A method and device for estimating the rotational speed of a permanent magnet synchronous motor in an ultra-low speed scenario

Abstract

This invention provides a method and apparatus for estimating the rotational speed of a permanent magnet synchronous motor (PMSM) in ultra-low-speed scenarios. The method includes: obtaining initial state parameters of the PMSM in ultra-low-speed scenarios from a vehicle simulation model; calculating particle parameters based on a pre-set particle covariance matrix; wherein the particle parameters include at least initial particle parameters and initial particle weights for multiple particles; calculating the current particle parameters for each particle based on a pre-established state equation; updating the particle parameters using a pre-set update equation to obtain the current particle weights for each particle; and calculating the current particle parameters and current particle weights for each particle based on a pre-established observation equation to obtain rotational speed estimation data for the PMSM. This invention can improve the accuracy of rotational speed estimation results for PMSMs in ultra-low-speed scenarios.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of vehicle technology, and more specifically, the embodiments of the present invention relate to a method and apparatus for estimating the speed of a permanent magnet synchronous motor in ultra-low speed scenarios. Background Technology

[0002] This section is intended to provide background or context for embodiments of the invention as set forth in the claims. The description herein is not an admission that it is prior art simply because it is included in this section.

[0003] The fully automated train control system (MAS) carries core functions such as safe train operation control and automatic driving. Typically, the onboard controller in an MAS requires a complete indoor simulation test platform to test its functions, interfaces, performance, and data. However, in practice, it has been found that current onboard controllers sometimes produce inaccurate speed estimates for the permanent magnet synchronous motor in ultra-low speed scenarios. Summary of the Invention

[0004] In this context, embodiments of the present invention aim to provide a method and apparatus for estimating the speed of a permanent magnet synchronous motor in ultra-low speed scenarios.

[0005] In a first aspect of the present invention, a method for estimating the speed of a permanent magnet synchronous motor in an ultra-low speed scenario is provided. The vehicle simulation fixture includes a vehicle simulation model, a speed control board, a motor drive board, a tachometer motor, and a speed sensor. The method is applied to the speed control board, and the method includes:

[0006] The initialization state parameters of the permanent magnet synchronous motor in the ultra-low speed scenario are obtained from the vehicle simulation model.

[0007] The initial state parameters are calculated based on the pre-set particle covariance matrix to obtain particle parameters; wherein, the particle parameters include at least the initial particle parameters and initial particle weights of multiple particles.

[0008] The particle parameters are calculated based on the pre-established state equations to obtain the current particle parameters of each particle.

[0009] The particle parameters are updated using a preset update equation to obtain the current particle weight of each particle.

[0010] Based on the pre-established observation equations, the current particle parameters and current particle weights of each particle are calculated to obtain the speed estimation data of the permanent magnet synchronous motor.

[0011] In one embodiment of this implementation:

[0012] The motor drive board is used to receive the speed estimation data sent by the speed control board, and control the operation of the speed measuring motor through speed control information containing the speed estimation data;

[0013] The speed measuring motor is used to operate based on the speed control information and drive the speed sensor to work;

[0014] The speed sensor is used to periodically output speed pulses during operation.

[0015] In one embodiment of this implementation, the calculation of the initialization state parameters based on a pre-set particle covariance matrix to obtain particle parameters includes:

[0016] The initial state parameters are calculated based on the pre-set particle covariance matrix and random numbers to obtain the initial particle parameters of multiple particles.

[0017] Determine the number of particles in a plurality of particles;

[0018] The initial particle weight of each particle is determined based on the number of particles.

[0019] The initial particle parameters and initial particle weights of each particle are determined as particle parameters.

[0020] In one embodiment of this implementation, the calculation of the particle parameters based on a pre-established state equation to obtain the current particle parameters of each particle includes:

[0021] Determine the sliding mode gain and the system sliding surface;

[0022] The initial particle parameters of each particle are calculated based on the sliding mode gain, the system sliding surface, random numbers, and pre-established state equations to obtain the current particle parameters of each particle.

[0023] In one embodiment of this implementation, updating the particle parameters using a preset update equation to obtain the current particle weight of each particle includes:

[0024] Determine the preset parameters;

[0025] The initial particle weights of each particle are updated based on the base of the natural logarithm, the preset parameters, and the preset update equation to obtain the current particle weights of each particle.

[0026] In one embodiment of this implementation, the calculation of the current particle parameters and current particle weights of each particle based on a pre-established observation equation to obtain the speed estimation data of the permanent magnet synchronous motor includes:

[0027] The current particle weights of each particle are normalized to obtain the normalized weights of each particle.

[0028] The normalized weights of each particle are resampled to obtain the resampled weights of each particle.

[0029] Based on the resampling weights of each particle and the current particle parameters of each particle, parameter estimation is performed to obtain the estimated parameters of each particle.

[0030] The estimated parameters of each particle are calculated based on the pre-established observation equations to obtain the speed estimation data of the permanent magnet synchronous motor.

[0031] In one embodiment of this implementation, after calculating the current particle parameters and current particle weights of each particle based on a pre-established observation equation to obtain the speed estimation data of the permanent magnet synchronous motor, the method further includes:

[0032] The current particle weights of each particle are reset to obtain the initial particle weights of each particle.

[0033] The current particle parameters are updated based on the state equation to obtain the updated current particle parameters of each particle. Then, the process is performed from updating the particle parameters through the preset update equation to obtain the current particle weight of each particle, to calculating the current particle parameters and current particle weight of each particle based on the pre-established observation equation to obtain the speed estimation data of the permanent magnet synchronous motor.

[0034] In a second aspect of the present invention, a speed estimation device for a permanent magnet synchronous motor in an ultra-low speed scenario is provided. The vehicle simulation fixture includes a vehicle simulation model, a speed control board, a motor drive board, a tachometer motor, and a speed sensor. The device is applied to the speed control board, and the device includes:

[0035] The acquisition unit is used to acquire the initialization state parameters of the permanent magnet synchronous motor in the ultra-low speed scenario from the vehicle simulation model.

[0036] The first calculation unit is used to calculate the initial state parameters based on the pre-set particle covariance matrix to obtain particle parameters; wherein, the particle parameters include at least the initial particle parameters and initial particle weights of multiple particles.

[0037] The second calculation unit is used to calculate the particle parameters based on the pre-established state equations to obtain the current particle parameters of each particle.

[0038] The update unit is used to update the particle parameters through a preset update equation to obtain the current particle weight of each particle.

[0039] The third calculation unit is used to calculate the current particle parameters and current particle weights of each particle based on the pre-established observation equations, so as to obtain the speed estimation data of the permanent magnet synchronous motor.

[0040] In a third aspect of the present invention, a computing device is provided, the computing device comprising: at least one processor, a memory, and an input / output unit; wherein the memory is used to store a computer program, and the processor is used to invoke the computer program stored in the memory to execute the method described in any one aspect.

[0041] In a fourth aspect of the present invention, a computer-readable storage medium is provided, comprising instructions which, when executed on a computer, cause the computer to perform the method described in any one of the first aspects.

[0042] The speed estimation method and apparatus for permanent magnet synchronous motors in ultra-low speed scenarios according to embodiments of the present invention can obtain the initial state parameters of the permanent magnet synchronous motor in ultra-low speed scenarios, and can calculate the speed estimation data of the permanent magnet synchronous motor by means of preset state equations, update equations and observation equations, so as to improve the accuracy of the speed estimation results of the permanent magnet synchronous motor in ultra-low speed scenarios. Attached Figure Description

[0043] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings. Several embodiments of the invention are illustrated in the drawings by way of example and not limitation, wherein:

[0044] Figure 1 This is a schematic diagram illustrating an application scenario of the speed estimation method for a permanent magnet synchronous motor in an ultra-low speed environment provided by an embodiment of the present invention.

[0045] Figure 2 This is a schematic diagram showing the motor speed measured using the traditional M / T method and the speed estimation method for permanent magnet synchronous motors in ultra-low speed scenarios provided by an embodiment of the present invention.

[0046] Figure 3 This is a schematic diagram of the speed estimation device for a permanent magnet synchronous motor in an ultra-low speed scenario provided in an embodiment of the present invention.

[0047] Figure 4 A schematic diagram of the structure of a medium according to an embodiment of the present invention is shown.

[0048] Figure 5 A schematic diagram of the structure of a computing device according to an embodiment of the present invention is shown.

[0049] In the accompanying drawings, the same or corresponding reference numerals indicate the same or corresponding parts. Detailed Implementation

[0050] The principles and spirit of the invention will now be described with reference to several exemplary embodiments. It should be understood that these embodiments are given merely to enable those skilled in the art to better understand and implement the invention, and are not intended to limit the scope of the invention in any way. Rather, these embodiments are provided to make this disclosure more thorough and complete, and to fully convey the scope of this disclosure to those skilled in the art.

[0051] Those skilled in the art will recognize that embodiments of the present invention can be implemented as a system, apparatus, device, method, or computer program product. Therefore, this disclosure can be specifically implemented in the following forms: entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0052] According to an embodiment of the present invention, a method and apparatus for estimating the speed of a permanent magnet synchronous motor in an ultra-low speed scenario are proposed.

[0053] It should be noted that the number of any elements in the accompanying drawings is for illustrative purposes only and not as a limitation, and any naming is for distinction only and has no limiting meaning.

[0054] The principles and spirit of the present invention will be explained in detail below with reference to several representative embodiments.

[0055] Exemplary methods

[0056] The following is for reference. Figure 1 , Figure 1 This is a flowchart illustrating a method for estimating the speed of a permanent magnet synchronous motor in an ultra-low speed scenario according to an embodiment of the present invention. It should be noted that the embodiments of the present invention can be applied to a speed control board in a vehicle simulation fixture, which includes a vehicle simulation model, a speed control board, a motor drive board, a tachometer motor, and a speed sensor.

[0057] Figure 1 The flowchart of the speed estimation method for a permanent magnet synchronous motor in an ultra-low speed scenario provided by an embodiment of the present invention, shown below, includes:

[0058] Step S101: Obtain the initialization state parameters of the permanent magnet synchronous motor in the ultra-low speed scenario from the vehicle simulation model.

[0059] In this embodiment of the invention, the vehicle simulation model can simulate the vehicle's operating state and periodically output the vehicle's operating speed. When the vehicle's operating speed is in an ultra-low speed scenario, the initialization state parameters of the permanent magnet synchronous motor can be obtained from the vehicle simulation model.

[0060] In this embodiment of the invention, the initialization state parameter can be the d-axis voltage of the motor. q-axis voltage d-axis current q-axis current Stator equivalent resistance d-axis equivalent inductance q-axis equivalent inductance Magnetic Link The number of pole pairs p, and the angular velocity and rotational speed measured using the M / T method. In this regard, the embodiments of the present invention do not limit the scope of the invention.

[0061] Optionally, the initialization state parameters of the permanent magnet synchronous motor can be represented by the following functional relationship:

[0062]

[0063]

[0064] in, The angular velocity of the motor. With mechanical angular velocity The relationship is .

[0065] Step S102: Calculate the initial state parameters based on the pre-set particle covariance matrix to obtain the particle parameters.

[0066] In this embodiment of the invention, the particle parameters include at least the initial particle parameters and initial particle weights of multiple particles.

[0067] As an optional implementation, step S102 calculates the initialization state parameters based on a pre-set particle covariance matrix, and the specific method for obtaining the particle parameters can be as follows:

[0068] The initial state parameters are calculated based on the pre-set particle covariance matrix and random numbers to obtain the initial particle parameters of multiple particles.

[0069] Determine the number of particles in a plurality of particles;

[0070] The initial particle weight of each particle is determined based on the number of particles.

[0071] The initial particle parameters and initial particle weights of each particle are determined as particle parameters.

[0072] In this embodiment of the invention, particle parameters are initialized. The calculation formula can be:

[0073]

[0074] Where rand represents a normally distributed random number with a mean of 0 and a variance of 1, N is the total number of particles, and P is the particle covariance matrix.

[0075] In this embodiment of the invention, the particle weights are initialized. The calculation formula can be:

[0076]

[0077] Step S103: Calculate the particle parameters based on the pre-established state equations to obtain the current particle parameters of each particle.

[0078] In this embodiment of the invention, particle parameters can be calculated using the Particle Sliding Mode Observer Filtering (PSMOF) algorithm to obtain the current particle parameters of each particle.

[0079] As an optional implementation, step S103 calculates the particle parameters based on a pre-established state equation to obtain the current particle parameters of each particle in the following specific ways:

[0080] Determine the sliding mode gain and the system sliding surface;

[0081] The initial particle parameters of each particle are calculated based on the sliding mode gain, the system sliding surface, random numbers, and pre-established state equations to obtain the current particle parameters of each particle.

[0082] In this embodiment of the invention, the pre-established state equation can be:

[0083]

[0084] , , .

[0085] in, The particle parameters represent the current sampling time. The particle parameters represent the parameters at the previous sampling time. The electric rotation angle of the permanent magnet synchronous motor. J is the motor torque of the permanent magnet synchronous motor, and J is the moment of inertia of the permanent magnet synchronous motor, which can be a constant; B is the rotational damping of the permanent magnet synchronous motor, which can also be a constant. This represents system noise and can be a random quantity.

[0086] In this embodiment of the invention, the formula for calculating the current particle parameters of each particle can be:

[0087]

[0088] in, For the pre-established state equations, Represents the sliding surface of the system, and The calculation formula can be:

[0089]

[0090] in, For system observations, These are measured values.

[0091] Step S104: Update the particle parameters using a preset update equation to obtain the current particle weight of each particle.

[0092] As an optional implementation, step S104 updates the particle parameters using a preset update equation to obtain the current particle weight of each particle in the following specific way:

[0093] Determine the preset parameters;

[0094] The initial particle weights of each particle are updated based on the base of the natural logarithm, the preset parameters, and the preset update equation to obtain the current particle weights of each particle.

[0095] In this embodiment of the invention, the preset update equation can be:

[0096]

[0097] Where e is the base of the natural logarithm; R is a preset parameter, which can be a constant selected by the user.

[0098] Step S105: Calculate the current particle parameters and current particle weights of each particle based on the pre-established observation equations to obtain the speed estimation data of the permanent magnet synchronous motor.

[0099] As an optional implementation, step S105, which calculates the current particle parameters and current particle weights of each particle based on a pre-established observation equation to obtain the speed estimation data of the permanent magnet synchronous motor, can specifically be as follows:

[0100] The current particle weights of each particle are normalized to obtain the normalized weights of each particle.

[0101] The normalized weights of each particle are resampled to obtain the resampled weights of each particle.

[0102] Based on the resampling weights of each particle and the current particle parameters of each particle, parameter estimation is performed to obtain the estimated parameters of each particle.

[0103] The estimated parameters of each particle are calculated based on the pre-established observation equations to obtain the speed estimation data of the permanent magnet synchronous motor.

[0104] In this embodiment of the invention, the formula for normalizing the current particle weight of each particle can be:

[0105]

[0106] In this embodiment of the invention, the normalized weights of each particle can be resampled by removing particles with smaller weights and copying particles with larger weights, while ensuring that the total number of particles is N.

[0107] In this embodiment of the invention, the estimated parameters The calculation formula can be:

[0108]

[0109] In this embodiment of the invention, the speed estimation data of the permanent magnet synchronous motor may include mechanical angular velocity. and motor angular velocity Mechanical angular velocity The calculation formula can be:

[0110]

[0111] in, It can be a pre-established observation equation, which can be:

[0112]

[0113] in, For measuring noise, it is a random quantity.

[0114] In this embodiment of the invention, the motor angular velocity The calculation formula can be:

[0115]

[0116] As an optional implementation, after step S105, the following steps may also be performed:

[0117] The current particle weights of each particle are reset to obtain the initial particle weights of each particle. The main purpose of this step is to avoid the increase in computational complexity caused by the increase in the dimensionality of the weight parameters. Resetting the weights in each iteration can reduce the consumption of computing resources and improve computational efficiency.

[0118] The current particle parameters are updated based on the state equation to obtain the updated current particle parameters of each particle, and steps S104 to S105 are executed.

[0119] As an optional implementation, the motor drive board is used to receive the speed estimation data sent by the speed control board, and control the operation of the speed measuring motor through speed control information containing the speed estimation data;

[0120] The speed measuring motor is used to operate based on the speed control information and drive the speed sensor to work;

[0121] The speed sensor is used to periodically output speed pulses during operation.

[0122] Please refer to the following: Figure 2 , Figure 2 This diagram illustrates the motor speeds measured using the traditional M / T method and the speed estimation method for permanent magnet synchronous motors in ultra-low speed scenarios provided in an embodiment of this invention. It can be seen that the motor speed directly measured using the traditional M / T method exhibits speed oscillations in ultra-low speed scenarios, failing to accurately determine the possible speed. In contrast, the motor speed obtained using the speed estimation method for permanent magnet synchronous motors proposed in this embodiment is stable and convergent.

[0123] This invention can obtain the initial state parameters of a permanent magnet synchronous motor in ultra-low speed scenarios, and can calculate the speed estimation data of the permanent magnet synchronous motor by using preset state equations, update equations and observation equations, so as to improve the accuracy of the speed estimation results of the permanent magnet synchronous motor in ultra-low speed scenarios.

[0124] Exemplary device

[0125] After introducing the method of exemplary embodiments of the present invention, the following references are made. Figure 3 An exemplary embodiment of the present invention describes a speed estimation device for a permanent magnet synchronous motor in an ultra-low speed scenario. This device is applied to a speed control board of a vehicle simulation fixture. The vehicle simulation fixture includes a vehicle simulation model, a speed control board, a motor drive board, a tachometer motor, and a speed sensor. The device includes:

[0126] The acquisition unit 301 is used to acquire the initialization state parameters of the permanent magnet synchronous motor in the ultra-low speed scenario from the vehicle simulation model;

[0127] The first calculation unit 302 is used to calculate the initial state parameters based on the pre-set particle covariance matrix to obtain particle parameters; wherein, the particle parameters include at least the initial particle parameters and initial particle weights of multiple particles.

[0128] The second calculation unit 303 is used to calculate the particle parameters based on the pre-established state equations to obtain the current particle parameters of each particle.

[0129] The update unit 304 is used to update the particle parameters through a preset update equation to obtain the current particle weight of each particle.

[0130] The third calculation unit 305 is used to calculate the current particle parameters and current particle weights of each particle based on the pre-established observation equations, so as to obtain the speed estimation data of the permanent magnet synchronous motor.

[0131] Exemplary media

[0132] After introducing the methods and apparatus of exemplary embodiments of the present invention, the following references are made. Figure 4 A computer-readable storage medium according to exemplary embodiments of the present invention will be described, please refer to... Figure 4 The computer-readable storage medium shown is an optical disc 40, on which a computer program (i.e., a program product) is stored. When the computer program is run by a processor, it implements the steps described in the above method implementation, such as: obtaining the initial state parameters of the permanent magnet synchronous motor in an ultra-low speed scenario from the vehicle simulation model; calculating the initial state parameters based on a pre-set particle covariance matrix to obtain particle parameters; wherein the particle parameters include at least the initial particle parameters and initial particle weights of multiple particles; calculating the particle parameters based on a pre-established state equation to obtain the current particle parameters of each particle; updating the particle parameters through a pre-set update equation to obtain the current particle weights of each particle; calculating the current particle parameters and current particle weights of each particle based on a pre-established observation equation to obtain the speed estimation data of the permanent magnet synchronous motor; the specific implementation of each step will not be repeated here.

[0133] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here.

[0134] Exemplary computing device

[0135] After introducing the methods, apparatus, and media of exemplary embodiments of the present invention, the following references are made. Figure 5 A computational device for speed estimation of a permanent magnet synchronous motor in an ultra-low speed scenario, according to an exemplary embodiment of the present invention.

[0136] Figure 5 A block diagram is shown of an exemplary computing device 50 suitable for implementing embodiments of the present invention, which may be a computer system or a server. Figure 5 The computing device 50 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0137] like Figure 5 As shown, the components of computing device 50 may include, but are not limited to: one or more processors or processing units 501, system memory 502, and bus 503 connecting different system components (including system memory 502 and processing unit 501).

[0138] The computing device 50 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computing device 50, including volatile and non-volatile media, removable and non-removable media.

[0139] System memory 502 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 5021 and / or cache memory 5022. Computing device 50 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, ROM 5023 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 5 (Not shown in the image, usually referred to as "hard drive"). Although not shown in... Figure 5The diagram illustrates that disk drives for reading and writing to removable non-volatile disks (e.g., "floppy disks") and optical disc drives for reading and writing to removable non-volatile optical discs (e.g., CD-ROMs, DVD-ROMs, or other optical media) can be provided. In these cases, each drive can be connected to bus 503 via one or more data media interfaces. System memory 502 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0140] A program / utility 5025 having a set (at least one) of program modules 5024 may be stored, for example, in system memory 502, and such program modules 5024 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment. Program modules 5024 typically perform the functions and / or methods described in the embodiments of the present invention.

[0141] The computing device 50 can also communicate with one or more external devices 504 (such as a keyboard, pointing device, display, etc.). This communication can be performed through the input / output (I / O) interface 505. Furthermore, the computing device 50 can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 506. Figure 5 As shown, network adapter 506 communicates with other modules of computing device 50 (such as processing unit 501) via bus 503. It should be understood that, although... Figure 5 As not shown, it can be used in conjunction with computing device 50 with other hardware and / or software modules.

[0142] The processing unit 501 executes various functional applications and data processing by running programs stored in the system memory 502. For example, it obtains the initial state parameters of the permanent magnet synchronous motor in an ultra-low speed scenario from the vehicle simulation model; calculates the initial state parameters based on a pre-set particle covariance matrix to obtain particle parameters; wherein the particle parameters include at least the initial particle parameters and initial particle weights of multiple particles; calculates the particle parameters based on a pre-established state equation to obtain the current particle parameters of each particle; updates the particle parameters through a preset update equation to obtain the current particle weights of each particle; and calculates the current particle parameters and current particle weights of each particle based on a pre-established observation equation to obtain the speed estimation data of the permanent magnet synchronous motor. The specific implementation methods of each step will not be repeated here. It should be noted that although several units / modules or sub-units / sub-modules of the speed estimation device for the permanent magnet synchronous motor in an ultra-low speed scenario are mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of a unit / module described above can be further divided into multiple units / modules for specificity.

[0143] In the description of this invention, it should be noted that the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0144] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0145] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0146] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0147] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0148] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0149] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0150] Furthermore, although the operations of the method of the present invention are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

Claims

1. A method for estimating the speed of a permanent magnet synchronous motor in ultra-low speed scenarios, characterized in that, The vehicle simulation fixture includes a vehicle simulation model, a speed control board, a motor drive board, a tachometer motor, and a speed sensor. The method is applied to the speed control board, and the method includes: The initialization state parameters of the permanent magnet synchronous motor in the ultra-low speed scenario are obtained from the vehicle simulation model. The initial state parameters are calculated based on the pre-set particle covariance matrix to obtain particle parameters; wherein, the particle parameters include at least the initial particle parameters and initial particle weights of multiple particles. The particle parameters are calculated based on the pre-established state equations to obtain the current particle parameters of each particle. The particle parameters are updated using a preset update equation to obtain the current particle weight of each particle. Based on the pre-established observation equations, the current particle parameters and current particle weights of each particle are calculated to obtain the speed estimation data of the permanent magnet synchronous motor.

2. The speed estimation method for permanent magnet synchronous motors in ultra-low speed scenarios according to claim 1, characterized in that: The motor drive board is used to receive the speed estimation data sent by the speed control board, and control the operation of the speed measuring motor through speed control information containing the speed estimation data; The speed measuring motor is used to operate based on the speed control information and drive the speed sensor to work; The speed sensor is used to periodically output speed pulses during operation.

3. The speed estimation method for a permanent magnet synchronous motor in an ultra-low speed scenario according to claim 1, wherein the calculation of the initial state parameters based on a pre-set particle covariance matrix to obtain particle parameters includes: The initial state parameters are calculated based on the pre-set particle covariance matrix and random numbers to obtain the initial particle parameters of multiple particles. Determine the number of particles in a plurality of particles; The initial particle weight of each particle is determined based on the number of particles. The initial particle parameters and initial particle weights of each particle are determined as particle parameters.

4. The speed estimation method for a permanent magnet synchronous motor in an ultra-low speed scenario according to claim 1, wherein the calculation of the particle parameters based on a pre-established state equation to obtain the current particle parameters of each particle includes: Determine the sliding mode gain and the system sliding surface; The initial particle parameters of each particle are calculated based on the sliding mode gain, the system sliding surface, random numbers, and pre-established state equations to obtain the current particle parameters of each particle.

5. The method for estimating the rotational speed of a permanent magnet synchronous motor in an ultra-low speed scenario according to claim 4, wherein updating the particle parameters through a preset update equation to obtain the current particle weight of each particle includes: Determine the preset parameters; The initial particle weights of each particle are updated based on the base of the natural logarithm, the preset parameters, and the preset update equation to obtain the current particle weights of each particle.

6. The method for estimating the rotational speed of a permanent magnet synchronous motor in an ultra-low speed scenario according to claim 1, wherein the step of calculating the current particle parameters and current particle weights of each particle based on a pre-established observation equation to obtain the rotational speed estimation data of the permanent magnet synchronous motor includes: The current particle weights of each particle are normalized to obtain the normalized weights of each particle. The normalized weights of each particle are resampled to obtain the resampled weights of each particle. Based on the resampling weights of each particle and the current particle parameters of each particle, parameter estimation is performed to obtain the estimated parameters of each particle. The estimated parameters of each particle are calculated based on the pre-established observation equations to obtain the speed estimation data of the permanent magnet synchronous motor.

7. The method for estimating the rotational speed of a permanent magnet synchronous motor in an ultra-low speed scenario according to claim 1, after calculating the current particle parameters and current particle weights of each particle based on a pre-established observation equation to obtain the rotational speed estimation data of the permanent magnet synchronous motor, the method further includes: The current particle weights of each particle are reset to obtain the initial particle weights of each particle. The current particle parameters are updated based on the state equation to obtain the updated current particle parameters of each particle. Then, the process is performed from updating the particle parameters through the preset update equation to obtain the current particle weight of each particle, to calculating the current particle parameters and current particle weight of each particle based on the pre-established observation equation to obtain the speed estimation data of the permanent magnet synchronous motor.

8. A speed estimation device for a permanent magnet synchronous motor in ultra-low speed scenarios, characterized in that, The vehicle simulation fixture includes a vehicle simulation model, a speed control board, a motor drive board, a tachometer motor, and a speed sensor. The device is applied to the speed control board and includes: The acquisition unit is used to acquire the initialization state parameters of the permanent magnet synchronous motor in the ultra-low speed scenario from the vehicle simulation model. The first calculation unit is used to calculate the initial state parameters based on the pre-set particle covariance matrix to obtain particle parameters; wherein, the particle parameters include at least the initial particle parameters and initial particle weights of multiple particles. The second calculation unit is used to calculate the particle parameters based on the pre-established state equations to obtain the current particle parameters of each particle. The update unit is used to update the particle parameters through a preset update equation to obtain the current particle weight of each particle. The third calculation unit is used to calculate the current particle parameters and current particle weights of each particle based on the pre-established observation equations, so as to obtain the speed estimation data of the permanent magnet synchronous motor.

9. A computing device, the computing device comprising: At least one processor, memory, and input / output unit; The memory is used to store computer programs, and the processor is used to call the computer programs stored in the memory to execute the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium comprising instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 1 to 7.

Images