A method for estimating clamping force of an electromechanical brake system

Abstract

The application discloses a clamping force estimation method of an electromechanical brake system and belongs to the technical field of vehicles. The method comprises the following steps: acquiring input signals, wherein the input signals comprise motor current sensor signals, motor rotation angle sensor signals and vibration acceleration sensor signals; performing feature extraction and determining a contact point according to the input signals; calculating a first clamping force based on an estimation model of dynamic stiffness; calculating a second clamping force based on an estimation model of torque balance; calculating a third clamping force based on an estimation model of current harmonic characteristics; and determining a final clamping force according to the first clamping force, the second clamping force and the third clamping force.

Description

Technical Field

[0001] This invention belongs to the field of vehicle technology, specifically relating to a method for estimating the clamping force of an electromechanical braking system. Background Technology

[0002] Electro-Mechanical Braking (EMB) systems, as a new generation of automotive braking technology, are gradually replacing traditional hydraulic braking systems. EMB systems directly drive the braking mechanism via an electric motor, offering advantages such as fast response, compact structure, and easy integration with the vehicle's control system. In EMB systems, accurately estimating the braking clamping force is a key technology for achieving high-performance braking control, directly impacting the vehicle's braking safety, comfort, and energy recovery efficiency.

[0003] Traditional methods assume that the mechanical contact point is fixed, but in actual applications, due to factors such as friction plate wear, thermal expansion effect, and changes in mechanical clearance, the position of the contact point will drift, resulting in systematic deviations in the clamping force estimation. Summary of the Invention

[0004] One object of the present invention is to provide a method for estimating the clamping force of an electromechanical braking system, which can solve the above-mentioned technical problems in the prior art.

[0005] According to a first aspect of the present invention, a method for estimating the clamping force of an electromechanical braking system is provided, comprising:

[0006] Acquire input signals, including motor current sensor signals, motor rotation angle sensor signals, and vibration acceleration sensor signals;

[0007] Feature extraction and contact point determination are performed based on the input signal;

[0008] The first clamping force is calculated based on the estimation model of dynamic stiffness;

[0009] The second clamping force is calculated based on the torque balance estimation model;

[0010] The third clamping force is calculated based on an estimation model of current harmonic characteristics.

[0011] The final clamping force is determined based on the first clamping force, the second clamping force, and the third clamping force.

[0012] Optionally, the first clamping force is calculated according to the following formula:

[0013] ;

[0014] ;

[0015] ;

[0016] in, For the first clamping force, The stiffness function in the angular domain. It is a non-linear exponent. This is the stiffness coefficient. For the lead screw, The change in angle. The motor rotation angle at the moment of contact. For the motor rotation angle, For temperature.

[0017] Optionally, the second clamping force is calculated according to the following formula:

[0018] ;

[0019] in, For the second clamping force, For the collected motor torque, For frictional torque, For transmission efficiency, For rotational inertia, Angular acceleration, For effective lead screw, For the motor rotation angle, For temperature;

[0020] Frictional torque Represented as:

[0021] ;

[0022] in, The Coulomb friction torque of the motor, This is the maximum static friction torque of the motor. The viscous friction coefficient of the motor, The critical Stribeck velocity, This is the Stribeck characteristic speed of the motor. This represents the total load torque acting on the motor shaft. The threshold for static friction is determined. ω is the rotational angular velocity of the motor shaft;

[0023] Transmission efficiency Represented as:

[0024] ;

[0025] ;

[0026] ;

[0027] in, This is a position-dependent efficiency term, used to characterize the mechanical losses caused by changes in the geometric position of the transmission mechanism. This is a temperature-dependent efficiency term, used to characterize the performance changes of a transmission mechanism due to temperature variations. and The coefficient is the location-related coefficient. and This is a temperature-dependent coefficient. This is the normalized temperature variable.

[0028] Optionally, the calculation process for the third clamping force includes:

[0029] Feature extraction of motor current sensor signals is performed using a one-dimensional convolutional neural network;

[0030] The extracted features are fused with additional features to output a third clamping force, where the additional features include motor angle, speed and temperature.

[0031] Optionally, determining the final clamping force based on the first clamping force, the second clamping force, and the third clamping force includes:

[0032] Calculate the final clamping force using the following formula:

[0033] ;

[0034] in, For the final clamping force, For the first clamping force, This is the first weighting coefficient corresponding to the first clamping force. For the second clamping force, This is the second weighting coefficient corresponding to the second clamping force. For the third clamping force, This is the third weighting coefficient corresponding to the third clamping force.

[0035] Optionally, before determining the final clamping force based on the first clamping force, the second clamping force, and the third clamping force, the method further includes:

[0036] Saturation region detection:

[0037] ;

[0038] in, The results are for the saturation region detection. This is an indicator function; its value is 1 when the condition is met and 0 when the condition is not met. For the present Torque at time t, for The motor's rotation angle at any given moment, express The motor's rotation angle at any given moment;

[0039] Temperature operating conditions are classified as follows:

[0040] ;

[0041] ;

[0042] ;

[0043] in, Indicates temperature. This is the result of low temperature testing. This is the result of a high-temperature test. These are test results at room temperature.

[0044] Define the basic weights based on the operating condition detection results. :

[0045] ;

[0046] Determine model confidence :

[0047] ;

[0048] ;

[0049] ;

[0050] ;

[0051] in, For the confidence level of the stiffness model, Location influence factor Used to reflect changes in the position of the contact point. The confidence level of the torque model. Temperature influence factor Used to reflect the smoothness of temperature changes, For the confidence of the CNN model, For signal quality factor, signal quality factor Used to reflect the quality of the current signal;

[0052] Based on the basic weights and model confidence Determine the first weighting coefficient, the second weighting coefficient, and the third weighting coefficient.

[0053] Optionally, location influence factor Represented as:

[0054] ;

[0055] in, The time decay coefficient, Indicates the current time. This is the latest contact time;

[0056] Temperature Influence Factors Represented as:

[0057] ;

[0058] in, The root mean square of the temperature change. The temperature change threshold;

[0059] Signal quality factor Represented as:

[0060] ;

[0061] ;

[0062] in, Indicates harmonic distortion rate. The maximum harmonic distortion rate, This is the effective value of the fundamental current. This represents the effective value of the nth harmonic current, where n is the harmonic order.

[0063] Optionally, feature extraction and contact point determination based on the input signal include:

[0064] Bandpass filtering is applied to the motor current sensor signal to remove DC components and high-frequency noise;

[0065] The filtered motor current sensor signal is converted to time and frequency using Fourier transform to obtain the current spectrum characteristics.

[0066] ;

[0067] in, It is a current frequency domain signal. It is a current time-domain signal. Where n is the signal length, n is the time-domain index, and k is the harmonic counter;

[0068] Define characteristic harmonic frequency band :

[0069] ;

[0070] in, This represents the number of pole pairs of the motor.

[0071] Calculate the energy of each frequency band as the current harmonic characteristic, and normalize it:

[0072] ;

[0073] ;

[0074] in, Indicates the harmonic order. The normalized current harmonic characteristics, For the maximum value of the characteristic, It exhibits characteristics of current harmonics.

[0075] Optionally, the process of determining the contact point includes:

[0076] The vibration acceleration sensor signal was processed using Morlet wavelet continuous wavelet transform.

[0077] Calculate the energy density of the target frequency band;

[0078] Determining background noise levels using a sliding window;

[0079] Determine the adaptive threshold based on the background noise level;

[0080] The binarized contact time series is determined based on the adaptive threshold and the average noise energy within the window.

[0081] Take three consecutive sampling points to determine whether a contact event has occurred;

[0082] Output timing angles to obtain the motor rotation angle at the moment of contact.

[0083] Optionally, after determining the final clamping force, the method further includes:

[0084] Determine the system operating mode, wherein the operating mode includes tracking mode, saturation mode, and startup mode;

[0085] The coefficients corresponding to the gain parameters of the PID controller are determined based on the system operating mode and the predefined gain scheduling rule table.

[0086] The actual gain parameters of the PID controller are determined based on the coefficients corresponding to the gain parameters of the PID controller and the preset basic gain parameters.

[0087] The system is subjected to PID control based on the actual gain parameters of the PID controller.

[0088] The beneficial effects of this invention are as follows: Through multi-signal fusion and complementarity, the overall estimation accuracy of the system is more accurate than that of the single-signal method, especially in the saturation region and under extreme conditions. The system maintains stable estimation performance in the clamping force saturation region, extreme temperatures (-40℃~120℃), and under different wear conditions, solving the limitations of traditional methods in terms of operating conditions. Online contact point identification based on vibration signals enables self-calibration of key parameters, and the system can automatically compensate for the influence of time-varying factors such as wear and thermal deformation. The multi-model parallel architecture provides natural redundancy; when a single sensor fails or a model fails, the system can automatically degrade its operation to ensure basic functions. Current harmonic analysis and vibration monitoring are not only used for force estimation but also for early detection and warning of transmission system faults (such as bearing wear, lead screw backlash, etc.). The adaptive fusion algorithm can automatically adjust model weights as the system ages, maintaining high-precision estimation throughout its entire service life. Attached Figure Description

[0089] Figure 1 This is a flowchart of a clamping force estimation method for an electromechanical braking system according to an embodiment of the present invention.

[0090] Figure 2 This is a flowchart of a clamping force estimation method for an electromechanical braking system according to another embodiment of the present invention. Detailed Implementation

[0091] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the invention.

[0092] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the invention or its application or use.

[0093] Techniques, methods, and apparatus known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and apparatus should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0094] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0095] In the specification of this invention, the terms "first" and "second" may explicitly or implicitly include one or more of the same feature. In the description of this invention, unless otherwise stated, "multiple" means two or more. Furthermore, in the specification, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0096] Currently, the industry mainly uses clamping force estimation methods based on motor torque and rotation angle signals. These methods have the following technical limitations:

[0097] (1) Estimation error caused by contact point drift: Traditional methods assume that the mechanical contact point is fixed, but in actual applications, due to factors such as friction plate wear, thermal expansion effect, and mechanical clearance change, the contact point position will drift, resulting in systematic deviation in clamping force estimation;

[0098] (2) Problem of estimation failure in saturation zone: In the stage of clamping force saturation, the deformation of the transmission system reaches the physical limit, and the change of motor angle is extremely weak. The traditional estimation method based on the change of angle completely fails under this working condition.

[0099] (3) Temperature sensitivity problem: Key parameters such as the electromagnetic characteristics of the motor, the stiffness coefficient of the material, and the viscosity of the lubricating grease all change with temperature. However, the existing methods lack an effective temperature compensation mechanism, which makes the estimation accuracy significantly affected by the ambient temperature.

[0100] (4) Limitations of a single signal source: Over-reliance on motor torque and angle signals, insufficient system robustness under complex working conditions or sensor failure, and lack of redundancy design;

[0101] (5) Lack of online adaptive capability: Existing methods cannot identify and calibrate key parameters (such as contact point position, transmission stiffness, etc.) online, making it difficult to adapt to performance changes throughout the vehicle's life cycle.

[0102] To address the aforementioned technical problems, this application proposes a method for estimating the clamping force of an electromechanical braking system, such as... Figure 1 As shown, steps 1100-1600 are included.

[0103] Step 1100: Acquire input signals, including motor current sensor signals, motor rotation angle sensor signals, and vibration acceleration sensor signals.

[0104] The motor current sensor is a Hall effect sensor with a sampling frequency greater than 10kHz, which acquires the three-phase current of the motor in real time. The motor angle sensor is an absolute photoelectric encoder with a dynamic response frequency greater than 5kHz. The vibration acceleration sensor is a MEMS triaxial accelerometer, placed in the caliper housing, to detect the impact vibration at the moment of braking contact.

[0105] Step 1200: Perform feature extraction and determine the contact point based on the input signal.

[0106] Step 1300: Calculate the first clamping force based on the estimation model of dynamic stiffness.

[0107] An empirical model for calculating clamping force is designed based on the nonlinear relationship between spring force and deformation, considering system transmission efficiency and temperature effects. For electromechanical braking systems, the motor rotation angle... Finally, the displacement is converted into a linear displacement by the leadscrew, which can then be expressed as:

[0108] ;

[0109] in, For the lead screw, when the motor moves from the contact point Turn to When the linear deformation of the system is such that:

[0110] ;

[0111] Based on the power-law model, the nonlinear elastic behavior of the braking system is described, and the effect of temperature is considered. The relationship between clamping force and linear deformation is given as follows:

[0112] ;

[0113] in, This is the stiffness coefficient, which varies with deformation and temperature. It is a non-linear exponent, based on the fitting of experimental data, and is expressed as: .

[0114] Substituting the linear deformation into the equation yields the first clamping force in the angular domain, which is calculated using the following formula:

[0115] ;

[0116] ;

[0117] ;

[0118] in, For the first clamping force, The stiffness function in the angular domain. It is a non-linear exponent. This is the stiffness coefficient. For the lead screw, The change in angle. The motor rotation angle at the moment of contact. For the motor rotation angle, For temperature. To standardize the data, a fitting was performed using experimental data. To balance the computational complexity and fitting ability of the model, a cubic polynomial was chosen for fitting. Expand:

[0119] ;

[0120] ;

[0121] in, The experimental data are fitted using a quadratic polynomial, where the temperature is the function.

[0122] Step 1400: Calculate the second clamping force based on the torque balance estimation model.

[0123] According to Newton's second law, the torque balance on the motor shaft can be expressed as:

[0124] ;

[0125] On the load side, the actual output torque is:

[0126] ;

[0127] Considering transmission efficiency, the load torque of the motor shaft can be expressed as:

[0128] ;

[0129] Substituting into the torque balance equation, we can obtain the formula for calculating the second clamping force:

[0130] ;

[0131] in, For the second clamping force, For the collected motor torque, For frictional torque, For transmission efficiency, For rotational inertia, Angular acceleration, For effective lead screw, For the motor rotation angle, For temperature;

[0132] Frictional torque Represented as:

[0133] ;

[0134] in, The Coulomb friction torque of the motor, This is the maximum static friction torque of the motor. The viscous friction coefficient of the motor, The critical Stribeck velocity, This is the Stribeck characteristic speed of the motor. This represents the total load torque acting on the motor shaft. The threshold for static friction is determined. ω is the rotational angular velocity of the motor shaft;

[0135] Transmission efficiency Represented as:

[0136] ;

[0137] ;

[0138] ;

[0139] in, This is a position-dependent efficiency term, used to characterize the mechanical losses caused by changes in the geometric position of the transmission mechanism. This is a temperature-dependent efficiency term, used to characterize the performance changes of a transmission mechanism due to temperature variations. and The coefficient is the location-related coefficient. and This is a temperature-dependent coefficient. This is the normalized temperature variable.

[0140] Transmission efficiency The location-dependent efficiency term is expressed as the product of the location-dependent efficiency term and the temperature-dependent efficiency term. This term characterizes the mechanical losses caused by changes in the geometric position of a transmission mechanism. The linear term reflects the fundamental losses as the stroke increases, while the quadratic term captures the nonlinear losses caused by mechanism deformation. and Fitting based on experimental data.

[0141] Temperature-related efficiency terms Using normalized temperature variables The linear term describes the conventional effects of lubrication characteristics and thermal expansion, while the quadratic term characterizes the nonlinear performance degradation under extreme temperature conditions. and Fitting based on experimental data.

[0142] The physical basis of the product form lies in the relative independence of the position effect and temperature effect in terms of physical mechanism, which facilitates the calibration of hierarchical parameters and the analysis of physical meaning.

[0143] Step 1500: Calculate the third clamping force based on the estimation model of current harmonic characteristics.

[0144] The calculation process of the third clamping force includes: extracting features from the motor current sensor signal using a one-dimensional convolutional neural network; fusing the extracted features with additional features to output the third clamping force, wherein the additional features include motor rotation angle, speed and temperature.

[0145] The network structure consists of three one-dimensional convolutional layers, each followed by batch normalization, ReLU activation function and pooling layer. Finally, a fully connected layer is used to fuse the extracted features with additional features (including rotation angle, rotation speed and temperature) to output an estimate of the clamping force.

[0146] The first convolutional layer of the network uses 32 convolutional kernels of size 5, padding to maintain the signal length, and downsampling using max pooling. The second convolutional layer uses 64 convolutional kernels of size 5, also performing pooling. The third convolutional layer uses 128 convolutional kernels of size 5, followed by adaptive average pooling to fix the feature map length to 16. The flattened convolutional features are then concatenated with the additional features. The forward propagation process can be represented as follows:

[0147] ;

[0148] ;

[0149] in, It is a current signal. For the motor rotation angle, This refers to the motor speed. For temperature.

[0150] After feature concatenation, a nonlinear transformation is performed through two fully connected layers. The two fully connected layers use the ReLU activation function and add a Dropout mechanism (0.3, 0.2) to prevent overfitting. Finally, a continuous clamping force value is output.

[0151] Step 1600: Determine the final clamping force value based on the first clamping force, the second clamping force, and the third clamping force. .

[0152] The final clamping force is obtained by weighted calculation of the first, second, and third clamping forces. Specifically, the final clamping force is calculated using the following formula:

[0153] ;

[0154] in, For the final clamping force, For the first clamping force, This is the first weighting coefficient corresponding to the first clamping force. For the second clamping force, This is the second weighting coefficient corresponding to the second clamping force. For the third clamping force, This is the third weighting coefficient corresponding to the third clamping force.

[0155] The sum of the three weighting coefficients is 1, which is expressed as:

[0156] ;

[0157] To adjust the weights to account for the impact of different operating conditions, we first define the operating condition detection function:

[0158] ;

[0159] The testing conditions for each operating condition are as follows:

[0160] Saturation region detection:

[0161] ;

[0162] in, The results are for the saturation region detection. This is an indicator function; its value is 1 when the condition is met and 0 when the condition is not met. For the present Torque at time t, for The motor's rotation angle at any given moment, express The motor's rotation angle at any given moment;

[0163] Temperature operating conditions are classified as follows:

[0164] ;

[0165] ;

[0166] ;

[0167] in, Indicates temperature. This is the result of low temperature testing. This is the result of a high-temperature test. These are test results at room temperature.

[0168] Define the basic weights based on the operating condition detection results. :

[0169] ;

[0170] Determine model confidence :

[0171] ;

[0172] ;

[0173] ;

[0174] ;

[0175] in, For the confidence level of the stiffness model, Location influence factor Used to reflect changes in the position of the contact point. The confidence level of the torque model. Temperature influence factor Used to reflect the smoothness of temperature changes, Confidence score for CNN (Convolutional Neural Network) models. For signal quality factor, signal quality factor Used to reflect the quality of current signals.

[0176] The confidence levels of three clamping force estimation models under the current working conditions are defined respectively. The confidence level calculation is based on historical data and the current state, including the confidence levels of the stiffness model, the torque model, and the CNN model.

[0177] Stiffness model confidence: depends on the location information of the contact point; if the contact point is updated, its location influence factor... The larger the value, the higher the confidence level of the stiffness model; the value is [value missing]. .

[0178] Torque model confidence: The torque model is greatly affected by temperature; therefore, its confidence is related to temperature stability, which is determined by the temperature stability factor. It reflects the stability of temperature changes, and its value is [value missing]. .

[0179] CNN model confidence: The current harmonic model depends on the quality of the current signal. If the signal quality is good, the signal quality factor will be higher. Larger values ​​indicate higher confidence levels; the value should be [value to be filled in]. .

[0180] Based on the basic weights and model confidence Determine the first weighting coefficient, the second weighting coefficient, and the third weighting coefficient.

[0181] Location Influence Factor Defined by the time decay function, it can be expressed as:

[0182] ;

[0183] in, The time decay coefficient, Indicates the current time. This is the latest contact time;

[0184] Temperature Influence Factors By considering the definition of the standard deviation of temperature change per unit time, it can be expressed as:

[0185] ;

[0186] in, The root mean square of the temperature change. This is a constant value, representing the temperature change threshold.

[0187] Signal quality factor The harmonic distortion rate is used for evaluation and is expressed as follows:

[0188] ;

[0189] ;

[0190] in, Indicates harmonic distortion rate. This is the effective value of the fundamental current. This represents the effective value of the nth harmonic current, where n is the harmonic order.

[0191] Based on basic weights and model confidence Adjust the weights and normalize:

[0192] ;

[0193] The final clamping force estimate is .

[0194] In this embodiment, step 1200 includes:

[0195] Bandpass filtering is applied to the motor current sensor signal to remove DC components and high-frequency noise;

[0196] The filtered motor current sensor signal is then converted to a time-frequency frequency using Fourier transform to obtain the current spectrum characteristics:

[0197] ;

[0198] in, For frequency domain signals, For time-domain signals, Where n is the signal length, n is the time-domain index, and k is the harmonic counter;

[0199] Define characteristic harmonic frequency band :

[0200] ;

[0201] in, This represents the number of pole pairs of the motor.

[0202] Calculate the energy of each frequency band as the current harmonic characteristic, and normalize it:

[0203] ;

[0204] ;

[0205] in, Indicates the harmonic order. The normalized current harmonic characteristics, For the maximum value of the characteristic, It exhibits characteristics of current harmonics.

[0206] In this embodiment, the process of determining the contact point includes:

[0207] The vibration acceleration sensor signal was processed using Morlet wavelet continuous wavelet transform, considering that the brake disc contact was a mechanical impact, and the typical frequency band for mechanical impact was 1-5kHz.

[0208] ;

[0209] ;

[0210] in, The wavelet center frequency, The wavelet transform scaling parameter, The wavelet transform translation parameters are... For Morlet wavelet complex conjugate;

[0211] Calculate the energy density of the target frequency band :

[0212] ;

[0213] Determining background noise level using a sliding window :

[0214] ;

[0215] ;

[0216] ;

[0217] in, This represents the average noise energy within the window. The signal length;

[0218] Determine the adaptive threshold based on the background noise level:

[0219] ;

[0220] in, For adaptive threshold, This is the threshold coefficient, and its value is empirically adjusted based on the Neyman-Pearson criterion.

[0221] The binarized contact time series is determined based on an adaptive threshold and the average noise energy within the window. :

[0222] ;

[0223] Define the location of the first contact event. The condition is met in The coordinates of the first True value in the sequence:

[0224] ;

[0225] To eliminate the influence of transient noise, and considering the continuity and reliability of contact, three consecutive sampling points are used to determine whether a contact event has occurred. The determination criteria are as follows:

[0226] ;

[0227] It also outputs the timing angle to obtain the motor rotation angle at the moment of contact. .

[0228] In this embodiment, after determining the final clamping force, the method further includes: determining the system operating mode, wherein the operating mode includes tracking mode, saturation mode, and start-up mode; determining the coefficients corresponding to the gain parameters of the PID controller according to the system operating mode and a predefined gain scheduling rule table; determining the actual gain parameters of the PID controller according to the coefficients corresponding to the gain parameters of the PID controller and a pre-set basic gain parameter; and performing PID control on the system according to the actual gain parameters of the PID controller.

[0229] like Figure 2As shown, after calculating the final clamping force, PID (Proportional Integral Derivative) control is also included. This embodiment designs a gain-scheduled PID controller, which adjusts the parameters of the PID controller according to the system's operating state (e.g., tracking, saturation, startup, etc.) to adapt to different operating conditions and improve control performance.

[0230] In practical control systems, a discrete PID controller is used, and its output can be expressed as:

[0231] ;

[0232] in, The sampling period is Clamping force for target Compared with the estimated final clamping force The error between them This is the gain coefficient.

[0233] This controller employs a multi-mode gain scheduling strategy, with the following basic gain parameters:

[0234] ;

[0235] Design PID controller gain scheduling rules based on different system operating modes:

[0236] (1) Tracking mode: Default mode, used for normal clamping force tracking;

[0237] (2) Saturation mode: When the system becomes saturated, reduce the gain to avoid integral saturation and overshoot;

[0238] (3) Startup mode: When the system starts up, a smaller gain is used to maintain stability.

[0239] The gain scheduling rules are shown in Table 1.

[0240] Table 1: Gain Scheduling Rules

[0241]

[0242] According to the gain scheduling rules, the gain parameter is defined as follows:

[0243] ;

[0244] in The coefficient for resisting integral saturation is adjusted according to the magnitude of the error.

[0245] .

[0246] While specific embodiments of the present invention have been described in detail by way of examples, those skilled in the art should understand that the above examples are for illustrative purposes only and are not intended to limit the scope of the invention. Those skilled in the art should understand that modifications can be made to the above embodiments without departing from the scope and spirit of the invention.

[0247] Those skilled in the art will recognize that the modules and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0248] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described apparatus and equipment can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0249] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0250] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of the embodiments of the present invention, depending on actual needs.

[0251] In addition, the functional modules in the embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0252] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0253] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

[0254] It should be understood that the sequence numbers of the steps in the invention's content and embodiments do not absolutely imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention. The foregoing description of embodiments of this disclosure has been provided for illustrative and descriptive purposes. The foregoing description is not exhaustive and is not intended to limit this disclosure to the exact form disclosed. Various modifications and variations may exist based on the foregoing teachings, or various modifications and variations may be derived from the practice of this disclosure. These embodiments were chosen and described to illustrate the principles of this disclosure and its practical application, so that those skilled in the art can utilize this disclosure in various implementations and modifications suitable for the specific purpose of the concept.

Claims

1. A method for estimating the clamping force of an electromechanical braking system, characterized in that, include: Acquire input signals, including motor current sensor signals, motor rotation angle sensor signals, and vibration acceleration sensor signals; Feature extraction and contact point determination are performed based on the input signal; The first clamping force is calculated based on the estimation model of dynamic stiffness; The second clamping force is calculated based on the torque balance estimation model; The third clamping force is calculated based on an estimation model of current harmonic characteristics. The final clamping force is determined based on the first clamping force, the second clamping force, and the third clamping force; The first clamping force is calculated according to the following formula: ； ； ； in, For the first clamping force, The stiffness function in the angular domain. It is a non-linear exponent. This is the stiffness coefficient. For the lead screw, The change in angle. The motor rotation angle at the moment of contact. For the motor rotation angle, For temperature; The second clamping force is calculated according to the following formula: ； in, For the second clamping force, For the collected motor torque, For frictional torque, For transmission efficiency, For rotational inertia, Angular acceleration, For effective lead screw, For the motor rotation angle, For temperature; Frictional torque Represented as: ； in, The Coulomb friction torque of the motor, This is the maximum static friction torque of the motor. The viscous friction coefficient of the motor, The critical Stribeck velocity, This is the Stribeck characteristic speed of the motor. This represents the total load torque acting on the motor shaft. For static friction, the threshold value is determined. The rotational angular velocity of the motor shaft; Transmission efficiency Represented as: ； ； ； in, This is a position-dependent efficiency term, used to characterize the mechanical losses caused by changes in the geometric position of the transmission mechanism. This is a temperature-dependent efficiency term, used to characterize the performance changes of a transmission mechanism due to temperature variations. and The coefficient is the location-related coefficient. and This is a temperature-dependent coefficient. Normalized temperature variable; The calculation process for the third clamping force includes: Feature extraction of motor current sensor signals is performed using a one-dimensional convolutional neural network; The extracted features are fused with additional features to output a third clamping force. The additional features include motor angle, speed and temperature. Determining the final clamping force based on the first clamping force, the second clamping force, and the third clamping force includes: Calculate the final clamping force using the following formula: ； in, For the final clamping force, For the first clamping force, This is the first weighting coefficient corresponding to the first clamping force. For the second clamping force, This is the second weighting coefficient corresponding to the second clamping force. For the third clamping force, This is the third weighting coefficient corresponding to the third clamping force.

2. The method for estimating the clamping force of an electromechanical braking system according to claim 1, characterized in that, Before determining the final clamping force based on the first clamping force, the second clamping force, and the third clamping force, the method further includes: Saturation region detection: ； in, The results are for the saturation region detection. This is an indicator function; its value is 1 when the condition is met and 0 when the condition is not met. For the present Torque at time t, for The motor's rotation angle at any given moment, express The motor's rotation angle at any given moment; Temperature operating conditions are classified as follows: ； ； ； in, Indicates temperature. This is the result of low temperature testing. This is the result of a high-temperature test. These are test results at room temperature. The basic weight is defined based on the working condition detection results. : ； Determine model confidence : ； ； ； ； in, For the confidence level of the stiffness model, Location influence factor Used to reflect changes in the position of the contact point. The confidence level of the torque model. Temperature influence factor Used to reflect the smoothness of temperature changes, For the confidence of the CNN model, For signal quality factor, signal quality factor Used to reflect the quality of the current signal; Based on the basic weights and model confidence Determine the first weighting coefficient, the second weighting coefficient, and the third weighting coefficient.

3. The clamping force estimation method for an electromechanical braking system according to claim 2, characterized in that, Location Influence Factor Represented as: ； in, The time decay coefficient, Indicates the current time. This is the latest contact time; Temperature Influence Factors Represented as: ； in, The root mean square of the temperature change. The temperature change threshold; Signal quality factor Represented as: ； ； in, Indicates harmonic distortion rate. The maximum harmonic distortion rate, This is the effective value of the fundamental current. This represents the effective value of the nth harmonic current, where n is the harmonic order.

4. The method for estimating the clamping force of an electromechanical braking system according to claim 1, characterized in that, Feature extraction and contact point determination based on the input signal include: Bandpass filtering is applied to the motor current sensor signal to remove DC components and high-frequency noise; The filtered motor current sensor signal is then converted to a time-frequency frequency using Fourier transform to obtain the current spectrum characteristics: ； in, It is a current frequency domain signal. It is a current time-domain signal. Where n is the signal length, n is the time-domain index, and k is the harmonic counter; Define characteristic harmonic frequency band : ； in, This represents the number of pole pairs of the motor. Calculate the energy of each frequency band as the current harmonic characteristic, and normalize it: ； ； in, Indicates the harmonic order. The normalized current harmonic characteristics, For the maximum value of the characteristic, It exhibits characteristics of current harmonics.

5. The clamping force estimation method for an electromechanical braking system according to claim 4, characterized in that, The process of determining the contact point includes: The vibration acceleration sensor signal was processed using Morlet wavelet continuous wavelet transform. Calculate the energy density of the target frequency band; Determining background noise levels using a sliding window; Determine the adaptive threshold based on the background noise level; The binarized contact time series is determined based on the adaptive threshold and the average noise energy within the window. Take three consecutive sampling points to determine whether a contact event has occurred; Output timing angles to obtain the motor rotation angle at the moment of contact.

6. The method for estimating the clamping force of an electromechanical braking system according to claim 1, characterized in that, After determining the final clamping force, the method further includes: Determine the system operating mode, wherein the operating mode includes tracking mode, saturation mode, and startup mode; The coefficients corresponding to the gain parameters of the PID controller are determined based on the system operating mode and the predefined gain scheduling rule table. The actual gain parameters of the PID controller are determined based on the coefficients corresponding to the gain parameters of the PID controller and the preset basic gain parameters. The system is subjected to PID control based on the actual gain parameters of the PID controller.

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