Motor model prediction method based on vector transformation, storage medium and electronic device

Abstract

This application provides a vector-conversion-based motor model prediction method, storage medium, and electronic device. The vector-conversion-based motor model prediction method includes the following steps: constructing prediction models for torque and flux linkage; selecting two parallel cost functions to eliminate weighting factors and extraneous parameters; obtaining the optimal voltage vector from the two parallel cost functions; and designing the control block diagram corresponding to the above control strategy. To address the issues of weighting factors and extraneous parameters in the torque prediction control cost function, two parallel cost functions are designed, and compensation measures are taken for time delay. Then, more theoretically grounded logic is used to filter the optimal voltage vector, effectively reducing torque ripple and stator flux, and improving system performance.

Description

Technical Field

[0001] This invention relates to the technical field of permanent magnet synchronous motor control, and in particular to a motor model prediction method, storage medium, and electronic device based on vector transformation. Background Technology

[0002] Permanent magnet synchronous motors have advantages such as high power density, high efficiency, and simple structure, and are widely used in industrial fields such as electric vehicles and rail traction.

[0003] Under the aforementioned application conditions, direct control of electromagnetic torque is typically required. Direct torque control (DTC) systems offer advantages such as simple structure, good dynamic performance, and strong robustness. However, because the optimal switching table for DTC is established offline, DTC suffers from drawbacks such as large stator flux and significant electromagnetic torque fluctuations.

[0004] To further improve the performance of Direct Current Turbocharging (DTC), Prediction Torque Control (PTC) has been proposed. While inheriting the advantages of DTC, it performs online calculations for voltage vector selection. Traditional PTC introduces weighting factors to address the issue of different units for flux linkage and torque, but its design still lacks theoretical support. Existing technologies have proposed a series of control schemes for weighting factors, such as online optimization of weighting factors using algebraic methods, decomposition of the cost function, or unit conversion to eliminate weighting factors. The first two schemes require enormous computational resources, while the third scheme has poor versatility and is difficult to promote.

[0005] In existing technologies, sequential torque control (S-PTC) and parallel torque control (P-PTC) have been proposed to eliminate weighting factors. However, fundamental issues with S-PTC remain to be addressed, such as the execution order of the cost function and the number of voltage vectors selected in the first step. Furthermore, the additional parameters introduced by P-PTC lack theoretical support. Therefore, resolving the issue of these additional parameters is crucial for achieving high-performance P-PTC. Summary of the Invention

[0006] The purpose of this invention is to address the shortcomings of existing technologies by providing a motor model prediction method based on vector conversion. By using two parallel cost functions, it eliminates weighting factors and employs a different logic for optimal voltage vector selection than traditional P-PTC to effectively solve the problem of introducing additional parameters. This allows for quantitative analysis of the specific effects of voltage vectors under different operating conditions, enabling the acquisition of the optimal voltage vector control method. This reduces torque ripple and stator flux, thereby improving system performance.

[0007] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0008] According to one aspect of the embodiments of this application, a motor model prediction method based on vector transformation is provided, comprising the following steps:

[0009] Construct predictive models for torque and flux linkage;

[0010] The cost function of the effect of the parallel voltage vector on the stator flux, and the cost function of the effect of the voltage vector on the electromagnetic torque;

[0011] The optimal voltage vector is obtained based on the optimal solution of two parallel cost functions.

[0012] In some embodiments, the prediction model for torque and flux linkage is defined by the dq axis of the coordinate system and expressed by the following expression:

[0013]

[0014] ψ sq =L q i sq

[0015] T e =n p (ψ sd i sq -ψ sq i sd )

[0016] u sd =R s i sd +pψ sd -ω r ψ sq

[0017] u sq =R s i sq +pψ sq +ω r ψ sd

[0018] In the formula, i sd i sq and ψ sd ψ sq These are the stator current and stator flux linkage on the dq axis, respectively, ψ f For permanent magnet flux linkage, L d and L q For the dq axis inductance, R s n is the stator resistance. pLet p be the extreme logarithm, p be the differential operator, and T be the extreme logarithm. e For electromagnetic torque, u sd and u sq These are the stator voltages on the dq axis, ω r ω is the electric angular velocity.

[0019] In some embodiments, a stator flux linkage observer based on a current model obtains the electromagnetic torque at the current moment and expresses it using the following expression:

[0020]

[0021] ψ sq [k]=L q i sq [k]

[0022] T e [k] = n p (ψ sd [k]i sq [k]-ψ sq [k]i sd [k])

[0023] In the formula, k is the value of the relevant variable at the current moment.

[0024] In some embodiments, the stator voltage equation is discretized using the first-order Euler method to obtain the stator flux linkage at time k+1, and expressed by the following expression:

[0025]

[0026]

[0027] ψ sd [k+1]=T s (u sd [k]-R s i sd [k]+ω r ψ sq [k])+ψ sd [k]

[0028] ψ sq [k+1]=T s (u sq [k]-R s i sq [k]-ω r ψ sd [k])+ψ sq [k]

[0029] In the formula, k+1 represents the value of the relevant variable at the next control time, ψ sd [k+1] and ψsq [k+1] represents the stator flux linkage and electromagnetic torque at time k+1.

[0030] In some embodiments, seven corresponding voltage vectors are obtained based on the effect of the voltage vector on the stator flux, wherein the cost function of the effect of the voltage vector on the stator flux is represented by the following expression:

[0031] g1=|Δψ s |

[0032] Δψ s =|ψ s | * -|ψ s [k+2]|

[0033] In the formula, |ψ s | * This is a reference value for the stator flux linkage;

[0034] Select three voltage vectors from the seven voltage vectors that minimize the cost function g1.

[0035] In some embodiments, seven corresponding voltage vectors are obtained based on the effect of the voltage vector on the electromagnetic torque, wherein the cost function of the effect of the voltage vector on the electromagnetic torque is represented by the following expression:

[0036] g2=|ΔT e |

[0037] ΔT e =T e * -T e [k+2]

[0038] In the formula, T e * This is a reference value for the electromagnetic torque;

[0039] Select three voltage vectors from the seven voltage vectors that minimize the cost function g2.

[0040] In some embodiments, the minimum value of the voltage vector is mapped to the three nearest points to a point on the x-axis or y-axis of the stator flux-oriented coordinate system, wherein the stator equations in the stator flux-oriented coordinate system are expressed by the following expression:

[0041] u sx =R s i sx +p|ψ s |

[0042] u sy =R s i sy +(ωr +pδ)|ψ s |

[0043] In the formula, i sx i sy and u sx u sy These are the stator current and stator voltage on the x and y axes, respectively, and δ is the angle between the rotor flux vector and the stator flux vector.

[0044] In some embodiments, based on the optimal solution ψ of the flux linkage control cost function s_min And the optimal solution T of the electromagnetic torque control cost function. e_min Determine the ψ in the optimal solution s_min and T e_min The intersection is used as the optimal voltage vector.

[0045] According to another aspect of the embodiments of this application, a storage medium is provided that stores computer-readable instructions thereon, which, when executed by a computer's processor, cause the computer to perform the above-described motor model prediction method based on vector transformation.

[0046] According to another aspect of the embodiments of this application, an electronic device is provided, comprising:

[0047] One or more processors;

[0048] A storage device for storing one or more programs that, when executed by one or more processors, enable the electronic device to implement the motor model prediction method based on vector transformation as described above.

[0049] In the technical solution of this application embodiment, a better voltage vector is obtained by setting two parallel cost functions. In order to address the issues of weighting factors and additional parameters in the torque prediction control cost function, two parallel cost functions are designed, and compensation measures are taken for time delay. Clear expressions are set to support the execution steps, thereby facilitating the selection of a better voltage vector to ensure better control of electromagnetic torque. At the same time, the control effect of electromagnetic torque is improved, torque ripple and stator flux are effectively reduced, and system performance is improved. Attached Figure Description

[0050] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0051] Figure 1 This is a flowchart of a model predictive control method for predicting the parallel torque of a permanent magnet synchronous motor.

[0052] Figure 2 It is ψ s_min A schematic diagram of the sector during the judgment process;

[0053] Figure 3 It is T e_min A schematic diagram of the sector during the judgment process;

[0054] Figure 4 This is an optimal voltage vector selection case for the stator flux linkage;

[0055] Figure 5 This is another optimal voltage vector selection case for the stator flux linkage;

[0056] Figure 6 This is another optimal voltage vector selection case for the stator flux linkage;

[0057] Figure 7 This represents an optimal voltage vector selection for electromagnetic torque.

[0058] Figure 8 This is another optimal voltage vector selection case for electromagnetic torque;

[0059] Figure 9 This is another optimal voltage vector selection case for electromagnetic torque;

[0060] Figure 10 This is an example of an IP-PTC control block diagram for predictive model control of parallel torque prediction in permanent magnet synchronous motors.

[0061] Figure 11 This is a schematic diagram of the structure of an electronic device illustrated in an exemplary embodiment of this application. Detailed Implementation

[0062] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art.

[0063] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0064] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0065] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0066] It should be noted that "multiple" in this article refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0067] like Figure 1 As shown, this application provides a motor model prediction method based on vector transformation, including the following steps:

[0068] Construct predictive models for torque and flux linkage;

[0069] The cost function of the effect of the parallel voltage vector on the stator flux, and the cost function of the effect of the voltage vector on the electromagnetic torque;

[0070] The optimal voltage vector is obtained based on the optimal solution of two parallel cost functions.

[0071] Combining the above steps, this application improves the existing PTC by using two parallel cost functions. Through these steps, it eliminates the weighting factor and uses a different logic for optimal voltage vector selection than the traditional P-PTC to effectively solve the problem of introducing additional parameters.

[0072] Specifically, the prediction model for torque and flux linkage is defined using the dq axis of the coordinate system and expressed by the following expressions:

[0073]

[0074] ψ sq =L q i sq (2)

[0075] T e =n p (ψ sd i sq -ψ sq i sd (3)

[0076] u sd =R s i sd +pψ sd -ω r ψ sq (4)

[0077] u sq =R s i sq +pψ sq +ω r ψ sd (5)

[0078] In the formula, i sd i sq and ψ sd ψ sq These are the stator current and stator flux linkage on the d-axis and q-axis, respectively, ψ f For permanent magnet flux linkage, L d and L q For the dq axis inductance, R s n is the stator resistance. p Let T be the extreme logarithm, p be the differential operator, and T be the extreme logarithm. e For electromagnetic torque, u sd and u sq These are the stator voltages on the d-axis and q-axis, respectively, ω r ω is the electric angular velocity.

[0079] The above expressions represent the functional relationships between stator flux linkage and stator current on the d-axis and q-axis, the electromagnetic torque, and the stator voltage. This allows for clear acquisition and recording of data relationships between variables and independent variables under different conditions.

[0080] Furthermore, this application employs a stator flux linkage observer based on a current model to obtain the electromagnetic torque at the current moment, which is expressed by the following expression:

[0081]

[0082] ψ sq [k]=L q i sq [k] (7)

[0083] T e [k] = n p (ψ sd [k]i sq [k]-ψ sq [k]i sd [k]) (8)

[0084] In the formula, k is the value of the relevant variable at the current time. It should be noted that the other variables are defined in the same way as the variables in the mathematical model on the dq axis, and will not be repeated here. Other expressions are explained here.

[0085] Combining the mathematical model on the dq axis, the stator voltage equation is discretized using the first-order Euler method. The stator flux linkage at time k+1 is obtained iteratively and expressed by the following expression:

[0086]

[0087]

[0088] ψ sd [k+1]=T s (u sd [k]-R s i sd [k]+ω r ψ sq [k])+ψ sd [k] (11)

[0089] ψ sq [k+1]=T s (u sq [k]-R s i sq [k]-ω r ψ sd [k])+ψ sq [k] (12)

[0090] In the formula, k+1 represents the value of the relevant variable at the next control time, ψ sd [k+1] and ψ sq [k+1] represents the stator flux linkage and electromagnetic torque at time k+1.

[0091] The above expressions facilitate the acquisition and recording of the values ​​of relevant variables at different times. It is worth noting that, to improve data reliability, this application considers a one-bit delay, including the variables at time k+2 in the calculation. Similar to time k, based on k+1, the following expression is used:

[0092] ψ sd [k+2]=T s (u sd [k+1]-R s i sd [k+1]+ω r ψ sq [k+1])+ψ sd [k+1] (13)

[0093] ψ sq [k+2]=T s (u sq [k+1]-R s i sq [k+1]-ω r ψ sd [k+1])+ψ sq [k+1] (14)

[0094] Simultaneously, based on the stator flux linkage and electromagnetic torque on the mathematical model along the dq axis, the stator flux linkage and electromagnetic torque at time k+2 are expressed, and the values ​​of relevant variables at that time are obtained, represented by the following expressions:

[0095]

[0096] T e [k+2]=n p (ψ sd [k+2]i sq [k+2]-ψ sq [k+2]i sd [k+2]) (16)

[0097] In the formula, ψ s [k+2] and T e [k+2] represents the stator flux linkage and electromagnetic torque at time k+2.

[0098] like Figure 2 As shown, seven corresponding voltage vectors are obtained based on the effect of the voltage vector on the stator flux. The cost function of the effect of the voltage vector on the stator flux is expressed by the following expression:

[0099] g1=|Δψ s | (17)

[0100] Δψ s=|ψ s | * -|ψ s [k+2]| (18)

[0101] In the formula, |ψ s | * This is the reference value for the stator flux linkage.

[0102] Three voltage vectors are selected from the seven voltage vectors that minimize the cost function g1, based on the following expression:

[0103] ψ s_min ={V x V y V z} (19)

[0104] g1(V x )≤g1(V y )≤g1(V z )≤g1(V l (20)

[0105] The three voltage vectors selected based on the above expression have a better control effect on the stator flux linkage than other voltage vectors.

[0106] like Figure 3 As shown, the voltage vectors are V(100), V(110), and V(111), where x, y, and z correspond to 1, 2, and 7, respectively. Here, x, y, and z represent three elements selected from seven sets, and the other set represents four elements other than these three. Thus, the three selected voltage vectors have a better control effect on the stator flux linkage than the other voltage vectors.

[0107] Similarly, such as Figure 3 As shown, seven corresponding voltage vectors are obtained based on the effect of the voltage vector on the electromagnetic torque. The cost function of the effect of the voltage vector on the electromagnetic torque is expressed by the following expression:

[0108] g2=|ΔT e | (21)

[0109] ΔT e =T e * -T e [k+2] (22)

[0110] In the formula, T e * This is a reference value for the electromagnetic torque.

[0111] Three voltage vectors are selected from the seven voltage vectors that minimize the cost function g2, based on the following expression:

[0112] T e_min ={V p V q V r} (twenty three)

[0113] g2(V p )≤g2(V q )≤g2(V r )≤g2(V k ) (twenty four)

[0114] The three voltage vectors selected based on the above expression have a better control effect on electromagnetic torque than other voltage vectors.

[0115] Where p, q, and r correspond to 1, 2, and 7 respectively. Therefore, the three selected voltage vectors exhibit better control over the stator flux linkage than other voltage vectors.

[0116] Based on the control method of the two parallel cost functions mentioned above, the stator flux linkage and electromagnetic torque are controlled by two independent cost functions respectively, so that each function contains only one controlled object, thereby achieving the purpose of eliminating weighting factors.

[0117] The minimum value of the voltage vector is mapped to the three nearest points to a point on the x-axis or y-axis of the stator flux-oriented coordinate system. In the stator flux-oriented coordinate system, the stator equation is expressed by the following expression:

[0118] u sx =R s i sx +p|ψ s | (25)

[0119] u sy =R s i sy +(ω r +pδ)|ψ s | (26)

[0120] In the formula, i sx i sy and u sx u sy These are the stator current and stator voltage on the x and y axes, respectively, and δ is the angle between the rotor flux vector and the stator flux vector.

[0121] Based on the above control relationship of stator flux, the control of stator flux is only related to the x-axis component of the stator voltage vector. By using this special parameter relationship, a new function model can be established, and the original parameters can be transformed in the same way. This transforms the above minimum value problem into finding the three points closest to a certain point on the x-axis or y-axis. In this way, the correlation data can be directly obtained from several points on the straight line, making the acquisition of the minimum value more intuitive and saving the time of parameter comparison.

[0122] On the one hand, based on the method for obtaining the minimum value mentioned above, the optimal solution of the flux linkage control cost function is sought, such as... Figure 2 As shown, the stator flux linkage is currently within the first sector. Points A, B, C, and D are the projections of voltage vectors V1(100), V2(110), V6(101), and V3(010) onto the x-axis, respectively. Point E is the midpoint of AO, and point F is the midpoint of BD. When Δψ s When the value is ≥0, the stator flux linkage should be increased. In this case, the voltage drop across the stator resistance is negligible. sx It must be on the positive half of the x-axis, now according to u sx The location will be discussed in three cases:

[0123] One: such as Figure 4 As shown, u sx To the right of point E, points A, B, and C are at a distance from u. sx The nearest point, i.e., ψ s_min It is {V1,V2,V6};

[0124] Second: such as Figure 5 As shown, u sx Between points E and F, points O, B, and C are at a distance u. sx The nearest point, i.e., ψ s_min It is {V7,V2,V6};

[0125] Three: such as Figure 6 As shown, u sx Between points F and O, points O, D, and C are at a distance from u. sx The nearest point, i.e., ψ s_min It is {V7,V3,V6}.

[0126] Let {V1,V2,V6}, {V7,V2,V6}, and {V7,V3,V6} be denoted as ψ. s_1 ,ψ s_2 and ψ s_3 Then there is Ψ. s_min {Ψ s_1 ,Ψ s_2 ,Ψ s_3}

[0127] To illustrate, based on the method for obtaining the minimum value described above, ΔΨ is...s The stator flux linkage values ​​for different ranges are summarized in Table 1 below:

[0128]

[0129] Table 1 ψ in the first sector s_min All possible components

[0130] On the other hand, based on the method for obtaining the minimum value mentioned above, the optimal solution of the electromagnetic torque control cost function is sought, such as... Figure 3 As shown, the stator flux linkage is currently within the first sector. Points G, H, I, and J are the projections of voltage vectors V3(010), V2(110), V4(011), and V1(100) onto the y-axis, respectively. Point L is the midpoint of OG, and M is the midpoint of JH. When ΔT e When ≥0, the electromagnetic torque should be increased. At this time, the voltage drop across the stator resistance is ignored. sy It must be on the positive half of the y-axis, now according to u sy The location will be discussed in three cases:

[0131] One: such as Figure 7 As shown, u sy Above point L, points I, H, and G are at a distance from u. sy The nearest point, i.e., T e_min It is {V4,V2,V3};

[0132] Second: such as Figure 8 As shown, u sy Between points L and M, points O, I, and H are at a distance from u. sy The nearest point, i.e., T e_min It is {V7,V4,V2};

[0133] Three: such as Figure 9 As shown, u sy Between points M and O, at this point, points J, O, and I are at a distance u. sy The nearest point, i.e., T e_min It is {V1,V7,V4}.

[0134] Let {V4,V2,V3}, {V7,V4,V2}, and {V1,V7,V4} be denoted as T respectively. e_1 ,T e_2 and T e_3 Then there is

[0135] Using the same method, an exemplary illustration will be given. Based on the method for obtaining the minimum value described above, ΔT... e The electromagnetic torque values ​​within different ranges are summarized in Table 2 below:

[0136]

[0137] Table 2, Sector 1, T e_min All possible components

[0138] Based on the optimal solution ψ of the flux linkage control cost function s_min And the optimal solution T of the electromagnetic torque control cost function. e_min Determine the ψ in the optimal solution s_min and T e_min The intersection is used as the optimal voltage vector.

[0139] The specific steps for obtaining the optimal voltage vector are as follows:

[0140] Taking the first sector as an example, ψ is listed s_min and T e_min All possible intersections are shown in Table 3 below:

[0141]

[0142]

[0143] Table 3 ψ in the first sector s_min and T e_min All possible intersections

[0144] As shown in the table above, ψ s_min and T e_min The number of elements in the intersection may only be 1 or 2. This conclusion also applies to other sectors, and based on the elements in the intersection, there are three different cases:

[0145] 1. If the number of elements in the intersection is 1, then this element is directly selected as the optimal voltage vector.

[0146] 2: The number of elements in the intersection is 2, and V p ∈ψ s_min ∩T e_min At this point, the voltage vector V that provides the best electromagnetic torque control effect P It is one of them, therefore the voltage vector V is chosen. P As the optimal voltage vector to ensure better control of electromagnetic torque;

[0147] 3: The number of elements in the intersection is 2, and At this point, neither of the two elements is the voltage vector V that provides the best electromagnetic torque control effect. P The common element is T. e_min The remaining two elements: V q and V r At this point, to ensure the control effect of the electromagnetic torque, V is selected. q As the optimal voltage vector.

[0148] To further clarify the disclosure of the vector transformation-based motor model prediction method of this application, the vector transformation-based motor model prediction method of this application includes the following steps:

[0149] Step S1: Construct predictive models for torque and flux linkage;

[0150] Step S2: Select two parallel cost functions to eliminate weighting factors and additional parameters;

[0151] Step S3: Obtain the optimal voltage vector from the two parallel cost functions.

[0152] Based on the above steps, two parallel cost functions were designed to address the issues of weighting factors and additional parameters in the torque predictive control cost function, and compensation measures were implemented to address time delay. Simultaneously, the optimal voltage vector was selected using logic with clear theoretical basis, effectively reducing torque ripple and stator flux, and improving system performance.

[0153] Embodiments of this application also provide an electronic device, including a processor and a memory, wherein the memory stores computer-readable instructions that, when executed by the processor, implement the vector-transformation-based motor model prediction method as described above.

[0154] like Figure 10 As shown, based on the above-mentioned motor model prediction method based on vector transformation, a control block diagram corresponding to the above control strategy is designed and executed by electronic equipment, thereby allowing for a more intuitive observation of the control effect of the permanent magnet synchronous motor model prediction. In the figure, D... A D B D C This indicates the duty cycle of each phase.

[0155] Figure 11 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown.

[0156] It should be noted that, Figure 11 The computer system 900 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0157] like Figure 11As shown, the computer system 900 includes a Central Processing Unit (CPU) 901, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 902 or programs loaded from storage portion 908 into Random Access Memory (RAM) 903, such as performing the methods described in the above embodiments. The RAM 903 also stores various programs and data required for system operation. The CPU 901, ROM 902, and RAM 903 are interconnected via a bus 904. An Input / Output (I / O) interface 905 is also connected to the bus 904.

[0158] The following components are connected to I / O interface 905: an input section 906 including a keyboard, mouse, etc.; an output section 907 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 908 including a hard disk, etc.; and a communication section 909 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to I / O interface 905 as needed. Removable media 911, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 910 as needed so that computer programs read from them can be installed into storage section 908 as needed.

[0159] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 909, and / or installed from removable medium 911. When the computer program is executed by central processing unit (CPU) 901, it performs various functions defined in the system of this application.

[0160] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. The transmitted data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0161] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0162] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0163] In another aspect, this application also provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable storage medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to implement the vector-conversion-based motor model prediction method described in the above embodiments.

[0164] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0165] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the method according to the embodiments of this application.

[0166] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0167] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A motor model prediction method based on vector transformation, characterized in that, Includes the following steps: Construct predictive models for torque and flux linkage; The cost function of the effect of the parallel voltage vector on the stator flux, and the cost function of the effect of the voltage vector on the electromagnetic torque; The optimal voltage vector is obtained based on the optimal solution of the two parallel cost functions; Seven corresponding voltage vectors are obtained based on the effect of the voltage vector on the stator flux. The cost function of the effect of the voltage vector on the stator flux is expressed by the following expression: In the formula, This is a reference value for the stator flux linkage; The stator flux linkage at the (k+2)th control moment; Select three from the seven voltage vectors to make the cost function The smallest voltage vector; Seven corresponding voltage vectors are obtained based on the effect of the voltage vector on the electromagnetic torque. The cost function of the effect of the voltage vector on the electromagnetic torque is expressed by the following expression: In the formula, This is a reference value for the electromagnetic torque; The electromagnetic torque at the (k+2)th control moment; Select three from the seven voltage vectors to make the cost function The smallest voltage vector.

2. The motor model prediction method based on vector transformation according to claim 1, characterized in that, The prediction model for torque and flux linkage is defined using the dq axis of the coordinate system and expressed by the following expressions: In the formula, and They are Stator current and stator flux linkage on the shaft, It is a permanent magnet flux linkage. and for Shaft inductor, For stator resistance, For extreme logarithms, For differential operators, For electromagnetic torque, and They are Stator voltage on the shaft, It represents the electric angular velocity.

3. The motor model prediction method based on vector transformation according to claim 2, characterized in that, A stator flux linkage observer based on a current model obtains the electromagnetic torque at the current moment and expresses it using the following expression: In the formula, This represents the value of the relevant variables at the current moment.

4. The motor model prediction method based on vector transformation according to claim 2, characterized in that, The stator voltage equation is discretized using the first-order Euler method to obtain the stator flux linkage at time k+1, which is expressed by the following expression: In the formula, k+1 represents the value of the relevant variable at the next control time. [k+1] and [k+1] represents the stator flux linkage and electromagnetic torque at time k+1.

5. The motor model prediction method based on vector transformation according to claim 1, characterized in that, The minimum value of the voltage vector is mapped to the three nearest points to a point on the x-axis or y-axis of the stator flux-oriented coordinate system. In the stator flux-oriented coordinate system, the stator equation is expressed by the following expression: In the formula, and They are Stator current and stator voltage on the shaft, The angle between the rotor flux vector and the stator flux vector.

6. The motor model prediction method based on vector transformation according to claim 5, characterized in that, Based on the optimal solution of the flux linkage control cost function And the optimal solution of the electromagnetic torque control cost function. Determining the optimal solution and The intersection is used as the optimal voltage vector.

7. A storage medium, characterized in that, It stores computer-readable instructions that, when executed by a computer's processor, cause the computer to perform the motor model prediction method based on vector transformation as described in any one of claims 1-6.

8. An electronic device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, cause the electronic device to implement the motor model prediction method based on vector transformation as described in any one of claims 1-6.

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