Segmented self-adaptive electronic mechanical braking clamping force estimation method and system

Abstract

The invention discloses a segmented self-adaptive electronic mechanical braking clamping force estimation method and system, and the method comprises the steps: obtaining motor rotation angle data, and building a clamping force estimation rigidity model; obtaining load torque observation data, and establishing a clamping force estimation moment balance model; dividing braking stages, and generating a segmented clamping force estimation strategy based on the characteristics of each stage; and establishing a state conversion model, outputting a state identifier, and switching an estimation strategy of a corresponding stage to output a clamping force estimation value. The whole clamping force estimation system is composed of a state conversion model and three clamping force estimation output models, the whole braking process is divided into a dynamic stage, a transition stage and a quasi-static stage, and corresponding output models can be selected according to characteristics of different stages and state identifiers output by the state conversion model; the clamping force estimation requirement under the full working condition can be met, and the clamping force estimation precision and stability under the full working condition are effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electronic mechanical brake, and particularly relates to a segmented adaptive EMB clamping force estimation method and system. BACKGROUND

[0002] The EMB system is a system that realizes the braking function through closed-loop control, and the core thereof is to realize closed-loop control by feeding back the actual clamping force, so as to ensure that the clamping force reaches the target clamping force, thereby implementing braking. The traditional EMB system relies on a force sensor to directly measure the clamping force, but has the problems of high cost, being easily disturbed by complex working conditions such as temperature and vibration, and the safety hazard that a sensor failure may cause the system to fail.

[0003] There are mainly two kinds of existing sensorless clamping force estimation methods: the estimation method based on stiffness characteristics is to estimate the clamping force by using a quadratic or cubic polynomial fitting according to the relationship between the clamping force and the motor angular displacement, which has the advantages of only needing a single angular displacement signal and high accuracy in the dynamic change stage of the rising or falling of the clamping force, but the error will greatly increase at the time of locking because of the mechanical deformation saturation and the load torque fluctuation; and the estimation method based on torque balance is to obtain the clamping force estimation value by establishing an equivalent dynamic balance equation of the motor output end, which can ensure high accuracy at the time of locking by relying on the relationship between the motor current and the torque, but there is a problem of estimation lag at the dynamic stage because of the inertia force and friction disturbance. SUMMARY

[0004] The present application relates to the technical field of electronic mechanical brake, and particularly relates to a segmented adaptive EMB clamping force estimation method and system.

[0005] To achieve the above-mentioned purpose, in a first aspect, the present application provides a segmented adaptive EMB clamping force estimation method, comprising: obtaining motor angular displacement data and establishing a clamping force estimation stiffness model; obtaining load torque observation data and establishing a clamping force estimation torque balance model; dividing braking stages, generating a segmented clamping force estimation strategy based on the characteristics of each stage; establishing a state transition model, outputting a state identifier, switching the estimation strategy of the corresponding stage to output a clamping force estimation value.

[0006] In some embodiments, the method comprises: inputting a motor angular displacement instruction and collecting corresponding clamping force data; establishing a clamping force estimation stiffness model in stages according to the corresponding relationship between the angular displacement and the clamping force.

[0007] In some embodiments, the load torque observation data is obtained, and a clamping force estimation torque balance model is established, including: A torque balance state equation is established based on motor angular velocity and load torque as state variables; A state observer is established based on motor angular velocity and load torque as observation objects; Load torque observation data is obtained, and a clamping force estimation torque balance model is established.

[0008] In some embodiments, the braking phase is divided, and a segmented clamping force estimation strategy is generated based on characteristics of each phase, including: Different braking phases are divided based on clamping state data corresponding to entry and exit conditions, and a segmented clamping force estimation strategy is imported based on the entry and exit conditions.

[0009] In some embodiments, a state transition model is established, a state identifier is output, an estimation strategy corresponding to the phase is switched to output a clamping force estimation value, including: A state transition model is established based on the entry and exit conditions to match the current clamping force phase; Motor state data is obtained, a current phase state identifier is output, and an estimation strategy corresponding to the phase is switched to output a clamping force estimation value.

[0010] In some embodiments, the entry and exit conditions are used to determine the matching current clamping force phase, and a state transition model is established, including: It is determined whether the motor speed is greater than the minimum threshold of the dynamic phase; If it is greater, it is determined to enter the dynamic phase, and a first state identifier is output; If it is less, it is determined whether the first transition condition is met.

[0011] In some embodiments, it is determined whether the first transition condition is met, including: If it is met, it is determined to enter the transition phase, a second state identifier is output, and it is determined whether the second transition condition is met; If it is not met, it is determined to maintain the dynamic phase, and the first state identifier is output.

[0012] In some embodiments, it is determined whether the second transition condition is met, including: If it is met, it is determined to enter the quasi-static phase, a third state identifier is output, and it is determined whether the third transition condition is met; If it is not met, it is determined whether the fourth transition condition is met.

[0013] In some embodiments, it is determined whether the third transition condition is met, including: If the fourth conversion condition is satisfied, it is judged to enter the dynamic stage, and the first state identifier is output. If the fourth conversion condition is not satisfied, it is judged to maintain the quasi-static stage, and the third state identifier is output. The judgment of whether the fourth conversion condition is satisfied includes: If the fourth conversion condition is satisfied, it is judged to enter the dynamic stage, and the first state identifier is output. If the fourth conversion condition is not satisfied, it is judged to enter the transition stage, and the second state identifier is output.

[0014] In a second aspect, the application further provides a segmented adaptive electronic mechanical braking clamping force estimation system, which is used to run the segmented adaptive electronic mechanical braking clamping force estimation method as described in the first aspect, and the estimation system includes: A data transceiver module is configured to obtain motor rotation angle data, obtain load torque observation data, and send the calculated clamping force estimation data. A state judgment module is configured to judge the state stage, output the state identifier, and switch the estimation strategy of the corresponding stage. A data processing module is configured to adopt the corresponding clamping force estimation strategy according to the state identifier, and calculate the clamping force estimation value.

[0015] The application has the following beneficial effects: The entire clamping force estimation system in the application is composed of a state conversion model and three clamping force estimation output models, and the entire braking process is divided into a dynamic stage, a transition stage and a quasi-static stage. The corresponding output model can be selected according to the characteristics of different stages and the state identifier output by the state conversion model, which can cover the clamping force estimation requirements in all working conditions, and effectively improve the clamping force estimation accuracy and stability in all working conditions. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 Flowchart of the estimation method proposed in the application Figure 1 ; Figure 2 Flowchart of the estimation method proposed in the application Figure 2 ; Figure 3 Flowchart of the estimation method proposed in the application Figure 3 ; Figure 4 Flowchart of the estimation method proposed in the application Figure 4 ; Figure 5 Flowchart of the estimation method proposed in the application Figure 5 ; Figure 6 Flowchart of the estimation method proposed in the application Figure 6 ; Figure 7The structural schematic diagram of the system model in the estimation method proposed in the application; Figure 8 The principle schematic diagram of the stiffness model in the estimation method proposed in the application; Figure 9 The principle schematic diagram of the estimation system proposed in the application. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the protection scope of the application.

[0018] The embodiment of the application provides a segmented adaptive electronic mechanical braking clamping force estimation method and system, which solves the problem that the estimation method based on the stiffness characteristics in the prior art will cause error to increase sharply due to mechanical deformation saturation and load torque fluctuation at the time of blocking, and the estimation method based on torque balance will cause estimation to lag due to inertial force and friction disturbance at the dynamic stage. The application can output corresponding output models according to different stage characteristics and state identification output by a state conversion model, and can cover the clamping force estimation requirement in all working conditions, so that the clamping force estimation precision and stability in all working conditions are effectively improved.

[0019] For details, please refer to the following embodiments: Referring to Figure 1 The embodiment of the application provides a segmented adaptive electronic mechanical braking clamping force estimation method, and the specific structure comprises: S100, acquiring motor angle data and establishing a clamping force estimation stiffness model; S200, acquiring load torque observation data and establishing a clamping force estimation torque balance model; S300, dividing braking stages, and generating a segmented clamping force estimation strategy based on characteristics of each stage; S400, establishing a state conversion model, outputting state identification, switching the estimation strategy of the corresponding stage to output a clamping force estimation value.

[0020] Please continue to refer to Figure 2 In the embodiment, S100 comprises: S110, inputting a motor angle instruction and collecting corresponding clamping force data; S120, establishing a clamping force estimation stiffness model in stages according to the corresponding relationship between the angle and the clamping force.

[0021] Exemplarily, the clamping force estimation stiffness model is fitted from motor rotation angle and clamping force data: By inputting motor rotation angle instructions, collecting corresponding clamping force data through the force sensor, and establishing a cubic polynomial according to the corresponding relationship between the rotation angle and the clamping force, wherein the motor rotation angle instructions can adopt ramp input or step input, and the amplitude of the input determines the range of the motor rotation angle covered by the dynamic estimation model, which needs to cover the working range of the actuator clamping force; Considering the motor hysteresis effect, the relationship needs to be established separately in the clamping and release two different stages, and the specific relationship is established as follows:

[0022] Among them 0 is the motor rotation angle, c i and d i (i=1, 2, 3, 4) are polynomial fitting coefficients, which can be obtained by least square method, spline interpolation method or neural network fitting; is the clamping force output by the stiffness model.

[0023] Please continue to refer to Figure 3 In this embodiment, S200 includes: S210, based on the motor angular velocity and the load torque as state variables, a torque balance state equation is established; S220, based on the motor angular velocity and the load torque as observation objects, a state observer is established; S230, load torque observation data is obtained, and a clamping force estimation torque balance model is established.

[0024] Exemplarily, first, according to the motor torque balance equation, the motor angular velocity and the load torque are taken as state variables to establish a state equation; wherein the torque balance equation is:

[0025] In the formula, represents the moment of inertia of the motor; represents the differential of the motor angular velocity with respect to time; represents the electromagnetic torque; represents the motor internal friction torque.

[0026] Among them, The expression of

[0027] In the formula, represents the number of motor pole pairs; represents the motor flux. This indicates the magnitude of the q-axis current of the motor.

[0028] also, Using the Stribeck friction model, the specific expression is as follows:

[0029] In the formula, w is the angular velocity of the motor; The frictional torque is the Coulomb torque. It is the viscous damping coefficient; This is the static friction torque.

[0030] in, For symbolic functions, the expression is:

[0031] Then, the motor angular velocity and load torque A state observer is built using the observed objects. Specifically, based on the torque balance equation established in the previous step, the motor angular velocity and load torque are used as the observed objects to build an observer, and the observer type can be a sliding diaphragm observer or a Luneburg observer.

[0032] Next, the clamping force estimated by torque balance is obtained based on the relationship between load torque and clamping force. Specifically, during braking, the motor reduces speed and increases torque through a gear reducer, and then converts the motor's rotational motion into linear motion (usually a ball screw) through a motion conversion mechanism, thereby pressing the brake disc to achieve braking. Based on the parameters and efficiency of the electromechanical brake transmission system, the relationship between clamping force and load torque can be obtained:

[0033] In the formula, The load torque observed by the observer; Clamping force for torque estimation; For the ball screw lead; This refers to the gear ratio of the reducer; For the gear transmission efficiency of the reducer; For ball screw transmission efficiency.

[0034] The expression for the load torque, derived from the torque balance equation, is as follows:

[0035] Then, substituting the above equation into the relationship between clamping force and load torque, we obtain the clamping force estimate of the torque balance model:

[0036] Please continue reading.Figure 4 In this embodiment, S300 includes: S310 divides the entry and exit conditions corresponding to different braking stages based on clamping state data, and imports a segmented clamping force estimation strategy based on the entry and exit conditions.

[0037] For example, the entire braking phase is divided into three phases: a dynamic phase, a transition phase, and a quasi-static phase; wherein: (1) The dynamic stage is characterized by a rapid increase or decrease in clamping force. During this stage, the motor speed is high, the mechanical system is in a state of significant motion, and the system's inertial force dominates. At this time, the nonlinearity of frictional resistance is obvious. The range covered by this stage is the actual rotational speed. greater than the speed threshold The specific range varies depending on the actuator; at this stage, the clamping force is estimated by outputting the motor rotation angle-clamping force stiffness model. This model has high linearity at this stage and is not affected by the drift of the motor's electromagnetic parameters. At this time, the state flag is the first state flag (Flag=1).

[0038] (2) The transition stage is the critical transition process from motion to stillness. During this stage, the clamping force approaches the target value and the rate of change decreases. At this time, the elastic deformation saturation effect occurs, and the state is marked as the second state mark (Flag=2).

[0039] During the transition phase, the stiffness model output becomes distorted due to deformation saturation, while the moment model is affected by inertial forces because the rotational speed has not returned to zero. Therefore, it is necessary to fuse the stiffness model and the moment balance model for output, specifically:

[0040] In the formula, The estimated clamping force output by the model; This is the estimated clamping force output by the torque balance model; The estimated clamping force is obtained by fusing the outputs of the two models during the transition phase. These are the weighting coefficients; in, The expression is:

[0041] In the formula, k is the slope factor, which is used to control the speed of model fusion weight switching during the transition phase; The expression for k is:

[0042] In the formula, The base slope is typically taken as 5-10; As the rate conversion coefficient, the value of k is increased for high-speed changing conditions, thereby increasing the switching rate and avoiding model lag, while the value of k is decreased for low-speed changing conditions to smoothly adjust the switching rate and suppress oscillations. in The expression is:

[0043] The normalized speed ratio is expressed as:

[0044] In the formula, w is the actual motor speed; This is the minimum speed during the transition phase; This is the highest speed during the transition phase.

[0045] (3) The quasi-static stage is a state of micro-force balance under macroscopic stillness, where the clamping force remains basically stable and the motor speed is... Less than The static friction torque of the system is approximately equal to the output torque of the motor. The influence of inertial force at this stage can be basically ignored. Therefore, the torque model is used to output the estimated clamping force. At this time, the state label is the third state label (Flag=3).

[0046] like Figure 7 As shown, the entire clamping force estimation system consists of four parts: a state transition model and three clamping force estimation output models. The system selects the corresponding output model based on the state flags output by the state transition model, and the final clamping force estimate is output as follows:

[0047] Please continue reading. Figure 5 In this embodiment, S400 includes: S410, based on the entry and exit conditions, determine the current stage of the clamping force and establish a state transition model; S420: Acquire motor status data, output the current stage status indicator, switch the estimation strategy for the corresponding stage, and output the clamping force estimate.

[0048] Please continue reading. Figure 6 In this embodiment, S410 includes: Determine if the motor speed is greater than the minimum threshold for the dynamic stage; If the value is greater than the threshold, the system enters the dynamic phase and outputs the first state flag. If it is less than, then continue to check whether the first transformation condition is met.

[0049] The determination of whether the first transformation condition is met includes: If the conditions are met, the system enters the transition phase, outputs the second state flag, and continues to check whether the second transition condition is met. If the condition is not met, the dynamic phase is maintained, and the first state flag is output.

[0050] Correspondingly, it is determined whether the second transformation condition is met, including: If the conditions are met, the system enters the quasi-static phase, outputs the third state flag, and continues to determine whether the third transition condition is met. If the condition is not met, continue to check whether the fourth transformation condition is met.

[0051] Specifically, determining whether the third transformation condition is met includes: If the condition is met, it is determined that the transition phase has begun, and the second state identifier is output. If the condition is not met, the system is determined to maintain the quasi-static phase and the third state flag is output.

[0052] Correspondingly, it is determined whether the fourth transformation condition is met, including: If the condition is met, it is determined that the dynamic phase is entered, and the first state flag is output. If the condition is not met, the system enters a transition phase and outputs a second state identifier.

[0053] For example, the state transition model is established for the braking stages divided in the above steps; it should be noted in detail that this state transition model only covers the state between the disc contact point and the separation point, and therefore does not include the gap elimination stage during clamping and the gap formation stage during release: like Figure 6 As shown, after the actual clamping force is generated, the motor speed is first determined. Is it greater than the minimum threshold of the dynamic stage? If the conditions are met, the system enters the dynamic phase and outputs the first state flag (Flag=1). If the conditions are not met, the system checks the first transition conditions of the dynamic and transition phases, specifically:

[0054] If the first transition condition is met, the transition phase begins, and the second state flag (Flag=2) is output, and the clamping force estimation strategy for the corresponding phase is executed. Next, after entering the transition phase, it is checked whether the second transition condition of the quasi-static phase is met, specifically:

[0055] in, It is the minimum speed threshold during the transition phase; The threshold for the rate of change of rotational speed; If the second transition condition is met, the system enters the quasi-static phase and outputs the third state flag (Flag=3); if the condition is not met, the system checks the fourth transition condition for switching back to the dynamic phase, specifically:

[0056] In addition, when transitioning from the transition phase to the dynamic phase, a 5% hysteresis protection mechanism is added to prevent critical speed oscillations and minor vibrations from triggering the transition. Once this condition is met, the model state returns to the dynamic phase and the first state flag (Flag=1) is restored.

[0057] Furthermore, when determining whether to exit and re-enter the transition phase during the quasi-static phase, the third transition condition is:

[0058] in, This is the minimum speed during the transition phase; In addition, if an external release brake command is detected, it is also considered that the exit condition is met. At this time, the quasi-static stage is converted to the transition stage, and the output is restored to the second state flag (Flag=2).

[0059] When in the dynamic stage, if the clamping state exit condition is met, the clamping state is exited. Specifically, when F=0 and an external brake release command is detected, the clamping force estimation work is completed or the process ends.

[0060] Reference Figure 2 The present invention also provides an embodiment of a segmented adaptive electromechanical braking clamping force estimation system for running the segmented adaptive electromechanical braking clamping force estimation method as described in the above embodiments. The estimation system includes: The data transceiver module is used to acquire motor rotation angle data, acquire load torque observation data, and send calculated clamping force estimation data. The state determination module is used to determine the state stage, output the state identifier, and switch the estimation strategy for the corresponding stage. The data processing module is used to adopt the corresponding clamping force estimation strategy based on the status identifier and calculate the clamping force estimate.

[0061] For example, the data transceiver module can realize real-time data flow with external devices and internal modules. By establishing communication connections with the motor encoder and clamping force sensor in the braking system, it can collect the rotation angle data (including rotation position, angular velocity and angular acceleration) and actual clamping force feedback data during the motor operation in real time, providing raw input for subsequent estimation calculations. At the same time, it can receive the load torque data output by the internal observer of the system. After completing the clamping force estimation calculation, the data transceiver module sends the processed estimation results to the execution unit or human-machine interface of the braking control system in real time according to the preset communication protocol, providing data support for braking strategy adjustment, fault diagnosis and status monitoring, and ensuring efficient and reliable closed-loop control of the entire braking process. The status judgment module receives motor angle data and load torque observation data transmitted by the data transceiver module in real time, and judges the status stage of the braking process by combining preset feature thresholds and criteria. It calls the matching algorithm model to calculate the clamping force estimate, ensuring that a high estimation accuracy can be maintained in each stage. The model building module is used to adopt the corresponding clamping force estimation strategy based on the status identifier, and then calculate the clamping force estimate accordingly.

[0062] Through the above technical solution, the entire clamping force estimation model system of this application consists of a state transition model and three clamping force estimation output models. The entire braking process is divided into a dynamic stage, a transition stage and a quasi-static stage. The corresponding output model can be selected according to the characteristics of different stages and the state indicators output by the state transition model. It can cover the clamping force estimation requirements under all working conditions and effectively improve the clamping force estimation accuracy and stability under all working conditions.

[0063] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A segmented adaptive electromechanical braking clamping force estimation method, characterized in that, include: Obtain motor rotation angle data and establish a clamping force estimation stiffness model; Obtain load torque observation data and establish a clamping force estimation torque balance model; Divide the braking phase and generate a segmented clamping force estimation strategy based on the characteristics of each phase; Establish a state transition model, output state identifiers, switch the estimation strategy for the corresponding stage, and output the clamping force estimate.

2. The estimation method according to claim 1, characterized in that, The process of acquiring motor rotation angle data and establishing a clamping force estimation stiffness model includes: Input the motor rotation angle command and collect the corresponding clamping force data; Based on the correspondence between rotation angle and clamping force, a clamping force estimation stiffness model is established in stages.

3. The estimation method according to claim 1, characterized in that, The process of acquiring load torque observation data and establishing a clamping force estimation torque balance model includes: Based on the motor angular velocity and load torque as state variables, a torque balance state equation is established; A state observer is established based on the motor angular velocity and load torque as the observation objects. Obtain load torque observation data and establish a clamping force estimation torque balance model.

4. The estimation method according to claim 1, characterized in that, The segmented braking stage, based on the characteristics of each stage, generates a segmented clamping force estimation strategy, including: Based on the clamping state data, the entry and exit conditions corresponding to different braking stages are divided, and a segmented clamping force estimation strategy is introduced based on the entry and exit conditions.

5. The estimation method according to claim 1, characterized in that, The process of establishing a state transition model, outputting state identifiers, and switching the estimation strategy for the corresponding stage to output clamping force estimates includes: Based on the entry and exit conditions, determine the current stage of the clamping force and establish a state transition model; Acquire motor status data, output the current stage status identifier, switch the estimation strategy for the corresponding stage, and output the clamping force estimate.

6. The estimation method according to claim 5, characterized in that, The process of establishing a state transition model based on the judgment of entry and exit conditions to match the current clamping force stage includes: Determine if the motor speed is greater than the minimum threshold for the dynamic stage; If the value is greater than the threshold, the system enters the dynamic phase and outputs the first state flag. If it is less than, then continue to check whether the first transformation condition is met.

7. The estimation method according to claim 6, characterized in that, The determination of whether the first conversion condition is met includes: If the conditions are met, the system enters the transition phase, outputs the second state flag, and continues to check whether the second transition condition is met. If the condition is not met, the dynamic phase is maintained, and the first state flag is output.

8. The estimation method according to claim 7, characterized in that, The determination of whether the second conversion condition is met includes: If the conditions are met, the system enters the quasi-static phase, outputs the third state flag, and continues to determine whether the third transition condition is met. If the condition is not met, continue to check whether the fourth transformation condition is met.

9. The estimation method according to claim 8, characterized in that, The determination of whether the third conversion condition is met includes: If the condition is met, it is determined that the transition phase has begun, and the second state identifier is output. If the condition is not met, it is determined that the quasi-static phase should be maintained, and the third state flag is output. The determination of whether the fourth transformation condition is met includes: If the condition is met, it is determined that the dynamic phase is entered, and the first state flag is output. If the condition is not met, the system enters a transition phase and outputs a second state identifier.

10. A segmented adaptive electromechanical braking clamping force estimation system, characterized in that, The estimation system is used to run the segmented adaptive electromechanical braking clamping force estimation method as described in any one of claims 1 to 9, the estimation system comprising: The data transceiver module is used to acquire motor rotation angle data, acquire load torque observation data, and send calculated clamping force estimation data. The state determination module is used to determine the state stage, output the state identifier, and switch the estimation strategy for the corresponding stage. The data processing module is used to adopt the corresponding clamping force estimation strategy based on the status identifier and calculate the clamping force estimate.

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