Electromechanical braking system clamping force estimation method based on stiffness curve and observer

Abstract

The invention relates to the technical field of vehicle braking, and provides an electronic mechanical braking system clamping force estimation method based on a stiffness curve and an observer. A method for learning a stiffness curve corresponding to a full-thickness friction plate and a stiffness curve under the latest condition, a moment balance equation, a corresponding state-space equation, a viscous friction coefficient, the stiffness curve and a state observer are fused, and a clamping force estimation system is complex in working condition when a real vehicle runs. Identifying according to a set full-thickness friction plate to obtain a rigidity curve, and obtaining an updated rigidity characteristic curve according to a maximum clamping force self-checking strategy; based on a stiffness characteristic curve, adding the stiffness characteristic curve to a debugged observer for correction feedback to obtain an estimated force system with high precision and good adaptability; the problem of force estimation without a force sensor is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle braking, and particularly to a method for estimating the clamping force of an electromechanical braking system based on a stiffness curve and an observer. Background Art

[0002] With the rapid development of automotive intelligence and autonomous driving, the requirements for the response time and accuracy of the actuators of various components are becoming increasingly strict. The electromechanical braking system uses full-wire control + four-wheel independent control, and the braking response time and control accuracy have been greatly improved, which can further meet the braking requirements during the rapid development of automobiles.

[0003] As an electromechanical braking system with a high degree of integration at the wheel end, in order to install a pressure sensor, the electromechanical braking system sacrifices a considerable amount of space, and considering the factor of sensor cost, the development of an actuator for an electromechanical braking system without a sensor is inevitable. However, in various actual vehicle working conditions, the electromechanical braking system is affected by a harsh working environment, many internal variables, strong coupling, difficult accurate estimation of the braking gap, non-linear characteristics, temperature changes, etc. How to design a clamping force estimation algorithm to ensure the estimation accuracy while also ensuring the robustness is the current difficulty. Therefore, how to accurately display the non-linear characteristics of the electromechanical braking system under complex actual vehicle working conditions: when affected by friction plate wear, braking gap change, temperature change, etc., and implement a clamping force estimation strategy without a sensor is an urgent problem to be solved. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to propose a method for estimating the clamping force of an electromechanical braking system based on a stiffness curve and an observer to solve the problems in the background art.

[0005] Based on the above purpose, the present invention provides a method for estimating the clamping force of an electromechanical braking system based on a stiffness curve and an observer, including the following steps: Step 1, first estimate the preliminary clamping force F_base by the method of the stiffness curve; Step 2, then use the observer method to correct F_base, and further estimate the accurate estimated clamping force.

[0006] Preferably, the stiffness curve is a two-dimensional curve of motor stroke - clamping force obtained by fitting a large amount of motor stroke and clamping force data, and the corresponding clamping force F_base can be obtained according to this curve for different motor strokes.

[0007] Preferably, based on the non-linear characteristics of the electro-mechanical braking system, a stroke-clamping force polynomial equation for different thicknesses is established, and a stiffness characteristic curve model covering the full-thickness friction plate is obtained according to the estimated different control zeros; at the same time, a maximum clamping force self-check strategy is carried out at regular intervals to obtain the latest stiffness characteristic curve of the current situation.

[0008] Preferably, the observer method includes the following steps: Step 2.1, establish the state space equation of the electro-mechanical braking system, and use the actual measurable variable motor speed ωm as the output of the state space equation. Step 2.2, calculate the observer gain matrix by the method of pole placement according to the system matrix and output matrix of the state space equation. Step 2.3, use the deviation Δω between the output ωm_est of the state space equation and the actual measured value ωm as the feedback quantity, and correct the stiffness curve value F_base with the result calculated by the observer gain matrix, so as to obtain a high-precision estimated clamping force.

[0009] Preferably, system modeling is also required before the above step 1: First, construct the electrical model of the electro-mechanical braking system, which includes the electrical characteristics of the motor, controller, and sensor. Based on the principle of torque balance, establish a model describing the relationship between each torque and the motor angular acceleration during the braking process.

[0010] Preferably, it also includes step 3 to analyze the test results: compare the difference between the clamping force estimated value provided by the observer and the actual measured value, evaluate the performance of the system, and adjust the model parameters or improve the algorithm according to the test feedback until a satisfactory accuracy level is achieved.

[0011] Preferably, it also includes step 4 optimization and deployment: make corresponding optimizations for the discovered problems, involving hardware upgrades and software algorithm improvements. After completing the design of the final version, deploy this solution on the target vehicle model to ensure that it can work reliably in a wide range of application scenarios.

[0012] The beneficial effects of the present invention: The present invention corresponds to the stiffness curve of the full-thickness friction plate and the learning method of the stiffness curve of the latest situation, the torque balance equation and the corresponding state space equation, the viscous friction coefficient, the stiffness curve and the state observer fusion. The clamping force estimation system is in a relatively complex working condition during actual vehicle driving. The stiffness curve is identified based on the preset full-thickness friction plate, and then the updated stiffness characteristic curve is obtained according to the maximum clamping force self-check strategy; based on the stiffness characteristic curve, it is added to the debugged observer for correction feedback to obtain an estimation force system with high precision and good self-adaptability; it solves the problem of force estimation without a force sensor.

[0013] The present invention does not require precise estimation of the braking gap: only the estimated control zero point needs to be obtained, and the estimated clamping force can be quickly converged through the observer, solving the problem of precise estimation of the braking gap.

[0014] The present invention has strong anti-interference ability and high precision: based on the stiffness characteristic curve, the estimated clamping force can be made to deviate abnormally, and the algorithm stability is excellent; it avoids the parts that have a great impact on the estimation and are difficult to accurately model: uncertain factors such as temperature and thickness. The role of the observer is to eliminate the interference of factors such as temperature and thickness. As long as the model estimated value does not match the actual value, it means that the system has a deviation and needs to be modified according to the deviation; therefore, by integrating the observer, the deformation of the stiffness curve caused by temperature change and the disturbances brought by the outside and the system itself can be compensated, realizing high-precision clamping force estimation and strong anti-interference ability.

[0015] The estimation method of the present invention has strong adaptability. Except for the necessary state bit identification, self-learning of the stiffness characteristic curve, and compensation of the observer, there is no other additional compensation, and it can adapt to the clamping force estimation requirements of different calipers and working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0017] Figure 1 is the system framework diagram of the present invention; Figure 2 is the partial working condition effect of verifying the clamping force estimation algorithm on the actual test bench of the present invention Figure 1 ; Figure 3 is the partial working condition effect of verifying the clamping force estimation algorithm on the actual test bench of the present invention Figure 2 . DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] In order to make the purpose, technical solutions and advantages of the present invention more clear and understandable, the following further details the present invention in conjunction with specific embodiments.

[0019] It should be noted that unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meanings understood by those with ordinary skills in the field to which the present invention belongs. The "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. Words such as "including" or "comprising" mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Words such as "connected" or "linked" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Upper", "lower", "left", "right", etc. are only used to represent relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0020] As Figures 1 to 3 shown, this embodiment provides a method for estimating the clamping force of an electromechanical braking system based on a stiffness curve and an observer, including the following steps: S1, system modeling: First, construct an electrical model of the electromechanical braking system, which includes the electrical characteristics of the motor, controller, and sensor. Based on the principle of torque balance, establish a model that describes the relationship between the torques and the motor angular acceleration during the braking process; S2, based on the non-linear characteristics of the electromechanical braking system, establish a stroke-clamping force polynomial equation for different thicknesses. Through multiple sets of actual tests, obtain a large amount of data on the motor rotation angle θ and the clamping force Fcl, and fit a fifth-order polynomial curve for different friction plate thicknesses, that is, the stiffness curve; at the same time, perform a maximum clamping force self-check strategy at regular intervals to obtain the latest clamping force stiffness characteristic curve of the current situation, and obtain the corresponding clamping force F_base for different motor strokes according to this curve; S3, accurately determine the various friction factors inside the electromechanical braking system: the viscous friction coefficient, the maximum static friction torque, and the Coulomb friction coefficient, and establish an accurate mechanical torque balance model based on this. According to this model, establish a torque balance equation, and based on this equation, establish a state space equation matrix, and then build an observer; In the formula, Jm is the moment of inertia; ωm is the motor speed; Tm is the motor output torque; Tf is the friction torque; T1 is the load torque.

[0021] Where R is the resistance; L is the inductance; K1 is the back electromotive force constant; K2 is the motor output torque coefficient; bf is the viscous friction coefficient; Jm is the moment of inertia; The input of the observer is set to parameters such as the clamping force Fcl, current i, voltage v, temperature T, etc. obtained from the stiffness curve, and the output is set to the motor speed ω (or the motor rotation angle θ). The feedback quantity Δω (Δθ) is obtained by taking the difference from the actual motor speed ωreal (or the actual motor rotation angle θreal), and the clamping force is corrected in real time according to the feedback (or the polynomial coefficients of the stiffness curve are corrected), so that the estimated clamping force is closer to the actual value; S4. Analyze the test results: Compare the difference between the estimated clamping force provided by the observer and the actual measured value, evaluate the performance of the system, and adjust the model parameters or improve the algorithm according to the test feedback until a satisfactory accuracy level is achieved.

[0022] S5. Optimization and deployment: Make corresponding optimizations for the discovered problems, involving hardware upgrades and software algorithm improvements. After completing the design of the final version, deploy this solution on the target vehicle model to ensure that it can work reliably in a wide range of application scenarios.

[0023] The stiffness curve corresponding to the full-thickness friction plate, the stiffness curve learning method for the latest condition, the moment balance equation and the corresponding state-space equation, the viscous friction coefficient, the fusion of the stiffness curve and the state observer. When the clamping force estimation system is in a complex working condition during actual vehicle driving, the stiffness curve is identified based on the preset full-thickness friction plate, and then the updated stiffness characteristic curve is obtained according to the maximum clamping force self-check strategy; based on the stiffness characteristic curve, it is added to the debugged observer for correction feedback to obtain an estimation force system with high accuracy and good self-adaptability; the problem of force estimation without a force sensor is solved.

[0024] This embodiment also provides a test bench, which integrates the above clamping force estimation method, and installs a force sensor to verify the estimation accuracy, and verifies the robustness of the clamping force estimation algorithm through different clamping methods. When the estimated clamping force is verified to be within the error range for multiple times, it can be used to replace the force sensor, greatly saving costs, saving space, and increasing the system stability.

[0025] Those of ordinary skill in the art should understand that: The discussion of any above embodiment is only exemplary and is not intended to imply that the scope of the present invention is limited to these examples; under the concept of the present invention, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations in different aspects of the present invention as described above, which are not provided in detail for the sake of brevity. Any omission, modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for estimating the clamping force of an electromechanical braking system based on a stiffness curve and an observer, characterized in that It includes the following steps: Step 1: First, estimate the preliminary clamping force F_base by the stiffness curve method; Step 2: Then, use the observer method to correct F_base, and further estimate the accurate estimated clamping force.

2. The method for estimating the clamping force of an electromechanical braking system based on a stiffness curve and an observer according to claim 1, characterized in that, The stiffness curve is a two-dimensional curve of motor stroke - clamping force obtained by fitting a large amount of motor stroke and clamping force data. Different motor strokes obtain their corresponding clamping forces F_base according to this curve.

3. The method for estimating the clamping force of an electromechanical braking system based on a stiffness curve and an observer according to claim 2, wherein Based on the non-linear characteristics of the electromechanical braking system, establish the stroke - clamping force polynomial equations of different thicknesses, and obtain the stiffness characteristic curve model covering the full-thickness friction plate according to the estimated different control zeros; at the same time, perform the maximum clamping force self-check strategy every certain period of time to obtain the latest clamping force stiffness characteristic curve of the current situation.

4. The method for estimating the clamping force of an electromechanical braking system based on a stiffness curve and an observer according to claim 3, wherein The observer method includes the following steps: Step 2.1: Establish the state space equation of the electromechanical braking system, and use the actually measurable variable motor speed ωm as the output of the state space equation; Step 2.2: Calculate the observer gain matrix by the pole placement method according to the system matrix and output matrix of the state space equation; Step 2.3: Use the deviation Δω between the output ωm_est of the state space equation and the actual measured value ωm as the feedback quantity, and correct the stiffness curve value F_base with the result calculated by the observer gain matrix, so as to obtain a high-precision estimated clamping force.

5. The method for estimating the clamping force of an electromechanical braking system based on a stiffness curve and an observer according to claim 1, wherein Before Step 1, system modeling is also required: First, construct the electrical model of the electromechanical braking system. This model includes the electrical characteristics of the motor, controller, and sensor. Based on the torque balance principle, establish a model describing the relationship between each torque and the motor angular acceleration during the braking process.

6. The method for estimating the clamping force of an electromechanical braking system based on a stiffness curve and an observer according to claim 5, wherein It also includes Step 3: Analyze the test results: Compare the difference between the clamping force estimated value provided by the observer and the actual measured value, evaluate the performance of the system, and adjust the model parameters or improve the algorithm according to the test feedback until a satisfactory accuracy level is achieved.

7. The method for estimating the clamping force of an electromechanical braking system based on a stiffness curve and an observer according to claim 6, characterized in that, It also includes Step 4: Optimization and deployment: Make corresponding optimizations for the discovered problems, involving hardware upgrades and software algorithm improvements. After completing the design of the final version, deploy this solution on the target vehicle model to ensure that it can work reliably in a wide range of application scenarios.

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