Electromechanical brake system motor control method based on error compensation

Abstract

The application relates to the technical field of automobile electromechanical brake system control, in particular to an electromechanical brake system motor control method based on error compensation, which comprises the following steps: determining the disturbance degree by analyzing the difference between each kind of motor data between all adjacent time points within a preset time length before each time point and the amplitude distribution of each kind of motor data in the frequency domain; determining the coupling correlation index by analyzing the correlation between each kind of motor data and all other kinds of motor data within the preset time length and the mutual dependence relationship between each kind of motor data and all other kinds of motor data; and controlling the current of the automobile motor at the current time point. The application solves the distortion problem of a single data source in the electromechanical brake system caused by noise and nonlinear interference, and the control deviation and error caused by multi-physical field coupling, and improves the accuracy and reliability of motor control.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of automobile electronic mechanical brake system control, in particular to an electronic mechanical brake system motor control method based on error compensation. BACKGROUND

[0002] With the development of electrification and intelligence, an electronic mechanical brake (EMB) system emerges as the times require. The EMB system directly drives a brake caliper through a motor of an automobile to realize "wire control braking". The core advantage of the EMB system lies in abandoning a hydraulic unit and accurately controlling braking force through an electronic signal, thereby significantly improving response speed. The control precision of the motor of the EMB system will directly affect braking safety and comfort. However, the EMB system is easily affected by errors introduced by parameter drift of the motor of the automobile, mechanical wear and temperature disturbance in actual operation.

[0003] The EMB system relies on sensors to detect errors in real time. However, the parameters of the motor of the automobile will drift due to temperature change or aging, and mechanical parts will change due to wear. A traditional parameter identification algorithm cannot quickly track time-varying parameters, resulting in compensation lag. Moreover, the parameter drift of the motor of the automobile, mechanical wear and temperature disturbance may occur simultaneously and are coupled with each other. The strong interaction of multi-physical field coupling causes a multivariable chain response triggered by a single parameter change. An ideal linear model is difficult to cover the dynamic behavior of time-varying and coupling, resulting in deviation and error in the motor control process, and reducing the accuracy and reliability of the motor control. SUMMARY

[0004] To solve the above technical problems, the application provides an electronic mechanical brake system motor control method based on error compensation to solve the existing problems.

[0005] The electronic mechanical brake system motor control method based on error compensation provided by the application adopts the following technical scheme:

[0006] One embodiment of the application provides an electronic mechanical brake system motor control method based on error compensation. The method comprises the following steps:

[0007] Real-time acquisition of various motor data of the motor of the automobile;

[0008] Determination of a first disturbance degree of each kind of motor data at each time point by analyzing the difference between each kind of motor data between all adjacent time points within a preset time length before each time point; determination of a second disturbance degree of each kind of motor data at each time point based on the amplitude distribution of each kind of motor data in the frequency domain within a preset time period after any time point within the preset time length, and determination of the disturbance degree of each kind of motor data at each time point in combination with the first disturbance degree;

[0009] The first correlation index of each motor data at each time is determined by analyzing the correlation of the disturbance between each motor data and all other motor data within the preset time period; the second correlation index of each motor data at each time is determined by analyzing the mutual dependence between each motor data and all other motor data within the preset time period, and the coupling correlation index of the automobile motor at each time is determined in combination with the first correlation index.

[0010] The differential gain at the current time is optimized by analyzing the difference between the coupling correlation index of the automobile motor at the current time and the average coupling correlation index within the preset historical time period before the current time, so as to control the current of the automobile motor at the current time.

[0011] Preferably, the various motor data includes the temperature of the automobile motor winding, the braking torque, the current and the temperature distribution data of the brake disc surface.

[0012] Preferably, the first disturbance of each motor data at each time is the mean value of the absolute value of all elements in the first-order difference sequence of each motor data within the preset time period before each time.

[0013] Preferably, the second disturbance of each motor data at each time is determined by:

[0014] Within the preset time period before each time, any time and each motor data within the preset time period after the time are taken as the input of the time-frequency conversion algorithm, the frequency domain signal of each motor data is output, the maximum amplitude in the frequency domain signal is taken as the main frequency amplitude of any time, and the discrete degree of the main frequency amplitudes of all times within the preset time period before each time is taken as the second disturbance of each motor data at each time.

[0015] Preferably, the disturbance of each motor data at each time is the positive fusion result of the first disturbance and the second disturbance of each motor data at each time.

[0016] Preferably, the first correlation index of each motor data at each time is the mean value of the correlation of the disturbance between each motor data and all other motor data within the preset time period before each time.

[0017] Preferably, the second correlation index of each motor data at each time is the mean value of the mutual information between each motor data and all other motor data within the preset time period before each time.

[0018] Preferably, the coupling correlation index of the automobile motor at each time is determined by:

[0019] The product of the first correlation index and the second correlation index of each motor data at each time is calculated, and the average of the products of all motor data at each time is taken as the coupling correlation index of the motor winding of the automobile at each time.

[0020] Preferably, the differential gain at the current time is optimized, including:

[0021] The expression of the differential gain at the current time is: ; in the formula, indicates the coupling correlation index of the motor of the automobile at the current time; indicates the average of the coupling correlation index at all times within a preset historical time length before the current time; and Kd indicates a preset differential gain.

[0022] Preferably, the current of the motor of the automobile at the current time is controlled, including:

[0023] The deviation between the current of the motor of the automobile at the current time and the rated current of the motor winding is taken as the input of a PID control algorithm, wherein the differential gain at the current time is taken as the differential gain in the PID control algorithm, and a current control signal is output to control the current of the motor of the automobile at the current time.

[0024] The application has at least the following beneficial effects:

[0025] The application avoids the problem that a single motor data source is easily distorted by noise and nonlinear interference, determines the disturbance degree of each motor data by analyzing the difference between each motor data at all adjacent times within a preset time length before each time and combining the amplitude distribution of each motor data in the frequency domain within a preset time period after any time, quantifies the dynamic distortion risk of the braking torque, temperature, current and temperature distribution data, avoids the system state misjudgment caused by the degradation of the quality of a single motor data, thereby specifically regulating and controlling the current of the motor of the automobile, improving the operating state of the motor of the automobile, and improving the control performance, ensuring the reliability and accuracy of motor control; further, the application determines the coupling correlation index of the motor of the automobile by analyzing the correlation between the disturbance degree of each motor data and all other motor data within the preset time length and the mutual dependence relationship between each motor data and all other motor data, represents the dynamic interaction intensity of the torque, temperature, current and temperature distribution, eliminates the problems of normal fluctuation misjudgment and coupling hidden danger omission caused by ignoring the multi-physical field interaction, and thereby improves the accuracy and reliability of motor control of the electronic mechanical braking system; further, the PID differential gain is dynamically adjusted based on the coupling correlation index to avoid the control lag or high-frequency noise amplification caused by the nonlinear interference of the fixed parameter PID, and thereby improves the accuracy and reliability of motor control of the electronic mechanical braking system. BRIEF DESCRIPTION OF DRAWINGS

[0026] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the accompanying drawings required by the embodiments or prior art description will be briefly introduced. Obviously, the accompanying drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor based on these drawings.

[0027] Figure 1 The step flow chart of the motor control method of the electronic mechanical brake system based on error compensation provided by an embodiment of the present application is shown in the following table.

[0028] Figure 2 The schematic diagram of the coupling correlation index extraction process provided by an embodiment of the present application is shown in the following table. DETAILED DESCRIPTION

[0029] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined purpose of the application, the specific embodiments, structures, features and effects of the motor control method of the electronic mechanical brake system based on error compensation according to the present application are described in detail as follows. 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.

[0030] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0031] The specific scheme of the motor control method of the electronic mechanical brake system based on error compensation provided by the present application is described in detail below with reference to the accompanying drawings.

[0032] The motor control method of the electronic mechanical brake system based on error compensation provided by an embodiment of the present application is specifically provided as follows. Please refer to Figure 1 The method comprises the following steps:

[0033] Step S1: Real-time acquisition of various motor data of the motor winding of the automobile.

[0034] The related data of the electromechanical brake system is collected. Specifically, a high-precision temperature sensor is installed on the automobile motor driving shaft to collect the temperature of the automobile motor winding in real time; a piezoelectric torque sensor is installed on the piston end of the brake caliper to collect the actual output brake torque of the automobile motor winding; a Hall current sensor is embedded in the automobile motor control circuit to monitor the current data of the automobile motor; an infrared thermal imager is installed on the contact surface of the brake disc to calculate the standard deviation of all temperature data on the surface of the brake disc as the temperature distribution of the surface of the brake disc. The data collection frequency of all sensors and equipment is set to f, and the collected data is subjected to real-time overall normalization processing to eliminate the influence of the dimension. For ease of description, the collected data is collectively referred to as motor data, that is, the motor data in this embodiment includes four types, namely the temperature of the automobile motor winding, the brake torque, the current and the temperature distribution data of the surface of the brake disc.

[0035] It should be noted that the value of the data collection frequency f is artificially set, and in this embodiment, the value of the data collection frequency is 100 Hz, and the implementer can also set it himself according to the specific circumstances, and this embodiment does not have special restrictions.

[0036] In addition, it should be understood that there are many commonly used normalization methods, and in this embodiment, the z-score standardization method is used to normalize the data. In actual application, as other implementation manners, the implementer can also use other normalization methods such as the maximum and minimum value normalization method. As for the selection of the normalization method, this embodiment does not have special restrictions.

[0037] Among them, the z-score standardization method is a known technology, and the specific process of normalizing the data will not be repeated.

[0038] Step S2: Determine the first disturbance degree of each motor data at each time by analyzing the difference between each motor data of all adjacent time points within a preset time length before each time; determine the second disturbance degree of each motor data at each time based on the amplitude distribution of each motor data in the frequency domain within a preset time period after any time point within the preset time length, and determine the disturbance degree of each motor data at each time in combination with the first disturbance degree.

[0039] Due to the dynamic characteristics of a single data category in an electronic mechanical brake (EMB) system are easily affected by environmental noise and hardware nonlinearity, resulting in deterioration of data quality. For example, the measurement noise of a motor winding temperature sensor can mask the real temperature rise trend. If the original temperature signal is directly used, the motor thermal state can be misjudged, causing false triggering or missed judgment of the over-temperature protection. The infrared thermal imager of the brake disc surface can cause inaccurate calculation of the temperature standard deviation due to local contamination or visual angle deviation, which cannot truly reflect the thermal distribution characteristics of the friction pair. The high-frequency vibration noise of the piezoelectric torque sensor can interfere with the steady-state characteristics of the torque signal, causing random fluctuations in the brake torque feedback value. If such distortion problems of a single data source are not effectively extracted and filtered, the control algorithm will misjudge the system state, further exacerbating the steady-state error and transient overshoot of the brake torque output.

[0040] Therefore, by analyzing the differences between each motor data between all adjacent time points within a preset time period before each time point, and the amplitude distribution of each motor data in the frequency domain within a preset time period after any time point, the disturbance degree of each motor data at each time point is determined to determine whether the motor data is disturbed by noise. The specific process is as follows:

[0041] (1) By analyzing the differences between each motor data between all adjacent time points within a preset time period before each time point, the first disturbance degree of each motor data at each time point is determined, specifically:

[0042] The mean value of the absolute values of all elements in the first-order difference sequence of each motor data within a preset time period before each time point is used as the first disturbance degree of each motor data at each time point, which is used for the change rate of the motor data within the preset time period. It can detect whether the power data has a transient mutation or abnormal fluctuation. The greater the first disturbance degree, the faster the change rate of the motor data in a short period of time, indicating that the power data is more likely to be disturbed by noise.

[0043] It should be noted that the value of the preset time period is artificially set, and in this embodiment, the value of the preset time period is 1h. In actual application, as another implementation manner, the implementer can also set it according to the specific situation, and this embodiment does not make special limitations.

[0044] Among them, the acquisition method of the first-order difference sequence is a known technology, and the specific acquisition principle and process will not be repeated.

[0045] (2) Further, based on the amplitude distribution of each motor data in the frequency domain within a preset time period after any time point within the preset time period, the second disturbance degree of each motor data at each time point is determined, specifically:

[0046] Within the preset time length before each time, each motor data within the preset time period after any time is taken as the input of the time-frequency conversion algorithm, the frequency domain signal of each motor data is output, the maximum amplitude in the frequency domain signal is taken as the main frequency amplitude of any time, and the discrete degree of the main frequency amplitude of all times within the preset time length before each time is taken as the second disturbance degree of each motor data at each time.

[0047] In particular, within the preset time length before each time, if the motor data after any time is less than the preset time period, the missing data is selected from the motor data before any time and adjacent to any time, so as to ensure that the motor data which is continuous in time and has a length meeting the preset time period is obtained.

[0048] It should be further understood that the length of the preset time period is artificially set, and in the embodiment, the length of the preset time period is 10s. In actual application, the implementer can also set it by himself according to the specific situation, and the embodiment does not have special limitation.

[0049] It should be noted that there are many commonly used time-frequency conversion algorithms, and in the embodiment, the fast Fourier transform algorithm is used to convert the motor data in the time domain into the frequency domain signal. In actual application, the implementer can also use other time-frequency conversion algorithms such as wavelet transform according to the specific situation, and the selection of the time-frequency conversion algorithm is not specially limited in the embodiment.

[0050] The fast Fourier transform is a known technology, and the specific principle process of converting the time domain signal to the frequency domain is not repeated.

[0051] In addition, it should be understood that there are many methods for measuring the discrete degree of a group of data, and in the embodiment, the standard deviation of the main frequency amplitude of all times within the preset time length before each time is taken as the discrete degree of the main frequency amplitude of all times within the preset time length before each time. In actual application, as other implementation manners, the implementer can also use other methods for measuring the discrete degree of data such as variance or dispersion coefficient, and the selection of the method for measuring the discrete degree of data is not specially limited in the embodiment.

[0052] According to the second disturbance degree of each kind of motor data at each time, it can be understood that the second disturbance degree is used to represent the fluctuation degree of the motor data in the frequency domain. If the dispersion degree is larger, it means that the distribution of the motor data is dispersed, and the volatility or uncertainty of the data is higher, and the obtained second disturbance degree is larger, which means that the degree of motor data to noise interference is larger. For example, for the brake torque in the motor data, the larger the second disturbance degree of the brake torque, the more likely the brake system is to cause the brake caliper to be stuck, the friction plate to be worn, and the brake torque to be disturbed, and the lower the authenticity of the collected brake torque. Conversely, if the dispersion degree is smaller, it means that the distribution of the motor data is concentrated, and the volatility or uncertainty of the data is lower, and the obtained second disturbance degree is smaller, which means that the degree of motor data to noise interference is smaller. For the brake torque in the motor data, it means that the brake torque is not significantly disturbed by abnormal interference.

[0053] (3) Further, based on the first disturbance and the second disturbance degree of each kind of motor data, the disturbance degree of each kind of motor data at each time is determined, specifically:

[0054] The disturbance degree of each kind of motor data at each time is the positive fusion result of the first disturbance degree and the second disturbance degree of each kind of motor data at each time.

[0055] It should be understood that the positive fusion means combining two or more indexes together through addition or multiplication, so as to obtain a comprehensive index, so as to more comprehensively and accurately evaluate a phenomenon or problem. This fusion method is not limited to simple arithmetic operation, but can also include more complex statistical models and analysis methods, and the implementer can select them according to the specific circumstances, and the present embodiment does not make special limitations.

[0056] Preferably, in the present embodiment, the disturbance degree of each kind of motor data at each time is the product of the first disturbance degree and the second disturbance degree of each kind of motor data at each time.

[0057] According to the disturbance degree of each kind of motor data at each time, it can be understood that the disturbance degree comprehensively considers the volatility of the change rate and the change amplitude of the motor data, and is used to evaluate the degree of disturbance of the motor data. If the first disturbance degree and the second disturbance degree of the current kind of motor data are larger, the obtained disturbance degree is larger, which means that the current kind of motor data is more seriously disturbed by environmental noise or hardware wear, etc. For example, for the brake torque, the larger the disturbance degree, the more likely the brake system is to cause the brake caliper to be stuck, the friction plate to be worn, or the noise abnormality to cause disturbance to the brake torque. Conversely, if the first disturbance degree and the second disturbance degree of the current kind of motor data are smaller, the obtained disturbance degree is smaller, which means that the current kind of motor data is less likely to be disturbed by environmental noise or hardware wear, etc. The motor data is relatively stable.

[0058] So far, in view of the problem that a single motor data source is easy to be distorted by noise and nonlinear interference, the dynamic distortion risk of torque, temperature, current and temperature distribution data is quantified by fusion analysis of first-order difference and main frequency amplitude fluctuation, and the disturbance degree is obtained to avoid system state misjudgment caused by single motor data quality degradation, so as to control the current of the automobile motor and improve the running state of the automobile motor, thereby improving the control performance and ensuring the reliability and accuracy of motor control.

[0059] Step S3: determining a first correlation index of each motor data at each time point by analyzing the correlation between each motor data and the rest of all motor data in the preset time period; determining a second correlation index of each motor data at each time point by analyzing the mutual dependence between each motor data and the rest of all motor data in the preset time period, and combining the first correlation index to determine the coupling correlation index of the automobile motor at each time point.

[0060] Step S2 has carried out feature analysis and abnormal interference degree calculation on each motor data, but cannot solve the problem of multi-physical field coupling. The occurrence of this problem is due to the complex interaction and coupling relationship between torque, temperature, current and temperature distribution and other multi-physical fields in the electronic mechanical braking system. This multi-physical field coupling problem will cause inaccurate system state judgment and unreasonable control strategy, etc. For example, temperature change will affect the current characteristics of the automobile motor and the friction characteristics between the brake disc and the brake caliper, thereby affecting the output of the braking torque; current fluctuation will also act on the heating of the automobile motor, changing the distribution of the temperature field. If these coupling relationships are ignored and only the abnormal interference degree of a single data is analyzed and decided, the normal fluctuation caused by multi-physical field coupling may be mistaken for an abnormal situation, or the actual fault hidden danger caused by coupling relationship may be missed, resulting in system performance degradation, and even brake failure and other serious problems.

[0061] Therefore, by analyzing the correlation between each motor data and the rest of all motor data in the preset time period, and combining the mutual dependence between each motor data and the rest of all motor data in the preset time period, the coupling correlation index of the automobile motor at each time point is determined, so as to judge the mutual coupling influence between different motor data, and the specific process is as follows:

[0062] (1) By analyzing the correlation between each motor data and the rest of all motor data in the preset time period, the first correlation index of each motor data at each time point is determined, which is specifically:

[0063] The average of the correlation between each motor data and the rest of all motor data in the preset time period before each time point, and the first correlation index of each motor data at each time point.

[0064] It should be noted that there are many methods of correlation, in the embodiment, as an implementation, all kinds of motor data in the preset time period before each time are taken as the input of the grey correlation algorithm, and the grey correlation degree between each kind of motor data and the rest of the motor data at each time is output, the grey correlation degree is taken as the correlation degree of the disturbed degree, which is used to measure the correlation between different kinds of motor data. In actual application, as other implementation, the implementer can also use Pearson correlation coefficient and other methods to measure the correlation between data groups. The selection of the method for measuring the correlation between data groups is not limited in the embodiment.

[0065] The grey correlation algorithm is a known technology, and its specific principle and process will not be repeated.

[0066] According to the first correlation index of each kind of motor data at each time, the first correlation index is used to measure the dynamic synergy of abnormal interference between each kind of motor data and all other kinds of motor data. If the correlation degree of the disturbed degree between each kind of motor data and all other kinds of motor data is greater, the first correlation index obtained is greater, which indicates that the correlation between each kind of motor data and all other kinds of motor data is stronger. In the electronic mechanical brake system, it indicates that the linear or quasi-linear coupling effect between torque, temperature, current and temperature distribution and other physical fields is more obvious, and the change of one physical field may significantly affect other physical fields.

[0067] On the contrary, if the correlation degree of the disturbed degree between each kind of motor data and all other kinds of motor data is smaller, the first correlation index obtained is smaller, which indicates that the correlation between each kind of motor data and all other kinds of motor data is weaker. In the electronic mechanical brake system, it may mean that the linear or quasi-linear coupling effect between multiple physical fields is not obvious, and the physical fields are relatively independent.

[0068] (2) Further, by analyzing the mutual dependence relationship between each kind of motor data and all other kinds of motor data in the preset time period, the second correlation index of each kind of motor data at each time is determined, specifically:

[0069] The average value of mutual information between each kind of motor data and all other kinds of motor data in the preset time period before each time is taken as the second correlation index of each kind of motor data at each time.

[0070] The calculation method of mutual information is a known technology, and its specific calculation process will not be repeated.

[0071] According to the second correlation index of each motor data at each time, it can be understood that the greater the mutual information between each motor data and the remaining motor data, the stronger the interaction and dependence between different physical fields, which means that the change of one physical field will significantly affect other physical fields, and therefore the greater the corresponding second correlation index.

[0072] On the contrary, the smaller the mutual information between each motor data and the remaining motor data, the weaker the interaction and dependence between different physical fields, which means that the change of one physical field will not significantly affect other physical fields, and therefore the smaller the corresponding second correlation index.

[0073] (3) Further, based on the first correlation index and the first correlation index of each motor data at each time, the coupling correlation index of the motor of the automobile at each time is determined, specifically:

[0074] The product of the first correlation index and the second correlation index of each motor data at each time is calculated, and the average of the products of all motor data at each time is taken as the coupling correlation index of the motor winding of the automobile at each time.

[0075] According to the coupling correlation index of each motor data at each time, it can be understood that the coupling correlation index is a comprehensive quantification of the interaction and dependence between the braking torque, temperature, current, temperature distribution and other multi-physical fields, considering the linear or quasi-linear coupling and nonlinear statistical dependence between physical fields. The greater the first correlation index and the second correlation index, the greater the coupling correlation index obtained, indicating that the interaction between multi-physical fields is stronger. On the contrary, the smaller the first correlation index and the second correlation index, the smaller the coupling correlation index obtained, indicating that the interaction between multi-physical fields is weaker.

[0076] Preferably, the coupling correlation index extraction process provided by the embodiment is shown in the schematic diagram as Figure 2 .

[0077] So far, for the problem of system state misjudgment caused by multi-physical field coupling, through joint analysis of correlation and mutual information, the dynamic interaction intensity of braking torque, temperature, current and temperature distribution is characterized, and the problems of normal fluctuation misjudgment and coupling hidden danger omission caused by ignoring multi-physical field interaction are eliminated, thereby improving the accuracy and reliability of the electronic mechanical brake system motor control.

[0078] Step S4: By analyzing the difference between the coupling correlation index of the motor of the automobile at the current time and the average coupling correlation index within the preset historical time length before the current time, the differential gain at the current time is optimized to control the current in the motor data of the motor of the automobile at the current time.

[0079] In the electromechanical brake system, the PID algorithm is widely used in brake torque control due to its simple structure and fast dynamic response, to realize fast torque tracking and suppress overshoot and oscillation. In this embodiment, the PID algorithm is used to control the current of the motor of the automobile, and the value range of the proportional gain Kp, the integral gain Ki and the differential gain Kd in the PID algorithm is respectively: to balance the response speed and stability; to balance the integral saturation and excessive response in the process of eliminating steady-state error during braking; to balance the overshoot suppression and noise sensitivity of the prediction error trend, specifically, in this embodiment, the specific values of the proportional gain Kp, the integral gain Ki and the differential gain Kd are 2, 0.05 and 0.2.

[0080] However, the fixed parameters of the existing PID are difficult to cope with complex working conditions caused by multi-physical field coupling. Therefore, by analyzing the difference between the coupling correlation index of the automobile motor at the current time and the average coupling correlation index within a preset historical time length before the current time, the differential gain Kd is dynamically improved based on the multi-physical field coupling efficiency value, and the specific improvement relationship is:

[0081] The expression of the differential gain at the current time is: ; in the formula, represents the coupling correlation index of the automobile motor at the current time; represents the average of the coupling correlation index at all times within a preset historical time length before the current time; Kd represents the preset differential gain, and the value of the preset differential gain Kd in this embodiment is 0.2.

[0082] It should be noted that the value of the preset historical time length is artificially set, and the value of the preset historical time length in this embodiment is 1h, and the implementer can also set it according to the specific situation, and this embodiment does not have special restrictions.

[0083] If the coupling correlation index of the automobile motor at the current time is greater than the average of the coupling correlation index , it means that the coupling state at the current time is greater than the average level of the coupling state at the historical time, and the current system is in a strong coupling state, and the multi-physical field interacts violently. The sensitivity of the differential gain control algorithm to the error change rate, at this time, increase the differential gain to strengthen the prediction ability of the sudden disturbance, suppress the trend of error change in advance, and reduce the transient overshoot and high-frequency oscillation of the brake torque output. For example, when the brake caliper is stuck or the friction plate is worn, the high-frequency noise and temperature rise of the torque signal will be reflected through the B value, and the Kd is improved to quickly stop the torque fluctuation caused by the coupling effect.

[0084] The coupling strength B is directly related to the system dynamic characteristics, and by dynamically adjusting Kd, the interference caused by the interaction of multiple physical fields can be targetedly suppressed. Compared with the fixed parameter PID, the improved algorithm reduces the drive current control overshoot under complex working conditions, avoids excessive suppression of the inherent dynamic characteristics of the system, prevents the amplification of high-frequency noise of the control signal caused by excessive differential gain, balances the system dynamic characteristics under complex interference, and significantly improves the control robustness and working condition adaptability.

[0085] The deviation between the current of the automobile motor at the current moment and the rated current of the automobile motor winding is taken as the input of the PID control algorithm, wherein the differential gain at the current moment is taken as the differential gain Kd in the PID control algorithm, the proportional gain Kp and the integral gain Ki are 2 and 0.05 respectively, and the current control signal is output to control the current of the automobile motor at the current moment.

[0086] At this point, the control strategy is optimized in real time according to the coupling state, the accuracy and stability of the automobile motor drive current control are improved, complex working conditions and fault risks are better dealt with, and the safe and stable operation of the brake system is ensured.

[0087] It should be noted that the above-mentioned sequence of the embodiments of the application is only for description, and does not represent the advantages and disadvantages of the embodiments. Moreover, the above-mentioned specific embodiments of the present application are described. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or may be advantageous.

[0088] Each embodiment in the specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other. Each embodiment focuses on the difference from other embodiments.

[0089] The above-described embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; modifying the technical solutions described in the above embodiments, or making equivalent replacement to part 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 be included in the protection scope of the present application.

Claims

1. A method of motor control for an electromechanical brake system based on error compensation, characterized in that, The method comprises the following steps: Real-time acquisition of various motor data of the automobile motor; Determination of a first disturbance degree of each motor data at each time point by analyzing the difference between each motor data at all adjacent time points within a preset time period before each time point; determination of a second disturbance degree of each motor data at each time point based on the amplitude distribution of each motor data in the frequency domain within a preset time period after any time point within the preset time period, and determination of the disturbance degree of each motor data at each time point in combination with the first disturbance degree; The first disturbance degree of each motor data at each time point is the mean value of the absolute values of all elements in the first-order difference sequence of each motor data within the preset time period before each time point. The determination method of the second disturbance degree of each motor data at each time point is as follows: Within the preset time period before each time point, each motor data within a preset time period after any time point is taken as the input of a time-frequency conversion algorithm, and the frequency domain signal of each motor data is outputted; the maximum amplitude in the frequency domain signal is taken as the main frequency amplitude of any time point; and the dispersion degree of the main frequency amplitudes of all time points within the preset time period before each time point is taken as the second disturbance degree of each motor data at each time point. Determination of a first correlation index of each motor data at each time point by analyzing the correlation between the disturbance degrees of each motor data and all other motor data within the preset time period; determination of a second correlation index of each motor data at each time point by analyzing the mutual dependence relationship between each motor data and all other motor data within the preset time period, and determination of the coupling correlation index of the automobile motor at each time point in combination with the first correlation index; Optimization of the differential gain of the current time point by analyzing the difference between the coupling correlation index of the automobile motor at the current time point and the average coupling correlation index within a preset historical time period before the current time point, so as to control the current of the automobile motor at the current time point.

2. The error compensation based electromechanical brake system motor control method of claim 1, wherein, The various motor data includes the temperature of the automobile motor winding, the braking torque, the current and the temperature distribution data of the brake disc surface.

3. The error compensation based electromechanical brake system motor control method of claim 1, wherein, The disturbance degree of each motor data at each time point is the positive fusion result of the first disturbance degree and the second disturbance degree of each motor data at each time point.

4. The error compensation based electromechanical brake system motor control method of claim 1, wherein, The first correlation index of each motor data at each time point is the mean value of the correlation degrees between each motor data and all other motor data within the preset time period before each time point.

5. The error compensation based electromechanical brake system motor control method according to claim 1, wherein, The second correlation index of each motor data at each time point is the mean value of the mutual information between each motor data and all other motor data within the preset time period before each time point.

6. The error compensation based electromechanical brake system motor control method according to claim 1, wherein, The determination method of the coupling correlation index of the automobile motor at each time point is as follows: Calculation of the product of the first correlation index and the second correlation index of each motor data at each time point, and calculation of the mean value of the products of all motor data at each time point as the coupling correlation index of the automobile motor at each time point.

7. The error compensation based electromechanical brake system motor control method according to claim 1, wherein, The optimization of the differential gain of the current time point comprises: The expression of the differential gain at the current time is: ; wherein, represents the coupling correlation index of the motor of the automobile at the current time; represents the average of the coupling correlation indexes at all times within a preset historical time length before the current time; and Kd represents a preset differential gain.

8. The error compensation based electromechanical brake system motor control method according to claim 1, wherein, The control of the current of the automobile motor at the current time point comprises: The deviation between the current of the automobile motor at the current time and the rated current of the automobile motor winding is taken as the input of the PID control algorithm, wherein the differential gain at the current time is taken 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 time.

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