EMB clamping force estimation method

Abstract

The invention provides an EMB clamping force estimation method. The method comprises the steps that an EMB clamping process mechanism model is built; the method comprises the following steps: acquiring data of EMB clamping force, a motor rotation angle and a brake disc temperature based on an EMB test bed, and building an EMB clamping force fitting model; acquiring data of motor current, motor voltage, motor rotating speed, motor rotating angle, brake disc temperature, clamping force and driving mileage based on an EMB test bench, and building an EMB clamping force data model; a first clamping force estimation value, a second clamping force estimation value and a third clamping force estimation value are calculated based on real vehicle EMB data, and a clamping force fusion estimation value is calculated in combination with a Kalman filtering algorithm; according to the clamping force estimation error, the weights of the first estimation value, the second estimation value and the third estimation value of the clamping force are adjusted in a self-adaptive mode, and the clamping force estimation precision is optimized. According to the method, the advantages of the mechanism model, the offline data and the online data can be fully combined, the fusion weight is continuously corrected according to the estimation error, and accurate estimation of the clamping force of the EMB without the clamping force sensor is realized.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle braking, and particularly to a method for estimating the clamping force of an EMB. Background Art

[0002] The electromechanical brake (EMB) system is widely regarded as a key component for the development of new energy vehicle chassis by-wire due to its advantages such as fast response speed, small volume, easy layout, and no hydraulic oil pollution. The clamping force of the EMB is an important parameter affecting the braking effect. However, due to cost and space limitations, the clamping force sensor cannot be a standard configuration for production vehicles. Therefore, how to accurately estimate the clamping force without a clamping force sensor has become the key to the control and optimization of the EMB system. Existing methods mainly include the estimation method based on the motor model, the estimation method based on the brake disc stiffness model, and the data-driven method. However, each single method has its own limitations, and a reliable and stable EMB clamping force estimation scheme has not been formed:

[0003] (1) The traditional motor model does not consider factors such as system temperature change and fatigue wear, resulting in model errors;

[0004] (2) The traditional brake disc stiffness model does not consider the influence of temperature change on stiffness and is only applicable to a single temperature scenario.

[0005] (3) The data-driven method has high requirements for the quantity and quality of data and poor reliability. Summary of the Invention

[0006] The present application aims to at least solve one of the technical problems in the related art to some extent.

[0007] To this end, the first object of the present application is to propose a method for estimating the clamping force of an EMB, so as to fully utilize measurable data to accurately estimate the clamping force of the EMB without a clamping force sensor, which helps to accurately control the clamping force of the EMB and improve the vehicle braking effect.

[0008] The second object of the present application is to propose an EMB clamping force estimation system.

[0009] The third object of the present application is to propose an electronic device.

[0010] The fourth object of the present application is to propose a computer-readable storage medium.

[0011] The fifth object of the present application is to propose a computer program product.

[0012] To achieve the above object, the first aspect embodiment of the present application proposes an EMB clamping force estimation, including:

[0013] Build a mechanism model for the EMB clamping process. By establishing the relationship between the motor speed, load torque, and friction torque, and considering the influence of temperature on the friction torque and transmission efficiency, as well as heat conduction, heat convection, and heat radiation, calculate the first estimated value of the EMB clamping force;

[0014] Based on the EMB test bench, obtain the data of the EMB clamping force, motor rotation angle, and brake disc temperature. Build an EMB clamping force fitting model. By using the polynomial fitting method, establish the relationship between the EMB clamping force and the motor rotation angle at different brake disc temperatures, and calculate the second estimated value of the EMB clamping force;

[0015] Based on the EMB test bench, obtain the data of the motor current, motor voltage, motor speed, motor rotation angle, brake disc temperature, clamping force, and driving mileage. Build an EMB clamping force data model and use machine learning or deep learning algorithms for training, and calculate the third estimated value of the EMB clamping force;

[0016] Fuse the first estimated value, second estimated value, and third estimated value of the EMB clamping force through the Kalman filter algorithm to obtain the fused estimated value of the EMB clamping force;

[0017] According to the clamping force estimation error, adaptively adjust the weights of the first estimated value, second estimated value, and third estimated value of the clamping force to optimize the clamping force estimation accuracy.

[0018] Optionally, the building of the EMB clamping process mechanism model, by establishing the relationship between the motor speed, load torque, and friction torque, and considering the influence of temperature on the friction torque and transmission efficiency, as well as heat conduction, heat convection, and heat radiation, to calculate the first estimated value of the EMB clamping force, includes:

[0019] Establish the relationship between the equivalent moment of inertia at the EMB motor end and the speed, and the expression is:

[0020] J m ω m =T m -T f -T L

[0021] T m =k m i q

[0022]

[0023] In the formula, J m is the equivalent moment of inertia at the EMB motor end; ω m is the EMB motor speed; T m is the output torque of the EMB motor; T fis the equivalent friction torque at the EMB motor end; T L is the load torque of the EMB motor; k m is the motor torque coefficient; i q is the q-axis current of the EMB motor; ε is the critical coefficient; T s is the static friction torque; F0 is the estimated value of the EMB clamping force in the previous step; G is the torque coefficient related to the clamping force; D is the viscous friction coefficient; F1 is the first estimated value of the EMB clamping force; P is the lead of the ball screw; i is the planetary gear transmission ratio; η is the transmission efficiency of the transmission mechanism;

[0024] Considering the influence of temperature on the friction torque and transmission efficiency, a correction model of the friction torque and transmission efficiency with respect to the brake disc temperature is constructed, and the expression is:

[0025] η = η r + k η (T - T r )

[0026] T f = T fr + k f (T - T r )

[0027] In the formula, T r is the standard temperature of the brake disc; η r , T fr are the transmission efficiency and friction torque of the transmission mechanism at the standard temperature respectively; k η , k f are the correction coefficients of the transmission efficiency and friction torque with respect to the brake disc temperature respectively;

[0028] Considering heat conduction, heat convection, and heat radiation, a brake disc temperature rise model during the braking process is constructed, and the expression is:

[0029]

[0030] P h = F0μv

[0031]

[0032] P b = hA a (T - T a )

[0033]

[0034] In the formula, m is the mass of the brake disc; c p is the specific heat capacity of the brake disc; T is the brake disc temperature; P h , P a , Pb 、P are respectively the frictional heat generation power, heat conduction power, heat convection power, and heat radiation power of the brake disc; μ is the brake disc friction coefficient; v is the relative rotational speed of the brake disc; k a is the thermal conductivity of the brake disc; A c is the contact area between the brake disc and the adjacent components; T a is the ambient temperature; d a is the length of the heat conduction path; h is the convective heat transfer coefficient; A a is the contact area between the brake disc and the air; ε c is the emissivity; δ is the Stefan-Boltzmann constant.

[0035] Optionally, obtaining the data of the EMB clamping force, motor rotation angle, and brake disc temperature based on the EMB test bench, building an EMB clamping force fitting model, establishing the relationship between the EMB clamping force and the motor rotation angle at different brake disc temperatures by using the polynomial fitting method, and calculating the second estimated value of the EMB clamping force, including:

[0036] Building an EMB test bench, configuring an encoder on the EMB motor, installing a pressure sensor between the EMB piston and the brake pad, and installing a temperature sensor inside the brake disc; outputting a triangular wave voltage signal of 0 to maximum voltage to 0 to the EMB motor, repeating this process multiple times, and after filtering, obtaining the motor rotation angle, brake disc temperature, and clamping force data during the EMB clamping and releasing processes respectively;

[0037] Based on the motor rotation angle, brake disc temperature, and clamping force data, using the polynomial fitting method to obtain the relationship between the clamping force and the motor rotation angle during the EMB clamping and releasing processes at different brake disc temperatures, and the expression is as follows:

[0038] EMB clamping process

[0039] EMB release process

[0040] In the formula, F2 is the second estimated value of the EMB clamping force; a i 、b、c i 、d are polynomial coefficients, and the polynomial coefficients are different at different temperatures; θ is the motor rotation angle; θ1 and θ2 are the motor rotation angle critical values during the EMB clamping and releasing processes respectively.

[0041] Optionally, obtaining the data of the motor current, motor voltage, motor speed, motor rotation angle, brake disc temperature, clamping force, and driving mileage based on the EMB test bench, building an EMB clamping force data model and training it using machine learning or deep learning algorithms, and calculating the third estimated value of the EMB clamping force, including:

[0042] Build an EMB test bench, configure an encoder for the EMB motor, install a pressure sensor and a temperature sensor between the EMB piston and the brake pads, output a voltage signal simulating the driver's braking torque demand to the EMB motor, repeat this process several times, and after filtering, obtain data on the EMB motor current, motor voltage, motor speed, motor rotation angle, brake disc temperature, clamping force, and driving mileage;

[0043] Build an EMB clamping force data model, where the EMB clamping force data model uses the EMB motor current, motor voltage, motor speed, motor rotation angle, brake disc temperature, clamping force, and driving mileage as input variables and the clamping force as the output variable;

[0044] Use machine learning or deep learning algorithms to train the EMB clamping force data model to obtain a trained clamping force data model, and the expression is:

[0045] F3 = f(i q ,V m ,ω m ,θ,T,L)

[0046] In the formula, F3 is the third estimated value of the EMB clamping force; V m is the motor voltage; f(·) is the trained clamping force data model; L is the driving mileage.

[0047] Optionally, fusing the first estimated value, second estimated value, and third estimated value of the EMB clamping force through the Kalman filter algorithm to obtain the fused estimated value of the EMB clamping force includes:

[0048] Based on the Kalman filter algorithm, fusing the first estimated value F3, second estimated value F2, and third estimated value F3 of the clamping force to obtain the fused estimated value F of the EMB clamping force, and the expression is:

[0049] F = K1F1 + K2F2 + K3F3

[0050] Among them, K1, K2, and K3 are the weight coefficients of the first estimated value, second estimated value, and third estimated value of the clamping force respectively.

[0051] Optionally, adaptively adjusting the weights of the first estimated value, second estimated value, and third estimated value of the clamping force according to the clamping force estimation error to optimize the clamping force estimation accuracy includes:

[0052] Calculate the variances of the estimation errors of the EMB clamping process mechanism model, EMB clamping force fitting model, and EMB clamping force data model within the sliding window, and the expression is:

[0053]

[0054] Wherein, N is the number of data in the sliding window, and δ1, δ2, and δ3 are the variances of the estimation errors of the EMB clamping process mechanism model, the EMB clamping force fitting model, and the EMB clamping force data model in the sliding window, respectively;

[0055] Based on the variances of the clamping force estimation errors, adaptively adjust the weight coefficients of the first estimated value, the second estimated value, and the third estimated value of the clamping force. The expression is:

[0056]

[0057] Wherein, K1, K2, and K3 are the weight coefficients of the first estimated value, the second estimated value, and the third estimated value of the clamping force, respectively.

[0058] To achieve the above object, the second aspect embodiment of the present application proposes an EMB clamping force estimation system, including:

[0059] An information acquisition module for acquiring real-time data related to the clamping force in the EMB system, including motor current, motor voltage, motor speed, motor rotation angle, brake disc temperature, clamping force, and driving mileage;

[0060] An EMB clamping process mechanism model calculation module for establishing a relationship between motor speed, load torque, and friction torque, and considering the influence of temperature on friction torque and transmission efficiency, as well as heat conduction, heat convection, and heat radiation, to calculate the first estimated value of the EMB clamping force;

[0061] An EMB clamping force fitting model calculation module for building an EMB clamping force fitting model based on the data of the EMB clamping force, motor rotation angle, and brake disc temperature, and establishing a relationship between the EMB clamping force and the motor rotation angle at different brake disc temperatures by using the polynomial fitting method to calculate the second estimated value of the EMB clamping force;

[0062] An EMB clamping force data model calculation module for building an EMB clamping force data model based on the data of motor current, motor voltage, motor speed, motor rotation angle, brake disc temperature, clamping force, and driving mileage, and training it using machine learning or deep learning algorithms to calculate the third estimated value of the EMB clamping force;

[0063] An EMB clamping force online fusion estimation module for fusing the first estimated value, the second estimated value, and the third estimated value of the EMB clamping force through the Kalman filter algorithm to obtain the fusion estimated value of the EMB clamping force, and adaptively adjusting the weights of each estimated value according to the clamping force estimation error to optimize the accuracy of the clamping force estimation.

[0064] To achieve the above object, the third aspect embodiment of the present application proposes an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0065] The memory stores computer-executable instructions;

[0066] The processor executes the computer-executable instructions stored in the memory to implement the method described in any one of the above first aspects.

[0067] To achieve the above object, an embodiment of the fourth aspect of the present application proposes a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, they are used to implement the method described in any one of the above first aspects.

[0068] To achieve the above object, an embodiment of the fifth aspect of the present application proposes a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the method described in any one of the above first aspects.

[0069] The technical solutions provided by the embodiments of the present application at least bring the following beneficial effects:

[0070] It can fully combine the advantages of the mechanism model, offline data, and online data, continuously correct the fusion weight according to the estimation error, achieve accurate estimation of the EMB clamping force without a clamping force sensor, have higher estimation accuracy and stability, and can adapt to complex working conditions for a long time. At the same time, since the present application does not use a clamping force sensor, the cost is reduced, which is beneficial to improving the economic benefits of mass-produced vehicles.

[0071] The additional aspects and advantages of the present application will be partially given in the following description, partially become obvious from the following description, or be understood through the practice of the present application. Description of the Drawings

[0072] The above and / or additional aspects and advantages of the present application will become obvious and easy to understand from the following description of the embodiments in conjunction with the drawings, where:

[0073] Figure 1 is a schematic flowchart of an EMB clamping force estimation method provided by an embodiment of the present application;

[0074] Figure 2 is a schematic structural diagram of an EMB clamping force estimation system provided by an embodiment of the present application. Detailed Embodiments

[0075] The embodiments of the present application will be described in detail below. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals denote the same or similar elements or elements with the same or similar functions from beginning to end. The embodiments described below with reference to the drawings are exemplary and are intended to explain the present application, but should not be construed as limiting the present application.

[0076] In view of the problems existing in the prior art, the embodiment of the present application provides an EMB clamping force estimation method. Figure 1 It is a schematic flow chart of an EMB clamping force estimation method provided by the embodiment of the present application. As Figure 1 shown, the method includes the following steps:

[0077] Step S1, build a mechanism model of the EMB clamping process, and calculate the first estimated value of the EMB clamping force by establishing the relationship between the motor speed, load torque, and friction torque, and considering the influence of temperature on the friction torque and transmission efficiency, as well as heat conduction, heat convection, and heat radiation.

[0078] The first step of the present application is to build a mechanism model of the EMB clamping process. The key to this step is to establish the relationship between the motor speed, load torque, and friction torque, comprehensively consider the influence of temperature on the friction torque and transmission efficiency, and also consider factors such as heat conduction, heat convection, and heat radiation, so as to calculate the first estimated value of the EMB clamping force.

[0079] In this process, the embodiment of the present application first expresses the working characteristics of the motor by establishing the relationship between the equivalent moment of inertia at the EMB motor end and the speed. The relationship is as follows:

[0080] J m ω m =T m -T f -T L

[0081] T m =k m i q

[0082]

[0083] In the formula, J m is the equivalent moment of inertia at the EMB motor end; ω m is the EMB motor speed; T m is the output torque of the EMB motor; T f is the equivalent friction torque at the EMB motor end; T L is the load torque of the EMB motor; k m is the motor torque coefficient; i q is the q-axis current of the EMB motor; ε is the critical coefficient; T s is the static friction torque; F0 is the estimated value of the EMB clamping force in the previous step; G is the torque coefficient related to the clamping force; D is the viscous friction coefficient; F1 is the first estimated value of the EMB clamping force; P is the lead of the ball screw; i is the planetary gear transmission ratio; η is the transmission efficiency of the transmission mechanism.

[0084] To consider the influence of temperature on the friction torque and transmission efficiency, the embodiment of the present application constructs a correction model of the friction torque and transmission efficiency with respect to the brake disc temperature, and the formula is as follows:

[0085] η = η r + k η (T - T r )

[0086] T f = T fr + k f (T - T r )

[0087] In the formula, T r is the standard temperature of the brake disc; η r , T fr are respectively the transmission efficiency and friction torque of the transmission mechanism at the standard temperature; k η , k f are respectively the correction coefficients of the transmission efficiency and friction torque with respect to the brake disc temperature.

[0088] In addition, the embodiment of the present application further considers the influence of heat conduction, heat convection and heat radiation on the temperature rise of the brake disc. By the heat conduction equation and combining with the calculation formula of heat power, a model of the temperature rise of the brake disc is established, and the expression is:

[0089]

[0090] P h = F0μv

[0091]

[0092] P b = hA a (T - T a )

[0093]

[0094] In the formula, m is the mass of the brake disc; c p is the specific heat capacity of the brake disc; T is the temperature of the brake disc; P h , P a , P b , P are respectively the frictional heat generation power, heat conduction power, heat convection power and heat radiation power of the brake disc; μ is the friction coefficient of the brake disc; v is the relative rotational speed of the brake disc; k a is the thermal conductivity of the brake disc; A c is the contact area between the brake disc and adjacent components; T a is the ambient temperature; d a is the length of the heat conduction path; h is the convective heat transfer coefficient; Aa is the contact area between the brake disc and air; ε c is the emissivity; δ is the Stefan-Boltzmann constant.

[0095] These formulas comprehensively consider the effects of heat generation due to friction, heat conduction, heat convection, and heat radiation, thereby helping this application to more accurately evaluate the temperature of the brake disc, and further adjust the frictional torque and transmission efficiency.

[0096] Combining all the above factors, the first estimated value of the EMB clamping force can be obtained based on the model. This estimated value provides a basis for subsequent steps and is further optimized by combining other data and models.

[0097] In practical applications, the first estimated value of the EMB clamping force can be obtained through vehicle test data. By real-time monitoring parameters such as motor current, motor speed, temperature, and load torque, and combining the calculation process of the aforementioned mechanism model, the EMB clamping force can be accurately estimated according to the actual vehicle operating conditions.

[0098] Step S2: Obtain data on the EMB clamping force, motor rotation angle, and brake disc temperature based on the EMB test bench, build an EMB clamping force fitting model, establish a relationship between the EMB clamping force and the motor rotation angle at different brake disc temperatures by using the polynomial fitting method, and calculate the second estimated value of the EMB clamping force.

[0099] The second step of this application mainly involves obtaining data such as the motor rotation angle, brake disc temperature, and clamping force based on the EMB test bench, building a fitting model of the EMB clamping force according to these data, establishing a relationship between the EMB clamping force and the motor rotation angle at different brake disc temperatures by using the polynomial fitting method, and then calculating the second estimated value of the EMB clamping force.

[0100] In this process, the embodiment of this application first builds an EMB test bench and configures relevant sensors and equipment to collect necessary data. The main configurations of the EMB test bench include:

[0101] Motor encoder: used to accurately measure the rotation angle of the motor;

[0102] Pressure sensor: installed between the EMB piston and the brake pad to monitor the clamping force;

[0103] Temperature sensor: installed in the brake disc to real-time monitor the temperature of the brake disc.

[0104] By controlling the EMB motor, the present application outputs a triangular wave voltage signal to it, which ranges from 0 to the maximum voltage and then returns to 0, and this process is repeated multiple times. The input of these signals prompts the EMB motor to operate during the clamping and releasing processes. After the signal output, through filtering, data under different conditions are obtained, which mainly include the motor rotation angle, brake disc temperature, and clamping force data during the EMB clamping and releasing processes. These data can reflect the dynamic behavior during the EMB clamping and releasing processes and are the basis for subsequent fitting and modeling.

[0105] Based on the collected motor rotation angle, brake disc temperature, and clamping force data, the embodiment of the present application adopts a polynomial fitting method to establish the relationship between the clamping force and the motor rotation angle. Specifically, the relational expression between the clamping force and the motor rotation angle will be different under different brake disc temperatures. Therefore, the polynomial coefficients need to be fitted for different temperature conditions, and the expression is:

[0106] EMB clamping process

[0107] EMB releasing process

[0108] In the formula, F2 is the second estimated value of the EMB clamping force; a i , b, c i , d are polynomial coefficients, and the polynomial coefficients are different when the temperature is different; θ is the motor rotation angle; θ1 and θ2 are respectively the critical values of the motor rotation angle during the EMB clamping and releasing processes.

[0109] It should be noted that when fitting the relationship between the clamping force and the motor rotation angle, the polynomial fitting methods adopted include but are not limited to the following common methods: Least squares polynomial fitting method: By minimizing the sum of the squares of the errors between the fitting curve and the actual data, the optimal polynomial coefficients are obtained; Lagrange interpolation polynomial fitting method: Based on the interpolation theory, a polynomial is calculated through multiple known data points and can accurately pass through all given points; Newton interpolation polynomial fitting method: Based on one of the interpolation methods, a polynomial is obtained by recursively calculating the divided differences and can adapt to different numbers of data points.

[0110] It should be noted that the present application does not limit the fitting method. The above polynomial fitting methods are applicable to different situations, and the most suitable fitting method can be selected according to the actual data characteristics. The polynomial coefficients obtained by these methods will vary with the different brake disc temperatures, so fitting needs to be carried out according to different temperature conditions.

[0111] Through the implementation of step S2, based on the actual data collected by the EMB test bench, the present application established a relationship between the EMB clamping force and the motor rotation angle using the polynomial fitting method. This step not only considered the influence of the brake disc temperature but also introduced various commonly used polynomial fitting methods, including the least squares method, Lagrange interpolation method, and Newton interpolation method, thereby optimizing the estimated value of the EMB clamping force.

[0112] In practical applications, the second estimated value of the EMB clamping force can be obtained through real vehicle data. Combining with the calculation process of the aforementioned clamping force fitting model, the EMB clamping force can be accurately estimated according to the real vehicle operating conditions.

[0113] Step S3: Based on the EMB test bench, obtain data on motor current, motor voltage, motor speed, motor rotation angle, brake disc temperature, clamping force, and driving mileage, build an EMB clamping force data model, and use machine learning or deep learning algorithms for training to calculate the third estimated value of the EMB clamping force.

[0114] The third step of the present application mainly involves obtaining data such as motor current, motor voltage, motor speed, motor rotation angle, brake disc temperature, clamping force, and driving mileage based on the EMB test bench, and building an EMB clamping force data model. Then, use machine learning or deep learning algorithms to train the data model to calculate the third estimated value of the EMB clamping force.

[0115] In this step, the embodiment of the present application first builds an EMB test bench and configures relevant sensors and equipment to collect necessary data. The main configurations of the EMB test bench include:

[0116] Motor encoder: used to accurately measure the rotation angle of the motor;

[0117] Pressure sensor: installed between the EMB piston and the brake pad to monitor the clamping force;

[0118] Temperature sensor: installed in the brake disc to monitor the temperature of the brake disc in real time;

[0119] Driving mileage sensor: records the mileage of the vehicle.

[0120] Next, the embodiment of the present application outputs a voltage signal to the EMB motor to simulate the driver's braking torque demand, prompting the EMB motor to operate under different conditions and repeating this process multiple times. After the signal is output, the following data is obtained through filtering: data on EMB motor current, motor voltage, motor speed, motor rotation angle, brake disc temperature, clamping force, and driving mileage. These data represent the working states of the EMB motor and the braking system, providing rich input information for subsequent data modeling and training.

[0121] According to the acquired data, an EMB clamping force data model is further established in an embodiment of the present application. This model uses the EMB motor current, motor voltage, motor speed, motor rotation angle, brake disc temperature, clamping force, and driving mileage as input variables, and the clamping force as the output variable.

[0122] In an embodiment of the present application, machine learning or deep learning algorithms are used to train the EMB clamping force data model. Common machine learning algorithms include, but are not limited to, gradient boosting trees, support vector regression, random forests, etc.; while deep learning algorithms can include long short-term memory networks (LSTM), convolutional neural networks (CNN), Transformer, etc. Through these algorithms, the model can automatically learn the variation law of the clamping force from the data, and then realize the estimation of the EMB clamping force.

[0123] The trained model can be expressed by the following formula:

[0124] F3 = f(i q , V m , ω m , θ, T, L)

[0125] In the formula, F3 is the third estimated value of the EMB clamping force; V m is the motor voltage; f(·) is the trained clamping force data model; L is the driving mileage.

[0126] Finally, the trained EMB clamping force data model can predict the third estimated value of the clamping force in real time. This model can handle complex non-linear relationships, and by analyzing various parameters of the motor and braking system, it provides an accurate clamping force estimate for practical applications. This estimated value will help better control and optimize the working state of the EMB system, and improve the stability and reliability of the system.

[0127] In practical applications, the third estimated value of the EMB clamping force can be obtained through real vehicle data. Combining the calculation process of the foregoing clamping force data model, the EMB clamping force can be accurately estimated according to the real vehicle operating state.

[0128] Step S4, fuse the first estimated value, the second estimated value, and the third estimated value of the EMB clamping force through the Kalman filter algorithm to obtain the fused estimated value of the EMB clamping force.

[0129] In an embodiment of the present application, the fourth step involves fusing the first estimated value, the second estimated value, and the third estimated value of the EMB clamping force through the Kalman filter algorithm, so as to obtain the fused estimated value of the EMB clamping force.

[0130] Kalman filtering is a recursive algorithm that uses a system dynamic model and observation data for optimal estimation, and is widely used in multi-sensor data fusion and dynamic system state estimation. In this step, the Kalman filtering algorithm is used to fuse different estimated values of the EMB clamping force, so as to obtain a more accurate fused estimated value. Through the weighted average mechanism of Kalman filtering, the embodiments of the present application can automatically adjust the weight coefficients of each estimated value and dynamically optimize the estimation result of the clamping force.

[0131] In the embodiments of the present application, based on the Kalman filtering algorithm, three different estimated values of the EMB clamping force are fused to calculate the final fused estimated value F of the clamping force. It is assumed that the first estimated value, the second estimated value, and the third estimated value of the clamping force have been obtained according to the real vehicle data. Then, the fused estimated value of the clamping force is calculated by the following weighted sum formula:

[0132] F = K1F1 + K2F2 + K3F3

[0133] Where K1, K2, and K3 are the weight coefficients of the first estimated value, the second estimated value, and the third estimated value of the clamping force respectively. These weight coefficients are adaptively adjusted according to the Kalman filtering algorithm, and are dynamically allocated according to the accuracy and reliability of each estimated value, and finally the optimal fused estimated value of the clamping force is obtained.

[0134] Step S5: According to the clamping force estimation error, adaptively adjust the weights of the first estimated value, the second estimated value, and the third estimated value of the clamping force to optimize the clamping force estimation accuracy.

[0135] In order to dynamically adjust the weight coefficients according to the clamping force estimation error, the embodiments of the present application first need to calculate the error of each estimated value. Through the historical data in the sliding window, the variance of the estimation errors of the EMB clamping process mechanism model, the EMB clamping force fitting model, and the EMB clamping force data model can be calculated. The variance of the error reflects the deviation size of each estimated value relative to the actual value, thereby helping to evaluate the reliability of the estimated value.

[0136] The specific error variance calculation formula is as follows

[0137]

[0138] In the formula, N is the number of data in the sliding window, and δ1, δ2, and δ3 are the variances of the estimation errors of the EMB clamping process mechanism model, the EMB clamping force fitting model, and the EMB clamping force data model in the sliding window respectively.

[0139] After calculating the error variance of each estimated value, the embodiments of the present application further dynamically adjust the weight coefficient of each estimated value according to the magnitude of the error. A smaller estimation error corresponds to a higher weight, thereby enhancing the influence of the estimated value in the fusion result. Conversely, a larger estimation error results in a lower weight, thereby reducing its impact on the final result.

[0140] The adaptive adjustment formula for the weight coefficient is as follows:

[0141]

[0142] Wherein, K1, K2, and K3 are the weight coefficients of the first estimated value, the second estimated value, and the third estimated value of the clamping force, respectively.

[0143] It can be understood that in practical applications, the estimation error of the clamping force fluctuates with changes in different working conditions. The Kalman filtering algorithm and the adaptive adjustment mechanism allow the present application to correct the estimation error and adjust the weight coefficient in real time according to new data. In this way, the estimation accuracy of the EMB clamping force will continuously improve over time and with the acquisition of more data.

[0144] Through the implementation of step S5, the present application adaptively adjusts the weight coefficient of the EMB clamping force estimated value by using the Kalman filtering algorithm in combination with the estimated error variance, thereby optimizing the estimation accuracy of the clamping force. This method can provide a high-precision estimation of the EMB clamping force in practical applications, which helps to improve the control accuracy and stability of the EMB system.

[0145] To implement the above embodiments, the present application also proposes an EMB clamping force estimation system. Figure 2 This is a schematic structural diagram of an EMB clamping force estimation system provided by the embodiments of the present application. As Figure 2 shown, the system includes:

[0146] An information acquisition module, configured to acquire real-time data related to the clamping force in the EMB system, including motor current, motor voltage, motor speed, motor rotation angle, brake disc temperature, clamping force, and driving mileage;

[0147] An EMB clamping process mechanism model calculation module, configured to establish a relationship between motor speed, load torque, and friction torque, and consider the influence of temperature on friction torque and transmission efficiency, as well as heat conduction, heat convection, and heat radiation, to calculate the first estimated value of the EMB clamping force;

[0148] The EMB clamping force fitting model calculation module is used to build an EMB clamping force fitting model based on the data of the EMB clamping force, motor rotation angle, and brake disc temperature, establish the relationship between the EMB clamping force and the motor rotation angle at different brake disc temperatures by using the polynomial fitting method, and calculate the second estimated value of the EMB clamping force;

[0149] The EMB clamping force data model calculation module is used to build an EMB clamping force data model based on the data of motor current, motor voltage, motor speed, motor rotation angle, brake disc temperature, clamping force, and driving mileage, train it using machine learning or deep learning algorithms, and calculate the third estimated value of the EMB clamping force;

[0150] The EMB clamping force online fusion estimation module is used to fuse the first estimated value, the second estimated value, and the third estimated value of the EMB clamping force through the Kalman filter algorithm to obtain the fusion estimated value of the EMB clamping force, and adaptively adjust the weights of each estimated value according to the clamping force estimation error to optimize the accuracy of the clamping force estimation.

[0151] Regarding the system in the above embodiments, the specific ways in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0152] To implement the above embodiments, the present application also proposes an electronic device, including: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided in the foregoing embodiments.

[0153] To implement the above embodiments, the present application also proposes a computer-readable storage medium storing computer-executable instructions, and the computer-executable instructions are used to implement the method provided in the foregoing embodiments when executed by a processor.

[0154] To implement the above embodiments, the present application also proposes a computer program product including a computer program, and the computer program implements the method provided in the foregoing embodiments when executed by a processor.

[0155] The collection, storage, use, processing, transmission, provision, and disclosure of the user's personal information involved in the present application all comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0156] It should be noted that personal information from users should be collected for legal and reasonable purposes and should not be shared or sold outside of such legal uses. In addition, such collection / sharing should be carried out after obtaining the informed consent of the user, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization including authorizing relevant user information before the user uses the function. In addition, any necessary steps should be taken to safeguard and protect access to such personal information data and ensure that others with access to the personal information data comply with their privacy policies and procedures.

[0157] This application is expected to provide an implementation where users can selectively block the use or access of personal information data. That is, the present disclosure is expected to provide hardware and / or software to prevent or block access to such personal information data. Once the personal information data is no longer needed, the risk can be minimized by restricting data collection and deleting the data. In addition, when applicable, personal identifiers are removed from such personal information to protect the privacy of the user.

[0158] In the description of the foregoing embodiments, the descriptions referring to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0159] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" can explicitly or implicitly include at least one of such features. In the description of the present application, "a plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0160] Any process or method description shown in the flowchart or described in other ways herein can be understood to represent a module, segment, or part of code including one or more executable instructions for implementing a customized logical function or process, and the scope of the preferred implementation of the present application includes additional implementations where the functions can be executed in a manner that is not in the order shown or discussed, including in a substantially simultaneous manner or in the reverse order according to the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application pertain.

[0161] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in conjunction with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion having one or more wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0162] It should be understood that various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0163] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the method of implementing the above embodiments can be completed by a program instructing relevant hardware, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0164] In addition, in each embodiment of the present application, each functional unit can be integrated into a processing module, or each unit can exist physically alone, or two or more units can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0165] The above-mentioned storage medium can be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

[0166] It should be understood that various forms of the processes shown above can be used, reordering, adding, or deleting steps. For example, the steps described in the present application can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present application can be achieved. This is not limited herein.

[0167] The above specific implementation manners do not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. A method for estimating EMB clamping force, characterized in that: include: Build the EMB clamping process mechanism model, and calculate the first estimate of the EMB clamping force by establishing the relationship between motor speed, load torque, and friction torque, and considering the influence of temperature on friction torque and transmission efficiency as well as heat conduction, heat convection, and heat radiation; Based on the EMB test bench, the data of EMB clamping force, motor angle and brake disc temperature are obtained, and the EMB clamping force fitting model is built. The relationship between EMB clamping force and motor angle at different brake disc temperatures is established by using the polynomial fitting method, and the second estimated value of EMB clamping force is calculated; Based on the EMB test bench, the data of motor current, motor voltage, motor speed, motor angle, brake disc temperature, clamping force, and mileage are obtained, and an EMB clamping force data model is built and trained using a machine learning or deep learning algorithm to calculate the third estimated value of the EMB clamping force; fusing the first estimated value, the second estimated value, and the third estimated value of the EMB clamping force through a Kalman filter algorithm to obtain a fused estimated value of the EMB clamping force; According to the clamping force estimation error, the weights of the first estimation value, the second estimation value, and the third estimation value of the clamping force are adaptively adjusted to optimize the clamping force estimation accuracy.

2. The method according to claim 1, characterized in that The EMB clamping process mechanism model is constructed, and a relationship between motor speed, load torque, and friction torque is established, and the influence of temperature on friction torque and transmission efficiency as well as heat conduction, heat convection, and heat radiation are considered to calculate a first estimated value of the EMB clamping force, including: The relationship between the equivalent moment of inertia of the EMB motor end and the speed is established as follows: J m ω m =T m -T f -T L T m =k m i q In the formula, J m is the equivalent moment of inertia of the EMB motor end; ω m is the EMB motor speed; T n is the output torque of the EMB motor; T f is the equivalent friction torque at the EMB motor end; T L is the EMB motor load torque; k m is the motor torque coefficient; i q is the q-axis current of the EMB motor; ε is the critical coefficient; T s is the static friction torque; F0 is the estimated value of the EMB clamping force in the previous step; G is the torque coefficient related to the clamping force; D is the viscous friction coefficient; F1 is the first estimated value of the EMB clamping force; P is the ball screw lead; i is the planetary gear ratio; η is the transmission efficiency of the transmission mechanism; Considering the influence of temperature on friction torque and transmission efficiency, a correction model of friction torque and transmission efficiency with respect to brake disc temperature is constructed, and the expression is: the=the r +k η (TT r ) T f =T fr +k f (T-T r ) Where, T r is the standard temperature of the brake disc; η r 、T fr are the transmission efficiency and friction torque of the transmission mechanism at standard temperature; k η , k f are the correction coefficients of transmission efficiency and friction torque with respect to brake disc temperature respectively; Considering heat conduction, heat convection and heat radiation, a brake disc temperature rise model during braking is constructed, and the expression is: P h =F0μv P b =hA a (T-T a ) Where m is the mass of the brake disc; c p is the specific heat capacity of the brake disc; T is the brake disc temperature; P h , P a , P b , P are the friction heat generation power, heat conduction power, heat convection power and heat radiation power of the brake disc respectively; μ is the friction coefficient of the brake disc; v is the relative speed of the brake disc; k s is the thermal conductivity of the brake disc; A c is the contact area between the brake disc and adjacent components; T a is the ambient temperature; d a is the heat conduction path length; h is the convection heat transfer coefficient; A a is the contact area between the brake disc and the air; c is the emissivity; δ is the Stefan-Boltzmann constant.

3. The method according to claim 2, characterized in that The method obtains data of EMB clamping force, motor rotation angle, and brake disc temperature based on the EMB test bench, builds an EMB clamping force fitting model, establishes a relationship between EMB clamping force and motor rotation angle at different brake disc temperatures by using a polynomial fitting method, and calculates a second estimated value of the EMB clamping force, including: Build an EMB test bench, configure an encoder on the EMB motor, install a pressure sensor between the EMB piston and the brake pad, and build a temperature sensor into the brake disc; output a triangular wave voltage signal of 0 to the maximum voltage to 0 to the EMB motor, repeat this process multiple times, and after filtering, obtain the motor rotation angle, brake disc temperature, and clamping force data during the EMB clamping and release process; Based on the motor angle, brake disc temperature and clamping force data, the polynomial fitting method is used to obtain the relationship between the clamping force and the motor angle during the EMB clamping and release process at different brake disc temperatures. The expression is as follows: Where F2 is the second estimated value of the EMB clamping force; i , b, c i , d are polynomial coefficients, and the polynomial coefficients are different at different temperatures; θ is the motor angle; θ1 and θ2 are the critical values ​​of the motor angle during the EMB clamping and loosening processes, respectively.

4. The method according to claim 3, characterized in that The method acquires data of motor current, motor voltage, motor speed, motor rotation angle, brake disc temperature, clamping force, and mileage based on the EMB test bench, builds an EMB clamping force data model, and uses a machine learning or deep learning algorithm for training to calculate a third estimated value of the EMB clamping force, including: Build an EMB test bench, configure an encoder on the EMB motor, install a pressure sensor and a temperature sensor between the EMB piston and the brake pad, output a voltage signal simulating the driver's braking torque demand to the EMB motor, repeat this process several times, and after filtering, obtain the data of EMB motor current, motor voltage, motor speed, motor angle, brake disc temperature, clamping force, and mileage; Building an EMB clamping force data model, wherein the EMB clamping force data model uses EMB motor current, motor voltage, motor speed, motor angle, brake disc temperature, clamping force and mileage as input variables, and uses clamping force as output variable; The EMB clamping force data model is trained using a machine learning or deep learning algorithm to obtain a trained clamping force data model, which is expressed as: F3=f(i q ,V m ,ω m ,θ,T,L) Where F3 is the third estimated value of the EMB clamping force; V m is the motor voltage; f(·) is the clamping force data model obtained through training; L is the mileage.

5. The method according to claim 4, characterized in that The step of fusing the first estimated value, the second estimated value, and the third estimated value of the EMB clamping force through a Kalman filter algorithm to obtain a fused estimated value of the EMB clamping force includes: Based on the Kalman filter algorithm, the first estimated value F3, the second estimated value F2, and the third estimated value F3 of the clamping force are fused to obtain the fused estimated value F of the EMB clamping force, which is expressed as follows: F=K1F1+K2F2+K3F3 Among them, K1, K2, and K3 are weight coefficients of the first estimated value, the second estimated value, and the third estimated value of the clamping force, respectively.

6. The method according to claim 5, characterized in that The method of adaptively adjusting the weights of the first estimated value, the second estimated value, and the third estimated value of the clamping force according to the clamping force estimation error to optimize the clamping force estimation accuracy includes: The variance of the estimation error of the EMB clamping process mechanism model, the EMB clamping force fitting model, and the EMB clamping force data model within the sliding window is calculated as follows: Where N is the number of data in the sliding window, δ1, δ2, and δ3 are the variances of the estimation errors of the EMB clamping process mechanism model, the EMB clamping force fitting model, and the EMB clamping force data model in the sliding window, respectively; Based on the variance of the clamping force estimation error, the weight coefficients of the first estimation value, the second estimation value, and the third estimation value of the clamping force are adaptively adjusted, and the expression is: Among them, K1, K2, and K3 are weight coefficients of the first estimated value, the second estimated value, and the third estimated value of the clamping force, respectively.

7. An EMB clamping force estimation system, characterized in that: include: Information acquisition module, used to collect real-time data related to clamping force in the EMB system, including motor current, motor voltage, motor speed, motor angle, brake disc temperature, clamping force, and mileage; The EMB clamping process mechanism model calculation module is used to establish the relationship between the motor speed, load torque, and friction torque, and consider the influence of temperature on the friction torque and transmission efficiency as well as heat conduction, heat convection, and heat radiation to calculate the first estimated value of the EMB clamping force; An EMB clamping force fitting model calculation module is used to build an EMB clamping force fitting model based on the data of EMB clamping force, motor rotation angle, and brake disc temperature, and to establish a relationship between EMB clamping force and motor rotation angle at different brake disc temperatures by using a polynomial fitting method to calculate a second estimated value of the EMB clamping force; An EMB clamping force data model calculation module is used to build an EMB clamping force data model based on the data of motor current, motor voltage, motor speed, motor angle, brake disc temperature, clamping force, and mileage, and train it using a machine learning or deep learning algorithm to calculate a third estimated value of the EMB clamping force; The EMB clamping force online fusion estimation module is used to fuse the first estimated value, the second estimated value and the third estimated value of the EMB clamping force through the Kalman filter algorithm to obtain the fused estimated value of the EMB clamping force, and adaptively adjust the weights of each estimated value according to the clamping force estimation error to optimize the accuracy of the clamping force estimation.

8. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 6 when executed by a processor.

10. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 6 when being executed by a processor.

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