Electromechanical brake clamping force estimation method and associated apparatus

Abstract

The embodiment of the invention provides an electronic mechanical braking clamping force estimation method and related equipment, and belongs to the technical field of electronic mechanical braking. The method comprises the following steps: acquiring a displacement time sequence signal of a brake driving part in response to a brake signal, performing clamping zero point identification according to the displacement time sequence signal, determining a displacement value of the brake driving part behind a clamping zero point, and acquiring a temperature time sequence signal of a clamping part according to a sampling moment of the displacement value, the method comprises the following steps of: acquiring a displacement value of a temperature sequence signal, performing clamping force mapping on the displacement value through a rigidity model to obtain a clamping force fitting value, performing clamping force compensation prediction on the temperature sequence signal through a temperature compensation model to obtain a clamping force compensation value, and finally determining a clamping force estimation value according to the clamping force fitting value and the clamping force compensation value. According to the invention, the accuracy of electronic mechanical braking clamping force estimation can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of electronic mechanical brakes, and in particular to an electronic mechanical brake clamping force estimation method and related equipment. Background Art

[0002] Electro-Mechanical Brake (EMB) systems require closed-loop control by providing feedback on the actual clamping force to ensure it reaches the target clamping force, thereby applying the brake. Traditional EMB systems directly measure clamping force using force sensors, but these sensors are expensive and prone to failure due to frequent braking, which can lead to brake system failure and pose a safety hazard.

[0003] To reduce hardware costs and the impact of hardware on detection, related technologies have proposed estimation methods based on stiffness characteristics. These methods use quadratic or cubic polynomial fitting based on the relationship between clamping force and motor angular displacement to estimate clamping force. This method requires only a single angular displacement signal to estimate clamping force, does not require a force sensor, and has good dynamic performance. However, this method is susceptible to interference from lining temperature changes. When the caliper is clamped, the high temperature generated by the friction between the brake lining and the brake disc is the main factor affecting the caliper's stiffness characteristics. Furthermore, the temperature of the brake lining increases with braking time, and the resulting offset in the clamping force also increases. This high temperature effect on the clamping force is difficult to directly estimate or measure in real-world environments, thus affecting the accuracy of electronic mechanical brake clamping force estimation. Summary of the Invention

[0004] The main purpose of the embodiments of the present application is to provide an electronic mechanical brake clamping force estimation method and related equipment, aiming to improve the accuracy of electronic mechanical brake clamping force estimation.

[0005] To achieve the above objectives, one aspect of an embodiment of the present application provides a method for estimating an electromechanical brake clamping force, comprising the following steps:

[0006] In response to the braking signal, collecting a displacement timing signal of the brake driving component;

[0007] Performing clamping zero point identification according to the displacement timing signal, determining the displacement value of the brake driving component after the clamping zero point, and acquiring the temperature timing signal of the clamping component according to the sampling time of the displacement value;

[0008] Performing clamping force mapping on the displacement value through a stiffness model to obtain a clamping force fitting value; the stiffness model is used to represent the characteristic relationship between the displacement and clamping force of the brake drive component;

[0009] Performing clamping force compensation prediction on the temperature time series signal through a temperature compensation model to obtain a clamping force compensation value;

[0010] A clamping force estimate value is determined based on the clamping force fitted value and the clamping force compensation value.

[0011] In some embodiments, the identifying the clamping zero point according to the displacement timing signal and determining the displacement value of the brake driving component after the clamping zero point comprises the following steps:

[0012] Performing differential processing on the displacement time series signal to obtain a velocity time series signal;

[0013] A maximum speed detection is performed on the speed time sequence signal, and a displacement state corresponding to when the maximum speed is detected is determined as a clamping zero point.

[0014] In some embodiments, the stiffness model is constructed by the following steps:

[0015] Acquire a first data set, the first data set including a plurality of first samples, the first samples including a displacement time series signal collected after the clamping zero point and a corresponding actual clamping force time series signal;

[0016] A cubic polynomial model is fitted according to the first data set to obtain a stiffness model.

[0017] In some embodiments, the temperature compensation model is obtained by the following steps:

[0018] Acquire a second data set, the second data set including a plurality of second samples at different moments, the second samples including a clamping force deviation value and a temperature time series signal of the clamping component;

[0019] A long short-term memory network model is trained according to the second data set to obtain a temperature compensation model.

[0020] In some embodiments, the second data set is collected by the following steps:

[0021] The temperature of the clamping parts of the target vehicle is sampled by a temperature sensor to obtain temperature series data;

[0022] Determine the clamping force deviation value based on the clamping force fitting value output by the stiffness model and the actual clamping force value collected by the force sensor;

[0023] For the clamping force deviation value at any sampling moment, the temperature sequence data is intercepted according to the sampling moment and the sliding window length of the clamping force deviation value to obtain the temperature time series signal corresponding to the sampling moment.

[0024] In some embodiments, training a long short-term memory network model based on the second data set to obtain a temperature compensation model comprises the following steps:

[0025] Inputting the second data set into the recurrent neural network model for forward transfer to obtain a compensation prediction value;

[0026] calculating a mean square error loss based on the compensation prediction value and the clamping force deviation value in the second data set;

[0027] The parameters of the recurrent neural network model are reversely updated according to the mean square error loss to obtain a temperature compensation model.

[0028] To achieve the above objectives, another aspect of the present application provides an electromechanical brake clamping force estimation system, comprising:

[0029] an acquisition module, configured to acquire a displacement timing signal of a brake driving component in response to a braking signal;

[0030] a zero point identification module, configured to identify the clamping zero point according to the displacement timing signal, determine the displacement value of the brake driving component after the clamping zero point, and obtain a temperature timing signal of the clamping component according to a sampling time of the displacement value;

[0031] a clamping force estimation module, configured to perform clamping force mapping on the displacement value using a stiffness model to obtain a clamping force fitting value; the stiffness model being configured to represent a characteristic relationship between the displacement of the brake drive component and the clamping force;

[0032] a deviation prediction module, configured to perform clamping force compensation prediction on the temperature time series signal using a temperature compensation model to obtain a clamping force compensation value;

[0033] A fusion module is used to determine a clamping force estimation value according to the clamping force fitting value and the clamping force compensation value.

[0034] To achieve the above-mentioned purpose, another aspect of an embodiment of the present application provides an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-mentioned method when executing the computer program.

[0035] To achieve the above-mentioned purpose, another aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program implements the above-mentioned method when executed by a processor.

[0036] To achieve the above-mentioned purpose, another aspect of an embodiment of the present application provides a computer program product, including a computer program, which implements the above-mentioned method when executed by a processor.

[0037] Embodiments of the present application include at least the following beneficial effects: The present application provides an electronic mechanical brake clamping force estimation method, system, device, storage medium, and program product. This solution, in response to a brake signal, collects a displacement timing signal of a brake drive component, identifies the clamping zero point based on the displacement timing signal, determines the displacement value of the brake drive component after the clamping zero point, and obtains a temperature timing signal of the clamping component based on the sampling moment of the displacement value. The displacement value is then mapped to a clamping force using a stiffness model to obtain a clamping force fitting value. The temperature timing signal is then used to predict clamping force compensation using a temperature compensation model to obtain a clamping force compensation value. Finally, an estimated clamping force value is determined based on the clamping force fitting value and the clamping force compensation value. This solution, while further considering the influence of temperature on the clamping force prediction based on stiffness characteristics, utilizes the temperature timing signal to compensate for the predicted clamping force, thereby improving the accuracy of electronic mechanical brake clamping force estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a flow chart of an electromechanical brake clamping force estimation method provided by an embodiment of the present application;

[0039] Figure 2 Schematic diagram of the displacement speed change of the ball screw provided in an embodiment of the present application;

[0040] Figure 3 This is a schematic diagram of the concept of estimating the electromechanical brake clamping force provided by an embodiment of the present application;

[0041] Figure 4 This is a schematic diagram of the hardware structure of the electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are merely examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the appended claims.

[0043] It will be understood that the terms "first", "second", etc. used in this application may be used herein to describe various concepts, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0044] The terms "at least one", "plurality", "each", "any", etc. used in this application include "at least one", "two" or more, "plurality" or "each", "any" or "any one", "each" or "any one" as used herein.

[0045] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0046] An embodiment of the present application provides an electronic mechanical brake clamping force estimation method and related equipment, which can improve the accuracy of electronic mechanical brake clamping force estimation.

[0047] The electronic mechanical brake clamping force estimation method provided in the embodiment of the present application relates to the field of electronic mechanical brake technology. The electronic mechanical brake clamping force estimation method provided in the embodiment of the present application can be applied in a terminal, can also be applied in a server, and can also be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a car terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or can be configured as a server cluster or distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network; the software can be an application that implements the electronic mechanical brake clamping force estimation method, etc., but is not limited to the above forms.

[0048] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments in which tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0049] In some embodiments, the electronic mechanical brake clamping force estimation method of the embodiments of the present application can be applied to an on-board control device. The on-board control device is connected to the vehicle's sensing system to obtain sensor data from various vehicle components, then analyzes and processes the sensor data, and controls the vehicle based on the analysis results. Specifically, for a vehicle's electronic mechanical braking system, the on-board control device requires real-time clamping force measurement to perform closed-loop braking control. The on-board control device can apply the electronic mechanical brake clamping force estimation method of the embodiments of the present application to achieve real-time detection of clamping force, thereby implementing closed-loop braking control of the vehicle.

[0050] Figure 1 is an optional flow chart of the method for estimating the electromechanical brake clamping force provided in an embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S105.

[0051] Step S101, in response to a braking signal, collecting a displacement timing signal of a brake driving component;

[0052] Step S102, performing clamping zero point identification based on the displacement timing signal, determining the displacement value of the brake driving component after the clamping zero point, and obtaining the temperature timing signal of the clamping component based on the sampling time of the displacement value;

[0053] Step S103, performing clamping force mapping on the displacement value using a stiffness model to obtain a clamping force fitting value; the stiffness model is used to represent the characteristic relationship between the displacement and clamping force of the brake drive component;

[0054] Step S104, performing clamping force compensation prediction on the temperature time series signal using a temperature compensation model to obtain a clamping force compensation value;

[0055] Step S105 , determining a clamping force estimation value according to the clamping force fitting value and the clamping force compensation value.

[0056] In step S101 of some embodiments, the brake signal refers to a control signal generated under the driver's command for implementing braking. The driver may generate the command by, for example, stepping on a foot pedal. The brake drive component refers to a component used to drive the brake's clamping component to clamp the brake disc. For example, the brake drive component may be a motor or a ball screw. Correspondingly, the displacement timing signal of the brake drive component may refer to a plurality of angular displacement data (i.e., motor rotation angle) collected from the drive motor at regular time intervals after the brake signal is generated. Alternatively, the displacement timing signal of the brake drive component may refer to a plurality of displacement data collected from the ball screw at regular time intervals after the brake signal is generated. The clamping component refers to a component that directly contacts the brake disc during braking. For example, the clamping component may be a brake lining.

[0057] It is understandable that after the displacement data of the driving component is collected, the collected displacement data can be further processed using smoothing filtering or Kalman filtering to suppress noise, and the processed displacement data at each sampling moment forms a displacement timing signal.

[0058] In some embodiments, in step S102, after the user brakes, displacement data of the brake drive component is continuously collected to generate a displacement time-series signal. The clamping zero point is then analyzed based on changes in the displacement time-series signal. The clamping zero point refers to the displacement state of the brake drive component corresponding to when the clamping component first contacts the brake disc. The vehicle is only in a braking state after the clamping zero point. Therefore, embodiments of the present application estimate the clamping force based on the displacement value of the brake drive component after the clamping zero point. Simultaneously, a temperature time-series signal of the clamping component is acquired based on the sampling time corresponding to the displacement value, allowing subsequent compensation of the clamping force estimate based on the temperature time-series signal. Specifically, a thermistor or thermocouple is embedded in the brake lining to form a temperature sensor, which measures the temperature of the brake lining contact surface. The temperature sensor continuously collects brake lining temperature data at a sampling frequency. For each displacement value collected after the clamping zero point, the system generates a temperature time-series signal based on the temperature data collected at the current sampling time and several temperature data points collected within a certain period of time before the sampling time.

[0059] According to some embodiments of the present application, step S102 may include, but is not limited to, the following steps:

[0060] Step S201, performing differentiation processing on the displacement time series signal to obtain a velocity time series signal;

[0061] Step S202 : performing speed maximum value detection on the speed time series signal, and determining the displacement state corresponding to the detection of the speed maximum value as the clamping zero point.

[0062] Specifically, when the brake lining and the brake disc have not yet contacted, the EMB system is in the gap elimination stage. At this time, the drive motor only needs to overcome the friction inside the system and is in an idling state. The displacement speed of the ball screw increases linearly, such as Figure 2 As shown in the ball screw displacement velocity curve, after the brake lining and brake disc contact at time x0, the displacement velocity begins to decrease. The corresponding ball screw displacement at this moment is the contact point between the brake lining and brake disc, and this position is set as the clamping zero point. To identify the clamping zero point, the velocity timing signal can be obtained by performing discrete differential processing on the displacement timing signal. The velocity timing signal is then subjected to maximum velocity detection, and the displacement state corresponding to the maximum velocity is determined as the clamping zero point. The maximum velocity detection process can be performed by subtracting the current velocity data from the previous moment. If the difference is positive, the clamping zero point identification process continues; if the difference is negative, it indicates that the clamping zero point was reached at the previous moment, and the clamping zero point identification process ends.

[0063] In step S103 of some embodiments, a clamping force mapping is performed on the displacement value using a stiffness model to obtain a clamping force fitting value. The stiffness model is obtained by fitting test data and is used to represent the characteristic relationship between the displacement of the brake drive component after the clamping zero point and the clamping force. The stiffness model can adopt a polynomial model.

[0064] According to some embodiments of the present application, the stiffness model in step S103 may be constructed by, but not limited to, the following steps:

[0065] Step S301, obtaining a first data set, the first data set including a plurality of first samples, the first samples including a displacement time series signal collected after the clamping zero point and a corresponding actual clamping force time series signal;

[0066] Step S302 : fitting a cubic polynomial model according to the first data set to obtain a stiffness model.

[0067] For example, the brake pad of the test device uses a force sensor to collect actual clamping force data. The force sensor can be a strain gauge force sensor. When the strain gauge is deformed by force, its resistance value changes accordingly. The resistance change is converted into a voltage signal output through a bridge circuit, and then the clamping force is calculated.

[0068] In the process of collecting test data for the test device, the ball screw displacement at the zero point is set to 0. The ball screw displacement and the corresponding actual clamping force data are collected after the clamping zero point. The collected ball screw displacement and actual clamping force data are filtered respectively, and the missing points in the data are supplemented by linear interpolation to make the displacement and actual clamping force data correspond one to one. The displacement time series signal X and the actual clamping force time series information F are obtained, which can be specifically expressed as:

[0069] X=x0,x1,…,xn;

[0070] F=f0,f1,…,fn;

[0071] Here, xn and fn are the ball screw displacement and actual clamping force corresponding to the maximum clamping force, respectively. A set of displacement time-series signals and the corresponding actual clamping force time-series signals constitutes a first sample. By performing multiple tests on several test devices, multiple first samples can be obtained, forming a first data set.

[0072] According to the first data set, a cubic polynomial model is used to fit the relationship between the screw displacement and the clamping force. The relationship is as follows:

[0073]

[0074] Where si and fi are the ball screw displacement and the corresponding actual clamping force at the i-th moment, i∈[0,n]. a, b, c, and d are the coefficients of the polynomial, obtained by least squares fitting. The resulting cubic polynomial model is the stiffness model, which can be expressed as a stiffness characteristic curve that shows the relationship between displacement and actual clamping force.

[0075] In some embodiments, step S104 is established under ideal test conditions, disregarding the vehicle's actual on-road driving conditions. The test setup typically collects test data from a single braking event, ignoring the effects of temperature. However, in actual vehicle driving conditions, multiple braking events often occur, leading to increased brake lining temperature and changes in the stiffness characteristics. For example, actual driving conditions are complex and diverse, including high-load braking conditions such as long downhill slopes, sudden deceleration, urban congestion, highways, and engineering operations, as well as conventional driving conditions. To mitigate the impact of temperature on clamping force estimation under these high-load conditions, embodiments of the present application simulate actual driving conditions (either on a real vehicle or on a test bench), obtain corresponding brake lining temperature data and clamping force error data, and construct a temperature compensation model to compensate for deviations in the clamping force output by the stiffness model. In other words, the stiffness model establishes an inherent characteristic that corresponds one-to-one between screw displacement and clamping force, while the temperature compensation model compensates for deviations in this characteristic during actual braking. Specifically, the temperature time series signal can be input into the trained temperature compensation model for prediction, yielding a corresponding clamping force compensation value.

[0076] According to some embodiments of the present application, the temperature compensation model in step S104 may be obtained by, but not limited to, the following steps:

[0077] Step S401, obtaining a second data set, the second data set including a plurality of second samples at different moments, the second samples including a clamping force deviation value and a temperature time series signal of a clamping component;

[0078] Step S402: training a long short-term memory network model according to the second data set to obtain a temperature compensation model.

[0079] In this embodiment, the rate of temperature rise of the lining under different braking intensities will also be different, that is, the temperature time series signals collected under different working conditions not only have different spatial characteristics, but also different time series characteristics. Therefore, the embodiment of the present application can adopt a long short-term memory network model to learn the time series characteristics of the temperature sequence affecting the stiffness characteristic offset value under different working conditions, thereby achieving accurate prediction of the clamping force compensation value.

[0080] In step S401 of some embodiments, the second data set in step S401 may be collected by, but not limited to, the following steps:

[0081] Step S501, sampling the temperature of the clamping component of the target vehicle through a temperature sensor to obtain temperature sequence data;

[0082] Step S502, determining a clamping force deviation value based on the clamping force fitting value output by the stiffness model and the actual clamping force value collected by the force sensor;

[0083] Step S503 : for the clamping force deviation value at any sampling moment, the temperature sequence data is intercepted according to the sampling moment of the clamping force deviation value and the sliding window length to obtain a temperature time series signal corresponding to the sampling moment.

[0084] Specifically, the brake lining temperature and clamping force error data are collected and preprocessed, including deleting outliers and denoising, and a data set is constructed and divided into a training set, a validation set, and a test set, wherein the training set is the second data set.

[0085] In the dataset, the model input is the brake lining temperature time series signal, which includes the current temperature and the historical temperature for a period of time before the current moment. The model output is the current clamping force deviation value. During model construction, the current clamping force deviation value serves as the sample label.

[0086] The specific process for acquiring temperature time series signals involves continuously sampling the temperature of the target vehicle's clamping components using a temperature sensor to generate temperature sequence data. This temperature sequence data is then segmented using a sliding window length, with the moment corresponding to the last temperature data segment being considered the current moment. The sliding window length is set to account for the hysteresis characteristics of the temperature effect on the brake lining, which is determined by different brake lining materials and brake structures. It should be noted that while ensuring real-time prediction, the sequence length should be as long as possible to extract more features and achieve more accurate model predictions.

[0087] The clamping force deviation value is the difference between the actual clamping force collected by the sensor and the clamping force output by the stiffness model, specifically expressed as: E = F 实际 -F 刚度模型 ;

[0088] In the dataset, the data structure of each second sample is as follows:

[0089] S i =[X i ,E i ], i∈N;

[0090] X i =[T i-m+1 ,T i-m+2 ,…T i ];

[0091] Among them, S i is the second sample at the current moment i, X i The input of the model is a time series consisting of the temperature data of m brake linings, where m is the sliding window length, T i is the temperature data at the current moment; E iis the output of the model, which is the clamping force deviation value of the sample at the current moment.

[0092] In some embodiments of step S402, step S402 may include, but is not limited to, the following steps:

[0093] Step S601: input the second data set into the recurrent neural network model for forward transfer to obtain a compensation prediction value;

[0094] Step S602 , calculating the mean square error loss based on the compensation prediction value and the clamping force deviation value in the second data set;

[0095] Step S603: Inversely update the parameters of the recurrent neural network model according to the mean square error loss to obtain a temperature compensation model.

[0096] In this embodiment, the temperature compensation model can employ a neural network capable of time series modeling, such as a long short-term memory network (LSTM) or a recurrent neural network (RNN). To ensure real-time prediction, the number of network layers can be set to no more than three. The number of network layers and the number of neurons in each layer are selected based on the size of the established dataset and the dimensionality of the input data. The activation function uses tanh, and the model is initialized using the Xavier initialization method.

[0097] Use the training set to train the recurrent neural network model, use the validation set to verify the model loss and optimize the parameters, and finally use the test set to test whether the model performance meets the requirements.

[0098] The Adam optimizer is used for training, the learning rate is set between 0.0001 and 0.001, the mean square error (MSE) loss function is used, and L2 regularization is added. The specific loss function is:

[0099]

[0100] Among them, y i is the true clamping force deviation value of the i-th sample (i.e., sample label), is the estimated clamping force deviation value of the i-th sample; λ is the regularization parameter used to control the strength of regularization; is the square of the L2 norm of the weight vector w.

[0101] The trained temperature compensation model is used to predict the clamping force compensation value based on temperature during the clamping process. Specifically, it is expressed as:

[0102] E i =f(T(i-m+1,i-m+2,…,i));

[0103] Where Ei is the clamping force compensation value output by the temperature compensation model at the i-th moment, f represents the temperature compensation model, T(i-m+1,i-m+2,…,i) is the temperature data of the brake lining from the (i-m+1)-th moment to the i-th moment, and m is the sliding window length.

[0104] In step S105 of some embodiments, the clamping force estimation value is determined based on the clamping force fitting value and the clamping force compensation value. Figure 3 The clamping force F is formed by the fusion of the output of the stiffness model and the output of the temperature compensation model. The output of the stiffness model is the clamping force fitting value of the stiffness characteristic curve to the ball screw displacement, and the output of the temperature compensation model is the clamping force compensation value predicted by the neural network to the temperature time series signal. The final output clamping force estimate is:

[0105] F i =f i +E i ;

[0106] Among them, F i is the estimated value of the clamping force at the i-th moment, f i is the clamping force fitting value at the i-th moment output by the stiffness model, E i is the clamping force compensation value at the i-th moment output by the neural network.

[0107] This embodiment of the present application provides a temperature-compensated EMB clamping force estimation method, enabling accurate estimation of the clamping force of an EMB system. This embodiment establishes a stiffness characteristic curve between ball screw displacement and clamping force. Through training and optimization of a large amount of braking data, a temperature compensation model is constructed. A neural network is used to predict the clamping force compensation value, thereby compensating for the effect of temperature on clamping force. This improves the robustness of the clamping force estimation and provides good dynamic performance.

[0108] The present application also provides an electronic mechanical brake clamping force estimation system, including:

[0109] an acquisition module, configured to acquire a displacement timing signal of a brake driving component in response to a braking signal;

[0110] A zero point identification module is used to identify the clamping zero point based on the displacement timing signal, determine the displacement value of the brake driving component after the clamping zero point, and obtain the temperature timing signal of the clamping component based on the sampling time of the displacement value;

[0111] A clamping force estimation module is used to map the displacement value to the clamping force using a stiffness model to obtain a clamping force fitting value; the stiffness model is used to represent the characteristic relationship between the displacement and clamping force of the brake drive component;

[0112] The deviation prediction module is used to predict the clamping force compensation of the temperature time series signal through the temperature compensation model to obtain the clamping force compensation value;

[0113] A fusion module is used to determine a clamping force estimation value based on the clamping force fitting value and the clamping force compensation value.

[0114] It can be understood that the method contents in the above method embodiments are all applicable to the present system embodiments, the functions specifically implemented by the present system embodiments are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0115] The present application also provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the above method when executing the computer program. The electronic device can be any smart terminal including a tablet computer, an in-vehicle computer, or the like.

[0116] It can be understood that the contents of the above method embodiments are applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0117] See also Figure 4 , Figure 4 The hardware structure of an electronic device according to another embodiment is shown. The electronic device includes:

[0118] The processor 901 can be implemented as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0119] The memory 902 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and is called by the processor 901 to execute the methods of the embodiments of this application.

[0120] Input / output interface 903, used to implement information input and output;

[0121] Communication interface 904, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.);

[0122] Bus 905 , which transmits information between various components of the device (e.g., processor 901 , memory 902 , input / output interface 903 , and communication interface 904 );

[0123] The processor 901 , the memory 902 , the input / output interface 903 and the communication interface 904 are connected to each other in communication within the device via a bus 905 .

[0124] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program, and the computer program implements the above method when executed by a processor.

[0125] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiment, the functions specifically implemented by the present storage medium embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0126] An embodiment of the present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0127] It is understandable that the contents of the above method embodiments are all applicable to the present program product embodiments, the functions specifically implemented by the present program product embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0128] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0129] The embodiments described in the embodiments of this application are intended to more clearly illustrate the technical solutions of the embodiments of this application and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0130] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0131] The system embodiment described above is merely illustrative. The modules described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network elements. Some or all of these modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0132] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0133] The terms "first," "second," "third," "fourth," etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential sequence. It should be understood that the numbers used in this way are interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions, e.g., a process, method, system, product, or apparatus comprising a series of steps or modules is not necessarily limited to those steps or modules clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or apparatus.

[0134] It should be understood that in the present application, "at least one (item)" or "several" refers to one or more, and "plurality" refers to two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0135] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the modules described above is merely a logical function division. In actual implementation, there may be other division methods, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0136] The modules described above as separate components may or may not be physically separate, and 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 elements. Some or all of these elements may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0137] In addition, the functional modules in the various embodiments of the present application may be integrated into a processing module, or each module may exist physically separately, or two or more modules may be integrated into a single module. The above-mentioned integrated modules may be implemented in the form of hardware or software functional modules.

[0138] If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes multiple instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store programs.

[0139] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. A method for estimating the clamping force of an electromechanical brake, characterized in that: The following steps are involved: In response to the braking signal, collecting a displacement timing signal of the brake driving component; Performing clamping zero point identification according to the displacement timing signal, determining the displacement value of the brake driving component after the clamping zero point, and acquiring the temperature timing signal of the clamping component according to the sampling time of the displacement value; Performing clamping force mapping on the displacement value through a stiffness model to obtain a clamping force fitting value; the stiffness model is used to represent the characteristic relationship between the displacement and clamping force of the brake drive component; Performing clamping force compensation prediction on the temperature time series signal through a temperature compensation model to obtain a clamping force compensation value; A clamping force estimate value is determined based on the clamping force fitted value and the clamping force compensation value.

2. The method according to claim 1, characterized in that The method of identifying the clamping zero point according to the displacement timing signal and determining the displacement value of the brake driving component after the clamping zero point comprises the following steps: Performing differential processing on the displacement time series signal to obtain a velocity time series signal; A maximum speed detection is performed on the speed time sequence signal, and a displacement state corresponding to when the maximum speed is detected is determined as a clamping zero point.

3. The method according to claim 1, characterized in that The stiffness model is constructed by the following steps: Acquire a first data set, the first data set including a plurality of first samples, the first samples including a displacement time series signal collected after the clamping zero point and a corresponding actual clamping force time series signal; A cubic polynomial model is fitted according to the first data set to obtain a stiffness model.

4. The method according to claim 1, wherein The temperature compensation model is obtained by the following steps: Acquire a second data set, the second data set including a plurality of second samples at different moments, the second samples including a clamping force deviation value and a temperature time series signal of the clamping component; A long short-term memory network model is trained according to the second data set to obtain a temperature compensation model.

5. The method according to claim 4, characterized in that The second data set is collected by the following steps: The temperature of the clamping parts of the target vehicle is sampled by a temperature sensor to obtain temperature series data; Determine the clamping force deviation value based on the clamping force fitting value output by the stiffness model and the actual clamping force value collected by the force sensor; For the clamping force deviation value at any sampling moment, the temperature sequence data is intercepted according to the sampling moment and the sliding window length of the clamping force deviation value to obtain the temperature time series signal corresponding to the sampling moment.

6. The method according to any one of claims 4 to 5, characterized in that The method of training a long short-term memory network model according to the second data set to obtain a temperature compensation model comprises the following steps: Inputting the second data set into the recurrent neural network model for forward transfer to obtain a compensation prediction value; calculating a mean square error loss based on the compensation prediction value and the clamping force deviation value in the second data set; The parameters of the recurrent neural network model are reversely updated according to the mean square error loss to obtain a temperature compensation model.

7. An electromechanical brake clamping force estimation system, characterized in that: include: an acquisition module, configured to acquire a displacement timing signal of a brake driving component in response to a braking signal; a zero point identification module, configured to identify the clamping zero point according to the displacement timing signal, determine the displacement value of the brake driving component after the clamping zero point, and obtain a temperature timing signal of the clamping component according to a sampling time of the displacement value; a clamping force estimation module, configured to perform clamping force mapping on the displacement value using a stiffness model to obtain a clamping force fitting value; the stiffness model being configured to represent a characteristic relationship between the displacement of the brake drive component and the clamping force; a deviation prediction module, configured to perform clamping force compensation prediction on the temperature time series signal using a temperature compensation model to obtain a clamping force compensation value; A fusion module is used to determine a clamping force estimation value according to the clamping force fitting value and the clamping force compensation value.

8. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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