Accelerator filter control method and device, computer device and storage medium

Abstract

The embodiment of the application belongs to the technical field of throttle control, and relates to a throttle filtering control method, which comprises the following steps: obtaining a target vehicle driving speed and a throttle pedal control signal; determining a target filtering control strategy based on the target vehicle driving speed; and updating the throttle pedal control signal based on the target filtering control strategy. The application also provides a throttle filtering control device, a computer device and a storage medium. The application can dynamically match appropriate filtering modes at different vehicle speeds according to the current vehicle driving speed, so as to realize the effect that the throttle signal is both stable and real-time responsive.

Description

Technical Field

[0001] This application relates to the field of throttle control technology, and in particular to throttle filtering control methods, devices, computer equipment and storage media. Background Technology

[0002] In modern automotive electronic control systems, the accelerator pedal, as a core input component for the driver's intentions, is crucial for the smoothness of vehicle power output, fuel economy, and driving safety due to the accurate acquisition and stable transmission of its signals. Currently, accelerator pedals typically use potentiometer-type, Hall-effect, or magnetoresistive sensors to convert pedal displacement into electrical signals. However, these signals are susceptible to electromagnetic interference (EMI), wiring noise, and sensor errors during transmission and processing, leading to high-frequency jitter or low-frequency drift, which in turn causes problems such as delayed engine power response and power output fluctuations.

[0003] To address the aforementioned issues, existing technologies primarily employ two methods: hardware filtering and software filtering. For hardware filtering, a common approach is to connect an RC low-pass filter in series at the sensor output, utilizing the frequency characteristics of capacitors and resistors to filter out high-frequency noise, or to use a π-type filter circuit to enhance the suppression of broadband interference. For software filtering, software algorithms such as mean filtering and median filtering are typically used for data processing to optimize signal quality.

[0004] However, hardware filtering circuits have inherent limitations. For example, once the filtering parameters are determined, they are difficult to adjust dynamically, cannot adapt to changes in noise characteristics under different operating conditions, over-filtering may cause signal response delays, affecting the real-time performance of driving controls, and complex hardware circuits increase system cost and space occupation. Regarding software filtering, while commonly used mean filtering and median filtering algorithms can optimize signal quality through data processing, these algorithms lack specificity for certain types of noise and are still difficult to effectively suppress signal fluctuations when the vehicle experiences severe vibrations or sudden changes in the electromagnetic environment. Summary of the Invention

[0005] The purpose of this application is to provide a throttle filtering control method, device, computer equipment, and storage medium to solve the problem that existing throttle pedal signal filtering technologies cannot dynamically adjust according to the actual driving conditions of the vehicle.

[0006] To address the aforementioned technical problems, this application provides a throttle filtering control method, which employs the following technical solution: Acquire the target vehicle's speed and accelerator pedal control signals; Based on the target vehicle's speed, a target filtering control strategy is determined; The accelerator pedal control signal is updated based on the target filtering control strategy.

[0007] Furthermore, the target filtering control strategy includes a high-speed control strategy and a low-speed control strategy. The step of determining the target filtering control strategy based on the target vehicle's speed specifically includes: When the target vehicle's speed is greater than or equal to a preset speed threshold, the current target vehicle is determined to be in a high-speed state. When the target vehicle's speed is less than a preset speed threshold, the target vehicle is determined to be in a low-speed state. When the target vehicle enters a high-speed state, the target filtering control strategy is determined to be the high-speed control strategy; When the target vehicle enters a low-speed state, the target filtering control strategy is determined to be the low-speed control strategy.

[0008] Furthermore, when the target filtering control strategy is the high-speed control strategy, the step of updating the current accelerator pedal control signal based on the target filtering control strategy specifically includes: Obtain the target digital filter; The accelerator pedal control signal is then subjected to a weighted moving average filter based on the target digital filter to update the current accelerator pedal control signal.

[0009] Furthermore, when the target filtering control strategy is the low-speed control strategy, the step of updating the accelerator pedal control signal based on the target filtering control strategy specifically includes: Obtain target noise information; The target noise information and the accelerator pedal control signal are imported into a preset accelerator pedal control model for processing, and the current accelerator pedal control signal is updated.

[0010] Furthermore, before the step of importing the target noise information and the accelerator pedal control signal into a preset accelerator pedal control model for processing and updating the current accelerator pedal control signal, the method further includes: Obtain the Kalman filter state matrix; Based on the Kalman filter state matrix, the accelerator pedal state equation and the accelerator pedal observation equation are established. The accelerator pedal state equation and the accelerator pedal observation equation are imported into a preset general model to obtain the preset accelerator pedal control model.

[0011] Furthermore, the step of importing the target noise information and the accelerator pedal control signal into a preset accelerator pedal control model for processing and updating the current accelerator pedal control signal specifically includes: The target noise information is dynamically modeled, measured offline, and weighted to obtain the measurement noise variance; The target noise information is segmented and calibrated to obtain the process noise; The measured noise variance, the process noise, and the accelerator pedal control signal are imported into a preset accelerator pedal control model for iterative filtering processing to update the current accelerator pedal control signal.

[0012] Furthermore, the step of importing the measured noise variance, the process noise, and the accelerator pedal control signal into a preset accelerator pedal control model for iterative filtering and updating the current accelerator pedal control signal specifically includes: Based on the process noise, the throttle prediction control signal is processed to generate a throttle prediction control signal. Based on the measured noise variance, the throttle prediction control signal is processed to generate a throttle gain control signal. Update the current throttle pedal control signal with the throttle gain control signal.

[0013] To address the aforementioned technical problems, this application also provides a throttle filter control device, which employs the following technical solution: The acquisition module is used to acquire the target vehicle's speed and accelerator pedal control signal; The determination module is used to determine the target filtering control strategy based on the target vehicle's driving speed; An update module is used to update the accelerator pedal control signal based on the target filtering control strategy.

[0014] To address the aforementioned technical problems, this application also provides a computer device that employs the following technical solution: A computer device includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the throttle filter control method.

[0015] To address the aforementioned technical problems, this application also provides a computer-readable storage medium, employing the technical solution described below: A computer-readable storage medium, characterized in that the computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the throttle filtering control method.

[0016] Compared with the prior art, the embodiments of this application have the following main advantages: This application embodiment acquires the target vehicle's driving speed and accelerator pedal control signal, determines a target filtering control strategy based on the target vehicle's driving speed, and updates the accelerator pedal control signal based on the target filtering control strategy. By dynamically matching an appropriate filtering method at different vehicle speeds, it achieves the technical effect of both stable and real-time responsive accelerator signal. Attached Figure Description

[0017] To more clearly illustrate the solutions in this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is an exemplary architecture diagram to which this application can be applied; Figure 2 A flowchart of an embodiment of the throttle filtering control method according to this application; Figure 3 This is a schematic diagram of one embodiment of the throttle filter control device according to this application; Figure 4 This is a schematic diagram of the structure of one embodiment of the computer device according to this application. Detailed Implementation

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings of this application, are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings of this application are used to distinguish different objects, not to describe a particular order.

[0020] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0021] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0022] like Figure 1 As shown, the system architecture 100 of the throttle filter control system may include a terminal device 101, a network 102, and a server 103. The terminal device 101 may be an in-vehicle computer 1011, a tablet computer 1012, or a mobile phone 1013. The network 102 is used as a medium to provide a communication link between the terminal device 101 and the server 103. The network 102 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0023] Users can use terminal device 101 to interact with server 103 via network 102 to receive or send messages, etc. Various communication client applications can be installed on terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social media platform software, etc.

[0024] Terminal device 101 can be various electronic devices with a display screen and support web browsing. In addition to in-vehicle computer 1011, tablet computer 1012 or mobile phone 1013, terminal device 101 can also be e-book reader, MP3 player (Moving Picture Experts Group Audio Layer III), MP4 player (Moving Picture Experts Group Audio Layer IV), laptop computer and desktop computer, etc.

[0025] Server 103 can be a server that provides various services, such as a backend server that provides support for the pages displayed on terminal device 101.

[0026] It should be noted that the throttle filtering control method provided in this application embodiment is generally executed by a server / terminal device, and correspondingly, the throttle filtering control device is generally installed in the server / terminal device.

[0027] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0028] Continue to refer to Figure 2 A flowchart of an embodiment of the throttle filtering control method according to this application is shown. The throttle filtering control method includes the following steps: Step S201: Obtain the target vehicle's driving speed and accelerator pedal control signal.

[0029] In this embodiment, the throttle filtering control method described above can be deployed in a throttle filtering control platform. This platform can be constructed using a server or server cluster. The server or server cluster can be any electronic device with data transmission and data storage functions. The electronic device on which the throttle filtering control method runs (e.g., Figure 1 The server / terminal device shown can receive the target vehicle's speed and accelerator pedal control signals via wired or wireless connection. It should be noted that the aforementioned wireless connection methods may include, but are not limited to, 3G / 4G / 5G connections, WiFi connections, Bluetooth connections, WiMAX connections, Zigbee connections, UWB (ultra-wideband) connections, and other currently known or future wireless connection methods.

[0030] Specifically, the aforementioned target vehicle speed can be the real-time vehicle speed calculated by the vehicle control unit (VCU) based on information such as motor speed, wheel speed sensors, or GPS; the aforementioned accelerator pedal control signal can be the pedal displacement electrical signal collected by the accelerator pedal position sensor (such as potentiometer, Hall effect, or magnetoresistive sensor) and converted into a digital signal through analog-to-digital conversion, used to reflect the driver's acceleration intention.

[0031] In one possible embodiment, the target vehicle's speed can be obtained by the Vehicle Control Unit (VCU) collecting vehicle speed information related to the vehicle's motion state in real time on the onboard control bus (such as Controller Area Network, CAN controller area network bus). The vehicle speed information can be obtained from multiple sources: first, based on the wheel speed signal obtained from the wheel speed sensor, the vehicle speed is calculated by converting the tire rolling radius; second, based on the speed sensor data of the drive motor, the vehicle speed is calculated by combining the transmission ratio; third, in some vehicles, the absolute driving speed can also be obtained through the onboard GPS module as redundant information to correct for speed deviations in situations such as wheel slippage. Finally, the vehicle speed data from the above different sources are compared and weighted by the fusion algorithm inside the VCU to obtain the final target vehicle speed.

[0032] In one possible embodiment, the accelerator pedal control signal can be acquired in real time by a position sensor mounted on the accelerator pedal. Specifically, this position sensor can be a potentiometer-type sensor, reflecting pedal displacement through resistance changes; or it can be a Hall effect or magnetoresistive sensor, obtaining the pedal angular displacement signal by detecting changes in the magnetic field. The acquired analog signal is first converted into a digital accelerator pedal opening signal through analog-to-digital conversion (A / D conversion). To ensure the accuracy and robustness of the accelerator pedal control signal, the accelerator pedal filtering control system typically employs a dual-channel redundancy design, i.e., configuring two independent sensor channels to output accelerator pedal opening signals separately, which are then used by the controller for consistency detection and fault diagnosis.

[0033] Step S202: Determine the target filtering control strategy based on the target vehicle's speed.

[0034] In this embodiment, the target filtering control strategy can be a filtering algorithm dynamically selected based on the target vehicle's speed. Specifically, when the target vehicle is traveling at low speed, an adjustable coefficient adaptive filtering strategy is used to enhance the response sensitivity to subtle accelerator pedal operations and suppress low-frequency noise; when the target vehicle is traveling at high speed, a weighted moving average filtering strategy is used to smooth high-frequency jitter and reduce computational load, thereby balancing the real-time performance and stability of the accelerator signal.

[0035] Specifically, the implementation process of determining the target filtering control strategy based on the target vehicle's speed will be described in more detail in subsequent specific embodiments of this application, and will not be elaborated on here.

[0036] Step S203: Update the accelerator pedal control signal based on the target filtering control strategy.

[0037] Specifically, the detailed implementation process of updating the accelerator pedal control signal based on the target filtering control strategy will be further described in subsequent specific embodiments of this application, and will not be elaborated on here.

[0038] This application obtains the target vehicle's driving speed and accelerator pedal control signal, determines a target filtering control strategy based on the target vehicle's driving speed, updates the accelerator pedal control signal based on the target filtering control strategy, and achieves the technical effect of both smooth and real-time response of the accelerator signal by dynamically matching an appropriate filtering method at different vehicle speeds.

[0039] In some alternative implementations, step S202 includes the following steps: When the target vehicle's speed is greater than or equal to a preset speed threshold, the target vehicle is determined to be in a high-speed state. When the target vehicle's speed is less than the preset speed threshold, the target vehicle is determined to be in a low-speed state. When the target vehicle enters a high-speed state, the target filtering control strategy is determined to be a high-speed control strategy. When the target vehicle enters a low-speed state, the target filtering control strategy is determined to be a low-speed control strategy.

[0040] In this embodiment, the high-speed state can refer to the operating condition when the target vehicle's speed is greater than or equal to a preset speed threshold. The preset speed threshold can be set according to the vehicle type, power system characteristics, and control requirements, such as 40km / h or 60km / h. The low-speed state can refer to the operating condition when the target vehicle's speed is less than the preset speed threshold, mainly covering scenarios such as vehicle start-up, low-speed cruising, and following other vehicles in congested traffic.

[0041] The aforementioned low-speed control strategy can be a control method based on an adaptive filtering algorithm. Specifically, by establishing the accelerator pedal state equation and observation equation, and combining dynamic measurement noise variance and process noise modeling, real-time prediction and updating of the accelerator pedal signal can be achieved, thereby enhancing the ability to capture subtle driver operations and effectively suppressing low-frequency drift or random noise.

[0042] The aforementioned high-speed control strategy can be based on a weighted moving average filtering method. Specifically, the accelerator pedal signal can be weighted and summed within a fixed-length sampling window, with higher weights for data from closer moments and lower weights for data from farther moments. This smooths out high-frequency jitter and reduces signal delay, meeting the stability and real-time requirements under high-speed driving conditions.

[0043] In one possible implementation, when the vehicle speed is below 20 km / h, it is in a low-speed state. At this time, a low-speed control strategy, namely an adaptive filtering algorithm, is adopted to enable the accelerator pedal signal to quickly follow the driver's slight acceleration actions and avoid delay caused by filtering. When the vehicle speed exceeds 50 km / h, it is in a high-speed state. At this time, a high-speed control strategy is adopted. For example, the accelerator pedal signal is filtered by a weighted moving average FIR filter, which can effectively suppress high-frequency jitter signals caused by road vibration during high-speed driving and ensure the smoothness of vehicle power output.

[0044] In some alternative implementations, step S203 includes the following steps: Obtain the target digital filter; The accelerator pedal control signal is then processed by a weighted moving average filter based on the target digital filter to update the current accelerator pedal control signal.

[0045] In this embodiment, the target digital filter can be a Finite Impulse Response (FIR) filter used to smooth the accelerator pedal control signal.

[0046] In this embodiment, the process of updating the current accelerator pedal control signal by performing weighted moving average filtering on the accelerator pedal control signal based on the target digital filter can be achieved through the following steps: A sampling window of fixed length is set to store the accelerator pedal control signal sequence acquired within a preset period; Assign corresponding weight coefficients to each sampling point within the sampling window. These weight coefficients can be set differently according to the time sequence, so that signal samples closer to the current time have a larger weight, while signal samples farther away from the current time have a smaller weight. The throttle filter control system calculates a weighted moving average based on the target digital filter, which involves multiplying the signal value of each sampling point within the window by the corresponding weight coefficient and summing the results, and then normalizing the results. The calculated weighted moving average is used as the updated throttle pedal control signal output. Through the above process, the impact of high-frequency jitter can be reduced while ensuring signal stability, thereby improving the stability and responsiveness of throttle control under high-speed conditions.

[0047] In one possible embodiment, at high vehicle speeds, a weighted moving average FIR filter can be used to filter the accelerator pedal signal. To ensure the influence weight of recent measurement signals and reduce accelerator signal lag, the weighting coefficient for the most recent moment can be increased. Let the weighting coefficient be... (satisfy ), Throttle filter output for: Weighted data can be set, such as: In some alternative implementations, step S203 may also include the following steps: Obtain target noise information; The target noise information and accelerator pedal control signal are imported into a preset accelerator pedal control model for processing, and the current accelerator pedal control signal is updated.

[0048] In this embodiment, the target noise information can be the measurement noise and process noise encountered by the accelerator pedal signal during acquisition and transmission. Specifically, the measurement noise can be caused by sensor accuracy limitations, electromagnetic interference, and line jitter, while the process noise is related to the driver's operating habits and vehicle vibration characteristics. The acquisition method can include a combination of static calibration and dynamic correction. That is, by collecting a large number of sampling points at different accelerator pedal openings (such as 0%, 20%, 40%, 60%, 80%, 100%) and calculating the variance to obtain the measurement noise distribution, and at the same time, by combining the vibration frequency and amplitude extracted from the motor speed fluctuation, a frequency-amplitude coupled noise correction factor is constructed, thereby forming a complete target noise information input, providing parameter support for the preset accelerator pedal control model.

[0049] In this embodiment, the aforementioned preset accelerator pedal control model can be a dynamic control model constructed based on state-space modeling and filtering algorithms. Specifically, the preset accelerator pedal control model consists of an accelerator pedal state equation and an accelerator pedal observation equation. The accelerator pedal state equation describes the dynamic evolution characteristics of the accelerator pedal opening and its rate of change over time, while the accelerator pedal observation equation reflects the actual measurement results of the accelerator pedal opening by the sensor. In one possible embodiment, the aforementioned preset accelerator pedal control model further incorporates a Kalman filter algorithm or other adaptive filtering methods. By iteratively predicting and updating the input accelerator pedal control signal and target noise information, an optimized accelerator pedal control signal output is obtained. This preset accelerator pedal control model effectively mitigates random noise and vibration interference while maintaining real-time tracking of the driver's operating intentions, thereby significantly improving the stability and reliability of the accelerator pedal control signal.

[0050] Specifically, the detailed implementation process of importing the target noise information and accelerator pedal control signal into the preset accelerator pedal control model for processing and updating the current accelerator pedal control signal will be further described in subsequent specific embodiments of this application, and will not be elaborated on here.

[0051] In some alternative implementations, the following steps may be included before the step "importing the target noise information and accelerator pedal control signal into a preset accelerator pedal control model for processing, and updating the current accelerator pedal control signal": Obtain the Kalman filter state matrix; Based on the Kalman filter state matrix, the accelerator pedal state equation and accelerator pedal observation equation are established. The accelerator pedal state equation and accelerator pedal observation equation are imported into a preset general model to obtain a preset accelerator pedal control model.

[0052] In this embodiment, the Kalman filter state matrix can be a state transition matrix used to characterize the evolution of the accelerator pedal state over time. Its parameters are determined by the sampling period and the accelerator pedal opening change rate. For example, with a sampling period of 10ms, a two-dimensional state matrix can be constructed with accelerator pedal opening and its rate of change as dual state variables. Specifically, the Kalman filter state matrix can include the accelerator pedal opening... and rate of change dual-state vector Among them, the accelerator pedal opening The accelerator pedal opening at time k reflects the depth to which the driver depresses the pedal. Rate of change The rate of change of accelerator pedal opening at time k reflects the driver's pedal speed.

[0053] In this embodiment, the aforementioned accelerator pedal state equation can be a discrete-time state equation used to describe the dynamic changes in accelerator pedal opening and its rate of change. A process noise term is introduced to reflect driver operational uncertainties. The aforementioned accelerator pedal observation equation can be an observation equation constructed from the accelerator pedal opening directly measured by a position sensor. This observation equation includes a measurement noise term to characterize the effects of sensor accuracy limitations and external interference. The aforementioned preset general model can be a Kalman filter framework model composed of both the state equation and the observation equation. This model can calculate the estimated value of the accelerator pedal signal based on the state transition relationship during the prediction phase and iteratively correct it in the update phase by combining sensor measurement results, thereby outputting a filtered and optimized accelerator pedal control signal.

[0054] Specifically, the formula for calculating the above-mentioned accelerator pedal state equation can be as follows: in This represents the accelerator pedal state vector at time k, typically including the accelerator pedal opening. and the rate of change of accelerator pedal opening , Represents the state transition matrix. , This represents the accelerator pedal state vector at the previous time k-1. This represents process noise, follows a Gaussian distribution with a mean of zero, and is used to reflect uncertainties caused by driver operation or external disturbances.

[0055] In one possible implementation, because the calculation of the low-speed control strategy is performed within a 10ms Task scheduling period, This indicates the current accelerator pedal opening. The rate of change of the accelerator pedal opening is approximately constant over a short period of time, therefore .

[0056] In one possible embodiment, process noise , representing the Time's up Time-of-flight noise It follows a mean of 0 and a covariance of It follows a normal distribution. The covariance matrix is ​​2×2. . Let Variance be the opening noise variance. The rate of change noise variance is used to characterize the model's tolerance to "non-uniform operation".

[0057] Specifically, the above accelerator pedal observation equation can be expressed as follows: Where the observation matrix The sensor directly measures the opening degree and measures the noise. , To measure the noise variance, which reflects the sensor noise level. Let be the accelerator pedal state vector at time k.

[0058] In one possible embodiment, the process of importing the accelerator pedal state equation and observation equation into a preset general model may include the following steps: I. Based on the dynamic characteristics of the accelerator pedal, define state information including accelerator pedal opening degree and accelerator pedal opening degree change rate, and establish a state equation to describe the state change over time; at the same time, based on the characteristic that the sensor can directly collect the accelerator pedal opening degree, establish an observation equation to describe the relationship between the observation and the actual state. 2. Input the state equation and observation equation into a general filtering modeling framework, and combine the calibration results of process noise and measurement noise to construct the accelerator pedal control model. Third, the above-mentioned accelerator pedal control model iterates through two stages, prediction and update, to correct and optimize the collected accelerator pedal signals in real time, and outputs stable and accurate accelerator control signals for subsequent vehicle control strategy execution.

[0059] In some alternative implementations, the step "importing the target noise information and accelerator pedal control signal into a preset accelerator pedal control model for processing, and updating the current accelerator pedal control signal" may include the following steps: The target noise information is dynamically modeled, measured offline, and weighted to obtain the measurement noise variance; The target noise information is segmented and calibrated to obtain the process noise; The measured noise variance, process noise, and accelerator pedal control signal are imported into a preset accelerator pedal control model for iterative filtering to update the current accelerator pedal control signal.

[0060] In this embodiment, the aforementioned measurement noise variance refers to the variance value calculated from the fluctuations of the measurement signal collected by the accelerator pedal position sensor under static conditions at different accelerator pedal openings. This measurement noise variance reflects the sensor's own measurement accuracy and the influence of external electromagnetic interference and circuit jitter on the measurement results. It can be obtained by repeatedly sampling at typical accelerator pedal opening positions such as 0%, 20%, 40%, 60%, 80%, and 100%, statistically analyzing the dispersion of the sampled values, thereby forming a measurement noise variance calibration table. This measurement noise variance can then be dynamically corrected in practical applications by considering the motor's speed and vibration characteristics.

[0061] In one possible embodiment, dynamic modeling, offline measurement, and weighted processing can be achieved through offline measurement calculation combined with motor speed fluctuation correction. Specifically, this may include the following steps: 1. Obtain an estimate of the measurement noise variance R at 0% opening.

[0062] With the pedal at 0% opening and the accelerator pedal not depressed, continuously collect N measurement values. ,suggestion The sampling frequency is 100Hz.

[0063] Calculate the sample variance, which is an estimate of the measurement noise variance R.

[0064] R 2. Obtain an estimate of the measurement noise variance R at 100% opening.

[0065] With the pedal fixed at 100% opening and the accelerator pedal fully depressed, N measurements were continuously collected. ,suggestion The sampling frequency is 100Hz.

[0066] Calculate the sample variance, which is an estimate of the measurement noise variance R.

[0067] R 3. Using the car's accelerator pedal lifter, obtain the measured noise variance values ​​at 20%, 40%, 60%, and 80% respectively. The calculation method for the measured noise variance under different accelerator pedal openings is the same as above.

[0068] IV. Based on the different measured noise variance values ​​obtained above, establish a two-dimensional measured noise variance table, where the horizontal axis of the table is input as the current estimated accelerator pedal opening, and the measured noise variance is... Regarding accelerator pedal opening The continuous mapping function is as follows: 5. Extract the vibration noise frequency and amplitude of the motor speed, and construct a noise amplification factor that couples frequency and amplitude. The specific formula is as follows: The parameters have the following meanings: Sensitivity coefficient (engineering calibration value, such as 0.001) controls the overall amplification intensity; The pedal resonant frequency (obtained experimentally, such as 15Hz, at which vibration has the greatest impact on the pedal). Frequency bandwidth (e.g., 10Hz) controls the rate attenuation when deviating from the resonant frequency. Vibration amplitude (calculated from rotational speed signal; the larger the value, the more intense the vibration). When the vibration frequency is close to the pedal resonance frequency hour, The influence factor is mainly composed of amplitude. Decide; When the vibration frequency is far away hour, Rapid decay reduces the impact on noise, even with large amplitude.

[0069] A noise fusion algorithm model is used to combine the frequency influence factor with the aperture noise model. In one possible embodiment, the segmented calibration process can be performed offline. Specifically, the calibration method involves establishing a mapping relationship between "rate of change interval → optimal Q value" and generating a two-dimensional calibration table. During actual vehicle operation, the calibration table is then used based on the currently estimated rate of change. By looking up a table and dynamically switching Q, stable operation with smooth noise and fast response in dynamic operation can be achieved.

[0070] Process noise can be adjusted according to the rate of change of accelerator pedal opening. The calibration and adaptation are performed in three stages: During constant speed cruising, gently press the accelerator, pay attention to noise suppression, and appropriately reduce process noise; Normal acceleration and overtaking maneuvers, balancing tracking accuracy and noise suppression; Rapid acceleration and deceleration, dynamic response speed, and appropriately increase process noise to minimize lag; In one possible embodiment, at low vehicle speeds (V<5km / h), to accommodate the condition of preventing accidental throttle pedal depressing, the process noise Q is appropriately increased to allow the throttle to quickly follow the driver's operation, avoiding lag in throttle filtering that would cause lag in the recognition of accidental throttle pedal depressing and thus output a portion of the drive torque.

[0071] Specifically, the implementation process of importing the measured noise variance, process noise, and accelerator pedal control signal into a preset accelerator pedal control model for iterative filtering and updating the current accelerator pedal control signal will be described in more detail in subsequent specific embodiments of this application, and will not be elaborated on here.

[0072] In some alternative implementations, the step "importing the measured noise variance, process noise, and accelerator pedal control signal into a preset accelerator pedal control model for iterative filtering and updating the current accelerator pedal control signal" may include the following steps: Based on process noise, the throttle prediction control signal is processed to generate a throttle prediction control signal. Gain processing is performed on the throttle prediction control signal based on the measurement noise variance to generate a throttle gain control signal. Update the current throttle pedal control signal with the throttle gain control signal.

[0073] In this embodiment, the aforementioned throttle prediction control signal can be a signal obtained by calculating the opening degree of the throttle pedal signal in the next sampling period based on process noise. This signal reflects the prior estimate of the pedal state and is used to reflect the driver's possible operating trend in the prediction stage of filtering iteration. The aforementioned throttle gain control signal can be obtained by weighting and correcting the predicted signal based on the measurement noise variance. It fuses the predicted value and the actual measured value through filtering gain, thereby effectively suppressing measurement noise while ensuring response speed, and realizing dynamic optimization and updating of the current throttle pedal control signal.

[0074] In one possible embodiment, the dynamic characteristics of the accelerator pedal are modeled and predicted based on process noise. In each sampling period, state information such as the rate of change of pedal opening is used to estimate the accelerator pedal signal for the next moment, thereby generating a throttle predictive control signal. Subsequently, combined with a pre-calibrated measurement noise variance, the throttle predictive control signal is gain-processed to balance the weighting of the predicted value and the actual sensor measurement value, generating an optimized throttle gain control signal. This throttle gain control signal is then output as a new accelerator pedal control signal for execution by the vehicle's powertrain. This process effectively suppresses sensor noise and environmental interference while ensuring a rapid response of the pedal signal under dynamic conditions, achieving a balance between smoothness and real-time performance in throttle control, thus improving driving comfort and the overall vehicle powertrain control precision.

[0075] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results. Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing related hardware through computer-readable instructions. These computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0076] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by 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 flowcharts of the accompanying figures 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, and their execution order 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.

[0077] Further reference Figure 3 As a response to the above Figure 2 The present application provides an embodiment of a throttle filter control device, which is similar to the method shown. Figure 2 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0078] like Figure 3 As shown, the throttle filter control device 300 described in this embodiment includes: an acquisition module 301, a determination module 302, and an update module 303. Wherein: The acquisition module 301 is used to acquire the target vehicle's driving speed and accelerator pedal control signal; The determining module 302 is used to determine a target filtering control strategy based on the target vehicle's driving speed; The update module 303 is used to update the accelerator pedal control signal based on the target filtering control strategy.

[0079] The determining module 302 includes: The first determining submodule is used to determine that the current target vehicle is in a high-speed state when the target vehicle's driving speed is greater than or equal to a preset vehicle speed threshold. The second determining submodule is used to determine that the current target vehicle is in a low-speed state when the target vehicle's driving speed is less than a preset vehicle speed threshold. The third determining submodule is used to determine the target filtering control strategy as the high-speed control strategy when the target vehicle enters the high-speed state. The fourth determination submodule is used to determine the target filtering control strategy as the low-speed control strategy when the target vehicle enters a low-speed state.

[0080] The update module 303 includes: The first acquisition submodule is used to acquire the target digital filter; The first update submodule is used to perform weighted moving average filtering on the accelerator pedal control signal based on the target digital filter, and update the current accelerator pedal control signal.

[0081] The update module 303 further includes: The second acquisition submodule is used to acquire target noise information; The second update submodule is used to import the target noise information and the accelerator pedal control signal into a preset accelerator pedal control model for processing, and update the current accelerator pedal control signal.

[0082] Prior to the second update submodule, the following is also included: The third acquisition submodule is used to acquire the Kalman filter state matrix; A submodule is established to establish the accelerator pedal state equation and the accelerator pedal observation equation based on the Kalman filter state matrix; The import submodule is used to import the accelerator pedal state equation and the accelerator pedal observation equation into a preset general model to obtain the preset accelerator pedal control model.

[0083] The second update submodule includes: The first processing unit is used to dynamically model, measure offline, and weight the target noise information to obtain the measurement noise variance. The second processing unit is used to perform segmented calibration processing on the target noise information to obtain process noise; The update unit is used to import the measurement noise variance, the process noise, and the accelerator pedal control signal into a preset accelerator pedal control model for iterative filtering processing, and update the current accelerator pedal control signal.

[0084] The update unit includes: The first processing subunit is used to perform predictive processing on the throttle predictive control signal based on the process noise, and generate the throttle predictive control signal. The second processing subunit is used to perform gain processing on the throttle prediction control signal based on the measurement noise variance to generate a throttle gain control signal. An update subunit is used to update the current throttle pedal control signal with the throttle gain control signal.

[0085] In this embodiment, the operations performed by the above-mentioned units or modules correspond one-to-one with the steps of the throttle filtering control method of the above-described embodiments, and will not be described again here.

[0086] To address the aforementioned technical problems, embodiments of this application also provide a computer device. Please refer to [link / reference needed]. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0087] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected via a system bus. It should be noted that only the computer device 4 with components 41-43 is shown in the figure; however, it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described here is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0088] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0089] The memory 41 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 41 may be an internal storage unit of the computer device 4, such as the hard disk or memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 4. Of course, the memory 41 may also include both the internal storage unit and its external storage device of the computer device 4. In this embodiment, the memory 41 is typically used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions for throttle filter control methods. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or will be output.

[0090] In some embodiments, the processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to execute computer-readable instructions stored in the memory 41 or to process data, for example, to execute computer-readable instructions for the throttle filter control method.

[0091] The network interface 43 may include a wireless network interface or a wired network interface, which is typically used to establish communication connections between the computer device 4 and other electronic devices.

[0092] This application also provides another embodiment, namely, providing a computer-readable storage medium storing computer-readable instructions that can be executed by at least one processor to cause the at least one processor to perform the steps of the throttle filter control method described above.

[0093] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0094] Obviously, the embodiments described above are only some embodiments of this application, not all embodiments. The accompanying drawings show preferred embodiments of this application, but do not limit the patent scope of this application. This application can be implemented in many different forms; rather, the purpose of providing these embodiments is to provide a more thorough and comprehensive understanding of the disclosure of this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this application's specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the scope of patent protection of this application.

Claims

1. A throttle filter control method characterized by, Includes the following steps: Acquire the target vehicle's speed and accelerator pedal control signals; Based on the target vehicle's speed, a target filtering control strategy is determined; when the target vehicle's speed is low, an adjustable coefficient adaptive filtering strategy is adopted, and when the target vehicle's speed is high, a weighted moving average filtering strategy is adopted. The accelerator pedal control signal is updated based on the target filtering control strategy.

2. The throttle filtering control method according to claim 1, characterized in that, The target filtering control strategy includes a high-speed control strategy and a low-speed control strategy. The step of determining the target filtering control strategy based on the target vehicle's speed specifically includes: When the target vehicle's speed is greater than or equal to a preset speed threshold, the current target vehicle is determined to be in a high-speed state. When the target vehicle's speed is less than a preset speed threshold, the target vehicle is determined to be in a low-speed state. When the target vehicle enters a high-speed state, the target filtering control strategy is determined to be the high-speed control strategy; When the target vehicle enters a low-speed state, the target filtering control strategy is determined to be the low-speed control strategy.

3. The throttle filtering control method according to claim 2, characterized in that, When the target filtering control strategy is the high-speed control strategy, the step of updating the current accelerator pedal control signal based on the target filtering control strategy specifically includes: Obtain the target digital filter; The accelerator pedal control signal is then subjected to a weighted moving average filter based on the target digital filter to update the current accelerator pedal control signal.

4. The throttle filtering control method according to claim 2, characterized in that, When the target filtering control strategy is the low-speed control strategy, the step of updating the accelerator pedal control signal based on the target filtering control strategy specifically includes: Obtain target noise information; The target noise information and the accelerator pedal control signal are imported into a preset accelerator pedal control model for processing, and the current accelerator pedal control signal is updated.

5. The throttle filtering control method according to claim 4, characterized in that, Before the step of importing the target noise information and the accelerator pedal control signal into a preset accelerator pedal control model for processing and updating the current accelerator pedal control signal, the method further includes: Obtain the Kalman filter state matrix; Based on the Kalman filter state matrix, the accelerator pedal state equation and the accelerator pedal observation equation are established. The accelerator pedal state equation and the accelerator pedal observation equation are imported into a preset general model to obtain the preset accelerator pedal control model.

6. The throttle filtering control method according to claim 4, characterized in that, The step of importing the target noise information and the accelerator pedal control signal into a preset accelerator pedal control model for processing and updating the current accelerator pedal control signal specifically includes: The target noise information is dynamically modeled, measured offline, and weighted to obtain the measurement noise variance; The target noise information is segmented and calibrated to obtain the process noise; The measured noise variance, the process noise, and the accelerator pedal control signal are imported into a preset accelerator pedal control model for iterative filtering processing to update the current accelerator pedal control signal.

7. The throttle filtering control method according to claim 6, characterized in that, The step of importing the measured noise variance, the process noise, and the accelerator pedal control signal into a preset accelerator pedal control model for iterative filtering and updating the current accelerator pedal control signal specifically includes: Based on the process noise, the throttle prediction control signal is processed to generate a throttle prediction control signal. Based on the measured noise variance, the throttle prediction control signal is processed to generate a throttle gain control signal. Update the current throttle pedal control signal with the throttle gain control signal.

8. A throttle filter control device, characterized in that, include: The acquisition module is used to acquire the target vehicle's speed and accelerator pedal control signal; The determination module is used to determine the target filtering control strategy based on the target vehicle's driving speed; When the target vehicle is traveling at a low speed, an adjustable coefficient adaptive filtering strategy is used; when the target vehicle is traveling at a high speed, a weighted moving average filtering strategy is used. An update module is used to update the accelerator pedal control signal based on the target filtering control strategy.

9. A computer device, characterized in that, The system includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the throttle filter control 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-readable instructions, which, when executed by a processor, implement the steps of the throttle filtering control method as described in any one of claims 1 to 7.

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