. First-order low-pass filtering smooths the input signal using fixed filter coefficients, while stepped calibration filtering quantizes the input signal using fixed stepped variables. Although these two methods can reduce noise interference to some extent, they still struggle to accurately reflect the driver's operating intentions. Summary of the Invention

[0004] This invention provides a method, apparatus, device, medium, and program product for filtering accelerator pedal signals, in order to improve the problem that the filtering results in related technologies are difficult to accurately reflect the driver's operating intentions.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] In a first aspect, this application provides a method for filtering accelerator pedal signals, comprising:

[0007] Acquire the first sampling sequence corresponding to the vertical acceleration signal of the electric vehicle at the current moment and the second sampling sequence corresponding to the accelerator pedal signal;

[0008] Determine the acceleration amplitude sequence corresponding to the first sampling sequence and the pedal signal rate of change sequence corresponding to the second sampling sequence;

[0009] Based on the acceleration amplitude sequence and the accelerator pedal signal rate of change sequence, the Pearson correlation coefficient, which characterizes the correlation between the vertical acceleration signal and the accelerator pedal signal, is determined.

[0010] Based on the Pearson correlation coefficient, determine whether the accelerator pedal signal is affected by vehicle vibration;

[0011] When it is determined that the accelerator pedal signal is affected by vehicle body vibration, the second sampling sequence is subjected to anti-vibration filtering to obtain the filtered sampling sequence corresponding to the accelerator pedal signal.

[0012] In one possible implementation, determining whether the accelerator pedal signal is affected by vehicle body vibration based on the Pearson correlation coefficient includes: if the Pearson correlation coefficient is greater than a set threshold, then the accelerator pedal signal is determined to be affected by vehicle body vibration; if the Pearson correlation coefficient is less than or equal to the set threshold, then the accelerator pedal signal is determined to be unaffected by vehicle body vibration.

[0013] In one possible implementation, the filtering method for the accelerator pedal signal further includes: when it is determined that the accelerator pedal signal is not affected by vehicle body vibration, performing bandpass filtering on the second sampling sequence within a first set frequency range to obtain the filtered sampling sequence corresponding to the accelerator pedal signal.

[0014] In one possible implementation, the second sampling sequence is subjected to anti-vibration filtering to obtain a filtered sampling sequence corresponding to the accelerator pedal signal. This includes: determining the difference sequence between the second sampling sequence and the filtered sampling sequence at the previous time step; determining the difference rate of change sequence corresponding to the difference sequence; for each difference rate of change in the difference rate of change sequence, determining the adaptive step size corresponding to the difference rate of change based on a pre-calibrated mapping relationship between the difference, the difference rate of change, and the adaptive step size; and summing the adaptive step size sequence corresponding to the difference rate of change sequence with the filtered sampling sequence at the previous time step to obtain the filtered sampling sequence corresponding to the accelerator pedal signal.

[0015] In one possible implementation, the mapping relationship between the difference, the rate of change of the difference, and the adaptive step size is dynamically changing. Assuming the accelerator pedal signal is not affected by vehicle body vibration, the accelerator pedal signal filtering method further includes: storing the rate of change of the difference data in the rate of change of the difference sequence and the difference data in the difference sequence into a memory buffer; when the data capacity stored in the memory buffer exceeds a set capacity threshold, updating the mapping relationship between the difference, the rate of change of the difference, and the adaptive step size based on the rate of change of the difference data and the difference data in the memory buffer.

[0016] In one possible implementation, updating the mapping relationship between difference, difference rate of change, and adaptive step size based on difference change rate data and difference data in a memory buffer includes: determining the mean value corresponding to the difference data in the memory buffer to obtain a first difference value; determining the mean value corresponding to the difference rate of change data in the memory buffer to obtain a second difference rate of change value; based on the mapping relationship between difference, difference rate of change, and adaptive step size, interpolating the associated adaptive step size related to the first difference value and the second difference rate of change value to obtain a target adaptive step size in the mapping relationship between difference, difference rate of change, and adaptive step size; determining the driver's driving habits based on the relationship between the second difference rate of change value and a set difference rate of change threshold; updating the target adaptive step size based on the driving habits to obtain an updated target adaptive step size; and updating the associated adaptive step size in the mapping relationship between difference, difference rate of change, and adaptive step size based on the updated target adaptive step size and a preset weight coefficient corresponding to the driving habits to obtain an updated mapping relationship between difference, difference rate of change, and adaptive step size.

[0017] In one possible implementation, determining the acceleration amplitude sequence corresponding to the first sampling sequence and the pedal signal rate of change sequence corresponding to the second sampling sequence includes: performing bandpass filtering on the first sampling sequence within a second predetermined frequency range to obtain the first sampling sequence after bandpass filtering; performing Fast Fourier Transform (FFT) on the first sampling sequence after bandpass filtering to obtain the acceleration amplitude sequence corresponding to the first sampling sequence; and for the second sampling sequence, determining the pedal opening rate of change between adjacent sampling points to obtain the pedal signal rate of change sequence corresponding to the second sampling sequence.

[0018] Secondly, this application provides a filtering device for an accelerator pedal signal, comprising:

[0019] The data acquisition module is used to acquire the first sampling sequence corresponding to the vertical acceleration signal of the electric vehicle at the current moment and the second sampling sequence corresponding to the accelerator pedal signal;

[0020] The first determining module is used to determine the acceleration amplitude sequence corresponding to the first sampling sequence and the pedal signal change rate sequence corresponding to the second sampling sequence;

[0021] The second determining module is used to determine the Pearson correlation coefficient, which characterizes the correlation between the vertical acceleration signal and the accelerator pedal signal, based on the acceleration amplitude sequence and the pedal signal rate of change sequence.

[0022] The interference detection module is used to determine whether the accelerator pedal signal is affected by vehicle body vibration based on the Pearson correlation coefficient.

[0023] The filtering module is used to perform anti-vibration filtering on the second sampling sequence when it is determined that the accelerator pedal signal is disturbed by vehicle body vibration, so as to obtain the filtered sampling sequence corresponding to the accelerator pedal signal.

[0024] Thirdly, this application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0025] Memory is used to store instructions executed by the computer;

[0026] A processor for executing computer-executable instructions stored in memory to implement the method described in any of the first aspects.

[0027] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed, are used to implement the method described in any of the first aspects.

[0028] Fifthly, this application provides a computer program product, including a computer program that, when executed, implements the method described in any of the first aspects.

[0029] The accelerator pedal signal filtering method, apparatus, device, medium, and program product provided in this application acquire a first sampling sequence corresponding to the vertical acceleration signal of an electric vehicle at the current moment and a second sampling sequence corresponding to the accelerator pedal signal; determine the acceleration amplitude sequence corresponding to the first sampling sequence and the pedal signal rate of change sequence corresponding to the second sampling sequence; determine the Pearson correlation coefficient, which characterizes the correlation between the vertical acceleration signal and the accelerator pedal signal, based on the acceleration amplitude sequence and the pedal signal rate of change sequence; determine whether the accelerator pedal signal is affected by vehicle body vibration based on the Pearson correlation coefficient; and, if it is determined that the accelerator pedal signal is affected by vehicle body vibration, perform anti-vibration filtering on the second sampling sequence to obtain the filtered sampling sequence corresponding to the accelerator pedal signal. In this process, by acquiring the sampling sequences corresponding to the vertical acceleration signal and the accelerator pedal signal of the electric vehicle respectively, and analyzing their correlation, it is possible to determine whether the pedal signal is affected by vehicle vibration. This allows for accurate identification of whether the accelerator pedal signal is affected by vehicle vibration. If it is determined that the accelerator pedal signal is affected by vehicle vibration, targeted anti-vibration filtering is implemented to accurately filter out interference components while retaining valid operating signals. Compared with traditional filtering schemes, this effectively reduces the misjudgment of the driver's operating intentions by fixed filtering, thereby significantly improving the accuracy and fidelity of the pedal signal in reflecting the driver's true intentions. This provides a more accurate and reliable input signal for vehicle power control, ensuring that the vehicle's power response can quickly and accurately match the driver's operating intentions, effectively guaranteeing the stability and safety of vehicle handling, and thus effectively improving the driving experience. Attached Figure Description

[0030] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0031] Figure 1 A flowchart illustrating a method for filtering accelerator pedal signals provided in an exemplary embodiment of this application;

[0032] Figure 2 Another schematic flowchart of a filtering method for accelerator pedal signals provided in an exemplary embodiment of this application;

[0033] Figure 3 A schematic diagram of a filtering device for accelerator pedal signals provided as an exemplary embodiment of this application;

[0034] Figure 4 This application provides a schematic diagram of the structure of an electronic device as an exemplary embodiment.

[0035] In the diagram, 30—accelerator pedal signal filtering device; 31—data acquisition module; 32—first determination module; 33—second determination module; 34—interference judgment module; 35—filtering module; 40—electronic device; 41—processor; 42—memory; 43—communication interface.

[0036] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0037] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0038] The terms “first,” “second,” etc., used in the specification and claims of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, products, or apparatus.

[0039] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with relevant laws, regulations and standards, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0040] In related technologies, the principle of the first-order low-pass filtering scheme is: Y(n) = a·X(n) + (1-a)·Y(n-1), where a is the filter coefficient (0 < a < 1), X(n) is the sampled value at the current moment, and Y(n) is the filtered output value at the current moment. Because this scheme cannot effectively distinguish between actual operation and vibration noise, it is prone to misjudgment. The principle of the stepped calibration filtering scheme is: Y(n) = Y(n-1) + ΔY, where ΔY is a stepped variable based on the calibration result. ΔY is positive when the signal changes in the direction of increase and negative when it decreases. The stepped calibration filtering scheme quantizes the signal using a fixed stepped variable ΔY. Although it can reduce noise interference to some extent, it is still susceptible to noise interference when the accelerator pedal switches states, leading to signal jitter. Overall, although these two schemes can reduce noise interference to some extent, they still have the problem of not accurately reflecting the driver's operating intentions.

[0041] To address the aforementioned issues, this application provides a filtering scheme for accelerator pedal signals. By real-time acquisition of the vertical acceleration signal and accelerator pedal signal of the electric vehicle, and based on the correlation between the rates of change of the vertical acceleration signal and the accelerator pedal signal, it determines whether the accelerator pedal signal is affected by vehicle vibration. If the accelerator pedal signal is determined to be affected by vehicle vibration, the filtering method is dynamically selected, effectively reducing the impact of vehicle vibration on the accelerator pedal signal. This significantly improves the accuracy and fidelity of the pedal signal in reflecting the driver's true intentions, providing a more precise and reliable input signal for vehicle power control. It ensures that the vehicle's power response can quickly and accurately match the driver's operating intentions, effectively guaranteeing the stability and safety of vehicle handling, and thus significantly improving the driving experience.

[0042] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0043] Figure 1 This is a flowchart illustrating a method for filtering accelerator pedal signals provided as an exemplary embodiment of this application. Figure 1 As shown, the filtering method for the accelerator pedal signal includes the following steps:

[0044] S101. Obtain the first sampling sequence corresponding to the vertical acceleration signal of the electric vehicle at the current moment and the second sampling sequence corresponding to the accelerator pedal signal.

[0045] Vertical acceleration signal refers to the acceleration data of the vehicle in the vertical direction collected by vehicle attitude sensors such as inertial measurement units (IMUs), and the unit is gravitational acceleration g. This signal reflects the vertical vibration characteristics of the vehicle body caused by road bumps, speed bumps, etc., and the typical frequency band is 5-20Hz. For example, when the vehicle goes over a speed bump, the vehicle attitude sensor may detect an instantaneous impact signal with an amplitude of 0.5g and a frequency of 12Hz. Accelerator pedal signal refers to the amount of driver operation collected by pedal position sensors such as potentiometers or Hall sensors, usually expressed as voltage or percentage (0-100%). This signal directly reflects the driver's pressing depth and rate of change of the accelerator pedal.

[0046] For example, when an electric vehicle is in motion, the vehicle attitude sensor continuously collects vertical acceleration signals at a fixed sampling frequency (e.g., once every 10 milliseconds). At any given moment, a series of vertical acceleration values ​​collected by the vehicle attitude sensor constitutes the first sampling sequence corresponding to the vertical acceleration signal at that moment. For example, if 100 vertical acceleration values ​​are collected within 1 second, the sequence of these 100 vertical acceleration values ​​is the first sampling sequence. At the same time, the accelerator pedal sensor collects the accelerator pedal opening signal in real time, also sampling at a fixed sampling frequency. Within the same time period, a series of accelerator pedal opening values ​​collected constitute the second sampling sequence corresponding to the accelerator pedal signal. For example, if 100 accelerator pedal opening values ​​are collected within 1 second, the sequence of these 100 accelerator pedal opening values ​​is the second sampling sequence.

[0047] S102. Determine the acceleration amplitude sequence corresponding to the first sampling sequence and the pedal signal rate of change sequence corresponding to the second sampling sequence.

[0048] The acceleration amplitude sequence is a sequence composed of the acceleration amplitude values ​​of each sampling point in the first sampling sequence arranged in sampling order; the pedal signal rate of change sequence is a sequence composed of the pedal signal rate of change of each sampling point in the second sampling sequence arranged in sampling order. In some embodiments, determining the acceleration amplitude sequence corresponding to the first sampling sequence and the pedal signal rate of change sequence corresponding to the second sampling sequence includes: performing bandpass filtering on the first sampling sequence within a second set frequency range to obtain a bandpass-filtered first sampling sequence; performing FFT on the bandpass-filtered first sampling sequence to obtain the acceleration amplitude sequence corresponding to the first sampling sequence; and for the second sampling sequence, determining the pedal opening rate of change between adjacent sampling points to obtain the pedal signal rate of change sequence corresponding to the second sampling sequence.

[0049] For example, during the operation of an electric vehicle, the vibration frequency caused by uneven road surfaces is generally within 5-15Hz, while the high-frequency noise such as harmonics generated by the motor or mechanical transmission system is generally above 20Hz. In order to effectively filter out high-frequency noise and extract the effective vibration frequency band of the vehicle body, a passband range of, for example, 5-20Hz is selected for bandpass filtering of the first sampling sequence. Specifically, a digital filter algorithm such as a Butterworth bandpass filter can be used to determine the filter parameters, including the filter order and cutoff frequency, based on the sampling frequency and the passband range of 5-20Hz. The data of each sampling point in the first sampling sequence is sequentially input into the filter algorithm. After filtering calculation, the first sampling sequence after bandpass filtering is output. For example, after the first sampling sequence is bandpass filtered, a first sampling sequence containing 100 data points is obtained. The high-frequency noise in this sequence has been effectively filtered out, and the effective vibration signal in the 5-20Hz range is retained. Further, the bandpass filtered first sampling sequence is further processed... Performing an FFT on a sampled sequence converts the time-domain vibration signal into a frequency-domain energy distribution. For example, for the first sampled sequence containing 100 data points after bandpass filtering, calling the FFT function yields a frequency-domain sequence containing 100 complex numbers. The acceleration amplitude corresponding to each frequency component is calculated, i.e., the modulus of the complex number is taken. These amplitudes are arranged in frequency order to obtain the acceleration amplitude sequence corresponding to the first sampled sequence. This sequence reflects the intensity distribution of the vertical acceleration signal at different frequencies. In the target frequency band of 5-20Hz, the peak frequency represents the most dominant vibration component, and the magnitude of the amplitude at this frequency point reflects the intensity of the vibration.

[0050] Accordingly, for the second sampling sequence, for two adjacent sampling points in the sequence, if the accelerator pedal opening values ​​of the two adjacent sampling points are 30% and 35% respectively, the sampling interval Δ t For example, if the interval is 10 milliseconds (i.e., 0.01 seconds), then the rate of change of the pedal signal at that sampling point is... Correspondingly, in this way, the rate of change of pedal opening between all adjacent sampling points in the second sampling sequence is calculated sequentially, and these pedal signal rate of change are arranged in the order of sampling time to obtain the pedal signal rate of change sequence corresponding to the second sampling sequence. This pedal signal rate of change sequence can reflect the speed of change of the accelerator pedal signal at different times, which helps to analyze the driver's driving intention and the dynamic response of the vehicle.

[0051] S103. Based on the acceleration amplitude sequence and the pedal signal rate of change sequence, determine the Pearson correlation coefficient, which characterizes the correlation between the vertical acceleration signal and the accelerator pedal signal.

[0052] The Pearson correlation coefficient is a statistical indicator used to measure the degree of linear correlation between two variables. Its value ranges from -1 to 1; the closer the absolute value is to 1, the stronger the correlation between the two variables, and the closer the absolute value is to 0, the weaker the correlation. For example, suppose the acceleration amplitude sequence is Z = {z1, z2, ..., z...} n The sequence of the rate of change of the pedal signal is as follows: Correspondingly, the Pearson correlation coefficient P satisfies the following formula:

[0053]

[0054] in, The mean of the data in the acceleration amplitude sequence, is the mean of the data in the pedal signal rate of change sequence, and n is the number of data in the acceleration amplitude sequence and the pedal signal rate of change sequence.

[0055] S104. Based on the Pearson correlation coefficient, determine whether the accelerator pedal signal is affected by vehicle body vibration.

[0056] For example, based on the relationship between the Pearson correlation coefficient and a set threshold, it can be determined whether the accelerator pedal signal is affected by vehicle body vibration.

[0057] S105. If it is determined that the accelerator pedal signal is disturbed by vehicle body vibration, the second sampling sequence is subjected to anti-vibration filtering to obtain the filtered sampling sequence corresponding to the accelerator pedal signal.

[0058] For example, when it is determined that the accelerator pedal signal is affected by vehicle body vibration, a preset anti-vibration filtering processing strategy is used to perform anti-vibration filtering processing on the second sampling sequence to filter out the vehicle body vibration interference noise in the second sampling sequence, thereby obtaining the filtered sampling sequence corresponding to the accelerator pedal signal.

[0059] The accelerator pedal signal filtering method provided in this application obtains the sampling sequences corresponding to the electric vehicle's vertical acceleration signal and the accelerator pedal signal, respectively, and analyzes their correlation to determine whether the pedal signal is interfered with by vehicle body vibration. It can accurately identify whether the accelerator pedal signal is interfered with by vehicle body vibration, and when it is determined that the accelerator pedal signal is interfered with by vehicle body vibration, it implements targeted anti-vibration filtering processing to accurately filter out interference components and retain effective operation signals. Compared with traditional filtering schemes, it effectively reduces the misjudgment of the driver's operation intention by fixed filtering, thereby significantly improving the accuracy and restoration of the pedal signal in reflecting the driver's true intention. It provides a more accurate and reliable input signal for vehicle power control, ensuring that the vehicle power response can quickly and accurately match the driver's operation intention, effectively guaranteeing the stability and safety of vehicle body handling, and thus effectively improving the driving experience.

[0060] In some embodiments, the determination of whether the accelerator pedal signal is affected by vehicle body vibration is based on the Pearson correlation coefficient, including: if the Pearson correlation coefficient is greater than a set threshold, then the accelerator pedal signal is determined to be affected by vehicle body vibration; if the Pearson correlation coefficient is less than or equal to the set threshold, then the accelerator pedal signal is determined to be unaffected by vehicle body vibration.

[0061] For example, assuming a threshold of 0.7, if the Pearson correlation coefficient is greater than 0.7, the accelerator pedal signal is determined to be affected by vehicle body vibration; if the Pearson correlation coefficient is less than or equal to 0.7, the accelerator pedal signal is determined to be unaffected by vehicle body vibration. Correspondingly, when an electric vehicle goes over a speed bump, the accelerometer signal, after processing, yields a vertical dominant frequency of 12Hz and a vibration amplitude of 0.5g. The accelerator pedal signal synchronously fluctuates at 12Hz with an amplitude of ±8% / s. Based on the formula for the Pearson correlation coefficient, the Pearson correlation coefficient is determined to be 0.82. Since 0.82 is greater than 0.7, it is determined that the accelerator pedal signal is affected by vehicle body vibration at that moment.

[0062] It should be noted that the above assumption of setting the threshold to 0.7 is only an example. In actual applications, it can be flexibly adjusted according to the actual application requirements, and no limitation is made here.

[0063] In this embodiment, the accelerator pedal signal can be determined to be affected by vehicle body vibration simply by comparing the Pearson correlation coefficient with a set threshold. This simplifies the judgment process, improves real-time processing capability, and makes the filtering process of the accelerator pedal signal more efficient.

[0064] Based on the above embodiments, in some embodiments, the filtering method for the accelerator pedal signal further includes: when it is determined that the accelerator pedal signal is not affected by vehicle body vibration, performing bandpass filtering processing on the second sampling sequence within a first set frequency range to obtain the filtered sampling sequence corresponding to the accelerator pedal signal.

[0065] For example, by observing the frequency domain diagram of the second sampling sequence and combining it with the common frequency range of accelerator pedal operation during normal operation of an electric vehicle, a first set frequency range is determined. For example, in normal driving, the frequency of the driver's operation of the accelerator pedal is mostly concentrated between 1-4Hz, so the first set frequency range is set to 1-4Hz. Correspondingly, based on the sampling frequency and the first set frequency range, a Butterworth bandpass filter is selected, for example, and the order of the filter is determined to be, for example, 4th order, with upper and lower cutoff frequencies of, for example, 1Hz and 4Hz, respectively. Using the set Butterworth bandpass filter, the data of each sampling point in the second sampling sequence is filtered. The input accelerator pedal opening value is calculated by the filter, and frequency components below 1Hz and above 4Hz are filtered out, retaining only the effective signal in the range of 1-4Hz. For example, the original second sampling sequence contains 100 accelerator pedal opening values. After filtering, a filtered sampling sequence containing 100 data points is obtained. The high-frequency noise and low-frequency drift in this sequence have been effectively suppressed.

[0066] It should be noted that setting the first frequency range to 1-4Hz is only an example. In actual applications, it can be flexibly adjusted according to the actual application requirements, and no limitation is made here.

[0067] In this embodiment, after determining that the accelerator pedal signal is not affected by vehicle vibration, bandpass filtering of the second sampling sequence within a first set frequency range can accurately filter out high-frequency noise unrelated to accelerator pedal operation, such as electromagnetic interference from electronic components and low-frequency drift signals. This makes the filtered accelerator pedal signal more stable, thereby providing high-quality input for vehicle power system control, making power output more in line with the driver's operating intention, thereby optimizing power response and driving comfort, and enhancing vehicle driving safety and reliability.

[0068] In related technologies, the first-order low-pass filter uses a fixed filter coefficient 'a', resulting in poor dynamic adaptability. Specifically, under rapid acceleration, the filtered output signal exhibits a significant response delay, failing to promptly track the driver's rapid actions. Furthermore, the output signal of the first-order low-pass filter consistently lags behind the input signal, further exacerbating the response delay problem under rapid acceleration. In contrast, the stepped calibration filter, with its fixed step variable ΔY, struggles to adapt to different signal change rates. Specifically, under rapid acceleration, the fixed step variable leads to insufficient signal tracking capability, failing to accurately reflect the driver's rapid operational intentions; while under gradual acceleration, it easily causes a step change in the output signal, affecting driving smoothness. Therefore, in some embodiments, the second sampling sequence is subjected to anti-vibration filtering to obtain the filtered sampling sequence corresponding to the accelerator pedal signal, including: determining the difference sequence corresponding to the second sampling sequence and the filtered sampling sequence at the previous moment; determining the difference rate of change sequence corresponding to the difference sequence; for each difference rate of change in the difference rate of change sequence, determining the adaptive step size corresponding to the difference rate of change based on the pre-calibrated mapping relationship between the difference, the difference rate of change and the adaptive step size; and summing the adaptive step size sequence corresponding to the difference rate of change sequence with the filtered sampling sequence at the previous moment to obtain the filtered sampling sequence corresponding to the accelerator pedal signal.

[0069] For example, suppose the second sampling sequence corresponding to the current time is RS i The filtered sampled sequence from the previous time step is TS. i-1 The difference sequence between the second sampling sequence and the filtered sampling sequence from the previous time step is ΔS. i =RS i -TS i-1 Furthermore, determine ΔS i The corresponding difference change rate sequence dΔS i / dt. Correspondingly, according to ΔS i and dΔS i / dt, by querying the pre-defined mapping relationship between the difference, the rate of change of the difference, and the adaptive step size, the adaptive step size sequence ΔTS is obtained. i ; Output ΔTS through query i Afterwards, based on TS i =TS i-1 +ΔTS i The filtered sampling sequence TS corresponding to the accelerator pedal signal is obtained. i .

[0070] Optionally, the mapping relationship between the difference, the rate of change of the difference, and the adaptive step size can be a predefined table containing the adaptive step size corresponding to different combinations of differences and rates of change of the difference; or, the mapping relationship between the difference, the rate of change of the difference, and the adaptive step size can also be a two-dimensional matrix, where one dimension represents different values ​​of the difference and the other dimension represents different values ​​of the rate of change of the difference, and each element in the matrix corresponds to an adaptive step size, etc.

[0071] For example, Table 1 is a schematic table provided by an exemplary embodiment of this application for characterizing the mapping relationship between difference, rate of change of difference, and adaptive step size. As shown in Table 1, the first column of data represents different differences, the first row of data represents different rates of change of difference, and the other data in the table represents the adaptive step size corresponding to different combinations of differences and rates of change of difference.

[0072] Table 1

[0073] 20％ / s 50％ / s 100％ / s 200％ / s 500％ / s 5％ 1.0 1.5 2.0 3.0 6.0 10％ 1.8 2.5 3.5 5.0 10.0 15％ 2.5 3.5 5.0 7.0 14.0 20％ 3.0 4.5 6.5 9.0 18.0 25％ 3.5 5.5 8.0 11.0 22.0

[0074] Accordingly, ΔTS is determined by the adaptive step size corresponding to different combinations of differences and rates of change of differences in Table 1. i Furthermore, based on TS i =TS i-1 +ΔTS i The filtered sampling sequence TS corresponding to the accelerator pedal signal is obtained. i .

[0075] It should be noted that Table 1 above is only an example of the mapping relationship between the difference, the rate of change of the difference, and the adaptive step size. In actual applications, it can be pre-calibrated according to the actual application requirements, and no limitation is made here.

[0076] In this embodiment, when the accelerator pedal signal is determined to be disturbed by vehicle body vibration, an adaptive step size is determined for filtering by real-time determination of the difference sequence and the difference change rate sequence, combined with a pre-calibrated mapping relationship. This method has significant advantages over traditional filtering schemes. For example, under rapid acceleration conditions, the step size can be adaptively adjusted according to the difference change rate, effectively overcoming the response delay problem of traditional schemes, quickly tracking the driver's rapid operation, and accurately reflecting the operation intention. Under slow acceleration conditions, the adaptive step size can reduce the step change of the output signal, ensure a smooth signal transition, thereby improving driving smoothness and further enhancing the dynamic adaptability of filtering to different operating conditions.

[0077] In some embodiments, the mapping relationship between the difference, the rate of change of the difference, and the adaptive step size is dynamically changing. When it is determined that the accelerator pedal signal is not affected by vehicle body vibration, the filtering method for the accelerator pedal signal further includes: storing the rate of change of the difference data in the rate of change of the difference sequence and the difference data in the difference sequence into a memory buffer respectively; when the data capacity stored in the memory buffer is greater than a set capacity threshold, updating the mapping relationship between the difference, the rate of change of the difference, and the adaptive step size based on the rate of change of the difference data and the difference data in the memory buffer.

[0078] For example, assuming the accelerator pedal signal is unaffected by vehicle body vibration, the difference rate of change data and the difference data in the aforementioned difference rate of change sequence are stored in a memory buffer using a "first-in, first-out" queue. When the data capacity stored in the memory buffer exceeds a set capacity threshold, such as 10,000 data points, a learning process is triggered. Online learning is performed based on the difference rate of change data and the difference data in the memory buffer to update the mapping relationship between the difference, the difference rate of change, and the adaptive step size, or other filtering parameters. Further, the data in the memory buffer is cleared, and the difference rate of change data in the new difference rate of change sequence and the difference data in the new difference sequence are stored in the memory buffer.

[0079] In this embodiment, when it is determined that the accelerator pedal signal is not affected by vehicle vibration, the rate of change of the difference and the difference data are stored in a memory buffer. The mapping relationship is updated after the data volume exceeds a set capacity threshold, allowing the mapping relationship to closely follow the characteristics of the accelerator pedal signal over time and under different driving conditions. By dynamically updating the mapping relationship, the filtering process can promptly perceive and adapt to these changes, making the adjustment of the adaptive step size more precise and reasonable. This enables the filtered output signal to more accurately reflect the driver's intention to operate the accelerator pedal, significantly improving the filtering process's adaptability to complex and changing conditions and providing a more reliable and practically suitable signal input for the vehicle's powertrain system. Furthermore, timely clearing of the data in the memory buffer after the data volume exceeds the set capacity threshold effectively reduces memory usage, providing strong support for the fast execution and accurate calculation of the filtering process, further improving the performance and response speed of the entire vehicle control system.

[0080] In some embodiments, updating the mapping relationship between difference, difference rate of change, and adaptive step size based on difference change rate data and difference data in a memory buffer includes: determining the mean value corresponding to the difference data in the memory buffer to obtain a first difference value; determining the mean value corresponding to the difference rate of change data in the memory buffer to obtain a second difference rate of change value; based on the mapping relationship between difference, difference rate of change, and adaptive step size, interpolating the associated adaptive step size related to the first difference value and the second difference rate of change value to obtain a target adaptive step size in the mapping relationship between difference, difference rate of change, and adaptive step size; determining the driver's driving habits based on the relationship between the second difference rate of change value and a set difference rate of change threshold; updating the target adaptive step size based on the driving habits to obtain an updated target adaptive step size; and updating the associated adaptive step size in the mapping relationship between difference, difference rate of change, and adaptive step size based on the updated target adaptive step size and a preset weight coefficient corresponding to the driving habits to obtain an updated mapping relationship between difference, difference rate of change, and adaptive step size.

[0081] For example, the mean of 10,000 difference data points in the memory buffer is determined to be 12.0%; the mean of 10,000 difference change rate data points in the memory buffer is determined to be 120.0%; based on the mapping relationship between difference, difference change rate and adaptive step size shown in Table 1, the adaptive step sizes related to 12.0% and 120.0%, such as 3.5, 5.0, 5.0 and 7.0, are interpolated to obtain the target adaptive step size ΔTS corresponding to 12.0% and 120.0% in Table 1, for example, ΔTS = 3.48; since the mean of 10,000 difference change rate data points is 120.0%, which is greater than the set difference change rate... If the rate threshold is, for example, 100%, then the driver's driving habit is determined to be rapid operation, and the target adaptive step size ΔTS+ = 0.3% (0.3% is an example of a weighting coefficient); correspondingly, if the mean value corresponding to 10,000 difference change rate data is less than or equal to the set difference change rate threshold, then the driver's driving habit is determined to be slow operation, and the target adaptive step size ΔTS- = 0.1% (0.1% is another example of a weighting coefficient); furthermore, based on the updated ΔTS, the associated adaptive step size is updated to 3.5, 5.0, 5.0 and 7.0, thereby obtaining the mapping relationship between the updated difference, the difference change rate and the adaptive step size.

[0082] Accordingly, if the mean of 10,000 difference data points in the memory buffer is 10.0%, and the mean of 10,000 difference change rate data points is 200.0%, based on the mapping relationship between difference, difference change rate, and adaptive step size shown in Table 1, the adaptive step size corresponding to 10.0% and 200.0% is 5.0%. Since the mean of the difference change rate data is greater than the set difference change rate threshold, the updated adaptive step size is ΔTS. new =5.3%; further, for ΔTS new A fixed weight coefficient is applied to its surrounding area, such as a 3x3 region, to improve local smoothness; the fixed weight coefficients are shown in Table 2.

[0083] Table 2

[0084] Grid position center Adjacent 4 sides diagonal 4 Weight 1.0 0.6 0.3

[0085] For example, the central grid point: ΔTS new =5.0% + 0.3%; Right grid point: ΔTS new =10.0% + 0.3% * 0.6% = 10.18%, right diagonal grid point: ΔTS new =6.0% + 0.3% * 0.3% = 6.09%. The calculation method for the updated adaptive step size for other grid points is similar, and will not be elaborated here.

[0086] It should be noted that the above-mentioned threshold for the rate of change of difference, such as 100%, the weight coefficient corresponding to the rapid operation, the weight coefficient corresponding to the slow operation, and the weight coefficient corresponding to the grid points at different positions shown in Table 2 are all examples. In actual applications, they can be flexibly adjusted according to actual needs, and no restrictions are imposed here.

[0087] In this embodiment, by relying on the relationship between the second difference change rate value and the set difference change rate threshold, the driver's driving habits can be effectively identified, and the target adaptive step size can be updated based on the driving habits. This allows for personalized dynamic adjustment of the filtering process according to the characteristics of different drivers, making the filtering output more in line with the driver's operating intentions. This further enhances the adaptability of the filtering process to complex driving scenarios, thereby improving the vehicle power system's processing effect on the accelerator pedal signal and the driver's driving experience.

[0088] Figure 2 Another flowchart illustrating a filtering method for accelerator pedal signals provided as an exemplary embodiment of this application. Figure 2 As shown, the filtering method for the accelerator pedal signal includes the following steps:

[0089] S201. Obtain the first sampling sequence corresponding to the vertical acceleration signal of the electric vehicle at the current moment and the second sampling sequence corresponding to the accelerator pedal signal.

[0090] S202. Determine the acceleration amplitude sequence corresponding to the first sampling sequence and the pedal signal rate of change sequence corresponding to the second sampling sequence.

[0091] For example, the first sampling sequence is subjected to bandpass filtering within a second set frequency range to obtain the first sampling sequence after bandpass filtering; the first sampling sequence after bandpass filtering is subjected to FFT to obtain the acceleration amplitude sequence corresponding to the first sampling sequence; for the second sampling sequence, the rate of change of pedal opening between adjacent sampling points is determined to obtain the pedal signal rate of change sequence corresponding to the second sampling sequence.

[0092] S203. Based on the acceleration amplitude sequence and the pedal signal rate of change sequence, determine the Pearson correlation coefficient, which characterizes the correlation between the vertical acceleration signal and the accelerator pedal signal.

[0093] For example, the Pearson correlation coefficient P satisfies the following formula:

[0094]

[0095] in, The mean of the data in the acceleration amplitude sequence, is the mean of the data in the pedal signal rate of change sequence, and n is the number of data in the acceleration amplitude sequence and the pedal signal rate of change sequence.

[0096] S204. Based on the Pearson correlation coefficient, determine whether the accelerator pedal signal is affected by vehicle body vibration.

[0097] If the Pearson correlation coefficient is greater than the set threshold, the accelerator pedal signal is determined to be affected by vehicle body vibration; if the Pearson correlation coefficient is less than or equal to the set threshold, the accelerator pedal signal is determined to be unaffected by vehicle body vibration.

[0098] If so, execute S205;

[0099] If not, proceed to S206.

[0100] S205. Perform anti-vibration filtering on the second sampling sequence to obtain the filtered sampling sequence corresponding to the accelerator pedal signal.

[0101] Specifically, the difference sequence corresponding to the second sampling sequence and the filtered sampling sequence of the previous time step is determined; the difference change rate sequence corresponding to the difference sequence is determined; for each difference change rate in the difference change rate sequence, based on the pre-calibrated mapping relationship between the difference, the difference change rate and the adaptive step size, the adaptive step size corresponding to the difference change rate is determined; the adaptive step size sequence corresponding to the difference change rate sequence is summed with the filtered sampling sequence of the previous time step to obtain the filtered sampling sequence corresponding to the accelerator pedal signal.

[0102] S206. Perform bandpass filtering on the second sampling sequence within a first set frequency range to obtain the filtered sampling sequence corresponding to the accelerator pedal signal.

[0103] S207. Store the difference change rate data in the difference change rate sequence and the difference data in the difference sequence into the memory buffer respectively.

[0104] S208. When the data capacity stored in the memory buffer is greater than the set capacity threshold, update the mapping relationship between the difference, the difference rate of change and the adaptive step size based on the difference change rate data and the difference data in the memory buffer.

[0105] For example, when the data capacity stored in the memory buffer exceeds a set capacity threshold, such as 10,000 data points, a learning process is triggered. Online learning is performed based on the difference change rate data and the difference data in the memory buffer to update the mapping relationship between the difference, the difference change rate, and the adaptive step size, or other filtering parameters. Further, the data in the memory buffer is cleared, and the difference change rate data from the new difference change rate sequence and the difference data from the new difference sequence are stored in the memory buffer respectively.

[0106] This application embodiment achieves differentiated filtering of the accelerator pedal signal of an electric vehicle under different operating conditions by real-time acquisition, processing, signal filtering, data output, and continuous online learning of the vehicle's operating data. Specifically, it can intelligently identify whether the electric vehicle is affected by vibration or not, and adopt appropriate filtering strategies to effectively eliminate noise interference and ensure accurate and stable signals. At the same time, based on the online learning function, it can continuously accumulate and analyze the driver's operating habits, and then dynamically optimize the filtering parameters and processing logic, thereby significantly improving the safety, smoothness, and comfort of accelerator pedal operation during vehicle driving, bringing a better driving experience to the driver.

[0107] In summary, this application has at least the following advantages:

[0108] I. By acquiring the sampling sequences corresponding to the vertical acceleration signal and accelerator pedal signal of the electric vehicle respectively, and analyzing their correlation, it is possible to determine whether the pedal signal is interfered with by vehicle body vibration. This method can accurately identify whether the accelerator pedal signal is interfered with by vehicle body vibration. When it is determined that the accelerator pedal signal is interfered with by vehicle body vibration, targeted anti-vibration filtering is implemented to accurately filter out interference components and retain valid operation signals. Compared with traditional filtering schemes, this method effectively reduces the misjudgment of driver operation intentions by fixed filtering, thereby significantly improving the accuracy and fidelity of the pedal signal in reflecting the driver's true intentions. This provides a more accurate and reliable input signal for vehicle power control, ensuring that the vehicle power response can quickly and accurately match the driver's operation intentions, effectively guaranteeing the stability and safety of vehicle handling, and thus effectively improving the driving experience.

[0109] Second, after confirming that the accelerator pedal signal is not affected by vehicle vibration, by performing bandpass filtering on the second sampling sequence within the first set frequency range, high-frequency noise unrelated to accelerator pedal operation, such as electromagnetic interference from electronic components and low-frequency drift signals, can be accurately filtered out. This makes the filtered accelerator pedal signal more stable, thereby providing high-quality input for vehicle power system control, making power output more in line with the driver's operating intention, thus optimizing power response and driving comfort, and enhancing vehicle driving safety and reliability.

[0110] Third, when the accelerator pedal signal is determined to be affected by vehicle body vibration, an adaptive step size is determined for filtering by real-time determination of the difference sequence and the difference change rate sequence, combined with a pre-calibrated mapping relationship. This method has significant advantages over traditional filtering schemes. For example, under rapid acceleration conditions, the step size can be adaptively adjusted according to the difference change rate, effectively overcoming the response delay problem of traditional schemes, quickly tracking the driver's rapid operation, and accurately reflecting the operation intention. Under slow acceleration conditions, the adaptive step size can reduce the step changes in the output signal, ensure a smooth signal transition, thereby improving driving smoothness and further enhancing the dynamic adaptability of filtering to different operating conditions. When the accelerator pedal signal is confirmed to be unaffected by vehicle vibration, the rate of change of the difference and the difference data are stored in a memory buffer. The mapping relationship is updated when the data volume exceeds a set capacity threshold, ensuring that the mapping relationship closely follows the characteristics of the accelerator pedal signal over time and under different driving conditions. By dynamically updating the mapping relationship, the filtering process can promptly perceive and adapt to these changes, making the adjustment of the adaptive step size more precise and reasonable. This allows the filtered output signal to more accurately reflect the driver's intention to operate the accelerator pedal, significantly improving the filtering process's adaptability to complex and changing conditions and providing a more reliable and practically suitable signal input for the vehicle's powertrain system. Furthermore, timely clearing of the data in the memory buffer after the data volume exceeds the set capacity threshold effectively reduces memory usage, providing strong support for the fast execution and accurate calculation of the filtering process, further improving the performance and response speed of the entire vehicle control system.

[0111] Fourth, by relying on the relationship between the second difference change rate value and the set difference change rate threshold, the driver's driving habits can be effectively identified, and the target adaptive step size can be updated based on the driving habits. This allows for personalized dynamic adjustment of the filtering process according to the characteristics of different drivers, making the filtering output more in line with the driver's operating intentions. This further enhances the adaptability of the filtering process to complex driving scenarios, thereby improving the vehicle's power system's processing effect on the accelerator pedal signal and the driver's driving experience.

[0112] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0113] Figure 3 A schematic diagram of a filtering device for accelerator pedal signals provided as an exemplary embodiment of this application. Figure 3 As shown, the accelerator pedal signal filtering device 30 includes a data acquisition module 31, a first determination module 32, a second determination module 33, an interference determination module 34, and a filtering processing module 35, wherein:

[0114] Data acquisition module 31 is used to acquire the first sampling sequence corresponding to the vertical acceleration signal of the electric vehicle at the current moment and the second sampling sequence corresponding to the accelerator pedal signal;

[0115] The first determining module 32 is used to determine the acceleration amplitude sequence corresponding to the first sampling sequence and the pedal signal change rate sequence corresponding to the second sampling sequence;

[0116] The second determining module 33 is used to determine the Pearson correlation coefficient, which characterizes the correlation between the vertical acceleration signal and the accelerator pedal signal, based on the acceleration amplitude sequence and the pedal signal rate of change sequence.

[0117] The interference determination module 34 is used to determine whether the accelerator pedal signal is affected by vehicle body vibration based on the Pearson correlation coefficient.

[0118] The filtering module 35 is used to perform anti-vibration filtering on the second sampling sequence when it is determined that the accelerator pedal signal is disturbed by vehicle body vibration, so as to obtain the filtered sampling sequence corresponding to the accelerator pedal signal.

[0119] In one possible implementation, the interference determination module 34 can be specifically used to: determine that the accelerator pedal signal is interfered with by vehicle body vibration when the Pearson correlation coefficient is greater than a set threshold; and determine that the accelerator pedal signal is not interfered with by vehicle body vibration when the Pearson correlation coefficient is less than or equal to the set threshold.

[0120] In one possible implementation, the filtering module 35 can be specifically used to: when it is determined that the accelerator pedal signal is not affected by vehicle body vibration, perform bandpass filtering on the second sampling sequence within a first set frequency range to obtain the filtered sampling sequence corresponding to the accelerator pedal signal.

[0121] In one possible implementation, the filtering module 35 can also be used to: determine the difference sequence corresponding to the second sampling sequence and the filtered sampling sequence of the previous time step; determine the difference change rate sequence corresponding to the difference sequence; for each difference change rate in the difference change rate sequence, determine the adaptive step size corresponding to the difference change rate based on the pre-calibrated mapping relationship between the difference, the difference change rate and the adaptive step size; and sum the adaptive step size sequence corresponding to the difference change rate sequence with the filtered sampling sequence of the previous time step to obtain the filtered sampling sequence corresponding to the accelerator pedal signal.

[0122] In one possible implementation, the mapping relationship between the difference, the rate of change of the difference, and the adaptive step size is dynamically changing. When it is determined that the accelerator pedal signal is not affected by vehicle body vibration, the filtering module 35 can also be used to: store the rate of change of the difference data in the rate of change of the difference sequence and the difference data in the difference sequence into a memory buffer respectively; when the data capacity stored in the memory buffer is greater than a set capacity threshold, update the mapping relationship between the difference, the rate of change of the difference, and the adaptive step size based on the rate of change of the difference data and the difference data in the memory buffer.

[0123] In one possible implementation, the filtering module 35 can also be used to: determine the mean value corresponding to the difference data in the memory buffer to obtain a first difference value; determine the mean value corresponding to the difference change rate data in the memory buffer to obtain a second difference change rate value; based on the mapping relationship between the difference, the difference change rate, and the adaptive step size, interpolate the associated adaptive step size related to the first difference value and the second difference change rate value to obtain a target adaptive step size in the mapping relationship between the difference, the difference change rate, and the adaptive step size; based on the relationship between the second difference change rate value and a set difference change rate threshold, determine the driver's driving habits; based on the driving habits, update the target adaptive step size to obtain an updated target adaptive step size; based on the updated target adaptive step size and the preset weight coefficients corresponding to the driving habits, update the associated adaptive step size in the mapping relationship between the difference, the difference change rate, and the adaptive step size to obtain an updated mapping relationship between the difference, the difference change rate, and the adaptive step size.

[0124] In one possible implementation, the first determining module 32 may specifically: perform bandpass filtering on the first sampling sequence within a second set frequency range to obtain a first sampling sequence after bandpass filtering; perform Fast Fourier Transform (FFT) on the first sampling sequence after bandpass filtering to obtain an acceleration amplitude sequence corresponding to the first sampling sequence; and for the second sampling sequence, determine the rate of change of pedal opening between adjacent sampling points to obtain a pedal signal rate of change sequence corresponding to the second sampling sequence.

[0125] The accelerator pedal signal filtering device provided in this application embodiment can execute the technical solution shown in the above-described accelerator pedal signal filtering method embodiment. Its implementation principle and beneficial effects are similar, and will not be repeated here.

[0126] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0127] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0128] It should be noted that the above-described device embodiments are merely illustrative, and the device of this application can also be implemented in other ways; and it should be understood that the division of the various modules of the above device is only a logical functional division, and in actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, these modules can all be implemented in software through processing element calls; they can all be implemented in hardware; or some modules can be implemented by processing element calls to software, and some modules can be implemented in hardware. For example, the filtering processing module can be a separately established processing element, or it can be integrated into a chip of the above device. Alternatively, it can be stored as program code in the memory of the above device, and the filtering processing module's function can be called and executed by a processing element of the above device. The implementation of other modules is similar. In addition, these modules can be fully or partially integrated together, or they can be implemented independently. The processing element here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each of the above modules can be completed through hardware integrated logic circuits in the processor element or software instructions.

[0129] For example, these modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more Digital Signal Processors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs). As another example, when a module is implemented using processing element scheduler code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together as a System-On-a-Chip (SOC).

[0130] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., Digital Video Discs, DVDs), or semiconductor media (e.g., solid-state disks (SSDs)).

[0131] Figure 4 This application provides a schematic diagram of the structure of an electronic device according to exemplary embodiments. For example... Figure 4 As shown, the electronic device 40 in this embodiment includes:

[0132] At least one processor 41; and a memory 42 communicatively connected to said at least one processor;

[0133] The memory 42 stores instructions that can be executed by the at least one processor 41 to cause the electronic device to perform the method as described in any of the above embodiments.

[0134] Alternatively, the memory 42 can be either standalone or integrated with the processor 41.

[0135] The memory 42 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device.

[0136] Processor 41 may be a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application. Specifically, when implementing the accelerator pedal signal filtering method described in the foregoing method embodiments, the electronic device may be, for example, a server or other electronic device with processing capabilities.

[0137] Optionally, the electronic device may also include a communication interface 43. In specific implementations, if the communication interface 43, memory 42, and processor 41 are implemented independently, they can be interconnected via a bus to complete communication. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc., but this does not imply that there is only one bus or one type of bus.

[0138] Optionally, in a specific implementation, if the communication interface 43, memory 42 and processor 41 are integrated on a single chip, then the communication interface 43, memory 42 and processor 41 can communicate through an internal interface.

[0139] The implementation principle and technical effects of the electronic device provided in this embodiment can be found in the foregoing embodiments, and will not be repeated here.

[0140] This application also provides a computer-readable storage medium storing computer-executable instructions. When the computer-executable instructions are executed, they are used to implement the method steps as described in the above method embodiments. The specific implementation methods and technical effects are similar and will not be repeated here.

[0141] The aforementioned computer-readable storage media can be implemented from any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0142] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an application-specific integrated circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in a filtering device for the accelerator pedal signal.

[0143] This application also provides a computer program product, including a computer program, which, when executed, implements the method steps as described in the above method embodiments. The specific implementation and technical effects are similar and will not be repeated here.

[0144] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0145] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0146] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A filtering method for accelerator pedal signals, characterized in that, include: Acquire the first sampling sequence corresponding to the vertical acceleration signal of the electric vehicle at the current moment and the second sampling sequence corresponding to the accelerator pedal signal; Determine the acceleration amplitude sequence corresponding to the first sampling sequence and the pedal signal rate of change sequence corresponding to the second sampling sequence; Based on the acceleration amplitude sequence and the pedal signal rate of change sequence, the Pearson correlation coefficient, which characterizes the correlation between the vertical acceleration signal and the accelerator pedal signal, is determined. Based on the Pearson correlation coefficient, it is determined whether the accelerator pedal signal is affected by vehicle body vibration. If it is determined that the accelerator pedal signal is disturbed by vehicle body vibration, the second sampling sequence is subjected to anti-vibration filtering to obtain the filtered sampling sequence corresponding to the accelerator pedal signal.

2. The filtering method for the accelerator pedal signal according to claim 1, characterized in that, The step of determining whether the accelerator pedal signal is affected by vehicle body vibration based on the Pearson correlation coefficient includes: If the Pearson correlation coefficient is greater than a set threshold, it is determined that the accelerator pedal signal is affected by vehicle body vibration. If the Pearson correlation coefficient is less than or equal to the set threshold, then it is determined that the accelerator pedal signal is not affected by vehicle body vibration.

3. The filtering method for the accelerator pedal signal according to claim 1 or 2, characterized in that, Also includes: If it is determined that the accelerator pedal signal is not affected by vehicle body vibration, the second sampling sequence is subjected to bandpass filtering within a first set frequency range to obtain the filtered sampling sequence corresponding to the accelerator pedal signal.

4. The filtering method for the accelerator pedal signal according to claim 1 or 2, characterized in that, The step of performing anti-vibration filtering on the second sampling sequence to obtain the filtered sampling sequence corresponding to the accelerator pedal signal includes: Determine the difference sequence between the second sampling sequence and the filtered sampling sequence from the previous time step; Determine the difference change rate sequence corresponding to the difference sequence; For each rate of change in the rate of change of difference in the sequence of rate of change of difference, the adaptive step size corresponding to the rate of change of difference is determined based on the pre-calibrated mapping relationship between the difference, the rate of change of difference and the adaptive step size. The adaptive step size sequence corresponding to the difference change rate sequence is summed with the filtered sampling sequence of the previous time step to obtain the filtered sampling sequence corresponding to the accelerator pedal signal.

5. The filtering method for the accelerator pedal signal according to claim 4, characterized in that, The mapping relationship between the difference, the rate of change of the difference, and the adaptive step size is dynamically changing. Assuming the accelerator pedal signal is not affected by vehicle body vibration, the following further applies: The difference change rate data in the difference change rate sequence and the difference data in the difference sequence are stored in a memory buffer respectively; When the data capacity stored in the memory buffer is greater than the set capacity threshold, the mapping relationship between the difference, the difference rate of change and the adaptive step size is updated based on the difference change rate data and the difference data in the memory buffer.

6. The filtering method for the accelerator pedal signal according to claim 5, characterized in that, The step of updating the mapping relationship between the difference, the rate of change of the difference, and the adaptive step size based on the difference change rate data and the difference data in the memory buffer includes: Determine the mean value corresponding to the difference data in the memory buffer to obtain the first difference value; Determine the mean value corresponding to the difference change rate data in the memory buffer to obtain the second difference change rate value; Based on the mapping relationship between the difference, the rate of change of the difference, and the adaptive step size, the target adaptive step size of the first difference value and the second rate of change of the difference value in the mapping relationship between the difference, the rate of change of the difference, and the adaptive step size is obtained by interpolating the associated adaptive step size related to the first difference value and the second rate of change of the difference value. Based on the relationship between the second difference change rate value and the set difference change rate threshold, the driver's driving habits are determined; Based on the driving habits, the target adaptive step size is updated to obtain the updated target adaptive step size. Based on the updated target adaptive step size and the preset weight coefficients corresponding to the driving operation habits, the associated adaptive step size in the mapping relationship between the difference, the rate of change of the difference and the adaptive step size is updated to obtain the updated mapping relationship between the difference, the rate of change of the difference and the adaptive step size.

7. The filtering method for the accelerator pedal signal according to claim 1 or 2, characterized in that, Determining the acceleration amplitude sequence corresponding to the first sampling sequence and the pedal signal rate of change sequence corresponding to the second sampling sequence includes: The first sampled sequence is subjected to bandpass filtering within a second set frequency range to obtain the first sampled sequence after bandpass filtering. Perform a Fast Fourier Transform on the first sampled sequence after bandpass filtering to obtain the acceleration amplitude sequence corresponding to the first sampled sequence; For the second sampling sequence, the rate of change of pedal opening between adjacent sampling points is determined to obtain the pedal signal rate of change sequence corresponding to the second sampling sequence.

8. A filtering device for accelerator pedal signals, characterized in that, include: The data acquisition module is used to acquire the first sampling sequence corresponding to the vertical acceleration signal of the electric vehicle at the current moment and the second sampling sequence corresponding to the accelerator pedal signal; The first determining module is used to determine the acceleration amplitude sequence corresponding to the first sampling sequence and the pedal signal rate of change sequence corresponding to the second sampling sequence; The second determining module is used to determine the Pearson correlation coefficient, which characterizes the correlation between the vertical acceleration signal and the accelerator pedal signal, based on the acceleration amplitude sequence and the pedal signal rate of change sequence. An interference determination module is used to determine whether the accelerator pedal signal is interfered with by vehicle body vibration based on the Pearson correlation coefficient. The filtering module is used to perform anti-vibration filtering on the second sampling sequence when it is determined that the accelerator pedal signal is disturbed by vehicle body vibration, so as to obtain the filtered sampling sequence corresponding to the accelerator pedal signal.

9. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory is used to store computer-executed instructions; The processor is configured to execute the computer execution instructions to implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed, are used to implement the method as described in any one of claims 1 to 7.

11. A computer program product, comprising a computer program, characterized in that, When the computer program is executed, it implements the method as described in any one of claims 1 to 7.

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