An AEB Braking Force Control Method Based on OneBox Vehicle Mass Estimation

Abstract

The present invention discloses an AEB braking force control method based on OneBox vehicle mass estimation, comprising the following steps: acquiring vehicle operating condition information: collecting driving torque, braking torque, wheel speed, and vehicle acceleration signals through an integrated hydraulic brake-by-wire system and verifying the availability of these signals; calculating the ratio of vehicle longitudinal force to acceleration; estimating vehicle mass in real time; and controlling AEB braking force: inputting the estimated vehicle mass into an AEB control module, calculating a braking force adjustment gradient through feedforward control and proportional-integral feedback control, converting the adjustment gradient into a brake pressure control command, and executing brake pressure adjustment through the OneBox system. The present invention has the beneficial effect of achieving high-precision braking force adjustment through dynamic mass estimation and adaptive control strategies, combined with the existing sensors of the OneBox system, significantly improving AEB collision avoidance effectiveness under different loads without the need for additional hardware.

Description

Technical Field

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

[0002] With the advancement of intelligent and integrated vehicles, automatic emergency braking (AEB) technology within advanced driver assistance systems (ADAS) has become crucial for improving vehicle safety. AEB systems use sensors to monitor obstacles ahead in real time and automatically trigger braking when a collision is imminent, thereby reducing accident rates. However, existing AEB systems often control braking force based on fixed vehicle mass parameters, ignoring actual mass fluctuations caused by passengers or cargo. This increases braking distances and limits collision avoidance effectiveness, especially under conditions with significant load fluctuations.

[0003] In existing technologies, integrated hydraulic brake-by-wire systems (OneBox) have gradually become the core actuator of AEB systems due to their high degree of integration and rapid response capabilities. For example, patent CN202410392820.3 discloses an integrated automatic emergency brake-by-wire system that implements AEB functionality through a combination of visual perception and drive-by-wire execution. However, its braking force control still relies on preset fixed mass parameters and does not involve dynamic mass estimation. Furthermore, the braking force calculation theory based on Newton's second law requires that the target braking force is proportional to the vehicle mass. However, due to cost or signal acquisition difficulties, existing engineering solutions generally do not incorporate dynamic mass changes into the control logic, resulting in insufficient brake pressure regulation accuracy.

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

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

[0006] To solve the above technical problems, the present invention adopts a technical solution: an AEB braking force control method based on OneBox vehicle mass estimation, the innovation of which is that it includes the following steps:

[0007] Step 1: Obtain vehicle operating condition information: Collect driving torque, braking torque, wheel speed, and vehicle acceleration signals through the integrated hydraulic brake-by-wire system (OneBox) and verify the availability of these signals.

[0008] Step 2: Calculating the ratio of the vehicle's longitudinal force to acceleration: Based on the operating condition information, the vehicle's longitudinal force is calculated using Newton's second law, and combined with the vehicle's longitudinal acceleration to generate a ratio of the longitudinal force to acceleration;

[0009] 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 vehicle mass value;

[0010] Step 4: AEB Braking Force Control: The estimated vehicle mass is input into the AEB control module, and the braking force adjustment gradient is calculated through feedforward control and proportional-integral feedback control. The adjustment gradient is converted into a brake pressure control command, and brake pressure adjustment is performed through the OneBox system.

[0011] In some embodiments, the verification of the availability of the signal in step 1 must meet the following conditions:

[0012] Vehicle torque information is valid;

[0013] The wheel speed sensor or vehicle acceleration sensor is valid;

[0014] The wheels are not slipping or locking.

[0015] In some implementations, the calculation of the vehicle longitudinal force in step 2 includes:

[0016] Calculate the longitudinal force of each wheel based on the driving torque, braking torque and wheel acceleration of each wheel;

[0017] The total longitudinal force is corrected by combining air resistance and is calculated as follows:

[0018]

[0019] in, 、 、 and are the longitudinal forces of the vehicle’s left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively. is the air density, is the windward area, is the vehicle longitudinal velocity, is the air resistance constant.

[0020] In some embodiments, the vehicle mass estimation in step 3 includes:

[0021] Directly obtain acceleration signals through acceleration sensors;

[0022] Calculate the indirect acceleration signal through the vehicle speed change rate;

[0023] Generate two mass estimates based on direct and indirect acceleration signals respectively;

[0024] 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.

[0025] In some embodiments, the calculation formula for each wheel longitudinal force is:

[0026]

[0027] in, 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 mass of the tires on 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; 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.

[0028] In some embodiments, the system model of the Kalman filter algorithm in step 3 is:

[0029]

[0030]

[0031] in, , , , ,

[0032] 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 and the current offset value; is the process noise, is the measurement noise, And the formula of; is the total longitudinal force at the current moment, is the longitudinal acceleration of the vehicle at the current moment, H is the row vector, and a x It consists of two elements, , and 1, which has no actual physical meaning and is only the matrix form of the operation formula; F x =m k +OFF k Matrix form of .

[0033] In some embodiments, the calculation of the braking force adjustment gradient in step 4 includes:

[0034] According to the deviation between the target deceleration and the actual deceleration, the total adjustment gradient is calculated through the feedforward coefficient, proportional coefficient and integral coefficient;

[0035] Combined with the estimated vehicle mass, the total adjustment gradient is converted into a braking force adjustment gradient using the formula:

[0036]

[0037] in, is the total adjustment gradient of deceleration, is the estimated final vehicle mass, is the adjustment gradient of acceleration.

[0038] 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.

[0039] In some embodiments, the brake pressure control command in step 4 is executed by a hydraulic interface module of the OneBox system, converting the braking force adjustment gradient into a pressure adjustment amount to achieve precise braking control under different loads.

[0040] 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, overcoming the defect of traditional AEB systems that rely on fixed mass parameters, significantly reducing the braking distance error caused by load changes, and improving the collision avoidance success rate;

[0041] This technical solution fully utilizes the existing OneBox system sensors (such as torque, wheel speed, and acceleration sensors), eliminating the need for dedicated mass sensors and reducing system complexity and cost.

[0042] This technical solution uses dual acceleration signals (direct sensor signal and vehicle speed change rate calculation signal) to estimate mass separately, and dynamically selects the optimal result through effectiveness judgment, ensuring reliable operation even when some sensors fail;

[0043] The combination of feedforward and PI feedback control in this technical solution takes into account both response speed and steady-state accuracy. In addition, feedback control is turned off under low-speed conditions to avoid integral saturation problems and optimize braking performance in different speed ranges.

[0044] This technical solution uses the OneBox architecture to achieve an integrated design of signal acquisition, quality estimation, and control execution, reducing communication delays and improving braking response speed and overall reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive work, among which:

[0046] Figure 1 This is a block diagram of the steps of an AEB braking force control method based on OneBox vehicle mass estimation according to the present invention. DETAILED DESCRIPTION

[0047] The following is a clear and complete description of the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0048] like Figure 1 As shown, the embodiment of the present invention includes:

[0049] Step 1: Mass estimation is performed under the following conditions: Because internal and external sensor information is required for the OneBox, the vehicle torque information must be valid, the wheel speed sensor must be valid, or the vehicle acceleration sensor must be valid; the vehicle has acceleration and the wheels cannot be in a slipping or locking state (this can be determined by whether other stability control functions are triggered).

[0050] Step 2: Utilize the existing internal and external signals of the OneBox integrated hydraulic brake-by-wire system to estimate vehicle mass based on Newton's second law:

[0051]

[0052] in, is the vehicle longitudinal force, is the vehicle longitudinal acceleration, For vehicle quality.

[0053] The longitudinal force of the vehicle is calculated from the longitudinal force on each wheel and the air resistance:

[0054]

[0055] in, 、 、 and are the longitudinal forces of the vehicle’s left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively. is the air density, is the windward area, is the vehicle longitudinal velocity, is the air resistance constant.

[0056] The longitudinal force on the wheel is calculated from the torque:

[0057]

[0058] in, 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 mass of the tires on 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; 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.

[0059] Driving torque information is provided by the PT (Power Train) module of the OneBox (integrated hydraulic brake-by-wire system), which processes torque information obtained from the entire vehicle to derive the required driving torque. Braking torque information is provided by the OneBox system's hydraulic interface module, which calculates braking torque based on information such as brake pressure. Wheel acceleration information is provided by the OneBox system's wheel speed sensor signal processing module, which calculates wheel acceleration based on factors such as wheel speed. The specific processing steps of each OneBox module are beyond the scope of this solution and will not be discussed here.

[0060] In order to ensure the reliability and stability of mass estimation, two acceleration signals are used to estimate the vehicle mass respectively; one signal uses the acceleration signal received by OneBox from the vehicle acceleration sensor. The other method uses vehicle speed information to calculate:

[0061]

[0062] in, For one cycle, is the change in speed within one cycle.

[0063] Step 3: Obtaining the vehicle longitudinal force and acceleration ( 、 ) information, the vehicle mass is estimated based on the Kalman filter algorithm to obtain the estimated value of the vehicle mass. The system model is as follows:

[0064]

[0065]

[0066] in, , , , ,

[0067] 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 and the current offset value; is the process noise, is the measurement noise, And the formula of; is the total longitudinal force at the current moment, is the longitudinal acceleration of the vehicle at the current moment, H is the row vector, and a x It consists of two elements, , and 1, which has no actual physical meaning and is only the matrix form of the operation formula; F x =m k +OFF k Matrix form of .

[0068] The above steps are used to calculate two mass estimates. and , the final output quality estimate Calculate according to the following rules: When the vehicle speed information and acceleration sensor are valid at the same time, the vehicle mass Take the arithmetic mean of the two mass estimates; when the vehicle speed information is invalid, Pick ; When the acceleration sensor is invalid, Pick ; When both the vehicle speed information and the acceleration sensor are invalid, Take the value of the preset curb weight.

[0069] Step 4: AEB control uses a combination of feedforward and PI (proportional-integral) control. The control target is braking deceleration, but the integrated hydraulic brake-by-wire system directly controls the brake fluid pressure. Therefore, the controlled deceleration needs to be converted into braking force and then into brake pressure.

[0070] First, calculate the deceleration deviation:

[0071]

[0072]

[0073] in, 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, It is the target deceleration value at the previous moment.

[0074] For feedforward control, the deceleration adjustment gradient is:

[0075]

[0076] in, Adjust the gradient for the deceleration of the feedforward part, Adjustment coefficient for the feedforward part.

[0077] For proportional control, the deceleration adjustment gradient is:

[0078]

[0079] in, Adjust the gradient for the proportional part of the deceleration, is the proportional part adjustment coefficient.

[0080] For integral control, the deceleration adjustment gradient is:

[0081]

[0082] in, Adjust the gradient for the integral part of the deceleration, Adjust the coefficient for the integral part.

[0083] The total adjustment gradient of the deceleration of the control algorithm is:

[0084]

[0085] When the vehicle speed is lower than the feedback control threshold season = , =0, =0, turn off feedback control.

[0086] Using the vehicle mass calculated in step 3, the deceleration control gradient is converted into the braking force adjustment gradient:

[0087]

[0088] The AEB module outputs the braking force adjustment gradient to the subsequent OneBox hydraulic interface module, converting the braking force adjustment gradient into a pressure adjustment gradient to achieve control of the braking pressure.

[0089] This technical solution works as follows: First, the OneBox system collects driving torque, braking torque, wheel speed, and vehicle acceleration signals in real time. Data reliability is ensured through sensor validity checks (e.g., valid torque signal, valid wheel speed / acceleration sensor, and no wheel slip or locking). The total longitudinal force is then calculated based on Newton's second law, combining each wheel's driving torque, braking torque, wheel acceleration, and an air resistance correction term. Acceleration signals are obtained directly from accelerometers or indirectly calculated from the vehicle's speed rate of change. A Kalman filter algorithm is then used to fuse the direct and indirect acceleration signals to generate two mass estimates. Based on signal validity, the final estimate is dynamically selected as the average, a single value, or a preset curb weight, enabling real-time updates of vehicle mass. Finally, the mass estimate is fed into the AEB control module. Through a combination of feedforward control (for rapid response to target deceleration deviations) and proportional-integral feedback control (for correcting steady-state errors), the braking force adjustment gradient is calculated and converted into a brake pressure command, which the OneBox system then implements for precise pressure regulation. When the vehicle speed falls below a threshold, only feedforward control is enabled to improve low-speed stability.

[0090] The advantages of this technical solution are: it estimates the vehicle mass in real time through internal and external signals from the OneBox, overcoming the drawback of traditional AEB systems that rely on fixed mass parameters, significantly reducing braking distance errors caused by load changes, and improving the collision avoidance success rate;

[0091] This technical solution fully utilizes the existing OneBox system sensors (such as torque, wheel speed, and acceleration sensors), eliminating the need for dedicated mass sensors and reducing system complexity and cost.

[0092] This technical solution uses dual acceleration signals (direct sensor signal and vehicle speed change rate calculation signal) to estimate mass separately, and dynamically selects the optimal result through effectiveness judgment, ensuring reliable operation even when some sensors fail;

[0093] The combination of feedforward and PI feedback control in this technical solution takes into account both response speed and steady-state accuracy. In addition, feedback control is turned off under low-speed conditions to avoid integral saturation problems and optimize braking performance in different speed ranges.

[0094] This technical solution uses the OneBox architecture to achieve an integrated design of signal acquisition, quality estimation, and control execution, reducing communication delays and improving braking response speed and overall reliability.

[0095] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for controlling AEB braking force based on OneBox vehicle mass estimation, characterized by: The steps include: Step 1: Obtain vehicle operating condition information: Collect driving torque, braking torque, wheel speed, and vehicle acceleration signals through the integrated hydraulic brake-by-wire system and verify the availability of these signals; Step 2: Calculating the ratio of the vehicle's longitudinal force to acceleration: Based on the operating condition information, the vehicle's longitudinal force is calculated using Newton's second law, and combined with the vehicle's longitudinal acceleration to generate a ratio of the longitudinal force to 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 vehicle mass value; The vehicle mass estimation includes: Directly obtain acceleration signals through acceleration sensors; Calculate the indirect acceleration signal through the vehicle speed change rate; Generate two mass estimates based on direct and indirect acceleration signals respectively; According to the validity of the acceleration signal, the arithmetic mean of the two-way estimated value, the single-way estimated value or the preset curb weight is selected as the final vehicle weight; Step 4: AEB Braking Force Control: The estimated vehicle mass is input into the AEB control module, and the braking force adjustment gradient is calculated through feedforward control and proportional-integral feedback control. The adjustment gradient is converted into a brake pressure control command, and brake pressure adjustment is performed through the OneBox system. When the vehicle speed is lower than a preset threshold, the proportional-integral feedback control is turned off, and the braking force gradient is adjusted only through feedforward control.

2. The AEB braking force control method based on OneBox vehicle mass estimation according to claim 1, characterized in that: Verification of the signal availability described in step 1 requires the following conditions to be met: Vehicle torque information is valid; The wheel speed sensor or vehicle acceleration sensor is valid; The wheels are not slipping or locking.

3. The AEB braking force control method based on OneBox vehicle mass estimation according to claim 1, characterized in that: The calculation of the vehicle longitudinal force described 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 follows: in, 、 、 and are the longitudinal forces of the vehicle’s left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively. is the air density, is the windward area, is the vehicle longitudinal velocity, is the air resistance constant.

4. The AEB braking force control method based on OneBox vehicle mass estimation according to claim 3, characterized in that: The calculation formula for the longitudinal force of each wheel is: in, 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 mass of the tires on 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; 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.

5. 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 described in step 3 is: in, , , , , 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 and the current offset value; is the process noise, is the measurement noise, is the total longitudinal force at the current moment, is the longitudinal acceleration of the vehicle at the current moment, H is the row vector, and a x It consists of two elements, , and 1, which has no actual physical meaning and is only the matrix form of the operation formula; F x =m k +OFF k Matrix form of .

6. The AEB braking force control method based on OneBox vehicle mass estimation according to claim 1, characterized in that: The calculation of the braking force adjustment gradient in step 4 includes: According to the deviation between the target deceleration and the actual deceleration, the total adjustment gradient is calculated through the feedforward coefficient, proportional coefficient and integral coefficient; Combined with the estimated vehicle mass, the total adjustment gradient is converted into a braking force adjustment gradient using the formula: in, is the total adjustment gradient of deceleration, is the estimated final vehicle mass, is the adjustment gradient of acceleration.

7. 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 by the hydraulic interface module of the OneBox system, converting the brake force adjustment gradient into a pressure adjustment amount to achieve precise brake control under different loads.

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