AEB braking force control method based on OneBox whole vehicle mass estimation

Abstract

The invention discloses an AEB braking force control method based on OneBox vehicle mass estimation, which comprises the following steps: acquiring vehicle working condition information: acquiring driving torque, braking torque, wheel speed and vehicle acceleration signals through an integrated hydraulic brake-by-wire system, and verifying the availability of the signals; calculating the ratio of vehicle longitudinal force to acceleration; estimating the mass of the whole vehicle in real time; and AEB braking force control is conducted, specifically, the whole vehicle mass estimation value is input into an AEB control module, the braking force adjusting gradient is calculated through feedforward control and proportional-integral feedback control, the adjusting gradient is converted into a braking pressure control instruction, and braking pressure adjustment is executed through an OneBox system. The method has the advantages that high-precision braking force adjustment is achieved through dynamic mass estimation and a self-adaptive control strategy in combination with an existing sensor of an OneBox system, and the AEB collision avoidance effect under different loads can be remarkably improved without additional hardware.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated hydraulic-by-wire braking systems, and particularly to an AEB braking force control method based on vehicle mass estimation of OneBox. Background Art

[0002] With the development of automotive intelligence and integration, the automatic emergency braking (AEB) technology in advanced driver assistance systems (ADAS) has become the key to enhancing vehicle safety. The AEB system monitors obstacles in front in real time through sensors and automatically triggers braking under collision risks to reduce the accident rate. However, the braking force control of existing AEB systems often relies on fixed vehicle mass parameters, ignoring the mass changes caused by passengers or goods during actual driving, resulting in an increase in braking distance and limited collision avoidance effect, which is more obvious especially in scenarios with large load fluctuations.

[0003] In the prior art, the integrated hydraulic-by-wire braking system (OneBox) has gradually become the core actuator of the AEB system due to its high integration and fast response capabilities. For example, Patent CN202410392820.3 discloses an integrated automatic emergency by-wire braking system, which realizes the AEB function through the combination of visual perception and by-wire execution, but its braking force control still relies on preset fixed mass parameters and does not involve dynamic mass estimation. In addition, the braking force calculation theory based on Newton's second law requires that the target braking force is proportional to the vehicle mass, and existing engineering solutions usually do not incorporate the dynamic mass changes into the control logic due to cost or signal acquisition difficulties, resulting in insufficient braking pressure regulation accuracy.

[0004] Therefore, how to utilize the internal and external sensor signals (such as driving torque, braking torque, wheel speed, acceleration, etc.) of the existing OneBox architecture to realize real-time dynamic estimation of the vehicle mass without increasing additional hardware costs and optimize the AEB braking force control based on this has become a key technical challenge for enhancing the collision avoidance effect. Summary of the Invention

[0005] The main technical problem to be solved by the present invention is to provide an AEB braking force control method based on vehicle mass estimation of OneBox to solve one or more of the above-mentioned prior art problems.

[0006] To solve the above technical problem, one technical solution adopted by the present invention is: an AEB braking force control method based on vehicle mass estimation of OneBox, and its innovation lies in: including the following steps: Step 1: Obtain vehicle operating condition information: Collect driving torque, braking torque, wheel speed, and vehicle acceleration signals through the integrated hydraulic-by-wire braking system (OneBox) and verify the availability of the signals; Step 2: Calculating the ratio of the longitudinal force to the acceleration of the vehicle: Based on the working condition information, the longitudinal force of the vehicle is calculated by Newton's second law, and combined with the longitudinal acceleration of the vehicle to generate the ratio of the longitudinal force to the acceleration; Step 3: Real-time estimation of vehicle mass: Based on the ratio in step 2, the Kalman filter algorithm is used to dynamically estimate the vehicle mass to obtain the estimated value of the vehicle mass; Step 4: AEB brake force control: The estimated vehicle mass is input into the AEB control module, the brake force adjustment gradient is calculated through feedforward control and proportional integral feedback control, and the adjustment gradient is converted into a brake pressure control instruction, and the brake pressure adjustment is performed through the OneBox system.

[0007] In some embodiments, the availability verification of the signal in step 1 needs to meet the following conditions: Vehicle torque information is valid; The wheel speed sensor or vehicle acceleration sensor is effective; The wheels are not slipping or locking.

[0008] In some implementations, the calculation of the vehicle longitudinal force in step 2 includes: Calculate the longitudinal force of each wheel based on the driving torque, braking torque and wheel acceleration of each wheel; The total longitudinal force is corrected by combining air resistance and is calculated as: in, , , and are the longitudinal forces of the left front wheel, right front wheel, left rear wheel and right rear wheel of the vehicle respectively, is the air density, is the windward area, is the vehicle longitudinal velocity, is the air resistance constant.

[0009] In some implementations, the vehicle mass estimation in step 3 includes: Directly obtain the acceleration signal through the acceleration sensor; Calculate the indirect acceleration signal through the vehicle speed change rate; Generate two mass estimation values ​​based on direct and indirect acceleration signals respectively; According to the validity of the acceleration signal, the arithmetic mean of the two-way estimated values, the single-way estimated value or the preset curb weight is selected as the final vehicle mass.

[0010] In some embodiments, the calculation formula for the longitudinal force of each wheel is: in, is the driving torque for each wheel, is the braking torque for each wheel, is the acceleration for each wheel, is the tire radius for each axle, is the tire mass for each axle; i is used to distinguish the front and rear axles of the vehicle, where F represents the front axle and R represents the rear axle, and n is used to distinguish the left and right wheels of the vehicle, where L represents the left side and R represents the right side.

[0011] In some embodiments, the system model of the Kalman filter algorithm described in step 3 is: where, , , , , is the estimated value of the vehicle mass at the current moment; is the longitudinal force offset at the current moment, which is mainly related to factors such as rolling resistance; is the proportional coefficient of the longitudinal force offset change rate to the current offset value; is the process noise, is the measurement noise, and the formula ; is the total longitudinal force at the current moment, is the longitudinal acceleration of the vehicle at the current moment, H is a row vector, consisting of two elements a x and 1, without actual physical meaning, only in the matrix form of the operation formula; is F x = m k + OFF k in matrix form.

[0012] In some embodiments, the calculation of the braking force adjustment gradient described in step 4 includes: According to the deviation between the target deceleration and the actual deceleration, calculate the total adjustment gradient through the feedforward coefficient, proportional coefficient and integral coefficient; Combined with the estimated value of the total vehicle mass, convert the total adjustment gradient into the braking force adjustment gradient, and the formula is: where, is the total adjustment gradient of deceleration, is the final estimated value of the total vehicle mass, is the adjustment gradient of acceleration.

[0013] In some embodiments, when the vehicle speed is lower than a preset threshold in step 4, the proportional-integral feedback control is turned off, and the braking force gradient is adjusted only through feedforward control.

[0014] In some embodiments, the braking pressure control instruction in step 4 is executed through the hydraulic interface module of the OneBox system, and the braking force adjustment gradient is converted into a pressure adjustment amount to achieve precise braking control under different loads.

[0015] The beneficial effects of the present invention are as follows: This technical solution estimates the vehicle mass in real time through internal and external signals of the OneBox, overcomes the defect that traditional AEB systems rely on fixed mass parameters, significantly reduces the braking distance error caused by load changes, and improves the collision avoidance success rate; This technical solution makes full use of the existing sensors in the OneBox system (such as torque, wheel speed, and acceleration sensors), without the need to install dedicated mass sensors, reducing system complexity and cost; This technical solution uses dual-channel acceleration signals (direct sensor signals and signals calculated from the vehicle speed change rate) to estimate the mass respectively, and dynamically selects the optimal result through effectiveness judgment to ensure reliable operation even when some sensors fail; The combination of feedforward and PI feedback control in this technical solution takes into account both the response speed and the steady-state accuracy, and turns off the feedback control under low-speed conditions to avoid the problem of integral saturation and optimize the braking performance in different vehicle speed ranges; This technical solution realizes an integrated design of signal acquisition, mass estimation, and control execution based on the OneBox architecture, reduces communication delay, and improves the braking response speed and overall reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings, where: Figure 1 is a step block diagram of an AEB braking force control method based on OneBox vehicle mass estimation of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0018] Such asFigure 1 As shown in the figure, the embodiments of the present invention include: Step 1: Quality estimation will be carried out under the following working conditions: Since the internal and external sensor information of the OneBox is required, the vehicle torque information must be valid, and the wheel speed sensor is valid or the vehicle acceleration sensor is valid; the vehicle has acceleration and the wheels cannot be in a slipping or locked state (which can be judged by whether other stability control functions are triggered).

[0019] Step 2: Utilize the existing internal and external signals of the OneBox integrated hydraulic line control braking system to estimate the vehicle mass based on Newton's second law: Wherein, is the longitudinal force of the vehicle, is the longitudinal acceleration of the vehicle, is the mass of the vehicle.

[0020] The longitudinal force of the vehicle is calculated from the longitudinal forces received by each wheel and the air resistance: Wherein, , , and are the longitudinal forces of the left front wheel, right front wheel, left rear wheel and right rear wheel of the vehicle respectively, is the air density, is the frontal area, is the longitudinal speed of the vehicle, is the air resistance constant.

[0021] The longitudinal force of the wheel is calculated from the torque: Wherein, is the driving torque of each wheel, is the braking torque of each wheel, is the acceleration of each wheel, is the tire radius of each axle, is the tire mass of each axle; i is the distinction between the front and rear axles of the vehicle, where F represents the front axle and R represents the rear axle, and n is the distinction between the left and right wheels of the vehicle, where L represents the left side and R represents the right side.

[0022] The driving torque information is provided by the PT (Power Train) module of the OneBox (integrated hydraulic-by-wire braking system). This module processes the torque information obtained from the entire vehicle to obtain the required driving torque. The braking torque information is provided by the hydraulic interface module of the OneBox system, which calculates the braking torque based on information such as braking pressure. The wheel acceleration information is provided by the wheel speed sensor signal processing module of the OneBox system, which calculates the wheel acceleration based on the wheel speed, etc. The specific processing procedures of each OneBox module are not covered in this solution and will not be discussed here.

[0023] To ensure the reliability and stability of the mass estimation, two acceleration signals are used to estimate the vehicle mass separately. One signal is the acceleration received by the OneBox from the vehicle acceleration sensor ; the other is obtained by calculating using the vehicle speed information: Among them, is one cycle, is the change in speed within one cycle.

[0024] Step 3: After obtaining the vehicle longitudinal force and acceleration ( , ) information, vehicle mass estimation is performed based on the Kalman filtering algorithm to obtain the estimated value of the vehicle mass. The system model is as follows: Among them, , , , , is the estimated value of the vehicle mass at the current moment; is the longitudinal force offset at the current moment; is the proportional coefficient of the longitudinal force offset change rate to the current offset value; is the process noise, is the measurement noise, and the formula ; is the total longitudinal force at the current moment, is the vehicle longitudinal acceleration at the current moment, H is a row vector, consisting of two elements a x and 1, without actual physical meaning, only the matrix form of the operation formula; is F x =m k +OFF kin matrix form.

[0025] Two mass estimates are obtained through the above steps and . The finally output mass estimate is calculated according to the following rules: When the vehicle speed information and the acceleration sensor are both valid, the vehicle mass takes the arithmetic mean of the two mass estimates; when the vehicle speed information is invalid, take ; when the acceleration sensor is invalid, take ; when both the vehicle speed information and the acceleration sensor are invalid, take the value of the preset curb mass.

[0026] Step 4: The AEB control adopts a combination of feedforward and PI (Proportional Integral) control. The control target is the braking deceleration. However, the integrated hydraulic line control braking system directly controls the object as the brake fluid pressure. Therefore, it is necessary to convert the control deceleration into braking force and then into brake pressure.

[0027] First, calculate the deceleration deviation: Among them, is the feedforward control deviation, is the feedback control deviation, is the target braking deceleration output by the upper layer, is the actual braking deceleration of the vehicle, is the value of the target deceleration at the previous moment.

[0028] For feedforward control, the deceleration adjustment gradient is: Among them, is the deceleration adjustment gradient of the feedforward part, is the adjustment coefficient of the feedforward part.

[0029] For proportional control, the deceleration adjustment gradient is: Among them, is the deceleration adjustment gradient of the proportional part, is the adjustment coefficient of the proportional part.

[0030] For integral control, the deceleration adjustment gradient is: Among them, is the deceleration adjustment gradient of the integral part, is the adjustment coefficient for the integral part.

[0031] The total adjustment gradient of the deceleration of the control algorithm is: When the vehicle speed is lower than the feedback control threshold let = , = 0, = 0, and the feedback control is turned off.

[0032] Using the vehicle mass calculated in Step 3, convert the deceleration regulation gradient into a braking force adjustment gradient: The AEB module outputs the braking force adjustment gradient to the subsequent OneBox hydraulic interface module, converts the braking force adjustment gradient into a pressure adjustment gradient, and realizes the control of the braking pressure.

[0033] The working principle of this technical solution is as follows: First, use the OneBox system to collect the driving torque, braking torque, wheel speed, and vehicle acceleration signals in real time, and ensure the data reliability through sensor validity judgment (such as the torque signal is valid, the wheel speed / acceleration sensor is valid, and the wheels are not slipping or locked); then, based on Newton's second law, combine the driving torque, braking torque, wheel acceleration, and air resistance correction term of each wheel to calculate the total longitudinal force; at the same time, directly obtain the acceleration signal through the acceleration sensor or indirectly calculate the acceleration through the vehicle speed change rate; then use the Kalman filter algorithm to fuse the direct and indirect acceleration signals, generate two mass estimation values, and dynamically select the mean value, single-channel value, or preset curb weight as the final estimation result according to the signal validity to realize the real-time dynamic update of the vehicle mass; finally, input the mass estimation value into the AEB control module, combine the feedforward control (quickly respond to the target deceleration deviation) and the proportional-integral feedback control (correct the steady-state error) to calculate the braking force adjustment gradient, and convert it into a braking pressure command, and the OneBox system executes precise pressure regulation; when the vehicle speed is lower than the threshold, only the feedforward control is enabled to improve the low-speed stability.

[0034] The advantages of this technical solution are as follows: This technical solution estimates the vehicle mass in real time through the internal and external signals of the OneBox, overcomes the defect of the traditional AEB system relying on fixed mass parameters, significantly reduces the braking distance error caused by load changes, and improves the collision avoidance success rate; This technical solution makes full use of the existing sensors in the OneBox system (such as torque, wheel speed, and acceleration sensors), without installing special mass sensors, reducing the system complexity and cost; This technical solution estimates the mass using two-channel acceleration signals (direct sensor signal and vehicle speed change rate calculation signal), dynamically selects the optimal result through effectiveness judgment, and ensures reliable operation even when some sensors fail; The combination of feedforward and PI feedback control in this technical solution takes into account both response speed and steady-state accuracy, and turns off the feedback control under low-speed conditions to avoid the problem of integral saturation and optimize the braking performance in different vehicle speed segments; This technical solution realizes an integrated design of signal acquisition, mass estimation, and control execution based on the OneBox architecture, reduces communication delay, and improves braking response speed and overall reliability.

[0035] The above are only embodiments of the present invention, and do not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the content of the specification of the present invention, or directly or indirectly applied in other related technical fields, shall be similarly included in the patent protection scope of the present invention.

Claims

1. A method for AEB braking force control based on OneBox vehicle mass estimation, characterized in that: It includes the following steps: Step 1: Obtain vehicle operating condition information: Collect drive torque, braking torque, wheel speed, and vehicle acceleration signals through an integrated hydraulic-by-wire braking system, and verify the availability of the signals; Step 2: Calculate the ratio of vehicle longitudinal force to acceleration: Based on the operating condition information, calculate the vehicle longitudinal force through Newton's second law, and combine with the vehicle longitudinal acceleration to generate the ratio of longitudinal force to acceleration; Step 3: Real-time estimate the vehicle mass: Based on the ratio in step (2), use the Kalman filter algorithm to dynamically estimate the vehicle mass and obtain the estimated value of the vehicle mass; Step 4: AEB braking force control: Input the estimated value of the vehicle mass into the AEB control module, calculate the braking force adjustment gradient through feedforward control and proportional-integral feedback control, and convert the adjustment gradient into a brake pressure control command to perform brake pressure adjustment through the OneBox system.

2. The AEB braking force control method based on OneBox vehicle mass estimation according to claim 1, wherein: The verification of the availability of the signals in step 1 needs to meet the following conditions: The vehicle torque information is valid; The wheel speed sensor or the vehicle acceleration sensor is valid; The wheels are not in a slipping or locked state.

3. The AEB braking force control method based on OneBox vehicle mass estimation according to claim 1, wherein: The calculation of the vehicle longitudinal force in step 2 includes: Calculate the longitudinal force of each wheel according to the drive torque, braking torque, and wheel acceleration of each wheel; Combine the air resistance to correct the total longitudinal force, and the calculation formula is: Among them, , , and are the longitudinal forces of the left front wheel, right front wheel, left rear wheel, and right rear wheel of the vehicle respectively, is the air density, is the frontal area, is the longitudinal speed of the vehicle, is the air resistance constant.

4. A method for AEB braking force control based on OneBox vehicle mass estimation according to claim 1, characterized in that: The vehicle mass estimation in step 3 includes: Directly obtain the acceleration signal through the acceleration sensor; Calculate the indirect acceleration signal through the vehicle speed change rate; Generate two paths of mass estimation values based on the direct and indirect acceleration signals respectively; According to the effectiveness of the acceleration signal, select the arithmetic mean of the two paths of estimation values, a single-path estimation value, or the preset curb weight as the final vehicle mass.

5. The AEB braking force control method based on OneBox vehicle mass estimation according to claim 3, wherein: The calculation formula of the longitudinal force of each wheel is: Among them, is the driving torque of each wheel, is the braking torque of each wheel, is the acceleration of each wheel, is the tire radius of each axle, is the tire mass of each axle; i is the distinction between the front and rear axles of the vehicle, where F represents the front axle and R represents the rear axle, and n is the distinction between the left and right wheels of the vehicle, where L represents the left side and R represents the right side.

6. The AEB braking force control method based on OneBox vehicle mass estimation according to claim 1, characterized in that: The system model of the Kalman filter algorithm in step 3 is: Among them, , , , , is the estimated vehicle mass at the current moment; is the longitudinal force offset at the current moment; is the proportionality coefficient of the longitudinal force offset change rate to the current offset value; is the process noise, is the measurement noise, is the total longitudinal force at the current moment, is the vehicle longitudinal acceleration at the current moment, H is a row vector, with a x and 1 as two elements, having no actual physical meaning, only being the matrix form of the operation expression; is F x =m k +OFF k in matrix form.

7. The AEB braking force control method based on OneBox vehicle mass estimation according to claim 1, wherein: The calculation of the braking force adjustment gradient in step 4 includes: Calculate the total adjustment gradient through the feedforward coefficient, proportional coefficient, and integral coefficient according to the deviation between the target deceleration and the actual deceleration; Combine the estimated value of the vehicle mass, and convert the total adjustment gradient into the braking force adjustment gradient, and the formula is: wherein, is the total deceleration adjustment gradient, is the estimated value of the final vehicle mass, is the adjustment gradient of the acceleration.

8. A method for AEB braking force control based on OneBox vehicle mass estimation according to claim 1, characterized in that: In step 4, when the vehicle speed is lower than the preset threshold, turn off the proportional-integral feedback control and only adjust the braking force gradient through feedforward control.

9. The AEB braking force control method based on OneBox vehicle mass estimation according to claim 1, characterized in that: The brake pressure control command in step 4 is executed through the hydraulic interface module of the OneBox system, and the braking force adjustment gradient is converted into a pressure adjustment amount to achieve precise braking control under different loads.

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