High-precision clamping force estimation method of electronic mechanical braking system

Abstract

The invention discloses a high-precision clamping force estimation method of an electronic mechanical braking system, and belongs to the technical field of automobile braking system control. The method comprises the following steps: acquiring real-time signals such as motor rotation angle, angular velocity and current, and acquiring a steady-state clamping force estimation value by using a seven-degree polynomial static model; then based on a motor angular speed judgment working condition, correcting a tightening force estimation value through an inertia and friction dynamic compensation model under a high-speed dynamic working condition to obtain a model output force; estimating unmodeled disturbances such as friction change and temperature excursion in real time through an extended state observer to obtain disturbance compensation force; and finally, obtaining a final clamping force estimated value by adopting working condition self-adaptive weighted fusion. According to the method, errors caused by factors such as friction change, temperature excursion and structure aging can be effectively reduced, and high-precision estimation of the clamping force is achieved.

Description

Technical Field

[0001] This invention relates to the field of automotive braking system control technology, specifically a high-precision clamping force estimation method for an electro-mechanical braking system (EMB). Background Technology

[0002] Electromechanical braking (EMB), as a new generation of brake-by-wire technology, has become an important development direction for future intelligent vehicle braking systems due to its advantages such as fast response speed, high control precision, and easy structural integration. However, the force transmission path of the motor-ball screw mechanism in the EMB system is complex, and nonlinear factors such as friction, backlash, and structural elastic deformation are significant, making it difficult to directly measure the clamping force. Therefore, it is usually necessary to rely on model-based indirect estimation strategies to obtain the real-time clamping force.

[0003] Existing clamping force estimation methods generally rely on linear displacement-force mapping models, simple calibration compensation strategies, or estimation methods based on motor current. However, these methods have significant shortcomings in practical applications. First, ball screw friction is significantly affected by factors such as temperature and wear, and the friction characteristics change over time, leading to a continuous accumulation of model bias. Second, the EMB actuator exhibits structural clearance, elastic deformation, and nonlinear stiffness changes during clamping, making it difficult for linear mapping models to accurately describe system characteristics. Third, traditional static models cannot reflect dynamic behaviors such as inertia and damping during rapid braking, resulting in large errors in the estimation results during the transient phase. Furthermore, with long-term vehicle operation, key parameters such as system stiffness and friction coefficient gradually drift, and biases caused by mechanism aging further reduce estimation accuracy.

[0004] In summary, existing clamping force estimation methods cannot simultaneously meet the requirements of high precision, strong robustness, and good dynamic adaptability for clamping force estimation in electromechanical braking systems. They also cannot effectively address issues such as friction variations, temperature drift, mechanism aging, and drastic changes in dynamic operating conditions in real-world applications. Therefore, there is an urgent need for a high-precision clamping force estimation method for electromechanical braking systems that can cope with friction variations, temperature drift, mechanism aging, and dynamic operating conditions. Summary of the Invention

[0005] The purpose of this invention is to provide a high-precision clamping force estimation method for an electromechanical braking system to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A high-precision clamping force estimation method for an electromechanical braking system includes the following steps:

[0008] Step S1: Obtain the real-time operating signal of the electromechanical braking system actuator, wherein the signal includes at least the motor rotation angle, motor angular velocity, and motor current;

[0009] Step S2: Construct a seventh-order polynomial static mechanical model based on the motor rotation angle to obtain the steady-state clamping force estimate;

[0010] Step S3: Determine whether the current braking condition is a high-speed dynamic condition based on the motor angular velocity. If it is a high-speed dynamic condition, correct the steady-state clamping force estimate using the inertia and friction dynamic compensation model to obtain the model output force. If it is a steady-state or mild dynamic condition, directly use the steady-state clamping force estimate as the model output force.

[0011] Step S4: Construct an Extended State Observer (ESO) to estimate the unmodeled disturbance in real time based on the model output force and the observable feedback signal of the electromechanical braking system actuator, and obtain the disturbance compensation force.

[0012] Step S5: The model output force and the disturbance compensation force are weighted and fused to obtain the final clamping force estimate.

[0013] Preferably, the seventh-order polynomial static mechanical model is as follows:

[0014]

[0015] in, This refers to the motor's rotation angle; to These are the polynomial coefficients obtained through offline calibration.

[0016] Preferably, the specific condition for determining the working condition is: setting an angular velocity threshold. When the motor angular velocity ω m absolute value If the condition is as described above, it is determined to be a high-speed dynamic operating condition; otherwise, it is determined to be a steady-state or slightly dynamic operating condition. The threshold is preset based on the dynamic characteristics of the EMB actuator.

[0017] Preferably, the dynamic compensation model for inertia and friction is as follows:

[0018]

[0019] in, This is the equivalent axial inertia coefficient; This is the equivalent axial damping coefficient; F is the angular velocity of the motor. c Equivalent Coulomb friction; This is a sign function of the motor's angular velocity, used to determine the direction of the Coulomb friction torque.

[0020] Preferably, the extended state observer is a first-order extended state observer, whose continuous form is expressed as:

[0021]

[0022] in, This is the observer's estimate of the clamping force; Output force for the model; y is the observer's estimate of the unmodeled disturbance; y is the observable feedback signal of the electromechanical braking system actuator; L1 and L2 are the observer gains, and =2 , = ², ω0 is the observer bandwidth parameter.

[0023] Preferably, the extended state observer is implemented in discrete form, and the discretization equation is:

[0024]

[0025] in, for Estimated clamping force at any given moment; for Time-perturbation estimate for The model outputs force at any given time. for The observable feedback signal of the actuator of the electromechanical braking system at all times; The sampling period for the observer.

[0026] Preferably, the weighted fusion step in step S5 is implemented using the following formula:

[0027]

[0028] in, for The estimated final clamping force at the moment; for Constantly changing the compensation force, and ; For fusion weighting coefficients.

[0029] Preferably, the fusion weighting coefficient The adjustment is adaptive based on the motor's maximum angular velocity, specifically as follows:

[0030] ;

[0031] in, Basic weights; This is the weighting adjustment coefficient; This is the maximum design angular velocity of the motor.

[0032] Preferably, the method further includes:

[0033] Step S6: The final clamping force estimate is converted into wheel-end braking torque using the braking torque relationship formula, providing real-time feedback for the braking torque distribution and control of the vehicle controller. The vehicle controller back-calculates the target braking torque into the target clamping force using the braking torque relationship formula, and generates a target motor current command through a control algorithm based on the deviation between the target clamping force and the final clamping force estimate, so as to realize closed-loop control of the clamping force of the electromechanical braking system actuator.

[0034] Preferably, the braking torque relationship is as follows: ;

[0035] in, This refers to the braking torque at the wheel end; This is the coefficient of friction between the brake pads and the brake disc; The equivalent radius of action of the brake disc; This represents the effective number of friction surfaces.

[0036] Compared with the prior art, the present invention has at least one of the following advantages or beneficial effects:

[0037] 1. High estimation accuracy: This invention accurately captures the nonlinear relationship between motor rotation angle and clamping force through the synergistic effect of a seventh-order polynomial static model, an inertia and friction dynamic compensation model, and an extended state observer. It corrects the effects of inertia and friction under dynamic working conditions in real time and compensates for unmodeled disturbances such as friction changes, temperature drift, and structural aging. Combined with a weighted fusion mechanism, it can effectively reduce the errors caused by factors such as friction changes, temperature drift, and structural aging, and achieve high-precision estimation of clamping force.

[0038] 2. Strong robustness: With the help of ESO's real-time tracking capability of disturbances, the system can automatically compensate for model deviations caused by factors such as friction changes, temperature fluctuations and mechanism aging, thereby improving the stability and accuracy of clamping force estimation in long-term operation and reducing the impact of changes in environmental and usage conditions on the results.

[0039] 3. Strong dynamic adaptability and high computational efficiency: The operating condition judgment mechanism based on motor angular velocity triggers dynamic compensation only under high-speed dynamic conditions. While ensuring estimation accuracy, it effectively reduces the computational burden of the controller, balances technical performance and equipment operating efficiency, and meets the actual needs of real-time control of electromechanical brake (EMB) systems.

[0040] 4. Improved control performance: By using the braking torque relationship to estimate the final clamping force and apply it to the feedback of the closed-loop control system, high braking torque control accuracy can be maintained even under conditions of frictional changes, temperature fluctuations, and system aging. This also effectively improves the control performance and stability of the EMB system under complex operating conditions such as ABS, ESC, and AEB. Attached Figure Description

[0041] The invention, its features, shape, and advantages will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings. Like reference numerals denote like parts throughout the drawings. The drawings are not drawn to scale; their focus is on illustrating the gist of the invention.

[0042] Figure 1 This is a flowchart of a high-precision clamping force estimation method for an electromechanical braking system in an embodiment of the present invention.

[0043] Figure 2 This is a schematic diagram of the electromechanical brake actuator in an embodiment of the present invention;

[0044] The components include: 1. Motor; 2. Gear assembly; 3. Ball screw; 4. Screw nut; 5. Brake pads; 6. Brake disc; 7. Caliper assembly. Detailed Implementation

[0045] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but this is not intended to limit the present invention.

[0046] like Figure 1 As shown, this invention discloses a high-precision clamping force estimation method for an electromechanical braking system. Specifically, the method includes the following steps:

[0047] Step S1: Obtain the real-time operating signal of the electromechanical braking system actuator, which includes the motor rotation angle, motor angular velocity, and motor current.

[0048] like Figure 2 As shown, the electromechanical braking system actuator consists of a motor 1, a gear assembly 2, a ball screw 3, a screw nut 4, a push rod, brake pads 5, a brake disc 6, and a caliper assembly 7. The motor 1 drives the ball screw 3 to rotate via the gear assembly 2. The screw nut 4 moves axially under the rotation of the screw, thereby pushing the push rod to press the brake pads 5 against the brake disc 6, generating braking force. The caliper assembly 7 supports the brake pads 5 and the screw mechanism, and provides reaction force support during braking. The controller receives the motor rotation angle θ. m ω of the motor mThe system also collects operating signals from the actuators, such as motor current I, and calculates the push rod stroke based on the motor rotation angle. It then determines whether the brake pads are in contact with the brake disc based on the push rod stroke and motor speed change characteristics. If they are not in contact, the system continues stroke calculation and signal updates; if they are in contact, the system proceeds to step S2.

[0049] Step S2: Construct a seventh-order polynomial static mechanical model based on the motor rotation angle to obtain the steady-state clamping force estimate.

[0050] During EMB braking, the motor rotates at an angle θ. m It is positively correlated with the brake pad deformation. At this time, the motor rotation angle θ... m The magnitude of the force determines whether the ball screw has eliminated backlash and is in contact with the brake pads. Once the ball screw mechanism eliminates backlash and begins to generate clamping force, a highly non-linear relationship emerges between the motor rotation angle and the clamping force. Based on the mechanical characteristics of the actuator, the screw pitch P... h Reduction ratio i and motor rotation angle θ m It can describe braking deformation displacement:

[0051]

[0052] After entering the contact zone (brake pad contacts brake disc), due to factors such as material compression, structural elastic deformation, and changes in contact stiffness, the relationship between the motor rotation angle and clamping force exhibits a high-order nonlinearity, which is difficult to accurately describe using a low-order model. Therefore, this method employs a seventh-order polynomial static mechanical model to describe the motor rotation angle θ. m The mapping relationship with the clamping force F, the seventh-order polynomial static mechanical model is as follows:

[0053]

[0054] in, i Let i represent the coefficients of each term, i∈[0,7].

[0055] The polynomial coefficients can be obtained through offline calibration, and the final coefficients are shown in Table 1.

[0056] Table 1. Coefficients of the seventh-order polynomial obtained by fitting experimental data

[0057]

[0058] Step S3, based on the motor angular velocity ω mThe system determines whether the current braking condition is a high-speed dynamic condition. If it is, the steady-state clamping force estimate is corrected using an inertia and friction dynamic compensation model to obtain the model output force. If it is a steady-state or slightly dynamic condition, the steady-state clamping force estimate is directly used as the model output force. Specifically, the condition for determining this condition is: setting an angular velocity threshold. When the motor angular velocity ω m absolute value If the condition is high, it is judged as a high-speed dynamic operating condition; otherwise, it is judged as a steady-state or slightly dynamic operating condition. The threshold is preset based on the dynamic characteristics of the EMB actuator.

[0059] Specifically, after estimating the steady-state clamping force based on the motor rotation angle, to enhance the adaptability of the estimation method under rapid braking and high-dynamic conditions, this method further includes a condition determination step to determine whether the current braking actuator is in a high-speed dynamic state. If the determination result is a high-speed dynamic condition, this method triggers dynamic compensation for inertia and friction, performing dynamic mechanical correction on the steady-state clamping force estimate to compensate for dynamic effects such as inertial force, viscous damping, and Coulomb friction during actuator acceleration and deceleration, ultimately obtaining the model output force. If the determination is a steady-state or mildly dynamic condition (non-high-speed dynamic condition), the dynamic compensation step is skipped, and the steady-state clamping force estimate is directly used as the model output force, subsequently entering the extended state observer for unmodeled disturbance estimation. Through the condition identification mechanism, when the motor is in a low-speed or steady-state phase, the estimation bias introduced by dynamic compensation can be avoided, improving the stability of the estimation results.

[0060] After obtaining the steady-state clamping force estimate F(θ) based on the motor rotation angle m Subsequently, to consider the dynamic characteristics of the motor-ball screw system during actual braking, this embodiment establishes an inertia and friction dynamic compensation model to correct the steady-state clamping force estimate. The dynamics of the motor side are equivalent to a single-degree-of-freedom rotational system, and its dynamic equation can be expressed as:

[0061]

[0062] Among them, J eq ω is the equivalent moment of inertia of the motor rotor, reduction mechanism, and ball screw referred to the motor side; m T is the angular velocity of the motor. m For motor torque; for T L Load torque; T f B is the equivalent torque generated by Coulomb friction; m This is the viscous damping coefficient on the motor side.

[0063] The ball screw converts motor torque into axial thrust, and its transmission dynamics can be expressed as:

[0064]

[0065] Among them, F b η is the axial clamping force (theoretical value) acting on the push rod. t For lead screw transmission efficiency.

[0066] Based on the above relationships, the effects of inertia and friction can be converted into forces along the axial direction, and the constructed dynamic compensation model for inertia and friction is as follows:

[0067]

[0068] in, The equivalent axial inertia coefficient is obtained by converting the moment of inertia. F is the equivalent axial damping coefficient obtained from viscous damping. c Equivalent Coulomb friction; The sign function of the motor angular velocity is used to determine the direction of the Coulomb friction force; the friction torque coefficient can be obtained through experimental calibration or calculation based on the structural parameters of the actuator.

[0069] Finally, the model output force is obtained by superimposing the steady-state clamping force estimate with the dynamic compensation force:

[0070]

[0071] This model can compensate for transient errors caused by acceleration, deceleration, friction and damping effects, making the force estimation under conditions such as rapid braking and frequent start-stop more closely approximate the actual value.

[0072] Step S4: Construct an extended state observer to estimate the unmodeled disturbance in real time based on the model output force and the observable feedback signal of the electromechanical braking system actuator, and obtain the disturbance compensation force.

[0073] Considering the unmodeled factors such as the variation of ball screw friction with temperature and lubrication conditions, the change in brake caliper structural stiffness with fatigue aging, and the deviation in motor current measurement, relying solely on the aforementioned structural model is insufficient to completely eliminate errors. This embodiment introduces an Extended State Observer (ESO) based on the static estimation model and the dynamic compensation model to estimate and compensate for unmodeled disturbances in real time.

[0074] The system is abstracted at the force estimation level as follows:

[0075]

[0076] in, This is the actual clamping force; The model output force is obtained by using a seventh-order polynomial plus dynamic compensation or the model output force obtained by using a seventh-order polynomial. This represents unmodeled disturbances, corresponding to a combination of errors including frictional changes, temperature drift, structural deformation, and efficiency degradation.

[0077] This embodiment constructs a first-order extended state observer to detect disturbances. This is considered an extended state, and it is observed online. The continuous form of this first-order extended state observer can be written as:

[0078]

[0079] in, This is the observer's estimate of the clamping force; L1 represents the observer's estimate of the unmodeled disturbance; L2 represents the observable feedback signal of the electromechanical braking system actuator (used to characterize the difference between the model output and the actual response of the actuator); L1 and L2 are the observer gains.

[0080] In practical controllers, ESOs are generally implemented in discrete form for ease of engineering implementation. Assuming the sampling period is , using forward Euler discretization, we can obtain:

[0081]

[0082] in for Estimated clamping force at any given moment; for Time-based disturbance estimates; for The model outputs force at any given time. for Observable feedback signal of the actuator of the electromechanical braking system at all times The sampling period.

[0083] Disturbance compensation force is Given:

[0084]

[0085] The observer gains L1 and L2 can be selected based on the desired observation bandwidth and noise level. For example, a characteristic polynomial can be constructed:

[0086]

[0087] And by dynamically satisfying the above pole configuration for the observer error, we obtain:

[0088]

[0089] in, This is the observer bandwidth parameter, which is generally taken to be slightly larger than the system closed-loop bandwidth to ensure that it can quickly track changes in disturbances.

[0090] Step S5: The model output force and the disturbance compensation force are weighted and fused to obtain the final clamping force estimate.

[0091] Obtaining the model output force Perturbation estimation of ESO output Subsequently, this embodiment uses a weighted fusion method to obtain the final clamping force estimate. The fusion formula in continuous form is:

[0092]

[0093] It can be written as:

[0094]

[0095] in, This reflects the weight of the ESO compensation in the final output. .

[0096] In discrete form, it can be written as:

[0097]

[0098] To balance steady-state accuracy and dynamic response, this embodiment designs a weighted adaptive strategy related to the operating conditions. For example, it can be defined as follows:

[0099]

[0100] in, This represents the basic weight under low-speed operating conditions. This is the weighting adjustment coefficient; This is the maximum design angular velocity of the motor.

[0101] When the angular velocity is large and the inertial and disturbance effects are significant during the braking process, Increasing the value of ESO compensation in the final estimate; when the system is near steady-state. Smaller size allows the structural model to dominate, improving the smoothness and stability of the estimated output.

[0102] Step S6: The final clamping force estimate is converted into wheel-end braking torque using the braking torque relationship formula, providing real-time feedback for the vehicle controller to distribute and adjust the braking torque. The vehicle controller can back-calculate the target braking torque into the target clamping force using the braking torque relationship formula, and generate a target motor current command based on the deviation between the target clamping force and the final clamping force estimate, so as to adjust the clamping force of the electromechanical braking system actuator.

[0103] Specifically, in obtaining the final axial clamping force estimate Furthermore, this embodiment can also map the brake caliper structure and brake disc parameters to wheel-end braking torque for easier association with the vehicle control strategy. The braking torque can be estimated using the following formula:

[0104]

[0105] Where μ is the coefficient of friction between the brake pads and the brake disc; R eff denoted as , where is the equivalent radius of the brake disc; z is the number of effective friction surfaces.

[0106] In the vehicle controller, the target braking torque T can be... brk,ref The target clamping force F is calculated by back-calculating the above braking torque relationship. ref Then, combining the final clamping force estimate obtained in real time using this method, a closed-loop control for the clamping force is constructed:

[0107]

[0108]

[0109] in, This is for clamping force error; This is the target current command applied to the motor; It can be a PI controller, a feedforward + feedback controller, or other nonlinear control law function.

[0110] By combining the above clamping force estimation with closed-loop control, this embodiment can maintain high braking torque control accuracy even under conditions of frictional changes, temperature fluctuations, and system aging, and effectively improve the control performance and stability of the EMB system under complex operating conditions such as ABS, ESC, and AEB.

[0111] In summary, this invention proposes a high-precision clamping force estimation method suitable for electromechanical brakes. By combining a seventh-order polynomial static model, a dynamic compensation model, and an extended state observer, it can accurately identify the nonlinear relationship between motor rotation angle and clamping force and compensate for dynamic disturbances in real time. This method can effectively improve the accuracy of clamping force estimation, reduce errors caused by factors such as friction changes, temperature drift, and structural aging, and ensure stable and accurate output of clamping force under complex operating conditions such as rapid braking, temperature changes, and structural aging. It avoids estimation deviations that occur in traditional methods under rapid braking or complex operating conditions, thereby improving the control stability and response performance of the EMB system.

[0112] Those skilled in the art should understand that variations can be implemented by combining existing technology with the above embodiments, which will not be elaborated here. Such variations do not affect the essence of the present invention, and will not be elaborated here either.

[0113] The preferred embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and the devices and structures not described in detail should be understood as being implemented in a conventional manner in the art. Any person skilled in the art can make many possible variations and modifications to the technical solutions of the present invention using the methods and techniques disclosed above, or modify them into equivalent embodiments with equivalent changes, without departing from the scope of the present invention. This does not affect the essential content of the present invention. Therefore, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the present invention's technical solutions still fall within the protection scope of the present invention.

Claims

1. A high-precision clamping force estimation method for an electromechanical braking system, characterized in that, Includes the following steps: Step S1: Obtain the real-time operating signal of the electromechanical braking system actuator, the signal including motor rotation angle, motor angular velocity and motor current; Step S2: Construct a seventh-order polynomial static mechanical model based on the motor rotation angle to obtain the steady-state clamping force estimate; Step S3: Determine whether the current braking condition is a high-speed dynamic condition based on the motor angular velocity. If it is a high-speed dynamic condition, correct the steady-state clamping force estimate using the inertia and friction dynamic compensation model to obtain the model output force. If it is a steady-state or mild dynamic condition, directly use the steady-state clamping force estimate as the model output force. Step S4: Construct an extended state observer to estimate the unmodeled disturbance in real time based on the model output force and the observable feedback signal of the electromechanical braking system actuator, and obtain the disturbance compensation force. Step S5: The model output force and the disturbance compensation force are weighted and fused to obtain the final clamping force estimate.

2. The high-precision clamping force estimation method for the electromechanical braking system according to claim 1, characterized in that, The seventh-order polynomial static mechanical model is as follows: ， in, This refers to the motor's rotation angle; to These are the polynomial coefficients obtained through offline calibration.

3. The high-precision clamping force estimation method for the electromechanical braking system according to claim 1, characterized in that, The specific conditions for determining the operating condition are: setting an angular velocity threshold. When the motor angular velocity ω m absolute value If the condition is as described above, it is determined to be a high-speed dynamic operating condition; otherwise, it is determined to be a steady-state or slightly dynamic operating condition. The threshold is preset based on the dynamic characteristics of the EMB actuator.

4. The high-precision clamping force estimation method for the electromechanical braking system according to claim 1, characterized in that, The inertia and friction dynamic compensation model is as follows: ， in, This is the equivalent axial inertia coefficient; F is the equivalent axial damping coefficient. c Equivalent Coulomb friction; This is a sign function of the motor's angular velocity, used to determine the direction of the Coulomb friction torque.

5. The high-precision clamping force estimation method for the electromechanical braking system according to claim 1, characterized in that, The extended state observer is a first-order extended state observer, and its continuous form is expressed as: ， in, This is the observer's estimate of the clamping force; Output force for the model; y is the observer's estimate of the unmodeled disturbance; y is the observable feedback signal of the electromechanical braking system actuator; L1 and L2 are the observer gains, and =2 , = ², ω0 is the observer bandwidth parameter.

6. The high-precision clamping force estimation method for the electromechanical braking system according to claim 5, characterized in that, The extended state observer is implemented in discrete form, and the discretization equation is: ， in, for Estimated clamping force at any moment; for Time-perturbation estimate for The model outputs force at any given time. for The observable feedback signal of the actuator of the electromechanical braking system at all times; The sampling period of the observer.

7. The high-precision clamping force estimation method for the electromechanical braking system according to claim 6, characterized in that, The weighted fusion step in step S5 is achieved through the following formula: ， in, for The estimated final clamping force at any given moment; for Constantly changing the compensation force, and ; For fusion weighting coefficients.

8. The high-precision clamping force estimation method for the electromechanical braking system according to claim 7, characterized in that, The fusion weight coefficient The adjustment is adaptive based on the motor's maximum angular velocity, specifically as follows: ； in, Basic weights; This is the weighting adjustment coefficient; This is the maximum design angular velocity of the motor.

9. The high-precision clamping force estimation method for the electromechanical braking system according to claim 1, characterized in that, The method further includes: Step S6: The final clamping force estimate is converted into wheel-end braking torque using the braking torque relationship formula, providing real-time feedback for the braking torque distribution and control of the vehicle controller. The vehicle controller back-calculates the target braking torque into the target clamping force using the braking torque relationship formula, and generates a target motor current command through a control algorithm based on the deviation between the target clamping force and the final clamping force estimate, so as to realize closed-loop control of the clamping force of the electromechanical braking system actuator.

10. The high-precision clamping force estimation method for the electromechanical braking system according to claim 9, characterized in that, The relationship between the braking torque and the braking force is as follows: ; in, This refers to the braking torque at the wheel end; This is the coefficient of friction between the brake pads and the brake disc; The equivalent radius of action of the brake disc; This represents the effective number of friction surfaces.

Images