Motor parameter identification method and device and electronic mechanical braking system

Abstract

The invention discloses a motor parameter identification method and device and an electronic mechanical braking system, and relates to the technical field of vehicles. The method is applied to the electronic mechanical braking system and comprises the steps that in the brake clearance stage, quadrature-axis current driving is conducted on a motor in the electronic mechanical braking system, so that the motor enters a first steady-speed state from a first acceleration state; when the motor is in the first steady-speed state, performing direct-axis current driving on the motor to enable the motor to enter a second steady-speed state from a second acceleration state; and obtaining first sampling data of the motor entering the first steady speed state from the first acceleration state and second sampling data of the motor entering the second steady speed state from the second acceleration state, and based on the first sampling data and the second sampling data, solving by using an optimization algorithm to obtain a motor parameter identification value. According to the invention, all motor parameter identification values can be directly obtained in the motor experiment.

Description

Technical Field

[0001] The present application belongs to the field of vehicle technology, and in particular relates to a motor parameter identification method, device and electronic mechanical braking system. Background Art

[0002] The electronic mechanical brake (EMB) system is a hot topic in the field of drive-by-wire chassis research. It is a highly integrated solution for braking systems and belongs to the electromechanical servo system driven by a permanent magnet synchronous motor. In a permanent magnet synchronous motor system, the parameter accuracy of both the electrical and mechanical models is crucial for condition monitoring and control system parameter adjustment.

[0003] Identifying motor parameters is crucial for motor control. Currently, most motor parameter estimation methods require using two motors in parallel, indirectly inferring electrical parameters by measuring data such as voltage, current, and speed. These methods rely on experimental equipment, increasing complexity and cost, and are unable to directly obtain the mechanical parameters of the motor. Summary of the Invention

[0004] The embodiments of the present application provide a motor parameter identification method, device, and electromechanical braking system, which can directly obtain motor parameters from the motor.

[0005] In a first aspect, an embodiment of the present application provides a motor parameter identification method applied to an electromechanical braking system, the method comprising:

[0006] During the braking gap phase, a quadrature-axis current is driven on the motor in the electromechanical braking system so as to cause the motor to enter a first steady-speed state from a first acceleration state;

[0007] When the motor is in the first steady-speed state, driving the motor with a direct-axis current so that the motor enters the second steady-speed state from the second acceleration state;

[0008] First sampling data of the motor entering a first steady-speed state from a first acceleration state and second sampling data of the motor entering a second steady-speed state from a second acceleration state are obtained, and based on the first sampling data and the second sampling data, an optimization algorithm is used to solve and obtain a motor parameter identification value.

[0009] In some feasible embodiments, the motor parameter identification value includes a first electrical parameter, the first electrical parameter includes a motor inductance and a flux linkage, the first sampled data includes a first steady-state data, the second sampled data includes a second steady-state data, the first steady-state data includes a first quadrature-axis target voltage, a first quadrature-axis current, and a first motor speed, and the second steady-state data includes a second quadrature-axis target voltage, a second quadrature-axis current, a direct-axis current, and a second motor speed;

[0010] The motor parameter identification value is obtained by using an optimization algorithm based on the first sampling data and the second sampling data, and includes:

[0011] A first cost function related to the flux is determined according to the first quadrature axis target voltage, the first quadrature axis current, the first motor speed and the first motor rotor position information;

[0012] A second cost function related to the flux and the motor inductance is determined according to the second quadrature axis target voltage, the second quadrature axis current, the direct axis current, the second motor speed and the second motor rotor position information;

[0013] The motor inductance and the flux are obtained by using an optimization algorithm based on the first cost function and the second cost function.

[0014] In some possible embodiments, the motor parameter identification value further includes the moment of inertia and the viscous friction coefficient, and the first sampling data further includes first acceleration data, and the first acceleration data includes a third motor speed and a third quadrature axis current;

[0015] The motor parameter identification value is obtained by using an optimization algorithm based on the first sampling data and the second sampling data, and further includes:

[0016] A third cost function related to the flux, the moment of inertia and the viscous friction coefficient is determined according to the third motor speed and the third quadrature axis current;

[0017] A fourth cost function related to the flux and the viscous friction coefficient is determined according to the first quadrature axis current and the first motor speed;

[0018] Correspondingly, the motor inductance and the flux are obtained by using an optimization algorithm based on the first cost function and the second cost function, and include:

[0019] The motor inductance, the flux, the moment of inertia and the viscous friction coefficient are obtained by using an optimization algorithm based on the first cost function, the second cost function, the third cost function and the fourth cost function.

[0020] In some possible embodiments, the first cost function is:

[0021]

[0022] In the formula, n1 is the total number of the first steady-state data, u q1 is the first quadrature axis target voltage, R s is the resistance, i q1 is the first quadrature axis current, P is the number of pole pairs, w1 is the first motor speed, is the flux, D q1 is a periodic function related to the motor rotor position related to the quadrature axis in the first steady-state condition, V deadis the dead zone voltage;

[0023] The second cost function is:

[0024]

[0025] Where, is the motor inductance, n2 is the total number of the second steady-state data, u q2 * is the second quadrature axis target voltage, i q2 is the second quadrature-axis current, i d2 is the direct axis current, w2 is the second motor speed, D q2 is a periodic function related to the motor rotor position related to the quadrature axis in the second steady-speed state.

[0026] In some possible embodiments, the third cost function is:

[0027]

[0028] Where, is the moment of inertia, is the viscous friction coefficient, is the flux linkage, n3 is the total number of the first acceleration data, w0 is the third motor speed, i q0 is the third quadrature axis current, t sample is the sampling time;

[0029] The fourth cost function is:

[0030]

[0031] Where n1 is the total number of the first steady-state data, i q1 is the first quadrature-axis current, P is the number of pole pairs, and w1 is the first motor speed.

[0032] In some feasible embodiments, the motor parameter identification value includes a second electrical parameter; and before driving the motor in the electronic mechanical braking system with a quadrature-axis current so that the motor enters the first steady-speed state from the first acceleration state, the method further includes:

[0033] Acquire third sampled data, where the third sampled data is data generated by the motor in response to a direct-axis current instruction, where the direct-axis current instruction is characterized as an instruction for gradually increasing the direct-axis current in a ramp form;

[0034] The second electrical parameter is obtained by calculation based on the third sampling data and a relationship expression corresponding to the direct-axis target voltage.

[0035] In some feasible embodiments, the second electrical parameter includes resistance and dead-zone voltage, and the third sampled data includes static direct-axis current and static direct-axis target voltage;

[0036] The second electrical parameter is obtained by calculating based on the third sampling data and a relationship expression corresponding to the direct-axis target voltage, including:

[0037] Based on the relationship between the resistance, the dead-zone voltage and the direct-axis target voltage, the resistance and the dead-zone voltage are calculated using the static direct-axis current and the static direct-axis target voltage;

[0038] Correspondingly, based on the first sampling data and the second sampling data, an optimization algorithm is used to obtain the motor parameter identification value, including:

[0039] Based on the resistance, the dead zone voltage, the first sampling data and the second sampling data, an optimization algorithm is used to obtain the motor parameter identification value.

[0040] In some feasible embodiments, after obtaining the dead zone voltage, the method further includes:

[0041] The dead zone voltage is compensated in real time according to a periodic function related to the motor rotor position.

[0042] In a second aspect, an embodiment of the present application provides a motor parameter identification device, which is applied to an electromechanical braking system. The device includes:

[0043] A first driving module is configured to drive the motor in the electronic mechanical braking system with a quadrature-axis current during a braking gap phase, so as to cause the motor to enter a first steady-speed state from a first acceleration state;

[0044] A second driving module is used to drive the motor with a direct-axis current when the motor is in the first steady-speed state, so that the motor enters the second steady-speed state from the second acceleration state;

[0045] The data acquisition and solution module is used to obtain the first sampling data of the motor entering the first steady-speed state from the first acceleration state and the second sampling data of the motor entering the second steady-speed state from the second acceleration state, and based on the first sampling data and the second sampling data, use the optimization algorithm to solve and obtain the motor parameter identification value.

[0046] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, any one of the motor parameter identification methods described above is implemented.

[0047] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, any one of the above motor parameter identification methods is implemented.

[0048] In a fifth aspect, an embodiment of the present application provides a computer program product, instructions in the computer program product are executed by a processor of an electronic device to cause the electronic device to perform the motor parameter identification method according to any one of the above.

[0049] In a sixth aspect, an embodiment of the present application provides a vehicle, including at least one of:

[0050] The motor parameter identification apparatus as above;

[0051] The electronic device as above;

[0052] The computer readable storage medium as above;

[0053] The computer program product as above.

[0054] In a seventh aspect, an embodiment of the present application provides an electromechanical brake system, including a processor and a memory storing computer program instructions; the processor executes the computer program instructions to implement the motor parameter identification method according to any one of the above.

[0055] The motor parameter identification method, apparatus and electromechanical brake system of the present application, wherein the motor parameter identification method is applied to the electromechanical brake system, and the method includes: in a brake clearance stage, performing cross-axis current driving on a motor in the electromechanical brake system to make the motor enter a first steady state from a first acceleration state; when the motor is in the first steady state, performing direct-axis current driving on the motor to make the motor enter a second steady state from a second acceleration state; obtaining first sampling data of the motor from the first acceleration state to the first steady state and second sampling data of the motor from the second acceleration state to the second steady state, and based on the first sampling data and the second sampling data, using an optimization algorithm to obtain a motor parameter identification value. In this way, in the present application, the motor is sequentially subjected to cross-axis current driving and direct-axis current driving to make the motor accelerate to a steady state first and then to another steady state, and the motor parameter identification value can be directly obtained based on the sampled data in this process, without relying on a large device such as a motor test bench, thereby reducing the complexity and cost of parameter identification. In addition, since the data can be directly sampled for the motor, the parameter identification can be performed when the electromechanical brake system is offline, so that the method of the present application is more widely applicable and easier to use. BRIEF DESCRIPTION OF DRAWINGS

[0056] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments of the present application will be briefly introduced as follows, and other drawings can also be obtained by those skilled in the art without creative labor on the premise of not paying creative labor.

[0057] Figure 1 is a flowchart of a motor parameter identification method provided by an embodiment of the present application;

[0058] Figure 2 is one of schematic diagrams of a permanent magnet synchronous motor provided by an embodiment of the present application;

[0059] Figure 3 is another schematic diagram of a permanent magnet synchronous motor provided by an embodiment of the present application;

[0060] Figure 4 is a flowchart of a motor parameter compensation design based method provided by an embodiment of the present application;

[0061] Figure 5 is a structural diagram of a motor parameter identification device provided by an embodiment of the present application;

[0062] Figure 6 is a structural diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0063] The features and exemplary embodiments of various aspects of the present application will be described in detail below with reference to the drawings. The following description is merely intended to explain the present application, and is not intended to limit the present application. The present application can be implemented without some of the specific details. The following description of the embodiments is merely intended to provide a better understanding of the present application by showing examples of the present application.

[0064] It should be noted that, in this document, the terms such as first and second are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between the entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such a process, method, article or device. Without more limitations, the elements defined by the statement "include" do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0065] To solve the problems in the prior art, the embodiments of the present application provide a motor parameter identification method, device and electronic mechanical brake system. First, the motor parameter identification method provided by the embodiments of the present application will be introduced.

[0066] Figure 1 A flowchart of a motor parameter identification method provided by an embodiment of the application is shown. As shown in Figure 1 A motor parameter identification method applied to an electromechanical brake system includes the following steps:

[0067] S101, during a brake clearance stage, a motor in the electromechanical brake system is driven by a cross-axis current to make the motor enter a first steady state from a first acceleration state.

[0068] The brake clearance refers to a physical clearance between a brake friction plate and a brake disc. During the brake clearance stage, since the friction plate is not in contact with the brake disc, the load of the motor mainly comes from the internal friction (such as bearing friction, air resistance, etc.) of the system, rather than the brake force. At this time, the dynamic behavior of the motor will show two stages of acceleration and steady speed. The motor in the electromechanical brake system can be a permanent magnet synchronous motor, an induction motor, a switched reluctance motor, a stepper motor, etc. Under the cross-axis current driving, the motor first accelerates and then stabilizes at a certain speed, and the acceleration process in this process is taken as the first acceleration state and the process after the steady speed is taken as the first steady state. When the motor is only driven by the cross-axis current, it only needs to overcome the viscous friction.

[0069] S102, when the motor is in the first steady state, the motor is driven by a direct-axis current to make the motor enter a second steady state from a second acceleration state.

[0070] In this step, the motor is additionally driven by the direct-axis current on the basis of the cross-axis current driving, so that the motor accelerates from the first steady state and reaches the second steady state. Since the direct-axis current is mainly used to adjust the magnetic field strength and the cross-axis current is mainly used to generate torque, in the process from the second acceleration state to the second steady state, the motor needs to overcome not only the viscous friction but also the additional torque caused by the magnetic field adjustment.

[0071] S103, first sampling data of the motor from the first acceleration state to the first steady state and second sampling data of the motor from the second acceleration state to the second steady state are obtained, and based on the first sampling data and the second sampling data, an optimization algorithm is used to obtain a motor parameter identification value.

[0072] The first sampling data is related to the mechanical parameter of viscous friction, the second sampling data is related to the viscous friction and other electrical parameters, and the motor parameter identification value can be directly determined by using the first sampling data and the second sampling data. The motor parameter identification value includes an electrical parameter identification value and a mechanical parameter identification value. The electrical parameter identification value can be one or more of resistance, direct-axis inductance, cross-axis inductance, flux linkage, mutual inductance, etc. The mechanical parameter identification value can be one or more of moment of inertia, viscous friction coefficient, coulomb friction torque, load torque, etc.

[0073] The optimization algorithm can be a genetic algorithm, a particle swarm optimization algorithm, a differential evolution algorithm, an ant colony algorithm, a firefly algorithm, etc.

[0074] In this step, the electrical parameter to be identified and the cost function related to the electrical parameter to be identified are determined, and the optimal electrical parameter identification value is obtained by solving through an optimization algorithm.

[0075] The motor parameter identification method of the embodiment of the application is applied to an electronic mechanical brake system, and the method comprises the following steps: in a brake clearance stage, a motor in the electronic mechanical brake system is driven by a cross-axis current to make the motor enter a first steady speed state from a first acceleration state; when the motor is in the first steady speed state, the motor is driven by a direct-axis current to make the motor enter a second steady speed state from a second acceleration state; first sampling data of the motor from the first acceleration state to the first steady speed state and second sampling data of the motor from the second acceleration state to the second steady speed state are obtained, and the motor parameter identification value is obtained by solving through an optimization algorithm based on the first sampling data and the second sampling data. In this way, in the embodiment of the application, the motor is sequentially driven by the cross-axis current and the direct-axis current to make the motor accelerate to a steady speed state and then accelerate to another steady speed state, and the motor parameter identification value can be directly obtained based on the sampled data in this process, without relying on a large device such as a motor test bench, so that the complexity and cost of parameter identification are reduced. In addition, the data can be directly sampled for the motor, so that parameter identification can be performed when the electronic mechanical brake system is offline, so that the method of the application has a wider application range and is easier to use.

[0076] The permanent magnet synchronous motor is taken as an example below, and the working principle of the permanent magnet synchronous motor is briefly described before the motor parameter identification method provided by the embodiment of the application is described in detail.

[0077] Referring to Figure 2 With Figure 3 , the permanent magnet synchronous motor is provided with a speed reduction and torque increasing mechanism, the speed reduction and torque increasing mechanism is provided with a motion conversion mechanism (ball screw), the ball screw nut moves horizontally to push the friction pad to press the brake disc. An angle sensor is integrated in the permanent magnet synchronous motor controller to sense the current motor angle. A current sensor is integrated in the permanent magnet synchronous motor controller to sense the current motor current. A force sensor is integrated in the motion conversion mechanism to sense the clamping force of the friction pad on the brake disc. When the permanent magnet synchronous motor controller receives the clamping instruction from the upper controller, the motor is driven to accelerate in the forward direction to first overcome the brake clearance, and then enters the brake clamping stage after the brake pad presses the brake disc.

[0078] Based on the working principle of the above-mentioned permanent magnet synchronous motor, in some embodiments, the motor parameter identification value includes a first electrical parameter, the first electrical parameter includes motor inductance and magnetic flux, the first sampling data includes first steady-state data, the second sampling data includes second steady-state data, the first steady-state data includes a first quadrature-axis target voltage, a first quadrature-axis current and a first motor speed, and the second steady-state data includes a second quadrature-axis target voltage, a second quadrature-axis current, a direct-axis current and a second motor speed.

[0079] Based on the first sampling data and the second sampling data, an optimization algorithm is used to obtain a motor parameter identification value, including:

[0080] A first cost function related to flux linkage is determined according to the first quadrature-axis target voltage, the first quadrature-axis current, the first motor speed, and the first motor rotor position information.

[0081] A second cost function related to flux linkage and motor inductance is determined according to the second quadrature-axis target voltage, the second quadrature-axis current, the direct-axis current, the second motor speed, and the second motor rotor position information.

[0082] A second cost function related to flux linkage and motor inductance is determined according to the second quadrature-axis target voltage, the second quadrature-axis current, the direct-axis current, the second motor speed, and the second motor rotor position information.

[0083] In other embodiments, the motor parameter identification value further includes the moment of inertia and the viscous friction coefficient, the first sampling data further includes first acceleration data, and the first acceleration data includes the third motor speed and the third quadrature-axis current.

[0084] Based on the first sampling data and the second sampling data, an optimization algorithm is used to solve and obtain a motor parameter identification value, which also includes:

[0085] A third cost function related to flux linkage, moment of inertia, and viscous friction coefficient is determined according to the third motor speed and the third quadrature-axis current.

[0086] A fourth cost function related to the flux viscous friction coefficient is determined according to the first quadrature-axis current and the first motor speed.

[0087] Based on the first, second, third, and fourth cost functions, the motor inductance, flux linkage, moment of inertia, and viscous friction coefficient are obtained through an optimization algorithm. The motor inductance and flux linkage are electrical parameters, while the moment of inertia and viscous friction coefficient are mechanical parameters.

[0088] In other embodiments of the present application, the second sampling data also includes second acceleration data, and the second acceleration data includes a fourth motor speed and a fourth quadrature-axis current. The third cost function related to magnetic flux, moment of inertia, and viscous friction coefficient can also be determined based on the fourth motor speed and the fourth quadrature-axis current. The principle is the same as that of determining the third cost function based on the third motor speed and the third quadrature-axis current, and will not be repeated here.

[0089] The motor parameter identification method provided in the embodiment of the present application can solve the motor inductance, magnetic flux, moment of inertia and viscous friction coefficient based on a first cost function related to magnetic flux, a second cost function related to magnetic flux and motor inductance, a third cost function related to magnetic flux, moment of inertia and viscous friction coefficient, and a fourth cost function related to magnetic flux and viscous friction coefficient. Among them, the first cost function focuses on the accuracy of magnetic flux to ensure the accuracy of the motor magnetic field model; the second cost function can simultaneously optimize magnetic flux and inductance to ensure the consistency of electrical parameters; the third cost function comprehensively considers electrical parameters and mechanical parameters to ensure the accuracy of the dynamic model; the fourth cost function can further optimize magnetic flux and viscous friction coefficient to improve the matching degree of steady-state and dynamic performance. Therefore, the motor inductance, magnetic flux, moment of inertia and viscous friction coefficient solved by the motor parameter identification method of the embodiment of the present application are highly accurate and robust, and each parameter is decoupled and independent to ensure that each parameter can reach the optimal value, and mutual interference between parameters can also be avoided.

[0090] In some embodiments of the present application, the first cost function is:

[0091]

[0092] Where n1 is the total number of the first steady-state data, u q1 * is the first quadrature-axis target voltage, R s is the resistance, i q1 is the first quadrature axis current, P is the number of pole pairs, w1 is the first motor speed, is the magnetic linkage, D q1 is a periodic function related to the motor rotor position related to the quadrature axis in the first steady-speed state, V dead is the dead zone voltage.

[0093] The second cost function is:

[0094]

[0095] Where, is the motor inductance, n2 is the total number of the second steady-state data, u q2 * is the second quadrature-axis target voltage, i q2 is the second quadrature-axis current, i d2is the direct axis current, w2 is the second motor speed, D q2 is a periodic function related to the motor rotor position relative to the quadrature axis in the second steady-speed state;

[0096] In the process of constructing the third cost function and the fourth cost function, the forms of the torque equation in the first acceleration state and the second acceleration state and the first steady-speed state and the second steady-speed state where the acceleration is approximately considered to be 0 are taken into account, so as to obtain the third cost function related to the magnetic flux, moment of inertia, and viscous friction coefficient and the fourth cost function related to the magnetic flux and viscous friction coefficient.

[0097] The torque equation is:

[0098]

[0099] Where, T e is the electromagnetic torque, J is the rotor moment of inertia or the moment of inertia concentrated at the rotor end, B is the viscous friction coefficient at the rotor end or concentrated at the rotor end, T m is the load torque at the rotor end.

[0100] The third cost function is:

[0101]

[0102] Where, is the moment of inertia, is the viscous friction coefficient, n3 is the total number of the first acceleration data, w0 is the third motor speed, i q0 is the third quadrature-axis current;

[0103] The fourth cost function is:

[0104]

[0105] Where n1 is the total number of the first steady-state data, i q1 is the first quadrature-axis current, P is the number of pole pairs, and w1 is the first motor speed.

[0106] The motor parameter identification method provided in the embodiment of the present application ensures the accuracy of the magnetic flux through the first cost function. The introduction of the dead zone voltage in the first cost function can more realistically reflect the nonlinear characteristics of the actual system and improve the reliability of the identification result. The use of the first steady-state data can effectively extract the key information of the magnetic flux and avoid noise interference in the dynamic data. The second cost function is used to achieve accurate identification of the motor inductance and magnetic flux. Considering the optimization process corresponding to the second steady-state data can help improve the adaptability of the motor model so that it can maintain good performance in different working conditions and dynamic changes. The third cost function can more accurately identify the motor's moment of inertia and viscous friction coefficient, so that the motor control system can respond quickly and achieve high-precision control. The fourth cost function can more accurately identify the viscous friction coefficient and magnetic flux, so that the motor control system can achieve more accurate control at low speed and in a stationary state.

[0107] In some embodiments of the present application, the motor parameter identification value includes a second electrical parameter; and before driving the motor in the electronic mechanical braking system with a quadrature-axis current so that the motor enters a first steady-speed state from a first acceleration state, the method further includes:

[0108] Acquire third sampled data, where the third sampled data is data generated by the motor in response to a direct-axis current instruction, where the direct-axis current instruction is characterized as an instruction for gradually increasing the direct-axis current in a ramp form;

[0109] The second electrical parameter is obtained by calculation based on the third sampling data and a relationship expression corresponding to the direct-axis target voltage.

[0110] In some embodiments of the present application, the second electrical parameter includes resistance and dead-zone voltage.

[0111] The second electrical parameter is obtained by calculating based on the third sampling data and a relationship expression corresponding to the direct-axis target voltage, including:

[0112] Acquire third sampled data. The third sampled data is data generated by the motor in response to a direct-axis current command. The direct-axis current command is characterized by a command for gradually increasing the direct-axis current in a ramped manner. The third sampled data includes a static direct-axis current and a static direct-axis target voltage. It should be understood that the third sampled data represents a separate direct-axis current drive of the motor, independent of the aforementioned method of first driving the motor with the quadrature-axis current and then with the direct-axis current.

[0113] Based on the relationship among the resistance, the dead-zone voltage and the direct-axis target voltage, the resistance and the dead-zone voltage are calculated using the static direct-axis current and the static direct-axis target voltage.

[0114] The relationship between the resistance, dead zone voltage, and direct-axis target voltage is as follows:

[0115]

[0116] Where u d *、u q *Respectively direct-axis target voltage and quadrature-axis target voltage, i d ,i q are the direct axis true current and the quadrature axis true current respectively, are the direct-axis and quadrature-axis current differentials, D d ,D q They are periodic functions related to the motor rotor position of the direct axis and quadrature axis respectively, P is the number of pole pairs, L dq is the direct-axis quadrature-axis inductance, and w is the motor speed. s , direct-axis and quadrature-axis inductance L dq , magnetic link and dead zone voltage V dead For electrical parameters.

[0117] In this embodiment, after the motor angle zero position calibration is completed, a slowly rising ramp direct-axis current command is given by the motor controller, so that the current slowly decreases from the rated current to 0. During this process, the motor does not output torque. Accordingly, the motor speed w-related terms and the direct-axis quadrature-axis current differential terms in the relationship between the resistance, dead-zone voltage and direct-axis target voltage are approximately considered to be 0. By collecting the direct-axis current and the direct-axis target voltage, the unknown term resistance R in the relationship is obtained. s And dead zone voltage V dead , the dead zone voltage V can be completed by the least squares method dead and resistor R s identification.

[0118] Correspondingly, based on the first sampling data and the second sampling data, an optimization algorithm is used to obtain the motor parameter identification value, including:

[0119] Based on the resistance, the dead zone voltage, the first sampling data and the second sampling data, an optimization algorithm is used to obtain the motor parameter identification value.

[0120] The motor parameter identification method provided in the embodiments of the present application calculates the resistance and dead-zone voltage based on the third sampled data collected under static conditions. The third sampled data is less affected by noise and dynamic interference, providing a more accurate basis for parameter identification. Furthermore, the ramp-like, gradually increasing direct-axis current avoids the impact of sudden current changes on the electromechanical braking system, ensuring the stability and accuracy of the sampled data. Furthermore, the accurate identification of the resistance and dead-zone voltage provides important prior information for the subsequent identification of parameters such as flux linkage, inductance, and moment of inertia, helping to improve the accuracy of the overall parameter identification.

[0121] In some embodiments of the present application, after obtaining the dead zone voltage, the method further includes:

[0122] The dead zone voltage is compensated in real time according to a periodic function related to the motor rotor position, thereby preventing the dead zone voltage from affecting the subsequent motor parameter identification.

[0123] In some embodiments of the present application, after obtaining the motor inductance, magnetic flux, moment of inertia and viscous friction coefficient, the inductance and magnetic flux can be used to perform back electromotive force compensation and current loop control parameter design of the motor controller, and the moment of inertia and viscous friction coefficient can be used to design the motor speed loop control parameters.

[0124] Specifically, after obtaining the resistance, dead zone voltage, motor inductance, flux linkage, moment of inertia and viscous friction coefficient, the following equations are used: Figure 4 The process shown in the figure realizes the back electromotive force compensation of the motor controller and the design of the current loop control parameters and the motor speed loop control parameters. Among them, ACR is an automatic current regulator, which adjusts the input direct axis current i d * , quadrature axis current i q * Adjustment is made, and the space vector pulse width modulation SVPWM module converts the direct axis voltage u d * , quadrature axis voltage u q * The PWM signal is converted into an actual PWM signal to drive the inverter. The inverter converts the DC voltage into a three-phase AC voltage to drive the permanent magnet synchronous motor (PMSM). The position sensor and speed calculation module obtain the motor's rotor position and speed through the position sensor and feeds this information back to the control system for vector control.

[0125] Based on the motor parameter identification method provided in the above embodiment, the present application also provides a specific implementation of a motor parameter identification device. Please refer to the following embodiments.

[0126] like Figure 5 As shown, the motor parameter identification device provided in the embodiment of the present application is applied to an electronic mechanical braking system, and the device includes:

[0127] The first driving module 501 is configured to drive the motor in the electronic mechanical braking system with a quadrature-axis current during a braking gap phase, so as to enable the motor to enter a first steady-speed state from a first acceleration state.

[0128] The second driving module 502 is configured to drive the motor with a direct-axis current when the motor is in the first steady-speed state, so as to enable the motor to enter the second steady-speed state from the second acceleration state.

[0129] The data acquisition and solution module 503 is used to obtain the first sampling data of the motor entering the first steady-speed state from the first acceleration state and the second sampling data of the motor entering the second steady-speed state from the second acceleration state, and based on the first sampling data and the second sampling data, use the optimization algorithm to solve and obtain the motor parameter identification value.

[0130] The motor parameter identification device of the embodiment of the present application, in the braking gap stage, drives the motor in the electronic mechanical brake system with a cross-axis current so that the motor enters the first steady-speed state from the first acceleration state; when the motor is in the first steady-speed state, drives the motor with a direct-axis current so that the motor enters the second steady-speed state from the second acceleration state; obtains the first sampling data of the motor entering the first steady-speed state from the first acceleration state and the second sampling data of the motor entering the second steady-speed state from the second acceleration state, and obtains the motor parameter identification value based on the first sampling data and the second sampling data using an optimization algorithm. Thus, in the embodiment of the present application, by sequentially driving the motor with a cross-axis current and a direct-axis current, so that the motor first accelerates to a steady-speed state and then accelerates to another steady-speed state, the motor parameter identification value can be directly obtained based on the data sampled in this process, without relying on large equipment such as a tow motor test bench, thereby reducing the complexity and cost of parameter identification. In addition, since data sampling can be performed directly on the motor, parameter identification can be performed when the electronic mechanical brake system is offline, making the method of the present application more widely applicable and easier.

[0131] In some embodiments of the present application, the motor parameter identification value includes a first electrical parameter, the first electrical parameter includes motor inductance and magnetic flux, the first sampled data includes first steady-state data, the second sampled data includes second steady-state data, the first steady-state data includes a first quadrature-axis target voltage, a first quadrature-axis current and a first motor speed, and the second steady-state data includes a second quadrature-axis target voltage, a second quadrature-axis current, a direct-axis current and a second motor speed.

[0132] The data acquisition and solution modules include:

[0133] The first cost function construction unit is configured to determine a first cost function related to flux linkage according to the first quadrature-axis target voltage, the first quadrature-axis current, the first motor speed, and the first motor rotor position information.

[0134] The second cost function construction unit is used to determine a second cost function related to flux linkage and motor inductance according to the second quadrature-axis target voltage, the second quadrature-axis current, the direct-axis current, the second motor speed and the second motor rotor position information.

[0135] A solving unit is configured to obtain the motor inductance and flux linkage based on the first cost function and the second cost function through an optimization algorithm.

[0136] In some embodiments of the present application, the motor parameter identification value further includes the moment of inertia and the viscous friction coefficient, the first sampling data further includes first acceleration data, and the first acceleration data includes the third motor speed and the third quadrature-axis current;

[0137] The data acquisition and solution module also includes:

[0138] The third cost function constructing unit is configured to determine a third cost function related to flux linkage, moment of inertia, and viscous friction coefficient according to the third motor speed and the third quadrature-axis current.

[0139] The fourth cost function constructing unit is configured to determine a fourth cost function related to the flux linkage viscous friction coefficient according to the first quadrature-axis current and the first motor speed.

[0140] The solving unit is used to obtain the motor inductance, magnetic flux, moment of inertia and viscous friction coefficient based on the first cost function, the second cost function, the third cost function and the fourth cost function through an optimization algorithm.

[0141] In some embodiments of the present application, the first cost function is:

[0142]

[0143] Where n1 is the total number of the first steady-state data, u q1 * is the first quadrature-axis target voltage, R s is the resistance, i q1 is the first quadrature axis current, P is the number of pole pairs, w1 is the first motor speed, is the magnetic linkage, D q1 is a periodic function related to the motor rotor position related to the quadrature axis in the first steady-speed state, V dead is the dead zone voltage;

[0144] The second cost function is:

[0145]

[0146] Where, is the motor inductance, n2 is the total number of the second steady-state data, u q2 * is the second quadrature-axis target voltage, i q2 is the second quadrature-axis current, i d2 is the direct axis current, w2 is the second motor speed, D q2 is a periodic function related to the motor rotor position relative to the quadrature axis in the second steady-speed state;

[0147] In some embodiments of the present application, the third cost function is:

[0148]

[0149] Where, is the moment of inertia, is the viscous friction coefficient, is the flux linkage, n3 is the total number of the first acceleration data, w0 is the third motor speed, i q0 is the third quadrature axis current, t sample is the sampling time;

[0150] The fourth cost function is:

[0151]

[0152] Where n1 is the total number of the first steady-state data, i q1 is the first quadrature-axis current, P is the number of pole pairs, and w1 is the first motor speed.

[0153] In some embodiments of the present application, the motor parameter identification value includes a second electrical parameter; the motor parameter identification device further includes a third sampling data acquisition module and an electrical parameter solution module.

[0154] The third sampling data acquisition module is used to acquire third sampling data, which is data generated by the motor in response to the direct-axis current instruction. The direct-axis current instruction is characterized by an instruction that the direct-axis current gradually increases in a ramp form.

[0155] The electrical parameter solving module is used to calculate and obtain the second electrical parameter based on the third sampling data and the relationship expression corresponding to the direct-axis target voltage.

[0156] In some embodiments of the present application, the second electrical parameter includes resistance and dead zone voltage, and the third sampled data includes static direct-axis current and static direct-axis target voltage;

[0157] The electrical parameter solution module is specifically used for:

[0158] Based on the relationship between the resistance, the dead-zone voltage and the direct-axis target voltage, the resistance and the dead-zone voltage are calculated using the static direct-axis current and the static direct-axis target voltage;

[0159] Correspondingly, the data acquisition and solution module is specifically used to:

[0160] Based on the resistance, the dead zone voltage, the first sampling data and the second sampling data, an optimization algorithm is used to obtain the motor parameter identification value.

[0161] In some embodiments of the present application, the motor parameter identification device further includes a voltage compensation module, which is used to compensate the dead zone voltage in real time according to a periodic function related to the motor rotor position.

[0162] Figure 6A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application is shown.

[0163] The electronic device may include a processor 601 and a memory 602 storing computer program instructions.

[0164] Specifically, the processor 601 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0165] The memory 602 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 602 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 602 may include removable or non-removable (or fixed) media. Where appropriate, the memory 602 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 602 is a non-volatile solid-state memory.

[0166] In certain embodiments, the memory 602 may include read-only memory (ROM), random access memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical / tangible memory storage devices. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to an aspect of the present disclosure.

[0167] The processor 601 reads and executes computer program instructions stored in the memory 602 to implement any one of the motor parameter identification methods in the above embodiments.

[0168] In one example, the electronic device may further include a communication interface 603 and a bus 610. Figure 6 As shown, the processor 601, the memory 602, and the communication interface 603 are connected via a bus 610 and communicate with each other.

[0169] The communication interface 603 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0170] Bus 610 comprises hardware, software or both, couples the parts of electronic equipment to each other.For example, and not limitation, bus can comprise accelerated graphics port (AGP) or other graphics bus, enhanced industry standard architecture (EISA) bus, front side bus (FSB), hypertransport (HT) interconnection, industry standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations.In suitable cases, bus 610 can comprise one or more buses.Although the present application embodiment describes and shows specific bus, the application considers any suitable bus or interconnection.

[0171] The electronic device can execute the motor parameter identification method in the embodiment of the present application, thereby realizing the combination Figure 1 and Figure 5 The invention relates to a method and device for identifying motor parameters.

[0172] In addition, in conjunction with the motor parameter identification method in the above embodiments, embodiments of the present application may provide a computer-readable storage medium for implementation. The computer-readable storage medium stores computer program instructions; when the computer program instructions are executed by a processor, any of the motor parameter identification methods in the above embodiments is implemented.

[0173] In combination with the motor parameter identification method in the above embodiment, an embodiment of the present application can provide a computer program product. When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes any one of the motor parameter identification methods above.

[0174] In conjunction with the motor parameter identification method in the above embodiment, the present application can provide a vehicle for implementation. The vehicle includes at least one of the following: the motor parameter identification device described above; the electronic device described above; the computer-readable storage medium described above; or the computer program product described above.

[0175] In combination with the motor parameter identification method in the above embodiments, an embodiment of the present application can provide an electronic mechanical braking system, which includes: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, it implements the motor parameter identification method as any one of the above items.

[0176] It should be understood that the present application is not limited to the specific configurations and processes described above and illustrated in the figures. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and illustrated as examples. However, the method process of the present application is not limited to the specific steps described and illustrated. Those skilled in the art can make various changes, modifications, and additions, or change the order of the steps after understanding the spirit of the present application.

[0177] The functional blocks shown in the above block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. Programs or code segments can be stored in machine-readable media, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable media" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROMs, flash memories, erasable ROMs (EROMs), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0178] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps. In other words, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0179] Aspects of the present disclosure have been described above with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor or a field programmable logic circuit. It is also understood that each box in the block diagram and / or flowchart and the combination of the boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0180] The above is only a specific implementation of the present application, and those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system, module and unit described above can refer to the corresponding process in the foregoing method embodiments, which will not be described here. It should be understood that the protection scope of the present application is not limited to this, and any person skilled in the art can easily think of various equivalent modifications or replacements within the technical range disclosed in the present application, and these modifications or replacements should be covered within the protection scope of the present application.

Claims

1. A method for identifying motor parameters, characterized in that: Applied to an electromechanical braking system, the method comprises: During the braking gap phase, a quadrature-axis current is driven on the motor in the electromechanical braking system so that the motor enters a first steady-speed state from a first acceleration state; When the motor is in the first steady-speed state, driving the motor with a direct-axis current so that the motor enters a second steady-speed state from a second acceleration state; First sampling data of the motor entering a first steady-speed state from a first acceleration state and second sampling data of the motor entering a second steady-speed state from a second acceleration state are obtained, and based on the first sampling data and the second sampling data, an optimization algorithm is used to solve and obtain a motor parameter identification value.

2. The method according to claim 1, characterized in that The motor parameter identification value includes a first electrical parameter, the first electrical parameter includes a motor inductance and a flux linkage, the first sampled data includes a first steady-state data, the second sampled data includes a second steady-state data, the first steady-state data includes a first quadrature-axis target voltage, a first quadrature-axis current, and a first motor speed, and the second steady-state data includes a second quadrature-axis target voltage, a second quadrature-axis current, a direct-axis current, and a second motor speed; The method of obtaining a motor parameter identification value by using an optimization algorithm based on the first sampling data and the second sampling data includes: determining a first cost function related to the flux linkage according to the first quadrature-axis target voltage, the first quadrature-axis current, the first motor speed, and first motor rotor position information; determining a second cost function related to the flux linkage and the motor inductance according to the second quadrature-axis target voltage, the second quadrature-axis current, the direct-axis current, the second motor speed, and second motor rotor position information; Based on the first cost function and the second cost function, the motor inductance and the magnetic flux are obtained by solving the optimization algorithm.

3. The method according to claim 2, characterized in that The motor parameter identification value further includes a moment of inertia and a viscous friction coefficient, the first sampling data further includes first acceleration data, and the first acceleration data includes a third motor speed and a third quadrature-axis current; The method of obtaining a motor parameter identification value by using an optimization algorithm based on the first sampling data and the second sampling data further includes: determining a third cost function related to the flux linkage, the moment of inertia, and the viscous friction coefficient according to the third motor speed and the third quadrature-axis current; Determining a fourth cost function related to the flux linkage and the viscous friction coefficient according to the first quadrature-axis current and the first motor speed; Accordingly, the obtaining of the motor inductance and the flux linkage by solving the optimization algorithm based on the first cost function and the second cost function includes: Based on the first cost function, the second cost function, the third cost function and the fourth cost function, the motor inductance, the magnetic flux, the moment of inertia and the viscous friction coefficient are obtained by solving the optimization algorithm.

4. The method according to claim 2, characterized in that The first cost function is: Where n1 is the total number of the first steady-state data, u q1 * is the first quadrature-axis target voltage, R s is the resistance, i q1 is the first quadrature axis current, P is the number of pole pairs, w1 is the first motor speed, is the magnetic linkage, D q1 is a periodic function related to the motor rotor position related to the quadrature axis in the first steady-speed state, V dead is the dead zone voltage; The second cost function is: Where, is the motor inductance, n2 is the total number of the second steady-state data, u q2 * is the second quadrature axis target voltage, i q2 is the second quadrature-axis current, i d2 is the direct axis current, w2 is the second motor speed, D q2 is a periodic function related to the motor rotor position related to the quadrature axis in the second steady-speed state.

5. The method according to claim 3, characterized in that The third cost function is: Where, is the moment of inertia, is the viscous friction coefficient, is the flux linkage, n3 is the total number of the first acceleration data, w0 is the third motor speed, i q0 is the third quadrature axis current, t sample is the sampling time; The fourth cost function is: Where n1 is the total number of the first steady-state data, i q1 is the first quadrature-axis current, P is the number of pole pairs, and w1 is the first motor speed.

6. The method according to claim 1, characterized in that The motor parameter identification value includes a second electrical parameter; before driving the motor in the electronic mechanical braking system with a quadrature-axis current so that the motor enters a first steady-speed state from a first acceleration state, the method further includes: Acquire third sampled data, where the third sampled data is data generated by the motor in response to a direct-axis current instruction, where the direct-axis current instruction is characterized as an instruction for gradually increasing the direct-axis current in a ramp form; The second electrical parameter is obtained by calculation based on the third sampling data and a relationship expression corresponding to the direct-axis target voltage.

7. The method according to claim 6, characterized in that The second electrical parameter includes resistance and dead zone voltage, and the third sampling data includes static direct axis current and static direct axis target voltage; The calculating and obtaining the second electrical parameter based on the third sampling data and a relationship expression corresponding to the direct-axis target voltage includes: Based on a relationship between the resistance, the dead-zone voltage and the direct-axis target voltage, the resistance and the dead-zone voltage are calculated using the static direct-axis current and the static direct-axis target voltage; Correspondingly, obtaining the motor parameter identification value by using an optimization algorithm based on the first sampling data and the second sampling data includes: An optimization algorithm is used to obtain motor parameter identification values ​​based on the resistance, the dead zone voltage, the first sampling data, and the second sampling data.

8. The method according to claim 7, characterized in that After obtaining the dead zone voltage, the method further includes: The dead zone voltage is compensated in real time according to a periodic function related to the motor rotor position.

9. A motor parameter identification device, characterized in that: Applied to an electronic mechanical braking system, the device comprises: a first driving module, configured to drive the motor in the electromechanical braking system with a quadrature-axis current during a braking gap phase, so as to cause the motor to enter a first steady-speed state from a first acceleration state; a second driving module, configured to drive the motor with a direct-axis current when the motor is in the first steady-speed state, so as to cause the motor to enter a second steady-speed state from a second acceleration state; The data acquisition and solution module is used to obtain first sampling data of the motor entering a first steady-speed state from a first acceleration state and second sampling data of the motor entering a second steady-speed state from a second acceleration state, and based on the first sampling data and the second sampling data, use an optimization algorithm to solve and obtain the motor parameter identification value.

10. An electromechanical braking system, characterized in that: The electronic mechanical braking system includes: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the motor parameter identification method according to any one of claims 1 to 8 is implemented.

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