Vehicle torque adjusting method and device and storage medium

Abstract

The invention discloses a vehicle torque adjusting method and device and a storage medium, and relates to the technical field of torque adjusting.The method comprises the steps that acceleration data and wheel speed data of a vehicle in the driving process are obtained; determining a vertical motion sickness coefficient according to the acceleration data; determining a pavement roughness coefficient according to the wheel speed data; and according to the pavement roughness coefficient and the vertical motion sickness coefficient, the current demand torque of the vehicle is filtered, and the target demand torque is obtained. The road condition and the carsickness condition of a user are comprehensively considered, so that the required torque of the vehicle is dynamically adjusted according to the road roughness coefficient and the vertical motion sickness coefficient, the torque adjusting effect is improved, sudden power change caused by sudden road change or driving behaviors is reduced, stable operation of a vehicle power system is kept, and the driving safety of the vehicle is improved. And the driving comfort is improved according to the carsickness degree of the passenger while the performance of each driving mode is not influenced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of torque adjustment, in particular to a vehicle torque adjustment method, device and storage medium. BACKGROUND

[0002] Road roughness will affect the driving comfort and the durability of various components during vehicle driving. When the vehicle drives on rough road conditions, there may be rapid torque changes, which exacerbate the impact and make the vehicle jolt, resulting in poor driving experience.

[0003] For the estimation of road roughness, the current scheme generally analyzes through the installation of additional special sensors inside the tire, through the images captured by the vehicle-mounted camera and the noise collected by the in-vehicle microphone, but there are problems such as additional cost increase, difficulty in analyzing images in dark environment and low signal-to-noise ratio, and the obtained road roughness is not accurate enough, thereby the effect of improving the vehicle comfort is poor. SUMMARY

[0004] The main purpose of the present application is to provide a vehicle torque adjustment method, device and storage medium, which aims to solve the technical problem that the current road roughness has poor effect on improving the vehicle comfort.

[0005] To achieve the above-mentioned purpose, the present application provides a vehicle torque adjustment method, which comprises:

[0006] obtaining acceleration data and wheel speed data of the vehicle during driving;

[0007] determining a vertical motion sickness coefficient according to the acceleration data;

[0008] determining a road roughness coefficient according to the wheel speed data;

[0009] filtering the current demand torque of the vehicle according to the road roughness coefficient and the vertical motion sickness coefficient to obtain a target demand torque.

[0010] In an embodiment, the step of determining a vertical motion sickness coefficient according to the acceleration data comprises:

[0011] calculating a vertical mean square displacement speed coefficient according to the acceleration data;

[0012] correcting the vertical mean square displacement speed coefficient based on the pedal signal of the vehicle to obtain a vertical motion sickness coefficient.

[0013] In an embodiment, the step of correcting the vertical mean square displacement speed coefficient based on the pedal signal of the vehicle to obtain a vertical motion sickness coefficient comprises:

[0014] The longitudinal acceleration is obtained based on the acceleration data, and the throttle opening rate and brake pedal switch flag are determined based on the vehicle's pedal signal.

[0015] When the rate of change of the throttle opening is greater than a preset rate of change threshold or the brake pedal switch flag is a preset value, the correction coefficient corresponding to the longitudinal acceleration is obtained.

[0016] The vertical mean square displacement velocity coefficient is corrected according to the correction factor to obtain the vertical halos coefficient.

[0017] In one embodiment, the step of determining the road surface roughness coefficient based on the wheel speed data includes:

[0018] The angular acceleration is obtained by discretely differentiating the wheel speed data;

[0019] Calculate the angular acceleration variance based on the angular acceleration;

[0020] The wheel speed data is subjected to bandpass filtering of at least two frequency bands to obtain multiple filtered wheel speed data.

[0021] Calculate the coefficient value related to road surface roughness based on multiple filtered wheel speed data;

[0022] The road surface roughness coefficient is determined based on the angular acceleration variance and the coefficient value.

[0023] In one embodiment, the step of determining the road surface roughness coefficient based on the angular acceleration variance and the coefficient value includes:

[0024] Obtain a fusion weight value, which characterizes the degree of influence of the angular acceleration variance and the coefficient value on the road surface roughness coefficient;

[0025] The road surface roughness coefficient is calculated based on the fusion weight value, the angular acceleration variance, and the coefficient value.

[0026] In one embodiment, the method further includes:

[0027] The filtered wheel speed signal is mapped to a confidence value;

[0028] Acquire road surface images while the vehicle is in motion, and analyze the road surface images to obtain roughness analysis results;

[0029] The roughness analysis results are compared with the historical pavement roughness coefficient of the previous period to obtain the comparison results;

[0030] Determine the error parameters based on the comparison results;

[0031] The fusion weight value is adjusted based on the confidence level and the error parameter.

[0032] In one embodiment, the step of filtering the current required torque of the vehicle based on the road surface roughness coefficient and the vertical sag coefficient to obtain the target required torque includes:

[0033] Obtain the initial filter coefficients corresponding to the vehicle's current gear and current driving mode;

[0034] Compare whether the vertical motion coefficient is greater than a preset motion coefficient threshold;

[0035] When the vertical sloshing coefficient is greater than the preset sloshing coefficient threshold, a corresponding filter adjustment coefficient is determined based on the vertical sloshing coefficient and the road surface roughness coefficient.

[0036] The current required torque of the vehicle is filtered based on the initial filter coefficient and the filter adjustment coefficient to obtain the target required torque.

[0037] In one embodiment, after the step of comparing whether the vertical motion coefficient is greater than a preset motion coefficient threshold, the method further includes:

[0038] When the vertical motion sickness coefficient is less than or equal to the preset motion sickness coefficient threshold, the current required torque of the vehicle is filtered by the initial filtering coefficient to obtain the target required torque.

[0039] Furthermore, to achieve the above objectives, this application also proposes a vehicle torque adjustment device, which includes:

[0040] The acquisition module is used to acquire acceleration data and wheel speed data of the vehicle during driving.

[0041] The determination module is used to determine the vertical holographic coefficient based on the acceleration data;

[0042] The determining module is also used to determine the road surface roughness coefficient based on the wheel speed data;

[0043] The filtering module is used to filter the current required torque of the vehicle based on the road surface roughness coefficient and the vertical sag coefficient to obtain the target required torque.

[0044] In addition, to achieve the above objectives, this application also proposes a vehicle torque adjustment device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the vehicle torque adjustment method as described above.

[0045] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the vehicle torque adjustment method described above.

[0046] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the vehicle torque adjustment method described above.

[0047] This application proposes one or more technical solutions to acquire vehicle acceleration and wheel speed data during driving; determine the vertical motion sickness coefficient based on the acceleration data; determine the road surface roughness coefficient based on the wheel speed data; and filter the vehicle's current torque demand based on the road surface roughness coefficient and the vertical motion sickness coefficient to obtain the target torque demand. By comprehensively considering road conditions and the user's motion sickness, the vehicle's torque demand is dynamically adjusted based on the road surface roughness coefficient and the vertical motion sickness coefficient, improving the torque adjustment effect, reducing sudden power changes caused by sudden road surface changes or driving behavior, maintaining the smooth operation of the vehicle's power system, and improving driving comfort based on the passenger's degree of motion sickness without affecting the performance of various driving modes. Attached Figure Description

[0048] 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.

[0049] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a flowchart illustrating an embodiment of the vehicle torque adjustment method of this application.

[0051] Figure 2 This is a schematic diagram of the system structure for adjusting vehicle torque according to an embodiment of the vehicle torque adjustment method of this application;

[0052] Figure 3 This is a flowchart illustrating Embodiment 2 of the vehicle torque adjustment method of this application;

[0053] Figure 4 A schematic diagram of the vertical motion coefficient calculation process provided in an embodiment of the vehicle torque adjustment method of this application;

[0054] Figure 5 This is a flowchart illustrating Embodiment 3 of the vehicle torque adjustment method of this application;

[0055] Figure 6 A schematic flowchart illustrating the determination of road surface roughness coefficient provided in an embodiment of the vehicle torque adjustment method of this application;

[0056] Figure 7 This is a flowchart illustrating Embodiment 4 of the vehicle torque adjustment method of this application;

[0057] Figure 8 This is a schematic flowchart illustrating the filtering of the current required torque, provided as an embodiment of the vehicle torque adjustment method of this application.

[0058] Figure 9 A simplified flowchart illustrating an embodiment of the vehicle torque adjustment method of this application;

[0059] Figure 10 This is a schematic diagram of the module structure of the vehicle torque adjustment device according to an embodiment of this application;

[0060] Figure 11 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the vehicle torque adjustment method in this application embodiment.

[0061] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0062] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0063] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0064] The main solution of this application embodiment is: to acquire the acceleration data and wheel speed data of the vehicle during driving; to determine the vertical motion coefficient based on the acceleration data; to determine the road surface roughness coefficient based on the wheel speed data; and to filter the current required torque of the vehicle based on the road surface roughness coefficient and the vertical motion coefficient to obtain the target required torque.

[0065] Existing technologies primarily rely on two methods to determine road surface roughness: the first involves installing additional sensors to acquire specific signals and assess road conditions; the second uses non-dedicated sensors, such as cameras, microphones, and wheel speed sensors, to estimate road roughness through complex algorithms. Current technologies that combine road surface conditions with MSDV (Motor-Driven Vehicle) for comfort enhancement mainly focus on suspension control, in-vehicle environment improvement, and smart seats, without considering torque control.

[0066] This application provides a solution that uses the vertical motion coefficient calculated from vertical acceleration and the road surface roughness coefficient calculated from wheel speed to dynamically filter the torque required by the motor. From the perspective of the power domain, this improves the driving experience of the vehicle on rough roads without interfering with normal driving needs.

[0067] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device or vehicle torque adjustment device capable of performing the above functions. The following description uses a vehicle torque adjustment device as an example to illustrate this embodiment and the subsequent embodiments.

[0068] Based on this, this application provides a vehicle torque adjustment method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the vehicle torque adjustment method of this application.

[0069] In this embodiment, the vehicle torque adjustment method includes steps S10 to S40:

[0070] Step S10: Obtain the vehicle's acceleration data and wheel speed data during driving.

[0071] It should be noted that during vehicle operation, changes in road conditions or adjustments in vehicle speed may cause motion sickness or discomfort for passengers. Current methods of adjustment involve modifying the vehicle's seats or suspension, but these adjustments are not very effective in adverse road conditions. This embodiment primarily collects vehicle driving data to adjust the MSDV (Mean Squared Displacement Velocity) coefficient. Combined with the adjusted MSDV coefficient and the road roughness coefficient calculated from the driving data, the motor's required torque is comprehensively adjusted. By considering the overall vehicle condition and incorporating multiple factors, the torque is dynamically filtered to improve user ride comfort.

[0072] It should be noted that acceleration data can include the vehicle's vertical and longitudinal acceleration during movement. Multiple acceleration values ​​can be obtained by collecting acceleration data over a certain period of time. Wheel speed data includes the vehicle's angular velocity over a period of time, such as the angular velocity at the current moment and the angular velocity in the previous cycle.

[0073] Step S20: Determine the vertical motion coefficient based on the acceleration data.

[0074] The vertical motion coefficient measures the intensity of a vehicle's vertical motion. Its magnitude is related to factors such as the vehicle's vertical acceleration and speed. Calculating the vertical motion coefficient allows for further analysis of the vehicle's vibration levels during operation, providing a more accurate reference for adjusting the required motor torque.

[0075] In practice, the vertical motion sickness coefficient can be determined by acceleration data. The vertical motion sickness coefficient can be the vertical MSDV coefficient calculated from acceleration data, or it can be the vertical MSDV coefficient corrected by a correction factor. It can be set and adjusted according to the specific driving conditions.

[0076] Step S30: Determine the road surface roughness coefficient based on the wheel speed data.

[0077] It should be noted that the road surface roughness coefficient can be calculated by processing wheel speed data. For example, multiple methods can be used to calculate wheel speed, and the results can be fused to obtain the final road surface roughness coefficient.

[0078] The road surface roughness coefficient can be used to characterize the roughness of the road surface on which the vehicle is currently traveling. For example, the larger the road surface roughness coefficient, the rougher the current road surface, and vice versa.

[0079] Step S40: Filter the current required torque of the vehicle based on the road surface roughness coefficient and the vertical motion coefficient to obtain the target required torque.

[0080] In practice, the required torque of the vehicle can be filtered based on the road surface roughness coefficient and vertical motion coefficient to obtain the target required torque, thereby adjusting the vehicle's driving state and improving the user's driving comfort.

[0081] like Figure 2 As shown, Figure 2The system structure diagram for vehicle torque adjustment includes a demand torque calculation module, a road surface roughness estimation module, a motion sickness coefficient calculation module, a torque filtering module, a torque limiting module, and a drive motor. Data such as gear position, driving mode, throttle opening, braking status, and vehicle speed are input to the demand torque calculation module to calculate the current demand torque, i.e., the base demand torque. Wheel speed is input to the road surface roughness estimation module to calculate the road surface roughness coefficient. Vertical acceleration is input to the motion sickness coefficient calculation module to calculate the vertical motion sickness coefficient. The gear position, driving mode, throttle opening, base demand torque, road surface roughness coefficient, and vertical motion sickness coefficient are sent to the torque filtering module. The filtered torque (target torque) is then sent to the torque limiting module, which outputs the motor command torque to the drive motor for execution. It should be noted that in actual use, a coefficient C1 related to suspension height fluctuations can also be considered; therefore, a suspension height coefficient calculation module can also be added to the system.

[0082] It should be noted that, in addition to considering the road surface roughness coefficient and vertical motion sickness coefficient, other factors related to comfort and motion sickness can also be considered, such as suspension height and in-vehicle ventilation. When a vehicle travels over different bumpy road surfaces, the suspension height will fluctuate at different frequencies and amplitudes. Therefore, by analyzing changes in the suspension height signal, the vehicle's driving stability can be reflected, thereby controlling torque changes. Thus, suspension height signals can be collected, and outlier processing can be performed, such as removing abnormal data points exceeding a threshold. A sliding window of length n can then be used to extract values, and calculations can be performed on the extracted values.

[0083] Calculate the range R3 and standard deviation σ. R3 = max(D) - min(D), and the standard deviation is calculated as follows:

[0084]

[0085] Where μ is the mean of the dataset and n is the number of data points, the coefficient C1 related to suspension height fluctuation is calculated, C1 = 0.5*R + 0.5*σ, and the coefficient C1 is taken into account in the calculation of the filter coefficient. The larger the coefficient C1 is, the more severe the vehicle bumps, and the smaller the filter coefficient is used to process the torque more smoothly and reduce the impact during driving.

[0086] It should be noted that in this embodiment, the vertical motion sickness coefficient and road surface roughness coefficient are obtained through acceleration data and wheel speed data. The required torque of the vehicle is dynamically adjusted based on the vertical motion sickness coefficient and road surface roughness coefficient. Taking into account road conditions and the user's motion sickness, the required torque of the vehicle is dynamically adjusted based on the road surface roughness coefficient and vertical motion sickness coefficient, thereby improving the torque adjustment effect, reducing sudden power changes caused by sudden road changes or driving behavior, maintaining the smooth operation of the vehicle's power system, and improving driving comfort according to the degree of motion sickness of passengers without affecting the performance of each driving mode.

[0087] This embodiment provides a vehicle torque adjustment method. It acquires vehicle acceleration and wheel speed data during driving; determines the vertical motion sickness coefficient based on the acceleration data; determines the road surface roughness coefficient based on the wheel speed data; and filters the vehicle's current torque demand based on the road surface roughness coefficient and the vertical motion sickness coefficient to obtain the target torque demand. By comprehensively considering road conditions and the user's motion sickness, the method dynamically adjusts the vehicle's torque demand based on the road surface roughness coefficient and the vertical motion sickness coefficient, improving the torque adjustment effect, reducing sudden power changes caused by sudden road surface changes or driving behavior, maintaining the smooth operation of the vehicle's powertrain, and enhancing driving comfort based on the passenger's degree of motion sickness without affecting the performance of various driving modes.

[0088] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 Step S20 includes steps S201 to S202: the pedal signal includes the accelerator pedal signal or the brake pedal signal, and the accelerator pedal opening or brake pedal change can be obtained through the pedal signal.

[0089] Step S201: Calculate the vertical mean square displacement velocity coefficient based on the acceleration data.

[0090] It should be noted that the acceleration data includes both vertical and longitudinal acceleration, so the vertical and longitudinal accelerations can be obtained from the acceleration data.

[0091] Understandably, the vertical mean square displacement velocity coefficient (MSDV) can be calculated from the vertical acceleration. Specifically, the MSDV coefficient can be obtained by filtering the vertical acceleration multiple times. This mainly includes: first, using a bandpass filter to obtain the motion-sensitive frequency band; then, using a low-pass filter for AV conversion; next, performing a high-pass filter; and finally, integrating the sum of squares over time and taking the square root to obtain the MSDV parameter.

[0092] For example, first, bandpass filtering is applied to the vertical acceleration. Specifically, the bandpass filter coefficients can be pre-set, and the vertical acceleration can be calculated using the filter transfer function to obtain the bandpass-filtered vertical acceleration. Then, acceleration-velocity conversion is performed on the bandpass-filtered vertical acceleration to remove high-frequency noise and smooth the signal. The converted signal is then high-pass filtered using the transfer function to obtain the high-pass-filtered vertical acceleration. The MSDV parameters are obtained by time integration of the high-pass-filtered vertical acceleration, as shown in the following formula:

[0093]

[0094] In the above formula, a w (t) represents the vertical acceleration after high-pass filtering, and T represents the entire period of motion, in seconds.

[0095] Step S202: Correct the vertical mean square displacement velocity coefficient based on the vehicle's pedal signal to obtain the vertical motion coefficient.

[0096] Understandably, the calculated vertical mean square displacement velocity coefficient can be corrected based on the vehicle's pedal signal to obtain the vertical motion coefficient. For example, the throttle opening change rate or brake pedal switch flag can be determined based on the vehicle's pedal signal, and then the vertical mean square displacement velocity coefficient can be corrected based on the throttle opening change rate or brake pedal switch flag.

[0097] In one feasible implementation, step S202 may include steps A11 to A13:

[0098] Step A11: Obtain the longitudinal acceleration based on the acceleration data, and determine the throttle opening rate of change and the brake pedal switch flag based on the vehicle's pedal signal;

[0099] It should be noted that the throttle opening change rate and brake pedal switch flag can be determined based on the pedal signal. The throttle opening change rate is the change of the throttle pedal over a period of time. For example, if the throttle opening change rate is 30%, it can be calculated from the throttle opening of the previous cycle and the throttle opening at the current moment. The brake pedal switch flag indicates whether the brake pedal is pressed. If the brake pedal is pressed, the brake pedal flag is 1; if the brake pedal is not pressed, the brake pedal flag is 0.

[0100] Step A12: When the rate of change of the throttle opening is greater than a preset rate of change threshold or the brake pedal switch flag is at a preset value, obtain the correction coefficient corresponding to the longitudinal acceleration;

[0101] Understandably, after calculating the vertical mean square displacement velocity coefficient, it can be further determined whether the vertical mean square displacement velocity coefficient needs to be corrected. Therefore, the throttle opening change rate is compared with the preset change rate threshold, or it is determined whether the brake pedal switch flag is at the preset value, thereby determining whether the vertical mean square displacement velocity coefficient needs to be corrected.

[0102] It should be noted that the preset change rate threshold can be set according to needs, such as 70%, 75%, etc., and the preset value is set to 1.

[0103] It should be noted that if the rate of change of throttle opening exceeds a preset threshold or the brake pedal switch flag is set to 1, the mapping table between longitudinal acceleration and correction coefficients can be queried using the current longitudinal acceleration. The specific data in the mapping table can be pre-calibrated to obtain the corresponding correction coefficient for longitudinal acceleration. The relationship between longitudinal acceleration and correction coefficients in the mapping table is set such that the correction coefficient is 1 when longitudinal acceleration <= 0.03g, and the larger the longitudinal acceleration, the smaller the correction coefficient (a larger longitudinal acceleration is considered to cause greater motion sickness in the longitudinal direction; to minimize motion sickness caused by longitudinal changes, a coefficient less than 1 should be used for correction). A minimum correction coefficient of 0.5 is set to avoid over-correction.

[0104] Step A13: Correct the vertical mean square displacement velocity coefficient according to the correction coefficient to obtain the vertical oscillation coefficient.

[0105] It should be noted that the vertical mean square displacement velocity coefficient can be corrected by looking up the correction coefficient in the table, thus obtaining the corrected vertical halos coefficient.

[0106] like Figure 4 As shown, Figure 4 The diagram illustrates the calculation process for the vertical motion coefficient. First, the vertical acceleration is obtained, and the vertical MSDV coefficient is calculated based on the vertical acceleration. Then, it is determined whether the throttle opening rate of change is greater than the threshold or the brake pedal is depressed. If so, the correction coefficient is obtained by looking up the table using the longitudinal acceleration. The vertical motion coefficient is obtained by multiplying the correction coefficient by the MSDV parameter. If not, the MSDV coefficient is directly used as the vertical motion coefficient.

[0107] In one feasible implementation, after step A11, the method further includes: when the rate of change of the throttle opening is less than or equal to a preset rate of change threshold or the brake pedal switch flag is not a preset value, the vertical mean square displacement velocity coefficient is used as the vertical sloshing coefficient.

[0108] It should be noted that if the throttle opening change rate is less than or equal to the preset change rate threshold or the brake pedal switch flag is not 1, it means that there is no emergency braking or sudden acceleration. In this case, there is no need to adjust the calculated MSDV coefficient, and the MSDV coefficient can be directly used as the vertical motion coefficient in the subsequent torque adjustment.

[0109] This embodiment calculates the vertical mean square displacement velocity coefficient based on the acceleration data; the vertical mean square displacement velocity coefficient is then corrected based on the vehicle's pedal signal to obtain the vertical motion coefficient. By calculating the vertical mean square displacement velocity coefficient, the vertical motion state of the vehicle can be accurately described. By introducing a correction coefficient to dynamically adjust the vertical mean square displacement velocity coefficient, torque can be more accurately controlled, thereby improving ride comfort.

[0110] Based on the first embodiment of this application, in the third embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 5 Step S30 includes steps S301 to S305:

[0111] Step S301: Discretely differentiate the wheel speed data to obtain angular acceleration.

[0112] It should be noted that the collected wheel speed data can be calculated using two different methods, and the results of the two methods can be combined to calculate the road surface roughness coefficient.

[0113] In practice, the wheel speed data can be discretely differentiated to obtain the angular acceleration. Since the vehicle speed usually fluctuates during driving, in order to remove the low-frequency components caused by the vehicle speed fluctuation, the wheel speed angular acceleration is calculated to characterize the higher-frequency vibration caused by the road surface on the vehicle body. Therefore, the angular velocity of the previous cycle and the angular velocity at the current moment can be obtained from the wheel speed data.

[0114] In practice, angular acceleration = (angular velocity at the current moment - angular velocity in the previous period) / sampling period.

[0115] Step S302: Calculate the angular acceleration variance based on the angular acceleration.

[0116] It should be noted that after calculating the angular acceleration, the angular acceleration data of a sliding window of length n can be statistically analyzed, and the angular acceleration variance R1 can be calculated based on the statistical angular acceleration data. Specifically, the mean of the angular acceleration can be calculated, and the angular acceleration variance R1 can be calculated using the mean of the angular acceleration and the angular acceleration data.

[0117] Step S303: Perform bandpass filtering on the wheel speed data for at least two frequency bands to obtain multiple filtered wheel speed data.

[0118] It should be noted that the second method of processing wheel speed data is to perform bandpass filtering on the wheel speed data in multiple preset frequency bands. Since the high-frequency fluctuations of wheel speed caused by vehicles traveling on road surfaces with different roughness have specific frequency bands, it is considered to use several bandpass filters of specific frequency bands to filter the wheel speed signal. This can simultaneously remove the low-frequency components caused by vehicle speed fluctuations and the high-frequency components caused by sensor electrical noise, and extract the corresponding road surface frequency band noise.

[0119] In practice, the filtering method can use the transfer function described above and the set parameters to filter in the corresponding frequency band, thereby obtaining the filtered wheel speed data.

[0120] Step S304: Calculate the coefficient value associated with road surface roughness based on multiple filtered wheel speed data.

[0121] Understandably, the energy spectrum can be calculated for each data point in the multiple filtered wheel speed data sets. The energy spectrum is calculated as follows:

[0122]

[0123] In the above formula, E is the energy spectrum, and x[n] is the filtered wheel speed data.

[0124] Each energy spectrum is assigned a corresponding weight, thereby calculating the coefficient value R2 that is positively correlated with the road surface roughness. Specifically, the filtering results of the frequency band with higher road surface roughness are given a greater weight, so that the weighted energy value is positively correlated with the road surface roughness.

[0125] The coefficient value R2 is calculated as follows:

[0126] R2 = A*E1 + B*E2 + C*E3

[0127] It is understandable that A, B, and C are the set weights, and E1, E2, and E3 are the energy spectra.

[0128] Step S305: Determine the road surface roughness coefficient based on the angular acceleration variance and the coefficient value.

[0129] It should be noted that the road surface roughness coefficient can be calculated by fusing the angular acceleration variance and the coefficient value. Specifically, a fusion weight value can be set to calculate the road surface roughness coefficient.

[0130] In one feasible implementation, step S305 may include: obtaining a fusion weight value, the fusion weight value representing the degree of influence of the angular acceleration variance and the coefficient value on the road surface roughness coefficient; and calculating the road surface roughness coefficient based on the fusion weight value, the angular acceleration variance, and the coefficient value.

[0131] It should be noted that the fusion weight value 'a' can be obtained through experimental calibration, or it can be set according to requirements. The fusion weight value 'a' can be a fixed value set in advance, or it can be dynamically adjusted in real time using an adaptive adjustment mechanism based on the error feedback and confidence level of the results from the previous calculation cycle. The fusion weight value represents the degree of influence of the angular acceleration variance and coefficient value on the road surface roughness; the greater the degree of influence, the larger the fusion weight value.

[0132] In practical implementation, the road surface roughness coefficient is calculated as follows:

[0133] R = a * R1 + (1 - a) * R2

[0134] In the above formula, R is the road surface roughness coefficient, a is the fusion weight value, R1 is the angular acceleration variance, and R2 is the coefficient value.

[0135] In one feasible implementation, the fusion weight value can also be updated based on the previously calculated road surface roughness coefficient. Therefore, after step S305, steps B11 to B15 are also included:

[0136] Step B11: Map the filtered wheel speed signal to a confidence value;

[0137] It should be noted that high-frequency electronic noise can be considered as a confidence level. The wheel speed signal is passed through a high-pass filter, and the filtered wheel speed signal is mapped to the execution level L. This confidence level L takes into account the influence of high-frequency noise on the signal. The specific mapping relationship is as follows: the smaller the confidence level, the greater the influence of high-frequency noise on the calculation result is considered, so a smaller value of 'a' is used to increase the weight of R2 (R2 takes into account the calculation result of the influence of high-frequency noise), and vice versa, a larger value of 'a' is used.

[0138] Step B12: Acquire road surface images while the vehicle is in motion, and analyze the road surface images to obtain roughness analysis results;

[0139] In practice, for vehicles equipped with cameras, the collected road surface images can be incorporated into the error feedback. By analyzing the road surface images, roughness analysis results can be obtained.

[0140] Step B13: Compare the roughness analysis results with the historical pavement roughness coefficient of the previous cycle to obtain the comparison results;

[0141] Step B14: Determine the error parameters based on the comparison results;

[0142] In practice, the historical road surface roughness coefficient of the previous cycle is the road surface roughness coefficient calculated based on the fusion weight value in the previous cycle. By comparing the roughness analysis result with the historical road surface roughness coefficient of the previous cycle, the error parameter E is obtained. The greater the difference between the roughness analysis result and the historical road surface roughness coefficient of the previous cycle, the larger the error parameter E; the smaller the difference, the smaller the error parameter E. A larger E indicates a greater gap between the result calculated in the previous cycle and the image detection result, in which case the fusion weight value a needs to be adjusted more significantly.

[0143] Step B15: Adjust the fusion weight value according to the confidence value and the error parameter.

[0144] In practice, the confidence value L, error parameter E, and fusion weight value are all set to values ​​between 0 and 1. After obtaining the confidence value and error parameter, the fusion weight value can be adjusted based on these values. Specifically, the fusion weight value for the current period is determined by the confidence value of the current period, the error parameter of the previous period, and the fusion weight value. The confidence value for the current period is obtained by mapping the filtered wheel speed signal from the current period to the previous n periods. The fusion weight value is calculated as follows:

[0145] a n =min(L n *(a n-1 +E n-1 ),1)

[0146] In the above formula, a n L represents the fusion weight value for the current period, i.e., the adjusted fusion weight value. n E represents the confidence level for the current period. n-1 Let a be the error parameter of the previous period. n-1 This is the fusion weight value from the previous period. If the value calculated using the confidence value of the current period, the error parameter of the previous period, and the fusion weight value is less than 1, then the value calculated using the confidence value of the current period, the error parameter of the previous period, and the fusion weight value is used as the fusion weight value for the current period. If the value calculated using the confidence value of the current period, the error parameter of the previous period, and the fusion weight value is greater than 1, then the fusion weight value for the current period is set to 1.

[0147] like Figure 6 As shown, Figure 6The flowchart for determining the road surface roughness coefficient is as follows: wheel speed is acquired through wheel speed sensor, angular acceleration is obtained by discrete differentiation of wheel speed, and the signal is filtered using a specific bandpass filter to obtain filtered wheel speed data. The variance R1 of wheel speed and angular acceleration is calculated. The energy of each filtered signal is weighted using preset weight coefficients to obtain R2. The road surface roughness coefficient R is obtained by using the fusion weight value a, variance R1 and coefficient value R2.

[0148] This embodiment obtains angular acceleration by discretely differentiating the wheel speed data; calculates the angular acceleration variance based on the angular acceleration; performs bandpass filtering on the wheel speed data in at least two frequency bands to obtain multiple filtered wheel speed data; calculates a coefficient value related to road surface roughness based on the multiple filtered wheel speed data; and determines the road surface roughness coefficient based on the angular acceleration variance and the coefficient value. Using two methods to calculate and then fuse the road surface roughness coefficient avoids the shortcomings and noise errors of a single method, improving the accuracy of the calculation.

[0149] Based on the first embodiment of this application, in the fourth embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 7 Step S40 includes steps S401 to S404:

[0150] Step S401: Obtain the initial filter coefficients corresponding to the vehicle's current gear and current driving mode.

[0151] It should be noted that there is a corresponding relationship between the vehicle's gear and driving mode and the filter coefficient. For example, if the gear is D and the driving mode is Sport, the corresponding filter coefficient is y1. The relationship between the vehicle's gear, driving mode and filter coefficient can be established in advance to create a filter coefficient table. Therefore, the corresponding initial filter coefficient can be obtained by querying the filter coefficient table based on the vehicle's current gear and current driving mode.

[0152] Step S402: Compare whether the vertical motion coefficient is greater than the preset motion coefficient threshold.

[0153] It should be noted that before comparing the vertical motion sickness coefficient with the preset motion sickness coefficient threshold, it is also necessary to determine whether the vehicle is currently in a performance switching operation state. Performance switching operation includes vehicle gear switching operation and driving mode switching operation.

[0154] Therefore, the premise for comparing the vertical motion sickness coefficient with the preset motion sickness coefficient threshold is that the vehicle is not in a performance switching operation, that is, the vehicle does not currently have a need for a performance switching operation. Then the calculated vertical motion sickness coefficient is compared with the preset motion sickness coefficient threshold. The preset motion sickness coefficient threshold can be set according to the needs, such as 0.8, 0.9, etc., and this embodiment does not limit it.

[0155] In one feasible implementation, when the vertical motion coefficient is less than or equal to the preset motion coefficient threshold, the current required torque of the vehicle is filtered by the initial filtering coefficient to obtain the target required torque.

[0156] It should be noted that if the vertical motion sickness coefficient is less than or equal to the preset motion sickness coefficient threshold, the initial filter coefficient is used. That is, the original data of the vehicle's current mode is not adjusted. The target torque is obtained by calculating the vehicle's current torque requirement using the initial filter coefficient, as shown in the following formula:

[0157] Y(n) = f1*X(n) + (1-f1)*Y(n-1)

[0158] In the above formula, Y(n) is the current filtered output value, i.e. the target required torque, X(n) is the current required torque, Y(n-1) is the filtered output value of the previous cycle, i.e. the historical required torque of the previous cycle, and f1 is the initial filter coefficient.

[0159] Step S403: When the vertical motion coefficient is greater than the preset motion coefficient threshold, determine the corresponding filter adjustment coefficient based on the vertical motion coefficient and the road surface roughness coefficient.

[0160] Understandably, if the vertical motion coefficient exceeds the preset motion coefficient threshold, the corresponding filter adjustment coefficient is obtained by consulting the filter adjustment coefficient table based on the vertical motion coefficient and the road surface roughness coefficient. There is a corresponding relationship between the vertical motion coefficient, the road surface roughness coefficient, and the filter adjustment coefficient; a larger motion coefficient and a larger road surface roughness coefficient correspond to a smaller filter coefficient. Finally, it is necessary to ensure that the minimum filter coefficient after adjustment is 0.015, that is, the filter adjustment coefficient is greater than or equal to 0.015, to ensure that the signal delay time meets the system response requirements. The filter adjustment coefficient table is obtained by calculating the basic data and then calibrating it. It can make torque changes smoother on rougher roads, thereby reducing vertical impact and improving driving comfort.

[0161] Step S404: Filter the current required torque of the vehicle according to the initial filter coefficient and the filter adjustment coefficient to obtain the target required torque.

[0162] In practice, the final filter coefficient can be determined based on the initial filter coefficient and the filter adjustment coefficient. Then, the current required torque of the vehicle can be calculated using the final filter coefficient to obtain the target required torque.

[0163] For example, if the initial filter coefficient is f1 and the filter adjustment coefficient is f2, then the final filter coefficient f3 = f1 * f2. The target torque requirement is calculated as follows:

[0164] Y(n) = f3*X(n) + (1-f3)*Y(n-1)

[0165] In one feasible implementation, if the vehicle is in a performance switching operation, the current required torque of the vehicle is not adjusted. Therefore, the vehicle torque adjustment method further includes steps S41 to S42:

[0166] Step S41: When the vehicle is in a performance switching operation, obtain the switching filter coefficients corresponding to the switched performance;

[0167] It should be noted that if the vehicle is performing a performance switching operation, that is, the vehicle is switching gears or driving modes, the performance switching is taken first. At this time, the required torque needs to meet the performance switching of the vehicle. Therefore, the switching filter coefficient corresponding to the performance that the vehicle is about to switch to can be obtained, or the switching filter coefficient corresponding to the gear or driving mode after the vehicle switches performance can be obtained.

[0168] The switching filter coefficient can be preset according to different gears or driving modes, which is used to quickly and smoothly adjust the vehicle's required torque when switching gears or modes, avoiding driving discomfort caused by sudden torque changes.

[0169] Step S42: Filter the current required torque of the vehicle according to the switching filter coefficient to obtain the target required torque.

[0170] In practice, the current required torque of the vehicle can be filtered by switching the filter coefficient. For example, if the filter coefficient is q1 and the current required torque is F1, then the target required torque F2 can be F2 = q1 * F1.

[0171] In practice, the target torque requirement can be sent to the drive motor for execution, thereby controlling the vehicle.

[0172] like Figure 8 As shown, Figure 8The flowchart illustrates the process of filtering the current required torque. First, it determines whether the user has not shifted gears or changed driving modes. If not, the current required torque is directly filtered using the specific filter coefficient (switching filter coefficient) corresponding to the shifted gear or driving mode to obtain the target required torque. If so, it determines whether the vertical motion coefficient is greater than a threshold. If not, the initial filter coefficient is determined by looking up a table using the current gear and driving mode, and the current required torque is filtered using the initial filter coefficient to obtain the target required torque. If it is greater than the threshold, the filter adjustment coefficient is obtained by looking up a table using the road roughness coefficient and vertical motion coefficient. The updated filter coefficient = adjustment coefficient * filter coefficient corresponding to the current gear and driving mode. The current required torque is then filtered using the updated filter coefficient to obtain the target required torque.

[0173] This embodiment obtains the initial filter coefficient corresponding to the vehicle's current gear and driving mode; compares whether the vertical motion sickness coefficient is greater than a preset motion sickness coefficient threshold; when the vertical motion sickness coefficient is greater than the preset threshold, determines the corresponding filter adjustment coefficient based on the vertical motion sickness coefficient and the road surface roughness coefficient; filters the vehicle's current required torque based on the initial filter coefficient and the filter adjustment coefficient to obtain the target required torque. The filter coefficient is corrected using the road surface roughness coefficient and the vertical motion sickness coefficient to achieve dynamic adjustment of the vehicle's required torque. This intelligently adjusts the filter coefficient according to actual road conditions, optimizes power transmission, avoids excessive or insufficient power output, improves driving experience and vehicle responsiveness, and enhances ride comfort based on passenger motion sickness levels without affecting the performance of each driving mode.

[0174] For example, to help understand the implementation flow of the vehicle torque adjustment method obtained in this embodiment combined with the above embodiment one, please refer to... Figure 9 , Figure 9 A simplified flowchart of a vehicle torque adjustment method is provided. Specifically: the vertical acceleration is obtained, the vertical slack velocity (MSDV) is calculated and corrected to obtain the vertical slack coefficient; the wheel speed is calculated using two methods and then fused to obtain the road surface roughness coefficient; the torque is dynamically filtered based on the vertical slack coefficient and the road surface roughness coefficient; and the filtered torque is comprehensively limited and sent to the drive motor for execution.

[0175] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the vehicle torque adjustment method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0176] This application also provides a vehicle torque adjustment device, please refer to... Figure 10 The vehicle torque adjustment device includes:

[0177] The acquisition module 10 is used to acquire the vehicle's acceleration data and wheel speed data during the driving process.

[0178] The determination module 20 is used to determine the vertical halos coefficient based on the acceleration data.

[0179] The determining module 20 is also used to determine the road surface roughness coefficient based on the wheel speed data.

[0180] The filtering module 30 is used to filter the current required torque of the vehicle based on the road surface roughness coefficient and the vertical motion coefficient to obtain the target required torque.

[0181] The vehicle torque adjustment device provided in this application, employing the vehicle torque adjustment method described in the above embodiments, can solve the technical problem that the effect of improving vehicle comfort through road surface roughness is currently poor. Compared with the prior art, the beneficial effects of the vehicle torque adjustment device provided in this application are the same as those of the vehicle torque adjustment method provided in the above embodiments, and other technical features in the vehicle torque adjustment device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0182] In one embodiment, the determining module 20 is further configured to calculate the vertical mean square displacement velocity coefficient based on the acceleration data; and to correct the vertical mean square displacement velocity coefficient based on the vehicle's pedal signal to obtain the vertical motion coefficient.

[0183] In one embodiment, the determining module 20 is further configured to obtain longitudinal acceleration based on the acceleration data, and determine the throttle opening change rate and brake pedal switch flag based on the vehicle's pedal signal; when the throttle opening change rate is greater than a preset change rate threshold or the brake pedal switch flag is a preset value, obtain a correction coefficient corresponding to the longitudinal acceleration; and correct the vertical mean square displacement velocity coefficient based on the correction coefficient to obtain a vertical motion coefficient.

[0184] In one embodiment, the determining module 20 is further configured to perform discrete differentiation on the wheel speed data to obtain angular acceleration; calculate the angular acceleration variance based on the angular acceleration; perform bandpass filtering on the wheel speed data for at least two frequency bands to obtain multiple filtered wheel speed data; calculate a coefficient value related to road surface roughness based on the multiple filtered wheel speed data; and determine the road surface roughness coefficient based on the angular acceleration variance and the coefficient value.

[0185] In one embodiment, the determining module 20 is further configured to acquire a fusion weight value; and calculate a road surface roughness coefficient based on the fusion weight value, the angular acceleration variance, and the coefficient value. In another embodiment, the determining module 20 is further configured to map the filtered wheel speed signal to a confidence value; acquire a road surface image while the vehicle is traveling, and analyze the road surface image to obtain a roughness analysis result; compare the roughness analysis result with the historical road surface roughness coefficient of the previous period to obtain a comparison result; determine an error parameter based on the comparison result; and adjust the fusion weight value based on the confidence value and the error parameter.

[0186] In one embodiment, the filtering module 30 is further configured to: obtain the initial filtering coefficient corresponding to the vehicle's current gear and current driving mode; compare whether the vertical motion coefficient is greater than a preset motion coefficient threshold; when the vertical motion coefficient is greater than the preset motion coefficient threshold, determine the corresponding filtering adjustment coefficient based on the vertical motion coefficient and the road surface roughness coefficient; and filter the vehicle's current required torque based on the initial filtering coefficient and the filtering adjustment coefficient to obtain the target required torque.

[0187] In one embodiment, the filtering module 30 is further configured to filter the current required torque of the vehicle using the initial filtering coefficient when the vertical motion coefficient is less than or equal to the preset motion coefficient threshold, so as to obtain the target required torque.

[0188] This application provides a vehicle torque adjustment device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the vehicle torque adjustment method in the above embodiment 1.

[0189] The following is for reference. Figure 11 This document illustrates a structural schematic diagram suitable for implementing a vehicle torque adjustment device according to embodiments of this application. The vehicle torque adjustment device in these embodiments may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 11 The vehicle torque adjustment device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of this application.

[0190] like Figure 11 As shown, the vehicle torque adjustment device may include a processing unit 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in ROM (Read Only Memory) 1002 or a program loaded from storage device 1003 into RAM (Random Access Memory) 1004. RAM 1004 also stores various programs and data required for the operation of the vehicle torque adjustment device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via bus 1005. Input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touch screens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, LCDs (Liquid Crystal Displays), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. The communication device 1009 allows the vehicle torque adjustment device to communicate wirelessly or wiredly with other devices to exchange data. Although the figures show vehicle torque adjustment devices with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0191] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0192] The vehicle torque adjustment device provided in this application, employing the vehicle torque adjustment method described in the above embodiments, can solve the technical problem that the effect of improving vehicle comfort through road surface roughness is currently poor. Compared with the prior art, the beneficial effects of the vehicle torque adjustment device provided in this application are the same as those of the vehicle torque adjustment method provided in the above embodiments, and other technical features of this vehicle torque adjustment device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0193] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0194] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0195] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the vehicle torque adjustment method in the above embodiments.

[0196] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable Read Only Memory or Flash Memory), optical fibers, CD-ROM (CD-Read Only Memory), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0197] The aforementioned computer-readable storage medium may be included in the vehicle torque adjustment device; or it may exist independently and not be installed in the vehicle torque adjustment device.

[0198] The aforementioned computer-readable storage medium carries one or more programs that, when executed by the vehicle torque adjustment device, cause the vehicle torque adjustment device to: acquire acceleration data and wheel speed data of the vehicle during driving; determine a vertical motion coefficient based on the acceleration data; determine a road surface roughness coefficient based on the wheel speed data; and filter the current required torque of the vehicle based on the road surface roughness coefficient and the vertical motion coefficient to obtain a target required torque.

[0199] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including LAN (Local Area Network) or WAN (Wide Area Network)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0200] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0201] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0202] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described vehicle torque adjustment method, which can solve the technical problem that the effect of improving vehicle comfort through road surface roughness is currently poor. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the vehicle torque adjustment method provided in the above embodiments, and will not be repeated here.

[0203] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the vehicle torque adjustment method described above.

[0204] The computer program product provided in this application can solve the technical problem that the effect of improving vehicle comfort by adjusting road surface roughness is currently poor. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the vehicle torque adjustment method provided in the above embodiments, and will not be repeated here.

[0205] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for adjusting vehicle torque, characterized in that, The vehicle torque adjustment method includes: Acquire vehicle acceleration and wheel speed data; The vertical vertigo coefficient is determined based on the acceleration data; The road surface roughness coefficient is determined based on the wheel speed data; The target torque is obtained by filtering the current torque requirement of the vehicle based on the road surface roughness coefficient and the vertical sag coefficient.

2. The method as described in claim 1, characterized in that, The step of determining the vertical halos coefficient based on the acceleration data includes: Calculate the vertical mean square displacement velocity coefficient based on the acceleration data; The vertical mean square displacement velocity coefficient is corrected based on the vehicle's pedal signal to obtain the vertical motion coefficient.

3. The method as described in claim 2, characterized in that, The step of correcting the vertical mean square displacement velocity coefficient based on the vehicle's pedal signal to obtain the vertical scintillation coefficient includes: The longitudinal acceleration is obtained based on the acceleration data, and the throttle opening rate and brake pedal switch flag are determined based on the vehicle's pedal signal. When the rate of change of the throttle opening is greater than a preset rate of change threshold or the brake pedal switch flag is a preset value, the correction coefficient corresponding to the longitudinal acceleration is obtained. The vertical mean square displacement velocity coefficient is corrected according to the correction factor to obtain the vertical halos coefficient.

4. The method as described in claim 1, characterized in that, The step of determining the road surface roughness coefficient based on the wheel speed data includes: The angular acceleration is obtained by discretely differentiating the wheel speed data; Calculate the angular acceleration variance based on the angular acceleration; The wheel speed data is subjected to bandpass filtering of at least two frequency bands to obtain multiple filtered wheel speed data. Calculate the coefficient value related to road surface roughness based on multiple filtered wheel speed data; The road surface roughness coefficient is determined based on the angular acceleration variance and the coefficient value.

5. The method as described in claim 4, characterized in that, The step of determining the road surface roughness coefficient based on the angular acceleration variance and the coefficient value includes: Obtain a fusion weight value, which characterizes the degree of influence of the angular acceleration variance and the coefficient value on the road surface roughness coefficient; The road surface roughness coefficient is calculated based on the fusion weight value, the angular acceleration variance, and the coefficient value.

6. The method as described in claim 5, characterized in that, The method further includes: The filtered wheel speed signal is mapped to a confidence value; Acquire road surface images while the vehicle is in motion, and analyze the road surface images to obtain roughness analysis results; The roughness analysis results are compared with the historical pavement roughness coefficient of the previous period to obtain the comparison results; Determine the error parameters based on the comparison results; The fusion weight value is adjusted based on the confidence level and the error parameter.

7. The method as described in claim 1, characterized in that, The step of filtering the current required torque of the vehicle based on the road surface roughness coefficient and the vertical sag coefficient to obtain the target required torque includes: Obtain the initial filter coefficients corresponding to the vehicle's current gear and current driving mode; Compare whether the vertical motion coefficient is greater than a preset motion coefficient threshold; When the vertical sloshing coefficient is greater than the preset sloshing coefficient threshold, a corresponding filter adjustment coefficient is determined based on the vertical sloshing coefficient and the road surface roughness coefficient. The current required torque of the vehicle is filtered based on the initial filter coefficient and the filter adjustment coefficient to obtain the target required torque.

8. The method as described in claim 7, characterized in that, After the step of comparing whether the vertical motion coefficient is greater than a preset motion coefficient threshold, the method further includes: When the vertical motion sickness coefficient is less than or equal to the preset motion sickness coefficient threshold, the current required torque of the vehicle is filtered by the initial filtering coefficient to obtain the target required torque.

9. A vehicle torque adjustment device, characterized in that, The device includes: The acquisition module is used to acquire acceleration data and wheel speed data of the vehicle during driving. The determination module is used to determine the vertical holographic coefficient based on the acceleration data; The determining module is also used to determine the road surface roughness coefficient based on the wheel speed data; The filtering module is used to filter the current required torque of the vehicle based on the road surface roughness coefficient and the vertical sag coefficient to obtain the target required torque.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the vehicle torque adjustment method as described in any one of claims 1 to 7.

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