Electric vehicle modularized disc type permanent magnet synchronous motor cooperative control method and system

Abstract

The invention discloses an electric vehicle modularized disc type permanent magnet synchronous motor cooperative control method and system, and the method comprises the steps: obtaining the total demand torque of a system, obtaining the efficiency in different operation modes according to a given rotating speed and the total demand torque of the system, obtaining the highest operation efficiency mode, and obtaining the highest operation efficiency mode; and a corresponding total demand torque instruction value of each group in the mode; based on the given rotating speed and the total demand torque instruction value of each group, solving a torque distribution coefficient of the two motors in the corresponding single group through a particle swarm optimization algorithm so as to obtain respective torque instruction values of the two motors in the single group; calculating a given stator current torque component corresponding to the torque instruction value, and determining a given stator current excitation component with the minimum total loss based on the minimum power loss of a loss model; and enabling the actual stator current of the motor to follow the given value through current closed-loop control. Cooperative management of multiple motors can be achieved through double-layer cooperative management, stability and efficiency are improved, and meanwhile cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle drive control, and in particular to a method and system for coordinated control of a modular disc-type permanent magnet synchronous motor of an electric vehicle. Background Art

[0002] New energy vehicles, especially pure electric vehicles, are attracting more and more attention due to their advantages such as high energy efficiency, energy saving and environmental protection. For commercial electric vehicles, high power, long endurance and high reliability are their development direction. The use scenario of electric vehicles is mainly in urban road conditions. Under complex urban road conditions, the motor drive control system needs to have better speed and stability to cope with various emergencies.

[0003] However, under urban road conditions, the motor's working range varies greatly, and it cannot be guaranteed that it will work in the high-efficiency range, which leads to low efficiency of the power system under comprehensive operating conditions. The traditional electric drive system using a single motor has been restricted in the substantial improvement of efficiency space under such complex industrial control, and cannot well meet the actual needs of improving the driving range; and the research on the coordinated control method of the multi-motor drive system is mostly limited to dual motors, and the multi-motor form lacks hierarchical management and coordinated optimization, and cannot achieve multi-level detailed control, and the reliability and stability are insufficient.

[0004] Therefore, a control method is needed that can improve the coordination of multiple motors to better achieve their reliability and economy. Summary of the invention

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a method and system for collaborative control of modular disc permanent magnet synchronous motors of electric vehicles to solve the problems that the existing methods are restricted in improving the efficiency space under complex industrial control, cannot meet the actual needs of improving the cruising range, lack hierarchical management and collaborative optimization under multi-motor drive, cannot achieve multi-level detailed control, and have insufficient reliability and stability.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a method for coordinated control of a modular disc-type permanent magnet synchronous motor of an electric vehicle, the method being applied to three groups of double-stator-single-rotor disc-type motors, the three groups of double-stator-single-rotor disc-type motors being equivalent to six single-stator-single-rotor permanent magnet synchronous motors, wherein the six single-stator-single-rotor motors are grouped in pairs;

[0009] The method comprises:

[0010] Obtaining the total required torque of the system, obtaining the efficiency under different operating modes according to the given speed and the total required torque of the system, obtaining the highest operating efficiency mode, and each group of total required torque command values ​​corresponding to the mode;

[0011] Based on the given speed and the total required torque command value of each group, the torque distribution coefficient of the two motors in the corresponding single group is obtained by particle swarm optimization algorithm to obtain the torque command value of each of the two motors in the single group;

[0012] Calculating a given stator current torque component corresponding to the torque command value, and determining a given stator current excitation component with the minimum total loss based on the minimum power loss of the loss model;

[0013] The actual stator current of the motor follows its given value through current closed-loop control.

[0014] As a preferred solution of the electric vehicle modular disc permanent magnet synchronous motor coordinated control method of the present invention, the different operation modes include:

[0015] In the first mode, a single motor is enabled to provide all the total required torque;

[0016] In the second mode, two motors are enabled, with each motor providing half of the total required torque;

[0017] In the third mode, three groups of motors are enabled, with each group of motors providing one-third of the total required torque.

[0018] As a preferred solution of the electric vehicle modular disc permanent magnet synchronous motor coordinated control method of the present invention, wherein: the efficiency under different operating modes is obtained according to the given speed and the total required torque of the system, including: defining the relationship between the total required torque of the system and the torque command value of each motor, expressed as:

[0019]

[0020] in, is the total required torque of the motor drive system, is the total required electromagnetic torque command value of the i-th group of motors, are the torque command values ​​of the two motors in the i-th group respectively;

[0021] The efficiency is expressed as:

[0022]

[0023] Where η is the overall efficiency of the drive system, p outi is the total output power of the two motors in the i-th group, ω e is the electrical angular velocity of the motor, N P is the motor speed, plossi is the total loss of the two motors in the i-th group.

[0024] As a preferred solution of the modular disc permanent magnet synchronous motor cooperative control method for electric vehicles described in the present invention, wherein: the torque distribution coefficient of the two motors in the group with the best efficiency is obtained by a particle swarm optimization algorithm, including: determining the solution variables of the objective function as the torque distribution coefficients α and β of the two motors, wherein β=1-α;

[0025] Determine that the dimension for solving the optimal power loss problem is one-dimensional;

[0026] Determine the position range of particles;

[0027] Initialize the particle swarm optimization parameters, including: population size m, maximum number of iterations n, inertia coefficient w, acceleration coefficients c1, c2;

[0028] Update the position and speed of each particle, calculate the fitness value of each particle, update the individual historical optimal position of each particle and the historical optimal position of the group, and update the number of iterations and inertia coefficient;

[0029] Output the optimal allocation coefficient.

[0030] As a preferred solution of the electric vehicle modular disc permanent magnet synchronous motor coordinated control method of the present invention, the inertia coefficient w is expressed as:

[0031]

[0032] Where k is the current iteration number.

[0033] As a preferred solution of the modular disc permanent magnet synchronous motor coordinated control method for electric vehicles of the present invention, wherein: the torque command value of each of the two motors in a single group is obtained by the torque relationship between the total required torque command value of the single group of motors and the torque of each of the two motors in the group after particle swarm optimization;

[0034] The torque relationship is expressed as:

[0035]

[0036] α is the torque distribution coefficient of the first motor in the group, β is the torque distribution coefficient of the second motor in the group,

[0037] are the torque command values ​​of the two motors in the i-th group respectively.

[0038] As a preferred solution of the electric vehicle modular disc permanent magnet synchronous motor coordinated control method of the present invention, wherein: the minimum power loss based on the loss model determines the given stator current excitation component with the minimum total loss, wherein:

[0039] The objective function of the optimization problem of the minimum power loss of the i-th group of disc motors is set as:

[0040]

[0041] Among them, P lossi is the total loss of the two motors in the i-th group, P lossiα , P lossiβ are the losses of the two motors in group i, n * For a given speed.

[0042] In a second aspect, the present invention provides a modular disc-type permanent magnet synchronous motor coordinated control system for an electric vehicle, comprising:

[0043] An inter-group collaborative allocation module is used to obtain the total required torque of the system, obtain the efficiency under different operating modes according to the given speed and the total required torque of the system, obtain the highest operating efficiency mode, and the corresponding total required torque command value of each group in the mode;

[0044] The intra-group collaborative allocation module is used to obtain the torque allocation coefficient of the two motors in the corresponding single group through a particle swarm optimization algorithm based on a given speed and a total required torque command value of each group, so as to obtain the torque command value of each of the two motors in the single group;

[0045] A calculation module, used for calculating a given stator current torque component corresponding to the torque command value, and determining a given stator current excitation component with the minimum total loss based on a minimum power loss of a loss model;

[0046] The control follower module is used to make the actual stator current of the motor follow its given value through current closed-loop control.

[0047] In a third aspect, the present invention provides a computing device, comprising:

[0048] Memory and processor;

[0049] The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the coordinated control method of the modular disc permanent magnet synchronous motor of an electric vehicle are implemented.

[0050] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the method for coordinated control of a modular disc permanent magnet synchronous motor of an electric vehicle.

[0051] Compared with the prior art, the present invention has the following beneficial effects: the present invention adopts three groups of dual-stator-single-rotor disc motors to form a modular drive system, and the dual-stator-single-rotor disc motor is equivalent to two single-stator-single-rotor permanent magnet synchronous motors. Through the inter-group collaborative allocation strategy, the torque provided by each group of motors is determined in real time according to the total required torque of the electric vehicle with the purpose of optimizing efficiency; through the intra-group collaborative allocation strategy, the torque provided by each of the two discs is determined in real time according to the total required torque of the single group of motors with the purpose of minimizing the loss of a single group of two motors, that is, minimizing the loss of a single disc motor; a torque-given motor closed-loop control system is established, and the three-phase voltage applied to each motor within the constraint range is determined according to the required torque, so as to realize the collaborative management of multiple motors, improve stability and efficiency while reducing costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0053] Figure 1 It is a schematic diagram of the overall process logic of the coordinated control method of the modular disc-type permanent magnet synchronous motor of an electric vehicle according to the first embodiment of the present invention;

[0054] Figure 2 It is a schematic diagram of the overall framework structure of the coordinated control method of the modular disc-type permanent magnet synchronous motor of an electric vehicle according to the first embodiment of the present invention;

[0055] Figure 3 This is a block diagram of the coordinated control principle of modular disc-type permanent magnet synchronous motors for electric vehicles, taking a single group as an example, in the coordinated control method of modular disc-type permanent magnet synchronous motors for electric vehicles according to the first embodiment of the present invention;

[0056] Figure 4 A flow chart of the inter-group cooperative allocation strategy in the cooperative control method of a modular disc-type permanent magnet synchronous motor of an electric vehicle according to the second embodiment of the present invention;

[0057] Figure 5 A schematic diagram of a particle swarm algorithm flow in a coordinated control method for a modular disc-type permanent magnet synchronous motor of an electric vehicle according to a first embodiment of the present invention;

[0058] Figure 6 This is a comparison chart of efficiency simulation results of three torque distribution strategies within the full torque range in the coordinated control method of the modular disc permanent magnet synchronous motor of an electric vehicle described in the second embodiment of the present invention. DETAILED DESCRIPTION

[0059] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0060] Example 1, reference Figure 1-Figure 5 , is an embodiment of the present invention, and provides a method for coordinated control of modular disc permanent magnet synchronous motors for electric vehicles, the method being applied to three sets of double-stator-single-rotor disc motors, the three sets of double-stator-single-rotor disc motors being equivalent to six single-stator-single-rotor permanent magnet synchronous motors, wherein the six single-stator-single-rotors are grouped in pairs;

[0061] It should be noted that each disk of the double-disk motor has a different current control loop control. In order to facilitate the control system design, the double-stator-single-rotor disk motor is equivalent to two single-stator-single-rotor permanent magnet synchronous motors.

[0062] like Figure 1-Figure 2 As shown, the method includes:

[0063] S100: Obtaining the total required torque of the system, obtaining the efficiency under different operating modes according to the given speed and the total required torque of the system, obtaining the highest operating efficiency mode, and each group of total required torque command values ​​corresponding to the mode;

[0064] It should be noted that the purpose of the inter-group collaborative allocation in this step is to determine the number of modules to be enabled under different working conditions and the torque contributed by each module so that the system can operate in a high-efficiency space under complex working conditions, thereby optimizing system efficiency.

[0065] In the embodiment of the present application, different operation modes include:

[0066] In the first mode, a single motor is enabled to provide all the total required torque;

[0067] In the second mode, two motors are enabled, with each motor providing half of the total required torque;

[0068] In the third mode, three groups of motors are enabled, with each group of motors providing one-third of the total required torque.

[0069] It should be noted that for multi-motor drive systems, there are two commonly used torque distribution strategies: average distribution and saturation distribution. Average distribution refers to distributing the total required torque to each motor evenly. The advantage of this method is that when the motor parameters are exactly the same, the system loss is minimized when the average distribution is performed. However, when the total torque is small, each motor operates in a low-power and low-efficiency state, making the overall efficiency of the drive system low. Saturation distribution refers to dividing the motor into a master motor and a slave motor. When the total required torque of the system is less than the maximum torque that the master motor can provide, only the master motor is enabled, and the slave motor does not work. When the total required torque of the system is greater than the maximum torque that the master motor can provide, the master motor contributes its maximum torque, and the remaining torque is borne by the slave motor. This method makes the master motor work at the peak torque state, but it does not work at a high efficiency state for the drive system as a whole. Under this distribution strategy, the running time of the master and slave motors is inconsistent, which will make the life span of the master and slave motors differ greatly.

[0070] The three groups of identical disc motor drive systems used in the present invention have the lowest system loss when the torque is evenly distributed. However, when the even distribution method is used, the system efficiency is obviously not high when the three groups are enabled for distribution when the total required torque is low. Therefore, the modular disc motor drive system is divided into the above three working modes. Under different working conditions, the most efficient mode among the three modes is obtained by calculation, the mode is switched, and the torque set value of each group of motors in the corresponding working mode is output.

[0071] In the embodiment of the present application, the efficiency under different operating modes is obtained according to the given speed and the total required torque of the system, including: defining the relationship between the total required torque of the system and the torque command value of each motor, which is expressed as:

[0072]

[0073] in, is the total required torque of the motor drive system, is the total required electromagnetic torque command value of the i-th group of motors, are the torque command values ​​of the two motors in the i-th group respectively;

[0074] The efficiency is expressed as:

[0075]

[0076] Where η is the overall efficiency of the drive system, p outi is the total output power of the two motors in the i-th group, ω e is the electrical angular velocity of the motor, N P is the motor speed, p lossi is the total loss of the two motors in the i-th group.

[0077] It should be noted that the efficiency calculation in the above three modes is generally based on this efficiency formula.

[0078] S200: Based on a given speed and a total required torque command value of each group, a particle swarm optimization algorithm is used to obtain a torque distribution coefficient of the two motors in the corresponding single group to obtain a torque command value of each of the two motors in the single group;

[0079] It should be noted that the purpose of the intra-group collaborative allocation in this step is to obtain the optimal torque allocation coefficients α and β that meet the constraints and minimize the loss of a single motor group under the conditions of a given speed and a total required torque command value for each group.

[0080] In the embodiments of the present application, Figure 5 The particle swarm optimization algorithm is used to obtain the torque distribution coefficient of the two motors in the group with the best efficiency, including:

[0081] S201: Determine that the solution variables of the objective function are the torque distribution coefficients α and β of the two motors, where β=1-α;

[0082] S202: Determine that the dimension for solving the power loss optimization problem is one dimension;

[0083] Therefore, we only need to solve α and we can get β according to the above constraints.

[0084] S203: Determine the position range of the particles. Since the initial position of each particle is random, it is necessary to satisfy various constraints in the mathematical model of the problem being solved. Suppose the solution variables and their constraints are:

[0085]

[0086] Where N is the dimension of the optimal power loss problem to be solved, and x is the solution variable, that is, the optimal torque distribution coefficient α of the motor.

[0087] Define the lower limit x of the particle position min , upper limit x max for:

[0088]

[0089] Then the particle position needs to satisfy:

[0090] x min ≤x≤x max

[0091] For the optimization problem, the expression of the particle position constraint, that is, the range of x values, is:

[0092] 0≤x≤1

[0093] S204: Initialize the particle swarm optimization parameters, including: population size m, maximum number of iterations n, inertia coefficient w, acceleration coefficients c1, c2;

[0094] It should be noted that the above parameters need to be reasonably set according to the characteristics and complexity of the problem to be solved.

[0095] Specifically, if the population size m is too large, the amount of computation for each iteration will be too large. If it is too small, the particles cannot cover the entire state space and are prone to falling into local optimality, affecting the globality and stability of the system. Therefore, the value of m is generally between [20, 1000].

[0096] The maximum number of iterations n represents the total number of iterations of the algorithm. In order to avoid too long a solution time and obtain the global optimal solution, n is generally set between [50-100] according to the complexity of the problem being solved.

[0097] The acceleration coefficients c1 and c2 represent the degree to which the next action of the particle is affected by its own experience and the experience of the group. When it is too small, the learning ability of the particle is not strong and the convergence time is slow. When it is too large, the particle speed changes too fast and will exceed the target area, affecting stability. Usually c1=c1=2 is used, at which time the algorithm has better optimization ability.

[0098] The inertia coefficient w is a coefficient that characterizes the influence of the previous particle velocity on the current particle velocity. Studies have shown that the optimization stability of the population is closely related to the inertia weight, and the system is more stable when w is smaller.

[0099] In the embodiment of the present application, the inertia coefficient w is expressed as:

[0100]

[0101] Where k is the current iteration number.

[0102] S205: Update the position and speed of each particle, calculate the fitness value of each particle, update the individual historical optimal position of each particle and the group historical optimal position, and update the number of iterations and inertia coefficient;

[0103] Specifically, in order to stabilize the initial state of the system, the initial velocity of the particle is generally set to 0. That is, v0 = 0. Since the problem to be solved in this paper is one-dimensional, the velocity of the jth (1≤j≤m) particle at the (k+1) iteration is It can be defined as:

[0104]

[0105] In the formula, is the historical optimal position of the jth particle at the kth iteration, is the position of the jth particle at the kth iteration, gbest k is the historical optimal position of the group at the kth iteration. r1 and r2 are random numbers between the interval [0,1], which are randomly generated in each iteration.

[0106] It can be seen from the above formula that the speed of a particle is determined by its speed of the last iteration, its individual optimal solution of the last iteration, and the group's optimal solution of the last iteration. This is also in line with the characteristics of mutual cooperation between the particle swarm optimization algorithm population particles and individual particles.

[0107] The position of the jth particle at the (k+1)th iteration is:

[0108]

[0109] Update the individual's historical optimal position and the population's historical optimal position, including:

[0110] The historical optimal position of the jth particle at the (k+1)th iteration is defined as:

[0111]

[0112] In the formula, is the fitness value of the jth particle at the kth iteration. For the particle swarm optimization algorithm with the optimization goal of minimizing the power loss of a single group of motors studied in this section, its fitness is The opposite of the value obtained by substituting it into the power formula is the opposite of the total power loss value of a single motor group.

[0113] Similarly, the optimal historical position of the group at the (k+1)th iteration is defined as:

[0114]

[0115] Record information about each particle, including:

[0116] The above formula has updated the particle's speed, position, individual historical optimal position, and group historical optimal position, thereby obtaining the optimal solution to the problem. However, if you need to obtain an image of the particle position changing with the number of iterations to analyze the stability of the algorithm, you need to use an array to store the optimal position and fitness value of this iteration during each iteration.

[0117] S206: Output the optimal allocation coefficient.

[0118] According to the above analysis, the process of particle swarm optimization algorithm implementation can be obtained as follows: Figure 5 , in the figure, n is the maximum number of iterations, and i is the current number of iterations.

[0119] In the embodiment of the present application, the torque command value of each of the two motors in a single group is obtained by the total required torque command value of the motors in the single group and the torque relationship between the torques of the two motors in the group after particle swarm optimization;

[0120] The torque relationship is expressed as:

[0121]

[0122] α is the torque distribution coefficient of the first motor in the group, β is the torque distribution coefficient of the second motor in the group,

[0123] are the torque command values ​​of the two motors in the i-th group respectively.

[0124] S300: Calculate a given stator current torque component corresponding to the torque command value, and determine a given stator current excitation component with the minimum total loss based on the minimum power loss of the loss model;

[0125] In the embodiment of the present application, the given stator current excitation component with the minimum total loss is determined based on the minimum power loss of the loss model, where:

[0126] The objective function of the optimization problem of the minimum power loss of the i-th group of disc motors is set as:

[0127]

[0128] Among them, P lossi is the total loss of the two motors in the i-th group, P lossiα , P lossiβ are the losses of the two motors in group i, n * For a given speed.

[0129] For a given stator current excitation component, the given speed and given torque are directly input into the LMC controller, and the optimal d-axis stator current excitation component that minimizes the total power loss of the SSSR-PMSM is output.

[0130] S400: Through current closed-loop control, the actual stator current of the motor follows its given value.

[0131] In summary, if Figure 3-Figure 4As shown, the overall working principle of the present invention is: under a given working condition, firstly, the working mode with the highest efficiency among the three working modes is determined by the method of online calculation of inter-group collaborative allocation. Different working modes start different numbers of modules and determine the torque allocation value between groups; secondly, the intra-group allocation is entered, and the torque allocation value that minimizes the loss of a single group of two motors, i.e., two discs of a disc motor, is obtained by searching for the optimal value through the particle swarm optimization algorithm; and then the stator d-axis given current with the minimum loss of each motor is obtained through the minimum power loss controller based on the loss model. This method can effectively broaden the high-efficiency operation space of the electric drive system of electric vehicles, and has redundancy, which can effectively improve the economy and stability of the drive system.

[0132] The above is a schematic scheme of a method for coordinated control of modular disc permanent magnet synchronous motors of electric vehicles in this embodiment. It should be noted that the technical scheme of the system for coordinated control of modular disc permanent magnet synchronous motors of electric vehicles and the technical scheme of the method for coordinated control of modular disc permanent magnet synchronous motors of electric vehicles described above belong to the same concept. For details not described in detail in the technical scheme of the coordinated control system of modular disc permanent magnet synchronous motors of electric vehicles in this embodiment, please refer to the description of the technical scheme of the method for coordinated control of modular disc permanent magnet synchronous motors of electric vehicles described above.

[0133] In this embodiment, the electric vehicle modular disc permanent magnet synchronous motor cooperative control system includes:

[0134] The inter-group collaborative allocation module is used to obtain the total required torque of the system, obtain the efficiency under different operating modes according to the given speed and the total required torque of the system, obtain the highest operating efficiency mode, and the corresponding total required torque command value of each group in the mode;

[0135] The intra-group collaborative allocation module is used to obtain the torque allocation coefficient of the two motors in the corresponding single group through a particle swarm optimization algorithm based on a given speed and a total required torque command value of each group, so as to obtain the torque command value of each of the two motors in the single group;

[0136] A calculation module, used for calculating a given stator current torque component corresponding to the torque command value, and determining a given stator current excitation component with the minimum total loss based on the minimum power loss of the loss model;

[0137] The control follower module is used to make the actual stator current of the motor follow its given value through current closed-loop control.

[0138] This embodiment further provides a computing device, which is applicable to the coordinated control of modular disc-type permanent magnet synchronous motors of electric vehicles, and includes:

[0139] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the method for collaborative control of modular disc permanent magnet synchronous motors of electric vehicles as proposed in the above embodiments.

[0140] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for realizing the coordinated control of a modular disc permanent magnet synchronous motor of an electric vehicle as proposed in the above embodiment is implemented.

[0141] The storage medium proposed in this embodiment and the method for realizing coordinated control of modular disc permanent magnet synchronous motor of electric vehicle proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0142] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (Random Access Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0143] Example 2, refer to Table 1-Table 3 and Figure 6 Based on the previous embodiment, this embodiment provides an application case of a coordinated control method for a modular disc permanent magnet synchronous motor of an electric vehicle to illustrate the feasibility of our solution.

[0144] MATLAB / Simulink has powerful computing power and many modules that are convenient for motor control design. It is a commonly used simulation software in motor control design. Therefore, the present invention chooses to build a simulation model of SSSR-PMSM in MATLAB / Simulink. The parameters of the two SSSR-PMSMs in a single group are different, and the parameters of the three groups of SSSR-PMSMs are exactly the same. The simulation parameters of the two SSSR-PMSMs in a single group are shown in Tables 1 and 2.

[0145] Table 1 Parameters of SSSR-PMSM1

[0146]

[0147] Table 2 Parameters of SSSR-PMSM 2

[0148]

[0149]

[0150] The s function is used in MATLAB / Simulink to build a simulation model of the intra-group collaborative allocator based on PSO, and a simulation test is carried out. According to experience, the main simulation parameters of the algorithm in the simulation model are shown in Table 3.

[0151] Table 3 Main simulation parameters of PSO model

[0152]

[0153] In order to verify the ability of the double-layer torque collaborative efficiency optimization control strategy designed in the present invention to optimize efficiency in the whole torque range, taking a group of motors as an example, the efficiency is compared and analyzed with the two torque distribution strategies of average distribution and saturation distribution under the test conditions of a speed of 1000r / min, a total required torque variation range of [20,300] (N·m) and a variation step of 20N·m. Figure 6 This is a comparison chart of the efficiency simulation results of the three torque distribution strategies within the full torque range. Figure 6 It can be seen that when the total required torque value of the drive system is small, the efficiency of the torque synergy efficiency optimization control strategy is similar to that of the saturation allocation strategy, which is significantly higher than the efficiency of the average allocation strategy. This is because when the torque value is small, the average allocation strategy makes the working torque of the two motors much smaller than their rated torque, and the motors work in a low efficiency range; when the total required torque of the drive system is large, under the saturation allocation strategy, the motors work in a low efficiency range, so its efficiency is lower than the other two allocation strategies. Overall, the torque synergy efficiency optimization allocation strategy ensures that the system works in a high efficiency space through torque synergy allocation, and its average efficiency is increased by 3.32% compared with the average allocation strategy and 2.93% compared with the saturation allocation strategy. It verifies the effectiveness and superiority of the double-layer torque synergy efficiency optimization control strategy designed in this chapter in reducing the loss of the drive system, improving the efficiency of the drive system, and broadening the high-efficiency operation space of the drive system within the full torque range.

[0154] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for coordinated control of modular disc-type permanent magnet synchronous motors in electric vehicles, characterized in that: The method is applied to three groups of double-stator-single-rotor disc motors, which are equivalent to six single-stator-single-rotor permanent magnet synchronous motors, wherein the six single-stator-single-rotor motors are grouped in pairs. The method comprises: Obtaining the total required torque of the system, obtaining the efficiency under different operating modes according to the given speed and the total required torque of the system, obtaining the highest operating efficiency mode, and each group of total required torque command values ​​corresponding to the mode; Based on the given speed and the total required torque command value of each group, the torque distribution coefficient of the two motors in the corresponding single group is obtained by particle swarm optimization algorithm to obtain the torque command value of each of the two motors in the single group; Calculating a given stator current torque component corresponding to the torque command value, and determining a given stator current excitation component with the minimum total loss based on the minimum power loss of the loss model; The actual stator current of the motor follows its given value through current closed-loop control.

2. The electric vehicle modular disc permanent magnet synchronous motor coordinated control method according to claim 1, characterized in that: The different operating modes include: In the first mode, a single motor is enabled to provide all the total required torque; In the second mode, two motors are enabled, with each motor providing half of the total required torque; In the third mode, three groups of motors are enabled, with each group of motors providing one-third of the total required torque.

3. The electric vehicle modular disc permanent magnet synchronous motor coordinated control method according to claim 2, characterized in that: The efficiency under different operation modes is obtained according to the given speed and the total required torque of the system, including: defining the relationship between the total required torque of the system and the torque command value of each motor, which is expressed as: in, is the total required torque of the motor drive system, is the total required electromagnetic torque command value of the i-th group of motors, are the torque command values ​​of the two motors in the i-th group respectively; The efficiency is expressed as: Where η is the overall efficiency of the drive system, p outi为 The total output power of the two motors in the i-th group, ω e is the electrical angular velocity of the motor, N P is the motor speed, p lossi is the total loss of the two motors in the i-th group.

4. The electric vehicle modular disc permanent magnet synchronous motor coordinated control method according to claim 3, characterized in that: The particle swarm optimization algorithm is used to obtain the torque distribution coefficient of the two motors in the group with the best efficiency, including: determining the solution variables of the objective function as the torque distribution coefficients α and β of the two motors, where β=1-α; Determine that the dimension for solving the optimal power loss problem is one-dimensional; Determine the position range of particles; Initialize the particle swarm optimization parameters, including: population size m, maximum number of iterations n, inertia coefficient w, acceleration coefficients c1, c2; Update the position and speed of each particle, calculate the fitness value of each particle, update the individual historical optimal position of each particle and the historical optimal position of the group, and update the number of iterations and inertia coefficient; Output the optimal allocation coefficient.

5. The electric vehicle modular disc permanent magnet synchronous motor coordinated control method according to claim 4, characterized in that: The inertia coefficient w is expressed as: Where k is the current iteration number.

6. The electric vehicle modular disc permanent magnet synchronous motor coordinated control method according to claim 5, characterized in that: The torque command values ​​of the two motors in the single group are obtained by the total torque command value of the motors in the single group and the torque relationship between the torques of the two motors in the group after particle swarm optimization; The torque relationship is expressed as: α is the torque distribution coefficient of the first motor in the group, β is the torque distribution coefficient of the second motor in the group, are the torque command values ​​of the two motors in the i-th group respectively.

7. The electric vehicle modular disc permanent magnet synchronous motor coordinated control method according to claim 6, characterized in that: The minimum power loss based on the loss model determines the given stator current excitation component with the minimum total loss, wherein: The objective function of the optimization problem of the minimum power loss of the i-th group of disc motors is set as: Among them, P lossi is the total loss of the two motors in the i-th group, P lossiα , P lossiβ are the losses of the two motors in group i, n * For a given speed.

8. A system using the electric vehicle modular disc permanent magnet synchronous motor coordinated control method as claimed in claim 1, characterized in that: include: An inter-group collaborative allocation module is used to obtain the total required torque of the system, obtain the efficiency under different operating modes according to the given speed and the total required torque of the system, obtain the highest operating efficiency mode, and the corresponding total required torque command value of each group in the mode; The intra-group collaborative allocation module is used to obtain the torque allocation coefficient of the two motors in the corresponding single group through a particle swarm optimization algorithm based on a given speed and a total required torque command value of each group, so as to obtain the torque command value of each of the two motors in the single group; A calculation module, used for calculating a given stator current torque component corresponding to the torque command value, and determining a given stator current excitation component with the minimum total loss based on the minimum power loss of the loss model; The control follower module is used to make the actual stator current of the motor follow its given value through current closed-loop control.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the electric vehicle modular disc permanent magnet synchronous motor collaborative control method as described in any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the modular disc permanent magnet synchronous motor collaborative control method for electric vehicles as described in any one of claims 1 to 6.

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