Quality-based vehicle braking force adjustment method, device, equipment and medium

Abstract

The present application relates to the field of unmanned driving, and provides a vehicle braking force adjustment method and device based on mass, equipment and medium, the method comprises: acquiring the road slope of the current driving road of the vehicle and the actual vehicle speed of the vehicle, and determining the estimated longitudinal acceleration of the vehicle according to the actual vehicle speed, when the road slope is zero, the estimated mass of the vehicle is determined according to the actual vehicle speed and the estimated longitudinal acceleration, otherwise the estimated mass of the vehicle calculated at the last moment of flat road driving is taken as the estimated mass of the vehicle at the current moment, the no-load brake oil pressure table of the vehicle is acquired, and the braking force of the vehicle is calculated based on the no-load brake oil pressure table, the estimated longitudinal acceleration, the estimated mass and the preset expected acceleration, so as to brake the vehicle based on the braking force. The present application can improve the accuracy of calculating the mass of the vehicle, and realize the adaptive adjustment of the braking force of the vehicle according to the total mass.

Description

Technical Field

[0001] This invention relates to the field of autonomous driving, and in particular to a method, apparatus, device, and medium for adjusting vehicle braking force based on mass. Background Technology

[0002] In daily life, when encountering emergencies while driving, rapid braking is often necessary to ensure driver safety. However, there is currently no method to adaptively adjust braking force based on the vehicle's total mass. In addition, most common methods for measuring the total mass of a vehicle are based on the vehicle's longitudinal dynamics model, relying on onboard sensors to directly measure longitudinal acceleration or obtaining longitudinal acceleration through speed versus time difference. The former requires the installation of onboard sensors, which greatly increases the vehicle's manufacturing cost, while the latter is sensitive to noise when measuring speed, resulting in inaccurate measurement results and thus inaccurate predictions of the vehicle's total mass. Summary of the Invention

[0003] This invention provides a method, apparatus, device, and medium for adjusting vehicle braking force based on mass, with the aim of improving the accuracy of calculating vehicle mass and enabling the vehicle to adaptively adjust braking force according to its total mass.

[0004] To achieve the above objectives, the present invention provides a mass-based method for adjusting vehicle braking force, the method comprising:

[0005] When the vehicle is in motion, the road gradient of the current road and the actual speed of the vehicle are obtained in real time. Based on the preset prediction time and the actual speed, the estimated longitudinal acceleration of the vehicle is determined by a preset differential tracker.

[0006] When the road gradient is zero, the estimated mass of the vehicle is determined based on the actual vehicle speed and the estimated longitudinal acceleration.

[0007] When the road gradient is non-zero, the estimated mass of the vehicle calculated at the last moment of driving on flat road is used as the estimated mass of the vehicle at the current moment. The last moment of driving on flat road is the time node at which the estimated mass of the vehicle was last calculated before the vehicle entered the road with a non-zero gradient.

[0008] The vehicle's unloaded brake pressure gauge is obtained, and the braking force of the vehicle is calculated based on the unloaded brake pressure gauge, the estimated longitudinal acceleration, the estimated mass, and the preset expected acceleration, so as to control the vehicle's braking based on the braking force.

[0009] To address the aforementioned problems, the present invention also provides a mass-based vehicle braking force adjustment device, the device comprising:

[0010] The vehicle acceleration prediction module is used to acquire the road gradient of the current road and the actual speed of the vehicle in real time when the vehicle is driving, and to determine the predicted longitudinal acceleration of the vehicle using a preset differential tracker based on the preset prediction time and the actual speed.

[0011] The vehicle mass estimation module is used to determine the estimated mass of the vehicle based on the actual vehicle speed and the estimated longitudinal acceleration when the road gradient is zero. When the road gradient is non-zero, the estimated mass of the vehicle calculated at the last moment of driving on flat road is used as the estimated mass of the vehicle at the current moment. The last moment of driving on flat road is the time node at which the estimated mass of the vehicle is last calculated before the vehicle enters the road with a non-zero gradient.

[0012] The vehicle braking control module is used to acquire the vehicle's unloaded brake oil pressure gauge, and calculate the vehicle's braking force based on the unloaded brake oil pressure gauge, the estimated longitudinal acceleration, the estimated mass, and the preset desired acceleration, so as to perform braking control on the vehicle based on the braking force.

[0013] To address the above problems, the present invention also provides an electronic device, the electronic device comprising:

[0014] Memory, storing at least one computer program; and

[0015] The processor executes the computer program stored in the memory to implement the quality-based vehicle braking force adjustment method described above.

[0016] To address the aforementioned problems, the present invention also provides a computer-readable storage medium storing at least one computer program, which is executed by a processor in an electronic device to implement the aforementioned mass-based vehicle braking force adjustment method.

[0017] This invention first determines the estimated longitudinal acceleration of the vehicle based on a preset forecast time and the vehicle's actual speed using a preset differential tracker. This reduces the vehicle's dependence on acceleration sensors. Compared to the existing speed difference method for calculating estimated longitudinal acceleration, the estimated longitudinal acceleration calculated using the preset differential tracker has less interference and is more accurate. Second, when the road gradient is zero, the estimated mass of the vehicle is determined based on the actual vehicle speed and the estimated longitudinal acceleration. When the road gradient is non-zero, the estimated mass of the vehicle calculated at the last moment of driving on a flat road is used as the estimated mass of the vehicle at the current moment, improving the accuracy of the estimated mass. Finally, the vehicle's no-load brake pressure gauge is obtained, and the braking force of the vehicle is calculated based on the no-load brake pressure gauge, the estimated longitudinal acceleration, the estimated mass, and the preset desired acceleration. This achieves adaptive adjustment of the vehicle's braking force according to the vehicle's mass, thereby greatly improving driver safety. Therefore, the present invention provides a mass-based vehicle braking force adjustment method, device, equipment, and storage medium, which can improve the accuracy of calculating vehicle mass, enable the vehicle to adaptively adjust braking force according to total mass, and ensure driver safety. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating a mass-based vehicle braking force adjustment method according to an embodiment of the present invention.

[0019] Figure 2 , Figure 3 This is a detailed implementation flowchart of one step in a mass-based vehicle braking force adjustment method provided in an embodiment of the present invention;

[0020] Figure 4 This is a schematic diagram of a mass-based vehicle braking force adjustment device according to an embodiment of the present invention.

[0021] Figure 5 A schematic diagram of the internal structure of an electronic device for implementing a mass-based vehicle braking force adjustment method according to an embodiment of the present invention;

[0022] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0023] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0024] This invention provides a quality-based vehicle braking force adjustment method. The executing entity of the quality-based vehicle braking force adjustment method includes, but is not limited to, at least one of electronic devices, such as a server or a terminal, that can be configured to execute the method provided in this application embodiment. In other words, the quality-based vehicle braking force adjustment method can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server can include an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0025] Reference Figure 1 The flowchart shown is a mass-based vehicle braking force adjustment method according to an embodiment of the present invention. In this embodiment, the mass-based vehicle braking force adjustment method includes:

[0026] S1. When the vehicle is driving, the road gradient of the current driving road and the actual speed of the vehicle are obtained in real time, and the estimated longitudinal acceleration of the vehicle is determined by a preset differential tracker based on the preset prediction time and the actual speed.

[0027] In this embodiment of the invention, the preset prediction time refers to the forward prediction time length in the preset differential tracker, wherein the preset differential tracker refers to a differential tracker with a mode of prediction followed by differentiation.

[0028] In an optional embodiment of the present invention, the road gradient of the current driving route of the vehicle can be obtained by looking up the road network information in the vehicle's built-in high-precision map. In addition, the actual speed of the vehicle can be the value displayed on the vehicle speedometer.

[0029] According to the preset prediction time and the actual vehicle speed, the preset differential tracker determines the estimated longitudinal acceleration of the vehicle, which reduces the vehicle's dependence on acceleration sensors. Compared with the existing speed difference method for calculating the estimated longitudinal acceleration, the estimated longitudinal acceleration calculated by the preset differential tracker has less interference and is more accurate.

[0030] In detail, as an optional embodiment of the present invention, refer to Figure 2 As shown, before determining the estimated longitudinal acceleration of the vehicle using a preset differential tracker based on the preset forecast time and the actual vehicle speed, the method further includes:

[0031] S11. Collect the vehicle speed data and plot the waveform based on the vehicle speed data to obtain the vehicle speed waveform;

[0032] S12. The vehicle speed waveform is smoothed and filtered offline using the original differential tracker to obtain a smoothed vehicle speed waveform.

[0033] S13. Calculate the lag time of the initial estimated speed based on the vehicle speed waveform and the smoothed vehicle speed waveform;

[0034] S14. Based on the lag time, determine the length of time required for forward aiming when calculating the initial estimated speed;

[0035] S15. The forward aiming time is used as the preset forecast time.

[0036] In this embodiment of the invention, the vehicle speed data refers to the vehicle speed under the influence of noise. The horizontal axis of the vehicle speed waveform and the smoothed vehicle speed waveform is the time axis, and the vertical axis is the velocity axis.

[0037] In an optional embodiment of the present invention, the signal time lag length caused by the differential tracker filtering is obtained by calculating the length difference of the time axis of the vehicle speed waveform and the smoothed vehicle speed waveform at the same speed. Then, the signal time lag length is used as the preset prediction time of the differential tracker, thereby obtaining a preset differential tracker with the prediction time parameter adjusted.

[0038] Further, the step of determining the estimated longitudinal acceleration of the vehicle using a preset differential tracker based on a preset forecast time and the actual vehicle speed includes:

[0039] The historical longitudinal acceleration of the vehicle at a historical time point is obtained. The historical time point is the previous time point adjacent to the current time point. The historical longitudinal acceleration is calculated by a preset differential tracker based on the actual vehicle speed at the historical time point.

[0040] The predicted speed of the vehicle is determined based on the historical longitudinal acceleration, the preset prediction time, and the actual vehicle speed at the current time point.

[0041] The predicted vehicle speed is input into the preset differential tracker to obtain the estimated longitudinal acceleration of the vehicle.

[0042] In this embodiment of the invention, after each estimated longitudinal acceleration is obtained, the estimated longitudinal acceleration needs to be stored so as to be used as calculation data for the next time node, thereby improving the accuracy of the estimated longitudinal acceleration for the next time node.

[0043] Further, in this embodiment of the invention, determining the predicted vehicle speed based on the historical longitudinal acceleration, the preset prediction time, and the actual vehicle speed at the current time point includes:

[0044] The predicted speed of the vehicle is determined by the following formula:

[0045] x(k)=v(k)+h1x2(k-1)

[0046] Where x(k) represents the predicted vehicle speed, v(k) represents the actual vehicle speed at the current time node, h1 represents the preset forecast time, and x2(k-1) represents the historical longitudinal acceleration.

[0047] In this embodiment of the invention, the sum of the vehicle's current actual speed and the speed at which the vehicle increases based on the historical longitudinal acceleration within the predicted time is used as the vehicle's predicted speed. This reduces the error caused by the lag of the differential tracker, thereby improving the accuracy of the estimated longitudinal acceleration.

[0048] Further, in this embodiment of the invention, the step of inputting the predicted vehicle speed into the preset differential tracker to obtain the estimated longitudinal acceleration of the vehicle includes:

[0049] Based on the predicted vehicle speed, the historical smooth vehicle speed corresponding to the historical time node, and the historical longitudinal acceleration corresponding to the historical time node, the function value of the nonlinear function in the preset differential tracker and the smooth vehicle speed corresponding to the current time node are determined. The historical time node is the previous time node adjacent to the current time node, and the historical smooth vehicle speed and the historical longitudinal acceleration are calculated by the preset differential tracker based on the actual vehicle speed at the historical time node.

[0050] The estimated longitudinal acceleration of the vehicle is determined based on the function value of the nonlinear function and the historical longitudinal acceleration.

[0051] In this embodiment of the invention, the nonlinear function refers to the computational function in the preset differential tracker.

[0052] In an optional embodiment of the present invention, the historical smooth vehicle speed and the historical longitudinal acceleration are both data calculated and stored based on the actual vehicle speed at the historical time point, and can be directly obtained by searching the data.

[0053] Further, in this embodiment of the invention, determining the function value of the nonlinear function within the preset differential tracker and the smoothed vehicle speed corresponding to the current time node based on the predicted vehicle speed, the historical smoothed vehicle speed, and the historical longitudinal acceleration corresponding to the historical time node includes:

[0054] The function value of the nonlinear function is determined using the following preset function:

[0055] fh=fhan(x1(k-1)-x(k),x2(k-1),r,h0)

[0056] x1(k)=x1(k-1)+T s x2(k-1)

[0057] Where fh represents the function value of the nonlinear function, fhan() represents the function expression used to process the nonlinear equation system in the differential tracker, x1(k-1) represents the historical smoothed vehicle speed, x(k) represents the predicted vehicle speed, x2(k-1) represents the historical longitudinal acceleration, r represents the speed factor of the preset differential tracker, h0 represents the filter factor of the preset differential tracker, x1(k) represents the smoothed vehicle speed, and T s This indicates the sampling period of the preset differential tracker.

[0058] In this embodiment of the invention, the sampling period of the preset differential tracker can be a preset prediction time or a sampling period set by the researchers.

[0059] In an optional embodiment of the present invention, the smoothed vehicle speed at the current time point is calculated based on the historical smoothed vehicle speed and the historical longitudinal acceleration, thereby obtaining a vehicle speed value with reduced noise interference and improving the accuracy of the estimated longitudinal acceleration.

[0060] Further, in this embodiment of the invention, determining the estimated longitudinal acceleration of the vehicle based on the function value of the nonlinear function and the historical longitudinal acceleration includes:

[0061] The estimated longitudinal acceleration is determined using the following formula:

[0062] x2(k)=x2(k-1)+T s fh

[0063] Where x2(k) represents the estimated longitudinal acceleration, x2(k-1) represents the historical longitudinal acceleration, and T s fh represents the sampling period of the preset differential tracker, and fh represents the function value of the nonlinear function.

[0064] In an optional embodiment of the present invention, the value obtained by adding the historical longitudinal acceleration to the function value calculated according to the nonlinear function within the sampling period is used as the estimated longitudinal acceleration of the vehicle. This reduces the vehicle's dependence on the acceleration sensor. Furthermore, since the prediction time parameters of the preset differential tracker are pre-tuned, the filtering accuracy and signal delay of the differential tracker are taken into account, thereby improving the accuracy of the estimated longitudinal acceleration calculation.

[0065] S2. When the road gradient is zero, the estimated mass of the vehicle is determined based on the actual vehicle speed and the estimated longitudinal acceleration.

[0066] In an optional embodiment of the present invention, when calculating the estimated mass of the vehicle based on the known actual vehicle speed and the estimated longitudinal acceleration using the vehicle longitudinal dynamics model, it is also necessary to know the road slope of the road on which the vehicle is currently traveling. The road slope data used is the road slope data provided by the road network information in the vehicle's built-in high-precision map. Therefore, when the road slope of the road on which the vehicle is currently traveling is not zero, the road slope data is not accurate enough, which can easily lead to errors in the calculation of the estimated mass of the vehicle.

[0067] Furthermore, in this embodiment of the invention, when the road gradient is zero, the estimated mass of the vehicle is calculated based on the actual vehicle speed and the estimated longitudinal acceleration, which can improve the accuracy of calculating the estimated mass of the vehicle.

[0068] In addition, in this embodiment of the invention, when the road slope is zero, the calculation difficulty of the estimated quality can be reduced, thereby improving the calculation efficiency of the estimated quality.

[0069] In detail, as an optional embodiment of the present invention, determining the estimated mass of the vehicle based on the actual vehicle speed and the estimated longitudinal acceleration when the road gradient is zero includes:

[0070] The estimated mass of the vehicle is obtained by substituting the vehicle's wheel radius, frontal area, empirical values ​​of rolling resistance coefficient and wind resistance coefficient, actual vehicle speed, estimated longitudinal acceleration, motor torque corresponding to the actual vehicle speed, and total transmission ratio into the vehicle's longitudinal dynamics model.

[0071] In an optional embodiment of the present invention, the vehicle's wheel radius, frontal area, and total transmission ratio are all data that have been measured in advance and can be directly obtained and used. Furthermore, the empirical values ​​of the vehicle's rolling resistance coefficient and wind resistance coefficient are both empirical values. In addition, the motor torque corresponding to the actual vehicle speed can be obtained through real-time measurement.

[0072] Further, the step of substituting the vehicle's wheel radius, frontal area, empirical values ​​of rolling resistance coefficient and drag coefficient, actual vehicle speed, estimated longitudinal acceleration, motor torque corresponding to the actual vehicle speed, and total transmission ratio into the vehicle's longitudinal dynamics model to obtain the estimated mass of the vehicle includes:

[0073] The estimated mass of the vehicle is determined using the following longitudinal dynamics model:

[0074]

[0075] in, The estimated longitudinal acceleration is represented by T, the motor torque by i, the overall transmission ratio by m, the estimated mass by r, the wheel radius by g, the gravitational acceleration by f, and the empirical value of the vehicle rolling resistance coefficient by C. D The value represents the empirical value of the vehicle's drag coefficient, A represents the frontal area, ρ represents the air density, and v represents the actual vehicle speed.

[0076] In an optional embodiment of the present invention, the reciprocal of the predicted quality can be estimated using the recursive least squares (RLS) method, and the estimated value of the predicted quality can be obtained by taking the reciprocal.

[0077] Furthermore, since the total mass of some vehicles varies greatly, and the maximum total mass of a vehicle can be many times the mass of the vehicle when unloaded, this embodiment of the invention selects the reciprocal of the estimated mass as the parameter for estimation by recursive least squares method, so that the reciprocal of the estimated mass is in the range of (0,1), thereby improving the robustness of numerical calculation.

[0078] S3. When the road gradient is non-zero, the estimated mass of the vehicle calculated at the last moment of driving on flat road is used as the estimated mass of the vehicle at the current moment. The last moment of driving on flat road is the time node at which the estimated mass of the vehicle was last calculated before the vehicle entered the road with a non-zero gradient.

[0079] In an optional embodiment of the present invention, when the vehicle navigation map encounters a road with a non-zero gradient, the accuracy of measuring the road gradient is not high. Therefore, when using the vehicle longitudinal dynamics model to calculate the estimated mass of the vehicle, the estimated mass will be incorrect, resulting in the failure of the vehicle braking force adjustment.

[0080] Furthermore, in an optional embodiment of the present invention, the total mass of the vehicle does not change significantly during the driving process. Therefore, when the road gradient is non-zero, the estimated mass of the vehicle calculated at the last moment of driving on a flat road is used as the estimated mass of the vehicle at the current moment, ensuring the accuracy of the estimated mass of the vehicle when the road gradient is non-zero.

[0081] In addition, to maximize the accuracy of the vehicle's estimated mass when the road gradient is non-zero, this embodiment of the invention requires the time node at which the vehicle's estimated mass is last calculated before it enters the road with a non-zero gradient to be taken as the last moment of driving on the flat road.

[0082] S4. Obtain the unloaded brake oil pressure gauge of the vehicle, and calculate the braking force of the vehicle based on the unloaded brake oil pressure gauge, the estimated longitudinal acceleration, the estimated mass and the preset expected acceleration, so as to perform braking control on the vehicle based on the braking force.

[0083] In this embodiment of the invention, the no-load brake oil pressure gauge refers to a table that shows a one-to-one correspondence between the vehicle's deceleration and brake oil pressure when the vehicle is unloaded.

[0084] In an optional embodiment of the present invention, the no-load brake pressure gauge can be manufactured by changing the brake oil pressure of the vehicle and measuring the deceleration of the vehicle.

[0085] In detail, as an optional embodiment of the present invention, refer to Figure 3 As shown, obtaining the vehicle's unloaded brake fluid pressure gauge includes:

[0086] S41. Maintaining the preset vehicle speed, send a step brake oil pressure signal to the chassis of the vehicle and obtain the deceleration data of the vehicle response.

[0087] S42. Sort the stepped brake oil pressure signals to obtain a brake oil pressure signal sequence;

[0088] S43. Sort the deceleration data according to the brake oil pressure signal sequence to obtain a deceleration data sequence;

[0089] S44. Perform numerical fitting on the brake oil pressure signal sequence and the deceleration data sequence to obtain the no-load brake oil pressure gauge.

[0090] In this embodiment of the invention, the preset vehicle speed refers to the vehicle's stable driving speed set by the researchers.

[0091] In an optional embodiment of the present invention, the accuracy and regularity of the no-load brake oil pressure gauge can be improved by numerically fitting the measured brake oil pressure signal sequence and deceleration data sequence.

[0092] Furthermore, in this embodiment of the invention, the braking force of the vehicle is calculated based on the unloaded brake oil pressure gauge, the estimated longitudinal acceleration, the estimated mass, and the preset desired acceleration, so as to perform braking control on the vehicle based on the braking force, thereby realizing adaptive adjustment of the braking force of the vehicle.

[0093] In detail, as an optional embodiment of the present invention, calculating the braking force of the vehicle based on the unloaded brake oil pressure gauge, the estimated mass, and the preset desired acceleration includes:

[0094] Measure the unloaded weight of the vehicle and calculate the amplification ratio between the estimated weight and the unloaded weight;

[0095] The estimated expected acceleration of the vehicle is calculated based on the preset expected acceleration and the amplification ratio.

[0096] The brake fluid pressure corresponding to the estimated desired acceleration is obtained from the brake fluid pressure gauge.

[0097] The feedforward PID control system of the vehicle under no-load conditions is debugged according to the preset driving requirements and the preset expected acceleration of the vehicle to obtain the target PID parameters, which include the target proportional coefficient, the target integral coefficient and the target derivative coefficient.

[0098] The braking force of the vehicle is calculated based on the no-load brake oil pressure gauge, the preset desired acceleration, the estimated longitudinal acceleration, and the target PID parameters.

[0099] In this embodiment of the invention, the preset driving requirements refer to the vehicle safety driving requirements set by the vehicle driver.

[0100] In an optional embodiment of the present invention, the vehicle's unloaded mass and the target PID parameters are obtained from measurements and adjustments made before the vehicle is driven.

[0101] In an optional embodiment of the present invention, the estimated expected acceleration of the vehicle is calculated by the amplification ratio between the estimated mass and the unloaded mass, thereby obtaining the brake oil pressure of the vehicle under the estimated expected acceleration. Finally, the braking force of the vehicle is calculated based on the unloaded brake oil pressure gauge, the preset expected acceleration, the estimated longitudinal acceleration, and the target PID parameters, thus realizing adaptive adjustment of the braking force of the vehicle according to the estimated mass of the vehicle.

[0102] Specifically, calculating the estimated expected acceleration of the vehicle based on the preset expected acceleration and the amplification ratio includes:

[0103] The desired acceleration of the vehicle is determined using the following formula:

[0104] a cmd_ =a cmd ·k

[0105]

[0106] Among them, a cmd_ a represents the desired acceleration. cmd The value represents the desired unloaded acceleration of the vehicle, k represents the magnification ratio, and m represents the desired acceleration of the vehicle. est The estimated mass is represented by m, and the unloaded mass is represented by m.

[0107] In an optional embodiment of the present invention, the ratio of the unloaded expected acceleration to the expected acceleration is equal to the ratio of the unloaded mass to the estimated mass. Therefore, the expected acceleration of the vehicle can be obtained simply by calculating the amplification ratio between the estimated mass and the unloaded mass.

[0108] Further, based on the brake pressure gauge, the preset desired acceleration, the estimated desired acceleration, the estimated longitudinal acceleration, and the target PID parameters, the braking force of the vehicle is calculated, including:

[0109] The braking force of the vehicle is determined using the following formula:

[0110]

[0111] Where P represents the braking force, f(a) cmd_ ) represents the brake fluid pressure corresponding to the estimated desired acceleration in the brake fluid pressure table, k p a represents the target proportional coefficient in the target PID parameters. real a represents the estimated longitudinal acceleration. cmd Let k represent the desired unloaded acceleration of the vehicle. i k represents the target integral coefficient in the target PID parameters. d This represents the target differential coefficient in the target PID parameters.

[0112] In this embodiment of the invention, the feedforward term in the vehicle feedforward PID system is adaptively adjusted according to the estimated mass of the vehicle, so that the braking force of the vehicle is explicitly related to the estimated mass of the vehicle.

[0113] Furthermore, in this embodiment of the invention, if it is desired that the vehicle has the same braking effect under different estimated masses, then the greater the estimated mass of the vehicle, the greater the braking force required for the vehicle to adaptively apply. The response speed of the vehicle's feedforward PID system is faster than that of increasing the integral coefficient in the target PID parameter, thereby enabling the vehicle to brake in time and improving the vehicle's driving safety.

[0114] In addition, in this embodiment of the invention, when the estimated mass of the vehicle increases, it is not necessary to increase the PID parameters in the vehicle's feedforward PID system, thus reducing the possibility of a deterioration in the smoothness of the vehicle's braking when it is driving unloaded.

[0115] This invention first determines the estimated longitudinal acceleration of the vehicle based on a preset forecast time and the vehicle's actual speed using a preset differential tracker. This reduces the vehicle's dependence on acceleration sensors. Compared to the existing speed difference method for calculating estimated longitudinal acceleration, the estimated longitudinal acceleration calculated using the preset differential tracker has less interference and is more accurate. Second, when the road gradient is zero, the estimated mass of the vehicle is determined based on the actual vehicle speed and the estimated longitudinal acceleration. When the road gradient is non-zero, the estimated mass of the vehicle calculated at the last moment of driving on a flat road is used as the estimated mass of the vehicle at the current moment, improving the accuracy of the estimated mass. Finally, the vehicle's no-load brake pressure gauge is obtained, and the braking force of the vehicle is calculated based on the no-load brake pressure gauge, the estimated longitudinal acceleration, the estimated mass, and the preset desired acceleration. This achieves adaptive adjustment of the vehicle's braking force according to the vehicle's mass, thereby greatly improving driver safety. Therefore, the mass-based vehicle braking force adjustment method provided by this invention can improve the accuracy of calculating vehicle mass, enable the vehicle to adaptively adjust braking force according to total mass, and ensure driver safety.

[0116] like Figure 4 The diagram shown is a functional block diagram of the mass-based vehicle braking force adjustment device of the present invention.

[0117] The mass-based vehicle braking force adjustment device 100 of the present invention can be installed in an electronic device. Depending on the functions implemented, the mass-based vehicle braking force adjustment device 100 may include a vehicle acceleration prediction module 101, a vehicle mass prediction module 102, and a vehicle braking control module 103. The module in this invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and which are stored in the memory of the electronic device.

[0118] In this embodiment, the functions of each module / unit are as follows:

[0119] The vehicle acceleration prediction module 101 is used to acquire the road slope of the current driving road and the actual speed of the vehicle in real time when the vehicle is driving, and to determine the predicted longitudinal acceleration of the vehicle using a preset differential tracker based on the preset prediction time and the actual speed.

[0120] The vehicle mass estimation module 102 is used to determine the estimated mass of the vehicle based on the actual vehicle speed and the estimated longitudinal acceleration when the road gradient is zero. When the road gradient is non-zero, the estimated mass of the vehicle calculated at the last moment of driving on flat road is used as the estimated mass of the vehicle at the current moment. The last moment of driving on flat road is the time node at which the estimated mass of the vehicle is last calculated before the vehicle enters the road with a non-zero gradient.

[0121] The vehicle braking control module 103 is used to acquire the vehicle's unloaded brake oil pressure gauge, and calculate the vehicle's braking force based on the unloaded brake oil pressure gauge, the estimated longitudinal acceleration, the estimated mass, and the preset expected acceleration, so as to perform braking control on the vehicle based on the braking force.

[0122] like Figure 5 The diagram shown is a structural schematic of the electronic device that implements the mass-based vehicle braking force adjustment method of the present invention.

[0123] The electronic device may include a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a mass-based vehicle braking force adjustment program.

[0124] The memory 11 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of an electronic device, such as a portable hard drive. In other embodiments, the memory 11 can be an external storage device of the electronic device, such as a plug-in portable hard drive, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc. Furthermore, the memory 11 can include both internal and external storage units of the electronic device. The memory 11 can be used not only to store application software and various types of data installed on the electronic device, such as code for a quality-based vehicle braking force adjustment program, but also to temporarily store data that has been output or will be output.

[0125] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., a mass-based vehicle braking force adjustment program) and calls data stored in the memory 11 to perform various functions of the electronic device and process data.

[0126] The communication bus 12 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into an address bus, a data bus, a control bus, etc. The communication bus 12 is configured to enable communication between the memory 11 and at least one processor 10, etc. For ease of illustration, only one thick line is used in the figure, but this does not indicate that there is only one bus or one type of bus.

[0127] Figure 5 Only electronic devices with components are shown; it will be understood by those skilled in the art that... Figure 5 The structure shown does not constitute a limitation on the electronic device and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0128] For example, although not shown, the electronic device may also include a power supply (such as a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0129] Optionally, the communication interface 13 may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, a Bluetooth interface, etc.), which is typically used to establish communication connections between the electronic device and other electronic devices.

[0130] Optionally, the communication interface 13 may further include a user interface, which may be a display, an input unit (such as a keyboard), or, optionally, a standard wired or wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device and to display a visual user interface.

[0131] It should be understood that the embodiments described are for illustrative purposes only and are not limited to this structure in the scope of the patent application.

[0132] The mass-based vehicle braking force adjustment program stored in the memory 11 of the electronic device is a combination of multiple computer programs that, when run in the processor 10, can achieve the following:

[0133] When the vehicle is in motion, the road gradient of the current road and the actual speed of the vehicle are obtained in real time. Based on the preset prediction time and the actual speed, the estimated longitudinal acceleration of the vehicle is determined by a preset differential tracker.

[0134] When the road gradient is zero, the estimated mass of the vehicle is determined based on the actual vehicle speed and the estimated longitudinal acceleration.

[0135] When the road gradient is non-zero, the estimated mass of the vehicle calculated at the last moment of driving on flat road is used as the estimated mass of the vehicle at the current moment. The last moment of driving on flat road is the time node at which the estimated mass of the vehicle was last calculated before the vehicle entered the road with a non-zero gradient.

[0136] The vehicle's unloaded brake pressure gauge is obtained, and the braking force of the vehicle is calculated based on the unloaded brake pressure gauge, the estimated longitudinal acceleration, the estimated mass, and the preset expected acceleration, so as to control the vehicle's braking based on the braking force.

[0137] Specifically, the processor 10's implementation method of the above-mentioned computer program can be found in [reference needed]. Figure 1 The descriptions of the relevant steps in the corresponding embodiments are not repeated here.

[0138] Furthermore, if the modules / units integrated into the electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable medium can be non-volatile or volatile. The computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0139] Embodiments of the present invention may also provide a computer-readable storage medium storing a computer program, which, when executed by a processor of an electronic device, can perform the following:

[0140] When the vehicle is in motion, the road gradient of the current road and the actual speed of the vehicle are obtained in real time. Based on the preset prediction time and the actual speed, the estimated longitudinal acceleration of the vehicle is determined by a preset differential tracker.

[0141] When the road gradient is zero, the estimated mass of the vehicle is determined based on the actual vehicle speed and the estimated longitudinal acceleration.

[0142] When the road gradient is non-zero, the estimated mass of the vehicle calculated at the last moment of driving on flat road is used as the estimated mass of the vehicle at the current moment. The last moment of driving on flat road is the time node at which the estimated mass of the vehicle was last calculated before the vehicle entered the road with a non-zero gradient.

[0143] The vehicle's unloaded brake pressure gauge is obtained, and the braking force of the vehicle is calculated based on the unloaded brake pressure gauge, the estimated longitudinal acceleration, the estimated mass, and the preset expected acceleration, so as to control the vehicle's braking based on the braking force.

[0144] Furthermore, the computer's usable storage medium may mainly include a program storage area and a data storage area, wherein the program storage area may store the operating system, applications required for at least one function, etc.; and the data storage area may store data created based on the use of blockchain nodes, etc.

[0145] In the several embodiments provided by this invention, it should be understood that the provided electronic devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0146] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0147] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0148] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0149] Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within the invention. No appended diagram markings in the claims should be construed as limiting the scope of the claims.

[0150] The blockchain referred to in this invention is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Essentially, a blockchain is a decentralized database, a chain of data blocks linked together using cryptographic methods. Each data block contains information about a batch of network transactions, used to verify the validity of the information (anti-counterfeiting) and generate the next block. A blockchain can include an underlying blockchain platform, a platform product service layer, and an application service layer.

[0151] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a system claim may also be implemented by a single unit or device through software or hardware. The term "second class" is used to indicate names and does not indicate any specific order.

[0152] Option 1: A mass-based method for adjusting vehicle braking force, the method comprising:

[0153] When the vehicle is in motion, the road gradient of the current road and the actual speed of the vehicle are obtained in real time. Based on the preset prediction time and the actual speed, the estimated longitudinal acceleration of the vehicle is determined by a preset differential tracker.

[0154] When the road gradient is zero, the estimated mass of the vehicle is determined based on the actual vehicle speed and the estimated longitudinal acceleration.

[0155] When the road gradient is non-zero, the estimated mass of the vehicle calculated at the last moment of driving on flat road is used as the estimated mass of the vehicle at the current moment. The last moment of driving on flat road is the time node at which the estimated mass of the vehicle was last calculated before the vehicle entered the road with a non-zero gradient.

[0156] The vehicle's unloaded brake pressure gauge is obtained, and the braking force of the vehicle is calculated based on the unloaded brake pressure gauge, the estimated longitudinal acceleration, the estimated mass, and the preset expected acceleration, so as to control the vehicle's braking based on the braking force.

[0157] Option 2, the mass-based vehicle braking force adjustment method as described in Option 1, further includes the following step before determining the estimated longitudinal acceleration of the vehicle using a preset differential tracker based on a preset prediction time and the actual vehicle speed:

[0158] Collect the vehicle speed data and plot the waveform based on the vehicle speed data to obtain the vehicle speed waveform;

[0159] The vehicle speed waveform is smoothed offline using the original differential tracker to obtain a smoothed vehicle speed waveform.

[0160] Calculate the lag time of the initial estimated speed based on the vehicle speed waveform and the smoothed vehicle speed waveform;

[0161] Based on the lag time, determine the length of forward aiming time required to calculate the initial estimated velocity;

[0162] The forward aiming time is used as the preset forecast time.

[0163] Option 3, the mass-based vehicle braking force adjustment method as described in Option 1, wherein determining the estimated longitudinal acceleration of the vehicle using a preset differential tracker based on a preset prediction time and the actual vehicle speed includes:

[0164] The historical longitudinal acceleration of the vehicle at a historical time point is obtained. The historical time point is the previous time point adjacent to the current time point. The historical longitudinal acceleration is calculated by a preset differential tracker based on the actual vehicle speed at the historical time point.

[0165] The predicted speed of the vehicle is determined based on the historical longitudinal acceleration, the preset prediction time, and the actual vehicle speed at the current time point.

[0166] The predicted vehicle speed is input into the preset differential tracker to obtain the estimated longitudinal acceleration of the vehicle.

[0167] Option 4, the mass-based vehicle braking force adjustment method as described in Option 3, wherein determining the predicted vehicle speed based on the historical longitudinal acceleration, the preset prediction time, and the actual vehicle speed at the current time point includes:

[0168] The predicted speed of the vehicle is determined by the following formula:

[0169] x(k)=v(k)+h1x2(k-1)

[0170] Where x(k) represents the predicted vehicle speed, v(k) represents the actual vehicle speed at the current time node, h1 represents the preset forecast time, and x2(k-1) represents the historical longitudinal acceleration.

[0171] Option 5, the mass-based vehicle braking force adjustment method as described in Option 3, wherein inputting the predicted vehicle speed into the preset differential tracker to obtain the estimated longitudinal acceleration of the vehicle includes:

[0172] Based on the predicted vehicle speed, the historical smooth vehicle speed corresponding to the historical time node, and the historical longitudinal acceleration corresponding to the historical time node, the function value of the nonlinear function in the preset differential tracker and the smooth vehicle speed corresponding to the current time node are determined. The historical time node is the previous time node adjacent to the current time node, and the historical smooth vehicle speed and the historical longitudinal acceleration are calculated by the preset differential tracker based on the actual vehicle speed at the historical time node.

[0173] The estimated longitudinal acceleration of the vehicle is determined based on the function value of the nonlinear function and the historical longitudinal acceleration.

[0174] Solution 6: The mass-based vehicle braking force adjustment method as described in Solution 5, wherein determining the function value of the nonlinear function within the preset differential tracker and the smoothed vehicle speed corresponding to the current time node based on the predicted vehicle speed, the historical smoothed vehicle speed, and the historical longitudinal acceleration corresponding to the historical time node includes:

[0175] The function value of the nonlinear function is determined using the following preset function:

[0176] fh=fhan(x1(k-1)-x(k),x2(k-1),r,h0)

[0177] x1(k)=x1(k-1)+T s x2(k-1)

[0178] Where fh represents the function value of the nonlinear function, fhan() represents the function expression used to process the nonlinear equation system in the differential tracker, x1(k-1) represents the historical smoothed vehicle speed, x(k) represents the predicted vehicle speed, x2(k-1) represents the historical longitudinal acceleration, r represents the speed factor of the preset differential tracker, h0 represents the filter factor of the preset differential tracker, x1(k) represents the smoothed vehicle speed, and T s This indicates the sampling period of the preset differential tracker.

[0179] Option 7, the mass-based vehicle braking force adjustment method as described in Option 5, wherein determining the estimated longitudinal acceleration of the vehicle based on the function value of the nonlinear function and the historical longitudinal acceleration includes:

[0180] The estimated longitudinal acceleration is determined using the following formula:

[0181] x2(k)=x2(k-1)+T s fh

[0182] Where x2(k) represents the estimated longitudinal acceleration, x2(k-1) represents the historical longitudinal acceleration, and T s fh represents the sampling period of the preset differential tracker, and fh represents the function value of the nonlinear function.

[0183] Option 8: The mass-based vehicle braking force adjustment method as described in Option 1, wherein when the road gradient is zero, determining the estimated mass of the vehicle based on the actual vehicle speed and the estimated longitudinal acceleration includes:

[0184] The estimated mass of the vehicle is obtained by substituting the vehicle's wheel radius, frontal area, empirical values ​​of rolling resistance coefficient and wind resistance coefficient, actual vehicle speed, estimated longitudinal acceleration, motor torque corresponding to the actual vehicle speed, and total transmission ratio into the vehicle's longitudinal dynamics model.

[0185] Solution 9, the mass-based vehicle braking force adjustment method as described in Solution 8, wherein the step of substituting the vehicle's wheel radius, frontal area, empirical values ​​of vehicle rolling resistance coefficient, empirical values ​​of vehicle drag coefficient, actual vehicle speed, estimated longitudinal acceleration, motor torque corresponding to the actual vehicle speed, and total transmission ratio into the vehicle's longitudinal dynamics model to obtain the estimated mass of the vehicle includes:

[0186] The estimated mass of the vehicle is determined using the following longitudinal dynamics model:

[0187]

[0188] in, The estimated longitudinal acceleration is represented by T, the motor torque by i, the overall transmission ratio by m, the estimated mass by r, the wheel radius by g, the gravitational acceleration by f, and the empirical value of the vehicle rolling resistance coefficient by C. D The value represents the empirical value of the vehicle's drag coefficient, A represents the frontal area, ρ represents the air density, and v represents the actual vehicle speed.

[0189] Option 10, the mass-based vehicle braking force adjustment method as described in Option 1, wherein obtaining the vehicle's no-load brake fluid pressure gauge includes:

[0190] While maintaining a preset vehicle speed, a step brake oil pressure signal is sent to the chassis of the vehicle, and the deceleration data of the vehicle in response is obtained.

[0191] The stepped brake oil pressure signals are sorted to obtain a brake oil pressure signal sequence;

[0192] Based on the brake oil pressure signal sequence, the deceleration data is sorted to obtain a deceleration data sequence;

[0193] Numerical fitting is performed on the brake oil pressure signal sequence and the deceleration data sequence to obtain the no-load brake oil pressure gauge.

[0194] Option 11: The mass-based vehicle braking force adjustment method as described in Option 1, wherein calculating the vehicle's braking force based on the unloaded brake fluid pressure gauge, the estimated mass, and the preset desired acceleration includes:

[0195] Measure the unloaded weight of the vehicle and calculate the amplification ratio between the estimated weight and the unloaded weight;

[0196] The estimated expected acceleration of the vehicle is calculated based on the preset expected acceleration and the amplification ratio.

[0197] The brake fluid pressure corresponding to the estimated desired acceleration is obtained from the brake fluid pressure gauge.

[0198] The feedforward PID control system of the vehicle under no-load conditions is debugged according to the preset driving requirements and the preset expected acceleration of the vehicle to obtain the target PID parameters, which include the target proportional coefficient, the target integral coefficient and the target derivative coefficient.

[0199] The braking force of the vehicle is calculated based on the no-load brake oil pressure gauge, the preset desired acceleration, the estimated longitudinal acceleration, and the target PID parameters.

[0200] Option 12, the mass-based vehicle braking force adjustment method as described in Option 11, wherein calculating the estimated expected acceleration of the vehicle based on the preset expected acceleration and the amplification ratio includes:

[0201] The desired acceleration of the vehicle is determined using the following formula:

[0202] a cmd_ =a cmd ·k

[0203]

[0204] Among them, a cmd_ a represents the desired acceleration. cmd The value represents the desired unloaded acceleration of the vehicle, k represents the magnification ratio, and m represents the desired acceleration of the vehicle. est The estimated mass is represented by m, and the unloaded mass is represented by m.

[0205] Solution 13, the mass-based vehicle braking force adjustment method as described in Solution 11, wherein calculating the vehicle braking force based on the brake fluid pressure gauge, the preset desired acceleration, the estimated desired acceleration, the estimated longitudinal acceleration, and the target PID parameters includes:

[0206] The braking force of the vehicle is determined using the following formula:

[0207]

[0208] Where P represents the braking force, f(a) cmd_ ) represents the brake fluid pressure corresponding to the estimated desired acceleration in the brake fluid pressure table, k p a represents the target proportional coefficient in the target PID parameters. real a represents the estimated longitudinal acceleration. cmd Let k represent the desired unloaded acceleration of the vehicle. i k represents the target integral coefficient in the target PID parameters. d This represents the target differential coefficient in the target PID parameters.

[0209] Option 14: A mass-based vehicle braking force adjustment device, the device comprising:

[0210] The vehicle acceleration prediction module is used to acquire the road gradient of the current road and the actual speed of the vehicle in real time when the vehicle is driving, and to determine the predicted longitudinal acceleration of the vehicle using a preset differential tracker based on the preset prediction time and the actual speed.

[0211] The vehicle mass estimation module is used to determine the estimated mass of the vehicle based on the actual vehicle speed and the estimated longitudinal acceleration when the road gradient is zero. When the road gradient is non-zero, the estimated mass of the vehicle calculated at the last moment of driving on flat road is used as the estimated mass of the vehicle at the current moment. The last moment of driving on flat road is the time node at which the estimated mass of the vehicle is last calculated before the vehicle enters the road with a non-zero gradient.

[0212] The vehicle braking control module is used to acquire the vehicle's unloaded brake oil pressure gauge, and calculate the vehicle's braking force based on the unloaded brake oil pressure gauge, the estimated longitudinal acceleration, the estimated mass, and the preset desired acceleration, so as to perform braking control on the vehicle based on the braking force.

[0213] Option 15: An electronic device, the electronic device comprising:

[0214] At least one processor; and,

[0215] A memory communicatively connected to the at least one processor; wherein,

[0216] The memory stores computer program instructions that can be executed by the at least one processor to enable the at least one processor to perform the mass-based vehicle braking force adjustment method as described in any one of Schemes 1 to 13.

[0217] Solution 16: A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the mass-based vehicle braking force adjustment method as described in any one of Solutions 1 to 13. Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention.

Claims

1. A mass-based method for adjusting vehicle braking force, characterized in that, The method includes: When the vehicle is in motion, the road gradient of the current road and the actual speed of the vehicle are obtained in real time. Based on the preset prediction time and the actual speed, the estimated longitudinal acceleration of the vehicle is determined by a preset differential tracker. When the road gradient is zero, the estimated mass of the vehicle is determined based on the actual vehicle speed and the estimated longitudinal acceleration. When the road gradient is non-zero, the estimated mass of the vehicle calculated at the last moment of driving on flat road is used as the estimated mass of the vehicle at the current moment. The last moment of driving on flat road is the time node at which the estimated mass of the vehicle was last calculated before the vehicle entered the road with a non-zero gradient. Obtain the no-load brake fluid pressure gauge of the vehicle; The brake pressure is determined based on the estimated mass, the no-load brake oil pressure gauge, and the preset expected acceleration. The feedforward PID control system of the vehicle under no-load conditions is debugged according to the preset driving requirements and the preset expected acceleration of the vehicle to obtain target PID parameters, which include target proportional coefficient, target integral coefficient and target derivative coefficient. The braking force of the vehicle is calculated based on the brake oil pressure, the estimated longitudinal acceleration, and the target PID parameters, so as to perform braking control on the vehicle based on the braking force.

2. The mass-based vehicle braking force adjustment method as described in claim 1, characterized in that, Before determining the estimated longitudinal acceleration of the vehicle using a preset differential tracker based on a preset forecast time and the actual vehicle speed, the method further includes: Collect the vehicle speed data and plot the waveform based on the vehicle speed data to obtain the vehicle speed waveform; The vehicle speed waveform is smoothed offline using the original differential tracker to obtain a smoothed vehicle speed waveform. Calculate the lag time of the initial estimated speed based on the vehicle speed waveform and the smoothed vehicle speed waveform; Based on the lag time, determine the length of forward aiming time required to calculate the initial estimated velocity; The forward aiming time is used as the preset forecast time.

3. The mass-based vehicle braking force adjustment method as described in claim 1, characterized in that, The step of determining the estimated longitudinal acceleration of the vehicle using a preset differential tracker based on a preset forecast time and the actual vehicle speed includes: The historical longitudinal acceleration of the vehicle at a historical time point is obtained. The historical time point is the previous time point adjacent to the current time point. The historical longitudinal acceleration is calculated by a preset differential tracker based on the actual vehicle speed at the historical time point. The predicted speed of the vehicle is determined based on the historical longitudinal acceleration, the preset prediction time, and the actual vehicle speed at the current time point. The predicted vehicle speed is input into the preset differential tracker to obtain the estimated longitudinal acceleration of the vehicle.

4. The mass-based vehicle braking force adjustment method as described in claim 3, characterized in that, Determining the predicted vehicle speed based on the historical longitudinal acceleration, the preset prediction time, and the actual vehicle speed at the current time point includes: The predicted speed of the vehicle is determined by the following formula: in, This indicates the predicted vehicle speed. This indicates the vehicle's actual speed at the current time point. This indicates the preset forecast time. This represents the historical longitudinal acceleration.

5. The mass-based vehicle braking force adjustment method as described in claim 3, characterized in that, The step of inputting the predicted vehicle speed into the preset differential tracker to obtain the estimated longitudinal acceleration of the vehicle includes: Based on the predicted vehicle speed, the historical smooth vehicle speed corresponding to the historical time node, and the historical longitudinal acceleration corresponding to the historical time node, the function value of the nonlinear function in the preset differential tracker and the smooth vehicle speed corresponding to the current time node are determined. The historical time node is the previous time node adjacent to the current time node, and the historical smooth vehicle speed and the historical longitudinal acceleration are calculated by the preset differential tracker based on the actual vehicle speed at the historical time node. The estimated longitudinal acceleration of the vehicle is determined based on the function value of the nonlinear function and the historical longitudinal acceleration.

6. The mass-based vehicle braking force adjustment method as described in claim 5, characterized in that, The step of determining the function value of the nonlinear function within the preset differential tracker and the smoothed vehicle speed corresponding to the current time point based on the predicted vehicle speed, the historical smoothed vehicle speed, and the historical longitudinal acceleration corresponding to the historical time point includes: The function value of the nonlinear function is determined using the following preset function: in, This represents the function value of the nonlinear function. This represents the function used within the differential tracker to process the nonlinear equation system. This represents the historical smoothed vehicle speed. This indicates the predicted vehicle speed. This represents the historical longitudinal acceleration. This represents the velocity factor of the preset differential tracker. This represents the filter factor of the preset differential tracker. Indicates the smoothed vehicle speed, This indicates the sampling period of the preset differential tracker.

7. The mass-based vehicle braking force adjustment method as described in claim 5, characterized in that, Determining the estimated longitudinal acceleration of the vehicle based on the function value of the nonlinear function and the historical longitudinal acceleration includes: The estimated longitudinal acceleration is determined using the following formula: in, This indicates the estimated longitudinal acceleration. This represents the historical longitudinal acceleration. This indicates the sampling period of the preset differential tracker. This represents the function value of the nonlinear function.

8. The mass-based vehicle braking force adjustment method as described in claim 1, characterized in that, When the road gradient is zero, determining the estimated mass of the vehicle based on the actual vehicle speed and the estimated longitudinal acceleration includes: The estimated mass of the vehicle is obtained by substituting the vehicle's wheel radius, frontal area, empirical values ​​of rolling resistance coefficient and wind resistance coefficient, actual vehicle speed, estimated longitudinal acceleration, motor torque corresponding to the actual vehicle speed, and total transmission ratio into the vehicle's longitudinal dynamics model.

9. The mass-based vehicle braking force adjustment method as described in claim 8, characterized in that, The process of substituting the vehicle's wheel radius, frontal area, empirical values ​​of rolling resistance coefficient and drag coefficient, actual vehicle speed, estimated longitudinal acceleration, motor torque corresponding to the actual vehicle speed, and total transmission ratio into the vehicle's longitudinal dynamics model to obtain the estimated mass of the vehicle includes: The estimated mass of the vehicle is determined using the following longitudinal dynamics model: in, This indicates the estimated longitudinal acceleration. This indicates the torque of the motor. This indicates the total transmission ratio. This indicates the estimated quality. This indicates the radius of the wheel. Represents gravitational acceleration. This represents the empirical value of the vehicle's rolling resistance coefficient. This represents the empirical value of the vehicle's drag coefficient. This indicates the windward area. Indicates air density, This indicates the actual vehicle speed.

10. The mass-based vehicle braking force adjustment method as described in claim 1, characterized in that, The step of obtaining the vehicle's no-load brake fluid pressure gauge includes: While maintaining a preset vehicle speed, a step brake oil pressure signal is sent to the chassis of the vehicle, and the deceleration data of the vehicle in response is obtained. The stepped brake oil pressure signals are sorted to obtain a brake oil pressure signal sequence; Based on the brake oil pressure signal sequence, the deceleration data is sorted to obtain a deceleration data sequence; Numerical fitting is performed on the brake oil pressure signal sequence and the deceleration data sequence to obtain the no-load brake oil pressure gauge.

11. The mass-based vehicle braking force adjustment method as described in claim 1, characterized in that, The step of determining the brake oil pressure based on the estimated mass, the no-load brake oil pressure gauge, and the preset expected acceleration includes: Measure the unloaded weight of the vehicle and calculate the amplification ratio between the estimated weight and the unloaded weight; The estimated expected acceleration of the vehicle is calculated based on the preset expected acceleration and the amplification ratio. The brake fluid pressure corresponding to the estimated desired acceleration is obtained from the brake fluid pressure gauge.

12. The mass-based vehicle braking force adjustment method as described in claim 11, characterized in that, The step of calculating the estimated expected acceleration of the vehicle based on the preset expected acceleration and the amplification ratio includes: The desired acceleration of the vehicle is determined using the following formula: in, This represents the desired acceleration. This represents the vehicle's desired unloaded acceleration. This indicates the magnification ratio. This indicates the estimated quality. This indicates the unloaded mass.

13. The mass-based vehicle braking force adjustment method as described in claim 11, characterized in that, The step of calculating the braking force of the vehicle based on the brake fluid pressure, the estimated longitudinal acceleration, and the target PID parameters includes: The braking force of the vehicle is determined using the following formula: in, This indicates the braking force. This indicates the brake oil pressure. This represents the target proportional coefficient in the target PID parameters. This indicates the estimated longitudinal acceleration. This represents the vehicle's desired unloaded acceleration. This represents the target integral coefficient in the target PID parameters. This represents the target differential coefficient in the target PID parameters.

14. A mass-based vehicle braking force adjustment device, characterized in that, The device includes: The vehicle acceleration prediction module is used to acquire the road gradient of the current road and the actual speed of the vehicle in real time when the vehicle is driving, and to determine the predicted longitudinal acceleration of the vehicle using a preset differential tracker based on the preset prediction time and the actual speed. The vehicle mass estimation module is used to determine the estimated mass of the vehicle based on the actual vehicle speed and the estimated longitudinal acceleration when the road gradient is zero. When the road gradient is non-zero, the estimated mass of the vehicle calculated at the last moment of driving on flat road is used as the estimated mass of the vehicle at the current moment. The last moment of driving on flat road is the time node at which the estimated mass of the vehicle is last calculated before the vehicle enters the road with a non-zero gradient. The vehicle braking control module is used to acquire the vehicle's unloaded brake fluid pressure gauge reading, determine the brake fluid pressure based on the estimated mass, the unloaded brake fluid pressure gauge reading, and a preset desired acceleration; adjust the vehicle's feedforward PID control system under unloaded conditions according to the vehicle's preset driving requirements and the preset desired acceleration to obtain target PID parameters, including a target proportional coefficient, a target integral coefficient, and a target derivative coefficient; calculate the vehicle's braking force based on the brake fluid pressure, the estimated longitudinal acceleration, and the target PID parameters, and perform braking control on the vehicle based on the braking force.

15. An electronic device, characterized in that, The electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores computer program instructions executable by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the mass-based vehicle braking force adjustment method as described in any one of claims 1 to 13.

16. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the mass-based vehicle braking force adjustment method as described in any one of claims 1 to 13.

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