Electromechanical braking system motor control method based on error compensation

Abstract

The invention relates to the technical field of automobile electronic mechanical braking system control, in particular to an electronic mechanical braking system motor control method based on error compensation, which comprises the following steps of: analyzing the difference of each kind of motor data between all adjacent moments within a preset duration before each moment and the amplitude distribution of each kind of motor data in a frequency domain; determining the disturbed degree; determining a coupling correlation index by analyzing the correlation of the disturbances between each type of motor data and all other types of motor data within the preset duration and the interdependence relationship between each type of motor data and all other types of motor data; and the current of the automobile motor at the current moment is controlled. The problem of distortion caused by noise and non-linear interference of a single data source in an electronic mechanical braking system is solved, control deviation and errors caused by multi-physical field coupling are solved, and the accuracy and reliability of motor control are improved.

Description

Technical Field

[0001] This application relates to the technical field of automotive electro-mechanical braking system control, and specifically to a motor control method for an electro-mechanical braking system based on error compensation. Background Art

[0002] With the development of electrification and intelligence, the electro-mechanical braking (EMB) system has emerged. It directly drives the brake caliper through an automotive motor to achieve "brake-by-wire". Its core advantage lies in abandoning the hydraulic unit and precisely controlling the braking force through electronic signals, significantly improving the response speed. The control accuracy of the automotive motor in the EMB system will directly affect braking safety and comfort. However, the EMB system is susceptible to errors introduced by factors such as parameter drift of the automotive motor, mechanical wear, and temperature disturbances during actual operation.

[0003] The EMB system relies on sensors to detect errors in real time. However, the parameters of the automotive motor will drift due to temperature changes or aging, and mechanical components will also change due to wear. Traditional parameter identification algorithms cannot quickly track time-varying parameters, resulting in compensation lag. Moreover, parameter drift, mechanical wear, and temperature disturbances of the automotive motor may occur simultaneously and interact with each other. And the strong interaction of the coupling of multiple physical fields leads to a chain response of multiple variables caused by a single parameter change. An idealized linear model is difficult to cover the time-varying and coupled dynamic behaviors, resulting in deviations and errors during the motor control process, reducing the accuracy and reliability of motor control. Summary of the Invention

[0004] To solve the above technical problems, this application provides a motor control method for an electro-mechanical braking system based on error compensation to solve the existing problems.

[0005] The motor control method for an electro-mechanical braking system based on error compensation in this application adopts the following technical solutions: An embodiment of this application provides a motor control method for an electro-mechanical braking system based on error compensation. The method includes the following steps: Obtain various motor data of the automotive motor in real time; By analyzing the differences between each motor data at all adjacent moments within a preset time period before each moment, determine the first degree of perturbation of each motor data at each moment; based on the amplitude distribution of each motor data in the frequency domain within a preset time period after any moment and within the preset time period before it, determine the second degree of perturbation of each motor data at each moment, and combine the first degree of perturbation to determine the degree of perturbation of each motor data at each moment; By analyzing the correlation of the disturbance degrees between each type of motor data and all the other types of motor data within the preset time period, determine the first correlation index of each type of motor data at each moment; by analyzing the mutual dependence relationship between each type of motor data and all the other types of motor data within the preset time period, determine the second correlation index of each type of motor data at each moment, and combine the first correlation index to determine the coupling correlation index of the automotive motor at each moment; By analyzing the difference between the coupling correlation index of the automotive motor at the current moment and the average coupling correlation index within the preset historical time period before the current moment, optimize the differential gain at the current moment to control the current of the automotive motor at the current moment.

[0006] Preferably, the various types of motor data include: the temperature of the automotive motor winding, the braking torque, the current, and the temperature distribution data of the brake disc surface.

[0007] Preferably, the first disturbance degree of each type of motor data at each moment is the mean value of the absolute values of all elements in the first-order difference sequence of each type of motor data within the preset time period before each moment.

[0008] Preferably, the method for determining the second disturbance degree of each type of motor data at each moment is as follows: Within the preset time period before each moment, take each type of motor data at any moment and within the preset time period after it as the input of the time-frequency conversion algorithm, output the frequency-domain signal of each type of motor data, take the maximum amplitude in the frequency-domain signal as the main frequency amplitude at any moment, and take the degree of dispersion of the main frequency amplitudes at all moments within the preset time period before each moment as the second disturbance degree of each type of motor data at each moment.

[0009] Preferably, the disturbance degree of each type of motor data at each moment is the result of the positive fusion of the first disturbance degree and the second disturbance degree of each type of motor data at each moment.

[0010] Preferably, the first correlation index of each type of motor data at each moment is the mean value of the correlation degrees of the disturbance degrees between each type of motor data and all the other types of motor data within the preset time period before each moment.

[0011] Preferably, the second correlation index of each type of motor data at each moment is the mean value of the mutual information between each type of motor data and all the other types of motor data within the preset time period before each moment.

[0012] Preferably, the method for determining the coupling correlation index of the automotive motor at each moment is as follows: Calculate the product of the first correlation index and the second correlation index of each type of motor data at each moment, and take the mean value of the products of all types of motor data at each moment as the coupling correlation index of the automotive motor winding at each moment.

[0013] Preferably, optimizing the differential gain at the current moment includes: The expression of the differential gain at the current moment is: ; where represents the coupling - related index of the automotive motor at the current moment; represents the average value of the coupling - related indexes at all moments within a preset historical duration before the current moment; Kd represents the preset differential gain.

[0014] Preferably, controlling the current of the automotive motor at the current moment includes: Taking the deviation between the current of the automotive motor at the current moment and the rated current of the automotive motor winding as the input of the PID control algorithm, where the differential gain at the current moment is used as the differential gain in the PID control algorithm, and outputting a current control signal to control the current of the automotive motor at the current moment.

[0015] This application has at least the following beneficial effects: Aiming at the problem that a single - motor data source is vulnerable to noise and non - linear interference, resulting in distortion, by analyzing the differences between each motor data at all adjacent moments within a preset duration before each moment, and combining the amplitude distribution of each motor data in the frequency domain within a preset period after any moment, the degree of disturbance of each motor data is determined, quantifying the dynamic distortion risk of braking torque, temperature, current, and temperature distribution data, avoiding misjudgment of the system state caused by the deterioration of a single - motor data quality, and thus regulating the current of the automotive motor targeted, improving the operating state of the automotive motor, thereby improving the control performance and ensuring the reliability and accuracy of motor control; further, aiming at the problem that system - state misjudgment is caused by multi - physical - field coupling, by analyzing the correlation of the degree of disturbance between each motor data and all other types of motor data within the preset duration, and the mutual - dependence relationship between each motor data and all other types of motor data, the coupling - related index of the automotive motor is determined, characterizing the dynamic interaction intensity of torque, temperature, current, and temperature distribution, eliminating the problems of misjudgment of normal fluctuations caused by ignoring multi - physical - field interactions and omission of coupling hidden dangers, thereby improving the accuracy and reliability of the motor control of the electro - mechanical braking system; further, based on the coupling - related index, dynamically adjusting the PID differential gain to avoid control lag or high - frequency noise amplification caused by non - linear interference of fixed - parameter PID, improving the accuracy and reliability of the motor control of the electro - mechanical braking system. Description of the Drawings

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

[0017] Figure 1 The flowchart of the steps of the motor control method for an electro-mechanical braking system based on error compensation provided by an embodiment of the present application; Figure 2 The schematic diagram of the coupling correlation index extraction process provided by an embodiment of the present application. Detailed implementation manners

[0018] In order to further elaborate on the technical means and effects adopted by the present application to achieve the intended invention purpose, the following will, in combination with the drawings and preferred embodiments, detail the specific implementation manners, structures, features and effects of the motor control method for an electro-mechanical braking system based on error compensation proposed according to the present application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present application belongs.

[0020] The following will specifically describe the specific solution of the motor control method for an electro-mechanical braking system based on error compensation provided by the present application in combination with the drawings.

[0021] An embodiment of the present application provides a motor control method for an electro-mechanical braking system based on error compensation. Specifically, the following motor control method for an electro-mechanical braking system based on error compensation is provided. Please refer to Figure 1 , and the method includes the following steps: Step S1: Real-time obtain various motor data of the automotive motor winding.

[0022] Collect relevant data of the electro-mechanical braking system. Specifically, install a high-precision temperature sensor on the motor drive shaft of the vehicle to collect the temperature of the motor winding of the vehicle in real time; install a piezoelectric torque sensor at the piston end of the brake caliper to collect the braking torque actually output by the motor winding of the vehicle; embed a Hall current sensor in the motor control circuit of the vehicle to monitor the current data of the motor of the vehicle; install an infrared thermal imager on the contact surface of the brake disc, and calculate the standard deviation of all temperature data on the surface of the brake disc as the temperature distribution on the surface of the brake disc. For the above collected data, set the data collection frequency of all sensors and devices to f, and perform real-time overall normalization processing on the collected data to eliminate the influence of dimensions. For the convenience of description, the above collected data is collectively referred to as motor data, that is, in this embodiment, the motor data includes four types, namely the temperature of the motor winding of the vehicle, the braking torque, the current, and the temperature distribution data on the surface of the brake disc.

[0023] It should be noted that the value of the data collection frequency f is set manually. In this embodiment, the value of the data collection frequency is 100Hz, and the implementer can also set it according to the specific situation by himself / herself. This embodiment does not make special restrictions.

[0024] In addition, it should be understood that there are many common normalization methods. In this embodiment, the z-score normalization method is used to normalize the data. In the actual application process, as other implementation methods, the implementer can also use other normalization methods such as the maximum-minimum normalization method. Regarding the selection of the normalization method, this embodiment does not make special restrictions.

[0025] Among them, the z-score normalization method is a well-known technology, and the specific process of normalizing the data will not be elaborated here.

[0026] Step S2: Determine the first disturbance degree of each type of motor data at each moment by analyzing the differences between each adjacent moment of each type of motor data within a preset duration before each moment; determine the second disturbance degree of each type of motor data at each moment based on the amplitude distribution of each type of motor data in the frequency domain within a preset time period after any moment within the preset duration, and combine the first disturbance degree to determine the disturbance degree of each type of motor data at each moment.

[0027] Due to the dynamic characteristics of a single data category in an electro-mechanical braking (EMB) system being vulnerable to environmental noise and hardware non-linearity, the data quality deteriorates. For example, the measurement noise of an automotive motor winding temperature sensor can mask the true temperature rise trend. If the original temperature signal is directly used, it may misjudge the thermal state of the automotive motor, triggering false triggering or missed judgment of over-temperature protection; the infrared thermal imager on the surface of the brake disc has inaccurate calculation of the temperature standard deviation due to local fouling or viewing angle deviation, and cannot truly reflect the thermal distribution characteristics of the friction pair; while the high-frequency vibration noise of the piezoelectric torque sensor will interfere with the steady-state characteristics of the torque signal, causing random fluctuations in the feedback value of the braking torque. Such distortion problems of single data sources, if not processed through effective feature extraction and filtering, will lead to misjudgment of the system state by the control algorithm, further exacerbating the steady-state error and transient overshoot of the braking torque output.

[0028] Therefore, by analyzing the differences between each type of motor data at all adjacent moments within a preset duration before each moment, and the amplitude distribution of each type of motor data in the frequency domain within a preset period after any moment, the degree of disturbance of each type of motor data at each moment is determined to judge whether various motor data are affected by noise. The specific process is as follows: (1) By analyzing the differences between each type of motor data at all adjacent moments within a preset duration before each moment, the first degree of disturbance of each type of motor data at each moment is determined, specifically: The mean value of the absolute values of all elements in the first-order difference sequence of each type of motor data within the preset duration before each moment is used as the first degree of disturbance of each type of motor data at each moment. It is used to represent the change rate of the motor data within the preset duration and can detect whether various power data have instantaneous mutations or abnormal fluctuations. The larger the first degree of disturbance, the faster the change rate of the motor data in a short period of time, indicating a greater possibility that the power data is affected by noise.

[0029] It should be noted that the value of the preset duration is set artificially. In this embodiment, the value of the preset duration is 1h. In actual application processes, as other implementation manners, the implementer can also set it according to specific situations by himself / herself, and this embodiment does not make special restrictions.

[0030] Among them, the method for obtaining the first-order difference sequence is a well-known technology, and its specific acquisition principle and process will not be elaborated here.

[0031] (2) Further, based on the amplitude distribution of each type of motor data in the frequency domain within a preset period after any moment within the preset duration, the second degree of disturbance of each type of motor data at each moment is determined, specifically: Within a preset duration before each moment, any motor data within a preset period after that moment is used as the input of the time-frequency conversion algorithm to output the frequency-domain signal of each motor data. The maximum amplitude in the frequency-domain signal is taken as the main frequency amplitude at any moment, and the degree of dispersion of the main frequency amplitudes at all moments within the preset duration before each moment is taken as the second disturbance degree of each motor data at each moment.

[0032] Specifically, within a preset duration before each moment, if the motor data after any moment is less than the length of the preset period, the missing data is selected from the motor data that is adjacent and continuous before any moment to ensure that the obtained motor data is continuous in time and the duration meets the length of the preset period.

[0033] It should be added that the value of the length of the preset period is set manually. In this embodiment, the length of the preset period is 10s. In the actual application process, the implementer can also set it according to the specific situation by himself / herself, and this embodiment does not make special restrictions.

[0034] It should be noted that there are many common time-frequency conversion algorithms. In this embodiment, the fast Fourier transform algorithm is used to convert the motor data in the time domain into a frequency-domain signal. In the actual application process, the implementer can also use other time-frequency conversion algorithms such as wavelet transform according to the specific situation. Regarding the selection of the time-frequency conversion algorithm, this embodiment does not make special restrictions.

[0035] Among them, the fast Fourier transform is a well-known technology, and the specific principle process of converting the time-domain signal to the frequency domain will not be elaborated here.

[0036] In addition, it should be understood that there are many methods to measure the degree of dispersion of a set of data. In this embodiment, the standard deviation of the main frequency amplitudes at all moments within the preset duration before each moment is taken as the degree of dispersion of the main frequency amplitudes at all moments within the preset duration before each moment. In the actual application process, as other implementation methods, the implementer can also use other methods to measure the degree of dispersion of data such as variance or coefficient of variation. Regarding the selection of the method to measure the degree of dispersion of data, this embodiment does not make special restrictions.

[0037] It can be understood from the second disturbance degree of each type of motor data at each moment that the second disturbance degree is used to characterize the fluctuation degree of motor data in the frequency domain. If the degree of dispersion is larger, it indicates that the distribution of motor data is discrete, and the volatility or uncertainty of the data is higher. The larger the obtained second received degree, the greater the degree of noise interference on the motor data. For example, for the braking torque in the motor data, the larger the second disturbance degree of the braking torque, it indicates that the caliper sticking and friction plate wear in the braking system will cause interference to the braking torque, and the authenticity of the collected braking torque is lower; conversely, if the degree of dispersion is smaller, it indicates that the distribution of motor data is concentrated, and the volatility or uncertainty of the data is lower. The smaller the obtained second received degree, the smaller the degree of noise interference on the motor data. For the braking torque in the motor data, it indicates that the braking torque is not significantly abnormally interfered.

[0038] (3) Further, based on the first disturbance and the second disturbance degree of each type of motor data, determine the disturbance degree of each type of motor data at each moment, specifically: The disturbance degree of each type of motor data at each moment is the result of the positive fusion of the first disturbance degree and the second disturbance degree of each type of motor data at each moment.

[0039] It should be understood that positive fusion means combining two or more indicators through addition, multiplication or other methods in order to obtain a comprehensive indicator, so as to more comprehensively and accurately evaluate a certain phenomenon or problem. This fusion method is not limited to simple arithmetic operations, but can also include more complex statistical models and analysis methods. The implementer can choose according to the specific situation, and this embodiment does not make special restrictions.

[0040] Preferably, in this embodiment, the disturbance degree of each type of motor data at each moment is the product of the first disturbance degree and the second disturbance degree of each type of motor data at each moment.

[0041] It can be understood from the disturbance degree of each type of motor data at each moment that the disturbance degree comprehensively considers the volatility of the change rate and change amplitude of the motor data, and is used to evaluate the degree of interference on the motor data. If the first disturbance degree and the second disturbance degree of the current type of motor data are larger, the obtained disturbance degree is larger, indicating that the current type of motor data is more severely interfered by environmental noise, hardware wear, etc. For example, for the braking torque, the larger the disturbance degree, it indicates that there may be caliper sticking, friction plate wear or noise abnormality in the braking system that causes interference to the braking torque; conversely, if the first disturbance degree and the second disturbance degree of the current type of motor data are smaller, the obtained disturbance degree is smaller, indicating that the current type of motor data is less likely to be interfered by environmental noise, hardware wear, etc., and the motor data is relatively stable.

[0042] So far, aiming at the problem that a single motor data source is vulnerable to noise and non-linear interference, resulting in distortion, through the fusion analysis of the first-order difference and the volatility of the main frequency amplitude, the dynamic distortion risk of torque, temperature, current and temperature distribution data is quantified, and the disturbance degree is obtained, avoiding misjudgment of the system state caused by the deterioration of the quality of a single motor data. Thus, the current of the automotive motor is regulated specifically to improve the operating state of the automotive motor, thereby improving the control performance and ensuring the reliability and accuracy of the motor control.

[0043] Step S3: By analyzing the correlation of the disturbance degrees between each type of motor data and all the other types of motor data within the preset time period, determine the first correlation index of each type of motor data at each moment; by analyzing the interdependence between each type of motor data and all the other types of motor data within the preset time period, determine the second correlation index of each type of motor data at each moment, and combine the first correlation index to determine the coupling correlation index of the automotive motor at each moment.

[0044] In step S2, feature analysis and abnormal interference degree calculation are performed on each type of motor data, but the problem of multi-physical-field coupling cannot be solved. This problem occurs because there are complex interactions and coupling relationships between multiple physical fields such as torque, temperature, current, and temperature distribution in the electromechanical braking system. This multi-physical-field coupling problem will cause consequences such as inaccurate system state judgment and unreasonable control strategy formulation. For example, temperature changes will affect the current characteristics of the automotive motor and the friction characteristics between the brake disc and the brake caliper, thereby affecting the output of the braking torque; current fluctuations will also act on the heating condition of the automotive motor and change the distribution of the temperature field. If these coupling relationships are ignored and only the abnormal interference degree of a single data is used for analysis and decision-making, it may misinterpret the normal fluctuations caused by multi-physical-field coupling as abnormal conditions, or miss the actual potential faults caused by the coupling relationship, resulting in a decline in system performance and even serious problems such as brake failure.

[0045] Therefore, by analyzing the correlation of the disturbance degrees between each type of motor data and all the other types of motor data within the preset time period, and combining the interdependence between each type of motor data and all the other types of motor data within the preset time period, determine the coupling correlation index of the automotive motor at each moment, so as to judge the mutual coupling influence between different types of motor data. The specific process is as follows: (1) By analyzing the correlation of the disturbance degrees between each type of motor data and all the other types of motor data within the preset time period, determine the first correlation index of each type of motor data at each moment, specifically: Take the mean value of the correlation degrees of the disturbance degrees between each type of motor data and all the other types of motor data within the preset time period before each moment as the first correlation index of each type of motor data at each moment.

[0046] It should be noted that there are many methods for relevance. In this embodiment, as an implementation manner, all types of motor data within the preset time period before each moment are used as the input of the grey relational algorithm, and the grey relational degrees between each type of motor data and all other types of motor data at each moment are output. The grey relational degree is used as the relevance of the disturbance degree to measure the correlation between different types of motor data. In the actual application process, as other implementation manners, implementers can also adopt other methods for measuring the relevance between data groups, such as the Pearson correlation coefficient. Regarding the selection of the method for measuring the relevance between data groups, this embodiment does not make special restrictions.

[0047] Among them, the grey relational algorithm is a well-known technology, and its specific principle and process will not be elaborated here.

[0048] From the first correlation index of each type of motor data at each moment, it can be understood that the first correlation index is used to measure the dynamic coordination of the abnormal interference suffered between each type of motor data and all other types of motor data. If the relevance of the disturbance degree between each type of motor data and all other types of motor data is greater, the obtained first correlation index is greater, indicating that the correlation between each type of motor data and the other types of motor data is stronger. In the electro-mechanical braking system, this indicates that the linear or quasi-linear coupling effect between multiple physical fields such as torque, temperature, current, and temperature distribution is more obvious, and the change of one physical field may significantly affect other physical fields. On the contrary, if the relevance of the disturbance degree between each type of motor data and all other types of motor data is smaller, the obtained first correlation index is smaller, indicating that the correlation between each type of motor data and the other types of motor data is weaker. In the electro-mechanical braking system, this may mean that the linear or quasi-linear coupling effect between multiple physical fields is not obvious, and the physical fields are relatively independent.

[0049] (2) Further, by analyzing the mutual dependence relationship between each type of motor data and all other types of motor data within the preset time period, the second correlation index of each type of motor data at each moment is determined, specifically: The mean value of the mutual information between each type of motor data and all other types of motor data within the preset time period before each moment is used as the second correlation index of each type of motor data at each moment.

[0050] Among them, the calculation method of mutual information is a well-known technology, and its specific calculation process will not be elaborated here.

[0051] From the second correlation index of each type of motor data at each moment, it can be understood that if the mutual information between each type of motor data and the other types of motor data is greater, it means that the interaction and dependence between different physical fields are stronger, indicating that the change of one physical field will significantly affect other physical fields. Therefore, the corresponding second correlation index is greater. Conversely, the smaller the mutual information between each motor data and the remaining motor data, the weaker the interaction and dependence between different physical fields, meaning that a change in one physical field will not significantly affect other physical fields. Therefore, the corresponding second correlation index is smaller.

[0052] (3) Further, based on the first correlation index and the first correlation index of each motor data at each moment, determine the coupling correlation index of the automotive motor at each moment, specifically: Calculate the product of the first correlation index and the second correlation index of each motor data at each moment, and take the mean of the products of all types of motor data at each moment as the coupling correlation index of the automotive motor winding at each moment.

[0053] From the coupling correlation index of each motor data at each moment, it can be understood that the coupling correlation index is a comprehensive quantification of the interaction and dependence relationship between multiple physical fields such as braking torque, temperature, current, and temperature distribution, considering the linear or quasi-linear coupling and non-linear statistical dependence between physical fields. If the first correlation index and the second correlation index are larger, the obtained coupling correlation index is larger, indicating that the interaction between multiple physical fields is stronger; conversely, if the first correlation index and the second correlation index are smaller, the obtained coupling correlation index is smaller, indicating that the interaction between multiple physical fields is weaker.

[0054] Preferably, the schematic diagram of the coupling correlation index extraction process provided in this embodiment is as Figure 2 shown.

[0055] So far, for the problem of misjudgment of the system state caused by multi-physical field coupling, through the joint analysis of correlation and mutual information, the dynamic interaction intensity of braking torque, temperature, current, and temperature distribution is characterized, eliminating the problems of misjudgment of normal fluctuations and omission of coupling hidden dangers caused by ignoring multi-physical field interaction, thereby improving the accuracy and reliability of the motor control of the electro-mechanical braking system.

[0056] Step S4: By analyzing the difference between the coupling correlation index of the automotive motor at the current moment and the average coupling correlation index within a preset historical duration before the current moment, optimize the differential gain at the current moment to control the current in various motor data of the automotive motor at the current moment.

[0057] In the electro-mechanical braking system, the PID algorithm is widely used for braking torque control because of its simple structure and fast dynamic response, realizing rapid torque tracking and suppressing overshoot and oscillation. In this embodiment, the PID algorithm is used to control the driving current of the automotive motor. The value ranges of the proportional gain Kp, integral gain Ki, and differential gain Kd in the PID algorithm are: , to balance the response speed and stability; , to balance the integral saturation and over-response in the elimination of steady-state error during the braking process; , to balance the overshoot suppression and noise sensitivity of the change trend of the prediction error. Specifically, in this embodiment, the specific values of the proportional gain Kp, integral gain Ki, and derivative gain Kd are 2, 0.05, and 0.2.

[0058] However, the fixed parameters of the existing PID are difficult to cope with the complex working conditions caused by multi-physical field coupling. Therefore, by analyzing the difference between the coupling correlation index of the automotive motor at the current moment and the average coupling correlation index within the preset historical duration before the current moment, the derivative gain Kd is dynamically improved based on the multi-physical field coupling energy efficiency value. The specific improvement relationship is as follows: The expression of the derivative gain at the current moment is: ; in the formula, represents the coupling correlation index of the automotive motor at the current moment; represents the mean value of the coupling correlation indexes at all moments within the preset historical duration before the current moment; Kd represents the preset derivative gain, and the value of the preset derivative gain Kd in this embodiment is 0.2.

[0059] It should be noted that the value of the preset historical duration is set manually. In this embodiment, the value of the preset historical duration is 1h, and the implementer can also set it according to the specific situation. This embodiment does not make special restrictions.

[0060] If the coupling correlation index of the automotive motor at the current moment is greater than the mean value of the coupling correlation indexes , it represents that the coupling state at the current moment is greater than the average level of the coupling state at the historical moment, and the current system is in a strong coupling state with intense multi-physical field interaction. The derivative gain control algorithm is sensitive to the change rate of the error. At this time, the derivative gain is increased to strengthen the prediction ability for sudden disturbances, and by suppressing the change trend of the error in advance, the transient overshoot and high-frequency oscillation of the braking torque output are reduced. For example, when the brake caliper is stuck or the friction pad is worn, the high-frequency noise and sudden temperature rise of the torque signal will be reflected by the B value. By enhancing Kd through the improvement relationship, the torque fluctuation caused by the coupling effect can be quickly stopped.

[0061] The coupling strength B is directly related to the dynamic characteristics of the system. Dynamically adjusting Kd through B can specifically suppress the interference caused by multi-physical field interaction. Compared with the fixed-parameter PID, the improved algorithm reduces the overshoot of the drive current control under complex working conditions, and at the same time avoids over-suppressing the inherent dynamic characteristics of the system, preventing the high-frequency noise of the control signal from being amplified due to excessive derivative gain, achieving the balance of the system dynamic characteristics under complex disturbances, and significantly improving the control robustness and working condition adaptability.

[0062] The deviation between the current of the automotive motor at the current moment and the rated current of the automotive motor winding is used as the input of the PID control algorithm. Among them, the differential gain at the current moment is used as the differential gain Kd in the PID control algorithm, and the proportional gain Kp and the integral gain Ki are 2 and 0.05 respectively, and an output current control signal is output to control the current of the automotive motor at the current moment.

[0063] So far, the control strategy is optimized in real time according to the coupling state, the accuracy and stability of the driving current control of the automotive motor are improved, complex working conditions and potential faults are better coped with, and the safe and stable operation of the braking system is ensured.

[0064] It should be noted that: the above order of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the specific embodiments of this specification have been described above. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous. Each embodiment in this specification is described in a progressive manner. For the same or similar parts between the embodiments, reference can be made to each other. The key point of each embodiment is to illustrate the differences from other embodiments.

[0065] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; any modification to the technical solutions recorded in the foregoing embodiments, or any equivalent replacement of some of the technical features, does not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. An electronic mechanical brake system motor control method based on error compensation, characterized in that: The method comprises the following steps: Obtain various motor data of automobile motors in real time; Determine the first disturbance degree of each motor data at each moment by analyzing the difference between each motor data at all adjacent moments within a preset time length before each moment; determine the second disturbance degree of each motor data at each moment based on the amplitude distribution of each motor data in the frequency domain at any moment within the preset time length and within a preset time period thereafter, and determine the disturbance degree of each motor data at each moment in combination with the first disturbance degree; By analyzing the correlation of the disturbance degree between each type of motor data and all other types of motor data within the preset time period, a first correlation index of each type of motor data at each moment is determined; by analyzing the mutual dependence between each type of motor data and all other types of motor data within the preset time period, a second correlation index of each type of motor data at each moment is determined, and in combination with the first correlation index, a coupling correlation index of the automobile motor at each moment is determined; By analyzing the difference between the coupling-related index of the automobile motor at the current moment and the average coupling-related index within a preset historical time period before the current moment, the differential gain at the current moment is optimized to control the current of the automobile motor at the current moment.

2. The method for controlling a motor of an electronic mechanical brake system based on error compensation according to claim 1, characterized in that: The various motor data include: temperature, braking torque, current of the automobile motor winding and temperature distribution data of the brake disc surface.

3. The method for controlling a motor of an electronic mechanical brake system based on error compensation according to claim 1, characterized in that: The first disturbance degree of each motor data at each moment is the average of the absolute values ​​of all elements in the first-order difference sequence of each motor data within a preset time length before each moment.

4. The method for controlling a motor of an electronic mechanical brake system based on error compensation according to claim 1, characterized in that: The method for determining the second disturbance degree of each motor data at each time is as follows: Within the preset time before each moment, each motor data at any moment and in the preset time period thereafter is used as the input of the time-frequency conversion algorithm, and the frequency domain signal of each motor data is output. The maximum amplitude in the frequency domain signal is used as the main frequency amplitude at any moment, and the discrete degree of the main frequency amplitude of all moments within the preset time before each moment is used as the second disturbance degree of each motor data at each moment.

5. The method for controlling a motor of an electronic mechanical brake system based on error compensation according to claim 1, characterized in that: The disturbance degree of each motor data at each moment is the result of forward fusion of the first disturbance degree and the second disturbance degree of each motor data at each moment.

6. The method for controlling a motor of an electronic mechanical brake system based on error compensation according to claim 1, characterized in that: The first correlation index of each type of motor data at each moment is the average value of the correlation between the disturbance degree of each type of motor data and all other types of motor data within a preset time period before each moment.

7. The method for controlling a motor of an electronic mechanical brake system based on error compensation according to claim 1, characterized in that: The second correlation index of each type of motor data at each moment is the average value of the mutual information between each type of motor data and all other types of motor data within a preset time period before each moment.

8. The method for controlling a motor of an electronic mechanical brake system based on error compensation according to claim 1, characterized in that: The method for determining the coupling-related index of the automobile motor at each moment is as follows: The product of the first correlation index and the second correlation index of each type of motor data at each moment is calculated, and the average of the product of all types of motor data at each moment is used as the coupling correlation index of the automobile motor winding at each moment.

9. The method for controlling a motor of an electronic mechanical brake system based on error compensation according to claim 1, characterized in that: The step of optimizing the differential gain at the current moment includes: The expression of the differential gain at the current moment is: ; In the formula, Indicates the coupling-related index of the automobile motor at the current moment; represents the average value of the coupling-related index at all moments within a preset historical time period before the current moment; Kd represents a preset differential gain.

10. The method for controlling a motor of an electronic mechanical brake system based on error compensation according to claim 1, characterized in that: The controlling of the current of the automobile motor at the current moment includes: The deviation between the current of the automobile motor and the rated current of the automobile motor winding at the current moment is used as the input of the PID control algorithm, wherein the differential gain at the current moment is used as the differential gain in the PID control algorithm, and a current control signal is output to control the current of the automobile motor at the current moment.

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