A method for collaborative optimization of mechanical and electrical control parameters of a dual-switch reluctance electric drive system

Abstract

This invention discloses a collaborative optimization method for electromechanical control parameters of a dual-switched reluctance electric drive system, belonging to the field of electric drive system optimization technology. The method employs a "double-layer nested optimization" architecture: the inner layer targets motor torque / radial force fluctuation, losses, and efficiency, optimizing control parameters such as switching angle and phase current reference values ​​using the SA simulated annealing algorithm; the outer layer targets the total system mass, total losses, and efficiency under typical operating conditions (such as CLTC), optimizing motor structural parameters (stator outer diameter, air gap) and gear transmission parameters (transmission ratio, module) using the particle swarm optimization algorithm. During the optimization process, a finite element model is established using JMAG, a dynamic model is built using Simulink / Matlab, and Isight is used for automated iteration. A "constraint judgment-parameter adjustment" loop ensures that dynamic and strength requirements are met. This invention can improve system efficiency, reduce mass, and decrease torque fluctuations, making it suitable for scenarios such as new energy vehicles and wind power generation, and solving the problem of poor collaborative optimization in existing modular systems.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of parameter optimization of double-switch reluctance electric drive systems, and in particular to a method for collaborative optimization of mechanical and electrical control parameters of double-switch reluctance electric drive systems for new energy vehicle, wind power and other scenarios. BACKGROUND

[0002] With the rapid development of new energy vehicles, wind power and other fields, the efficiency, power density and reliability of electric drive systems as core power components directly affect the overall performance. Currently, the design of electric drive systems mostly adopts the "module optimization" mode: optimizing the motor structure parameters (such as stator outer diameter, air gap length) alone, optimizing the transmission system parameters (such as gear transmission ratio, modulus) alone, or optimizing the control parameters (such as switching angle, phase current) alone, without realizing the collaborative linkage of "motor - transmission - control" (mechanical and electrical control).

[0003] The prior art has the following defects:

[0004] 1. Constraint conflict is difficult to balance: if only the motor efficiency is optimized, it may lead to an increase in motor volume and transmission system load exceeding the limit (such as gear contact stress exceeding the standard); if only the transmission system strength is strengthened, it may increase the system mass and reduce the power density; if only the control parameters are adjusted, it is difficult to break through the inherent design bottlenecks of the motor and transmission.

[0005] 2. Limited efficiency improvement: module optimization ignores the coupling relationship between parameters (such as changes in motor inductance affecting the optimal value of control parameters, gear transmission ratio affecting the motor operating point), resulting in a system overall efficiency improvement of only 2-3%, which cannot meet the needs of high power density scenarios.

[0006] 3. Poor dynamic adaptability: existing methods do not combine actual operating conditions (such as CLTC cycle conditions of new energy vehicles), and the applicability of the optimization results under complex conditions is low, which may cause problems such as excessive torque fluctuation and surge in loss.

[0007] Therefore, there is an urgent need for a method that can realize collaborative optimization of mechanical and electrical control parameters while meeting the power performance (such as maximum speed, maximum torque), strength (such as gear contact strength, motor structural strength) constraints, in order to improve the efficiency of electric drive systems, reduce mass and torque fluctuation. SUMMARY

[0008] The present application aims to solve the technical problems of poor coordination, limited efficiency improvement and difficult constraint conflict balancing caused by modular design in the optimization of existing double-switch reluctance electric drive systems. The method optimizes through inner and outer double-layer nesting. The inner layer optimizes motor control parameters, and the outer layer optimizes motor structure parameters and transmission system structure parameters. Through inner and outer layer nested collaborative optimization, the efficiency of the electric drive system can be improved, system loss can be reduced, system mass can be reduced as much as possible under the satisfaction of system power and strength checking, and the power density of the electric drive system can be improved.

[0009] The method creates a switched reluctance motor model through JMAG, simulates the phase flux, phase current and other parameters of the switched reluctance motor rotor at different positions, and obtains the efficiency and loss of the double-switch reluctance electric drive system through joint simulation of simulink and matlab. The isight software is used for automatic iteration optimization of parameters to obtain optimal motor control parameters. The following goals are achieved:

[0010] 1. Meet the power constraints (such as maximum speed, maximum torque) and strength constraints (such as motor structure strength, gear contact strength) of the electric drive system.

[0011] 2. Improve the overall efficiency and power density of the system.

[0012] 3. Reduce the total mass of the system, motor torque fluctuation and radial force fluctuation.

[0013] To achieve the above purposes, the technical scheme adopted by the present application is: a double-switch reluctance electric drive system motor control parameter collaborative optimization method, comprising the following steps:

[0014] (1) Calculate the initial motor parameters of the switched reluctance motor (SRM) and the structure parameters of the gear transmission system.

[0015] (2) Judge the switched reluctance motor structure constraints of the motor parameters. If the constraints are not met, modify the motor parameters and re-execute step (2) until the motor parameters that meet the motor structure constraints are obtained.

[0016] (3) Judge the gear strength constraints of the structure parameters of the gear transmission system. If the constraints are not met, modify the structure parameters of the gear transmission system and re-execute step (3) until the gear parameters that meet the gear strength constraints are obtained.

[0017] (4) Based on the motor parameters that meet the motor structure constraints obtained in step (2) and the gear parameters that meet the gear strength constraints obtained in step (3), couple the dual-switch reluctance electric drive system and build a system dynamic model. After simulating the dynamic characteristics of the system, perform a dynamic constraint judgment on the system. If the dynamic constraints are not met, return to step (2) to readjust the motor parameters until electromechanical parameters that meet all constraints are obtained.

[0018] (5) Based on the electromechanical parameters that meet the constraints obtained in step (4), establish a finite element model of the switched reluctance motor, and take the rotor angle and stator current as the operating conditions. The inductance and flux linkage of the motor under different operating conditions are obtained through automated script simulation.

[0019] (6) Based on the electromechanical parameters in step (4) and the inductance and flux linkage parameters in step (5), establish the torque and radial force model of the dual-switch reluctance electric drive system, and calculate the average value and fluctuation value of the motor torque and radial force.

[0020] (7) A set of torque distribution coefficients is obtained by equal step size. The operating point of a single motor is obtained by using the operating point of the dual motor system and the torque distribution coefficients. The loss and efficiency of the two motors are calculated by using the motor loss calculation model. The operating point includes: the speed n1 and torque T1 of motor 1, and the speed n2 and torque T2 of motor 2.

[0021] (8) Using the average value and fluctuation value of motor torque and radial force in step (6), and motor loss and efficiency in step (7) as objective functions, and motor control parameters as optimization variables, the SA simulated annealing algorithm is used to perform multi-objective optimization to obtain the optimal control parameters and complete the inner layer optimization. The motor control parameters include: switching angle, phase current reference value, and torque distribution coefficient.

[0022] (9) Take the motor loss in step (7) as the loss of a single operating point, traverse all operating points of the dual-switch reluctance electric drive system to obtain the total operating loss, and combine the CLTC classic cycle operating condition to calculate the total loss and total efficiency of the system under typical operating conditions.

[0023] (10) Based on the motor structure parameters in step (2) and the gear transmission system structure parameters in step (3), calculate the total mass of the dual-switch reluctance electric drive system;

[0024] (11) Taking the total mass of the system in step (10), the total loss and efficiency of the system under the typical working conditions in step (9) as the objective functions, and the motor structural parameters in step (2) and the gear transmission system structural parameters in step (3) as the optimization variables, the particle swarm algorithm is used to perform multi-objective optimization to obtain the optimal motor structural parameters and the optimal gear transmission system structural parameters, thus completing the outer layer optimization; through the above-mentioned coordinated optimization of the inner and outer layers, the optimal electromechanical control parameters of the dual-switch reluctance electric drive system are obtained.

[0025] Furthermore, the motor structure constraints in step (2) include:

[0026] (a) Polar arc constraint: , ;

[0027] (b) First air gap constraint: ;

[0028] (c) Second air gap constraint: ;

[0029] (d) Rotor yoke height constraint: ;

[0030] (e) Stator yoke height constraint: ;

[0031] (f) Shaft diameter constraint: ;

[0032] (g) Other constraints: ;

[0033] Where, β s For the stator pole arc, β r For rotor pole arc, , q is the number of phases, N r1 D is the number of rotor stages. s1 D is the stator outer diameter. s2 W is the inner diameter of the stator. s4 For the stator yoke height, W s2 For the stator to be extremely wide, D r1 D is the rotor outer diameter. r2 W is the inner diameter of the rotor. r2 For rotor yoke height, W r1 For rotor yoke width, D r3 This is the shaft diameter.

[0034] Furthermore, the gear strength constraint mentioned in step (3) includes:

[0035] (a) Not following the number of cutting teeth constraint: ;

[0036] (b) Single-pole transmission ratio constraint: ;

[0037] (c) Tooth surface contact strength constraint: , σ H Calculate the contact stress at the node, in N / mm. 2 , σ HG To calculate the contact limit stress of a gear, in N / mm², the contact strength check formula can be expanded as follows:

[0038] (1)

[0039] (d) Tooth root bending strength constraint: , σ F Calculate the contact stress at the node, in N / mm. 2 , σ FG To calculate the contact limit stress of the gear, the unit is N / mm. 2 The bending strength check formula can be further expanded as follows:

[0040] (1.1).

[0041] Commonly used safety factor reference values ​​are shown in Table 1 below:

[0042] Table 1. Commonly Used Minimum Safety Factors for Gears

[0043]

[0044] Finally, gear parameters that meet the strength requirements of the gear transmission system are obtained.

[0045] Furthermore, the system dynamic constraints mentioned in step (4) include;

[0046] (a) The maximum torque of the electric drive system is greater than the peak value of the torque required by the electric drive system;

[0047] (b) The maximum power of the electric drive system is greater than the peak power required by the electric drive system.

[0048] Furthermore, the finite element model mentioned in step (5) is established using JMAG software, and the value range of the stator current is 0~300A.

[0049] Furthermore, the SA simulated annealing algorithm in step (8) includes the following steps:

[0050] 1) Generate an initial solution x0 and calculate the energy function E0 = f(x0); set the initial temperature T0 and the final temperature T. f Cooling coefficient α, iteration number L for each temperature;

[0051] 2) External circulation: Simulates the annealing process, when the temperature T is greater than the termination temperature T f At that time, perform the following operations L times:

[0052] ① The perturbation generates a new solution x';

[0053] ② Calculate ΔE = f(x') – f(x), where ΔE is the energy difference;

[0054] ③ Decide whether to accept x' according to the Metropolis criterion;

[0055] 3) Cool down, T = αT, use the new temperature T to return to step (2) to generate x', α is the cooling coefficient;

[0056] 4) The temperature drops to the termination temperature T f When x is equal to or less than the given value, return the final solution, i.e., the optimal solution x. best .

[0057] Furthermore, the operating range of "traversing all operating points" in step (9) is the CLTC-P cyclic operating point.

[0058] Furthermore, the particle swarm optimization algorithm described in step (11) is specifically an optimization algorithm based on swarm intelligence. By simulating cooperation and competition among individuals, it efficiently searches for the optimal solution in the solution space. The particles modulate their own motion by observing two "optimal solutions," namely the individual optimal solution and the swarm optimal solution, and finally reach the final optimal solution.

[0059] Furthermore, the motor loss calculation model in step (7) is a fast calculation model for the losses of a switched reluctance motor based on analytical solutions and numerical interpolation. The specific calculation process includes:

[0060] 1) Derive the A-phase flux linkage waveform Ψ using the SRM dynamic torque interpolation model. A (t), and calculate the B-phase flux linkage waveform Ψ. B (t), C-phase flux linkage waveform Ψ C (t);

[0061] 2) Based on the magnetic flux data and structural data of each part of the SRM stator and rotor, through... Calculate the magnetic flux density data, where B i (t) represents the magnetic flux density waveform, φ i (t) represents the magnetic flux waveform, S i Let be the cross-sectional area through which the magnetic flux passes;

[0062] 3) Through Calculate the hysteresis loss Ph and eddy current loss Pe, where K h K is the hysteresis correction factor.e C is the eddy current correction factor. h C is the hysteresis loss coefficient. e f is the eddy current loss coefficient. c B is the magnetization frequency. m α is the amplitude of the magnetic flux density waveform, and α is the Strinmetz factor, ranging from 1.6 to 2.0.

[0063] 4) Calculate the hysteresis loss and eddy current loss of the stator pole, stator yoke, rotor pole, and rotor yoke using the superposition principle, and add them together to obtain the overall iron loss P of the SRM. Fe ;

[0064] (3)

[0065] In the formula P hsp P hS P hrp P hr These are the hysteresis losses of the stator pole, stator yoke, rotor pole, and rotor yoke, respectively; P esp P eS P erp P er These are the eddy current losses of the stator pole, stator yoke, rotor pole, and rotor yoke, respectively.

[0066] Furthermore, the system dynamics model described in step (4) is built using Simulink and Matlab, and the simulation of the total system loss and total efficiency described in step (9) is performed using Isight software for automated iterative calculation.

[0067] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0068] 1) Significantly improved efficiency: Through electromechanical control collaborative optimization, the overall efficiency of the system under CLTC conditions is improved, especially in low- and medium-speed conditions (such as urban road driving), where the efficiency improvement is even greater;

[0069] 2) Increased power density: Under the premise of meeting strength constraints, the total system mass is reduced, the power density is increased, and the system meets the lightweight requirements of new energy vehicles;

[0070] 3) Optimized operational stability: Reduced motor torque fluctuation and radial force fluctuation, reduced system vibration and noise, and extended component life;

[0071] 4) Strong adaptability to working conditions: Combined with actual working conditions optimization such as CLTC, it avoids the problem of "optimal in the laboratory but failure in reality". The optimization results can still perform stably under complex working conditions. Attached Figure Description

[0072] Figure 1This is a schematic diagram of the method flow of the present invention;

[0073] Figure 2 A diagram showing the CLTC-P cycle operating point distribution;

[0074] Figure 3 Here is a flowchart of the particle swarm optimization algorithm;

[0075] Figure 4 The flux linkage diagram of each phase of the stator and rotor of the SRM motor;

[0076] Figure 5 The schematic diagram of the system dynamics model was built using Simulink and Matlab.

[0077] Figure 6 shows the efficiency MAP of the dual-switch magnetoresistive electric drive system;

[0078] Figure 7 A comparison chart of input and loss power of the electric drive system under CLTC-P cycle conditions; Detailed Implementation

[0079] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. However, it should be understood that the specific embodiments described herein are merely illustrative and are not intended to limit the scope of the invention.

[0080] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention.

[0081] Taking the overall vehicle parameters (Table 2) of a certain electric vehicle on the market as an example, this paper elaborates on a method for coordinated optimization of electromechanical control parameters of a dual-switch reluctance electric drive system.

[0082] Table 2. Vehicle parameters of a certain electric vehicle

[0083]

[0084] Includes the following steps:

[0085] (1) A set of motor parameters of the switched reluctance motor (SRM) and structural parameters of the gear transmission system were calculated using traditional methods as initial parameters for collaborative optimization design;

[0086] (2) Perform a switch reluctance motor structure constraint judgment on the motor parameters. If the constraint requirements are not met, modify the motor parameters and repeat step (2) until motor parameters that meet the motor structure constraints are obtained.

[0087] (3) The structural parameters of the gear transmission system are judged for gear strength constraints. If they do not meet the constraints, the structural parameters of the gear transmission system are modified and step (3) is repeated until gear parameters that meet the gear strength constraints are obtained.

[0088] (4) Based on the motor parameters that meet the motor structure constraints obtained in step (2) and the gear parameters that meet the gear strength constraints obtained in step (3), couple the dual-switched reluctance electric drive system and build a system dynamic model as follows: Figure 4 After simulating the dynamic characteristics of the system, the dynamic constraints of the system are judged; if the dynamic constraints are not met, return to step (2) to readjust the motor parameters until the electromechanical parameters that meet all constraints are obtained.

[0089] (5) Based on the electromechanical parameters that meet the constraints obtained in step (4), establish a finite element model of the switched reluctance motor, and take the rotor angle and stator current as the operating conditions. The inductance and flux linkage of the motor under different operating conditions are obtained through automated script simulation.

[0090] (6) Based on the electromechanical parameters in step (4) and the inductance and flux linkage parameters in step (5), establish the torque and radial force model of the dual-switch reluctance electric drive system, and calculate the average value and fluctuation value of the motor torque and radial force.

[0091] (7) A set of torque distribution coefficients is obtained by equal step size. The operating point of a single motor is obtained by using the operating point of the dual motor system and the torque distribution coefficients. The operating point of a single motor includes the speed n1 and torque T1 of motor 1, and the speed n2 and torque T2 of motor 2. The losses and efficiencies of the two motors are calculated by using the motor loss calculation model.

[0092] (8) Using the average value and fluctuation value of motor torque and radial force in step (6), and motor loss and efficiency in step (7) as objective functions, and motor control parameters (including switching angle, phase current reference value, and torque distribution coefficient) as optimization variables, the SA simulated annealing algorithm is used to perform multi-objective optimization to obtain the optimal control parameters and complete the inner layer optimization. The optimal control parameters include: optimal switching angle, phase current reference value, and torque distribution coefficient.

[0093] (9) Take the motor loss in step (7) as the loss of a single operating point, traverse all operating points of the dual-switch reluctance electric drive system to obtain the total operating loss, and combine the CLTC classic cycle operating condition to calculate the total loss and total efficiency of the system under typical operating conditions.

[0094] (10) Based on the motor structure parameters in step (2) and the gear transmission system structure parameters in step (3), calculate the total mass of the dual-switch reluctance electric drive system;

[0095] (11) Taking the total mass of the system in step (10), the total loss and efficiency of the system under the typical working conditions in step (9) as the objective functions, and the motor structural parameters in step (2) and the gear transmission system structural parameters in step (3) as the optimization variables, the particle swarm algorithm is used to perform multi-objective optimization, and the optimal motor structural parameters and the optimal gear transmission system structural parameters are shown in Table 3, thus completing the outer layer optimization.

[0096] Table 3 Optimal SRM Motor Parameters and Gear Transmission System Parameters

[0097]

[0098] Through the synergistic optimization of the inner and outer layers described above, the optimal electromechanical control parameters of the dual-switched reluctance electric drive system are obtained. The optimal electromechanical control parameters include: optimal motor parameters, optimal gear system parameters, and optimal control parameters.

[0099] The motor structural constraints in step (2) include:

[0100] (a) Polar arc constraint: , ;

[0101] (b) First air gap constraint: ;

[0102] (c) Second air gap constraint: ;

[0103] (d) Rotor yoke height constraint: ;

[0104] (e) Stator yoke height constraint: ;

[0105] (f) Shaft diameter constraint: ;

[0106] (g) Other constraints: ;

[0107] Where, β s For the stator pole arc, β r For rotor pole arc, , q is the number of phases, N r1 D is the number of rotor stages. s1 D is the stator outer diameter. s2 W is the inner diameter of the stator. s4 For the stator yoke height, W s2 For the stator to be extremely wide, D r1 D is the rotor outer diameter. r2 W is the inner diameter of the rotor. r2 For rotor yoke height, Wr1 For rotor yoke width, D r3 The shaft diameter is used to obtain the final motor parameters that conform to the motor structure parameters.

[0108] The gear strength constraint mentioned in step (3) includes:

[0109] (a) Not following the number of cutting teeth constraint: ;

[0110] (b) Single-pole transmission ratio constraint: ;

[0111] (c) Tooth surface contact strength constraint: , σ H Calculate the contact stress at the node, in N / mm. 2 ;σ HG To calculate the contact limit stress of a gear, in N / mm², the contact strength check formula can be expanded as follows:

[0112] (1)

[0113] (d) Tooth root bending strength constraint: , σ F Calculate the contact stress at the node, in N / mm. 2 ;σ FG To calculate the contact limit stress of the gear, the unit is N / mm. 2 The bending strength check formula can be further expanded as follows:

[0114] (1.1)

[0115] Commonly used safety factor reference values ​​are shown in Table 1 below:

[0116] Table 1. Commonly Used Minimum Safety Factors for Gears

[0117] Finally, gear parameters that meet the strength requirements of the gear transmission system are obtained.

[0118] The system dynamic constraints mentioned in step (4) include:

[0119] (a) The maximum torque of the electric drive system is greater than the peak value of the torque required by the electric drive system;

[0120] (b) The maximum power of the electric drive system is greater than the peak power required by the electric drive system;

[0121] Finally, the electromechanical parameters that meet the constraints are obtained.

[0122] The finite element model mentioned in step (5) is established using JMAG software, and the value range of the stator current is 0~300A.

[0123] Step (8) of the SA simulated annealing algorithm includes the following steps:

[0124] 1) Generate an initial solution x0 and calculate the energy function E0 = f(x0); set the initial temperature T0 and the final temperature T. f Cooling coefficient α, iteration number L for each temperature;

[0125] 2) External circulation: Simulates the annealing process, when the temperature T is greater than the termination temperature T f At that time, perform the following operations L times:

[0126] ① The perturbation generates a new solution x';

[0127] ② Calculate ΔE = f(x') – f(x), where ΔE is the energy difference;

[0128] ③ Decide whether to accept x' according to the Metropolis criterion;

[0129] 3) Cool down, T = αT, use the new temperature T to return to step (2) to generate x', where α is the cooling coefficient;

[0130] 4) The temperature drops to the termination temperature T f When x is equal to or less than the given value, return the final solution, i.e., the optimal solution x. best .

[0131] The operating condition range for "traversing all operating points" mentioned in step (9) is: CLTC-P cyclic operating points, such as... Figure 2 As shown.

[0132] The particle swarm optimization algorithm described in step (11) is specifically an optimization algorithm based on swarm intelligence. It efficiently searches for the optimal solution in the solution space by simulating cooperation and competition among individuals. Particles modulate their motion by observing two "optimal solutions"—the individual optimal solution and the swarm optimal solution—ultimately reaching the final optimal solution. Figure 3 As shown.

[0133] Step (7) The motor loss calculation model is a fast calculation model for switched reluctance motor losses based on analytical solutions and numerical interpolation. First, the magnetic flux density distribution of the SRM during operation needs to be calculated; for example... Figure 4 As shown, during constant speed operation, the magnetic flux linkages of each phase are Ψ A Ψ B Ψ C .

[0134] Among them, the flux linkage waveform Ψ of phase A A(t) can be derived through the SRM dynamic torque interpolation model. The flux linkage waveform Ψ of the remaining phases... B (t), Ψ C (t) can be obtained through the flux linkage waveform Ψ of phase A. A (t) is calculated. Based on the magnetic flux data and structural data of each part of the SRM stator and rotor, the magnetic flux density data of each part of the SRM stator and rotor can be calculated: In the formula B i (t) represents the magnetic flux density waveforms of the stator and rotor of the SRM; φ i (t) represents the magnetic flux waveforms of the stator and rotor of the SRM; S i This refers to the cross-sectional area through which the magnetic flux passes in each part of the stator and rotor of the SRM.

[0135] Then, the hysteresis loss Ph and eddy current loss Pe are calculated using magnetic flux density data.

[0136] (2)

[0137] K h K is the hysteresis correction factor. e C is the eddy current correction factor. h C is the hysteresis loss coefficient. e f is the eddy current loss coefficient. c B is the magnetization frequency. m The amplitude of the magnetic flux density waveform is given by α, which is the Strinmetz factor, ranging from 1.6 to 2.0. Then, the hysteresis loss and eddy current loss of the stator pole, stator yoke, rotor pole, and rotor yoke are calculated using the superposition principle.

[0138] Then, the hysteresis loss and eddy current loss of each part of the SRM are added together to obtain the total iron loss P of the SRM. Fe ;

[0139]

[0140] In the formula P hsp P hS P hrp P hr These are the hysteresis losses of the stator pole, stator yoke, rotor pole, and rotor yoke, respectively; P esp P eS P erp P er These are the eddy current losses of the stator pole, stator yoke, rotor pole, and rotor yoke, respectively.

[0141] This approach significantly reduces the time required compared to traditional finite element method-based motor loss calculations, providing a foundation for numerous iterative optimizations during the optimization process.

[0142] The system dynamics model described in step (4) is built using Simulink and Matlab, and its schematic diagram is shown below. Figure 5 As shown, the simulation of the total system loss and total efficiency in step (9) is achieved by automated iterative calculation using Isight software. In Figure 5, (a) is the SRM dynamic torque model and (b) is the dynamic model of the dual-switched reluctance motor drive system.

[0143] Figure 6 shows (a) the efficiency MAP of the dual-switched reluctance electric drive system obtained by the traditional design optimization method and (b) the efficiency MAP of the dual-switched reluctance electric drive system obtained by the collaborative design optimization method.

[0144] Depend on Figure 6 It is known that in traditional design, the system efficiency is less than 85% for almost half of the operating points, followed by the 85% to 90% range, with only a small number of operating points having a system efficiency of more than 85%. However, when the dual-switch reluctance electric drive system with collaborative optimization design is tested under operating conditions, the system efficiency is above 85% for most operating points, and above 90% for a small number of operating points.

[0145] Figure 7(a) shows the input-loss power comparison of the electric drive system under the CLTC-P cycle condition obtained by the traditional design optimization method; (b) shows the input-loss power comparison of the electric drive system under the CLTC-P cycle condition obtained by the collaborative design optimization method.

[0146] according to Figure 7 As shown, after collaborative optimization design, the power loss of the system under cyclic operating conditions decreased by 35-50% at different operating time points compared to the traditional design.

[0147] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions or improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for collaborative optimization of electromechanical control parameters in a dual-switch reluctance electric drive system, characterized in that, Includes the following steps: (1) Calculate the initial motor parameters of the switched reluctance motor (SRM) and the structural parameters of the gear transmission system; (2) Perform a switch reluctance motor structure constraint judgment on the motor parameters. If the constraint requirements are not met, modify the motor parameters and repeat step (2) until motor parameters that meet the motor structure constraints are obtained. (3) The structural parameters of the gear transmission system are judged for gear strength constraints. If they do not meet the constraints, the structural parameters of the gear transmission system are modified and step (3) is repeated until gear parameters that meet the gear strength constraints are obtained. (4) Based on the motor parameters that meet the motor structure constraints obtained in step (2) and the gear parameters that meet the gear strength constraints obtained in step (3), couple the dual-switched reluctance electric drive system and build a system dynamic model. After simulating the dynamic characteristics of the system, perform a dynamic constraint judgment on the system. If the dynamic constraints are not met, return to step (2) to readjust the motor parameters until electromechanical parameters that meet all constraints are obtained. (5) Based on the electromechanical parameters that meet the constraints obtained in step (4), establish a finite element model of the switched reluctance motor, and take the rotor angle and stator current as the operating conditions. The inductance and flux linkage of the motor under different operating conditions are obtained through automated script simulation. (6) Based on the electromechanical parameters in step (4) and the inductance and flux linkage parameters in step (5), establish the torque and radial force model of the dual-switch reluctance electric drive system, and calculate the average value and fluctuation value of the motor torque and radial force. (7) A set of torque distribution coefficients is obtained by equal step size. The operating point of a single motor is obtained by using the operating point of the dual motor system and the torque distribution coefficients. The loss and efficiency of the two motors are calculated by using the motor loss calculation model. The operating point includes: the speed n1 and torque T1 of motor 1, and the speed n2 and torque T2 of motor 2. (8) Using the average value and fluctuation value of motor torque and radial force in step (6), and motor loss and efficiency in step (7) as objective functions, and motor control parameters as optimization variables, the SA simulated annealing algorithm is used to perform multi-objective optimization to obtain the optimal control parameters and complete the inner layer optimization. The motor control parameters include: switching angle, phase current reference value, and torque distribution coefficient. (9) Take the motor loss in step (7) as the loss of a single operating point, traverse all operating points of the dual-switch reluctance electric drive system to obtain the total operating loss, and combine the CLTC classic cycle operating condition to calculate the total loss and total efficiency of the system under typical operating conditions. (10) Based on the motor structure parameters in step (2) and the gear transmission system structure parameters in step (3), calculate the total mass of the dual-switch reluctance electric drive system; (11) Taking the total mass of the system in step (10), the total loss and efficiency of the system under the typical working conditions in step (9) as the objective functions, and the motor structural parameters in step (2) and the gear transmission system structural parameters in step (3) as the optimization variables, the particle swarm algorithm is used to perform multi-objective optimization to obtain the optimal motor structural parameters and the optimal gear transmission system structural parameters, thus completing the outer layer optimization; through the above-mentioned coordinated optimization of the inner and outer layers, the optimal electromechanical control parameters of the dual-switch reluctance electric drive system are obtained.

2. The method for coordinated optimization of electromechanical control parameters of a dual-switch reluctance electro-drive system according to claim 1, characterized in that, The motor structure constraints in step (2) include: (a) Polar arc constraint: , ; (b) First air gap constraint: ; (c) Second air gap constraint: ; (d) Rotor yoke height constraint: ; (e) Stator yoke height constraint: ; (f) Shaft diameter constraint: ; (g) Other constraints: ; Where, β s For the stator pole arc, β r For rotor pole arc, , q is the number of phases, N r1 D is the number of rotor stages. s1 D is the stator outer diameter. s2 W is the inner diameter of the stator. s4 For the stator yoke height, W s2 For the stator to be extremely wide, D r1 D is the rotor outer diameter. r2 W is the inner diameter of the rotor. r2 For rotor yoke height, W r1 For rotor yoke width, D r3 This is the shaft diameter.

3. The method for coordinated optimization of electromechanical control parameters of a dual-switch reluctance electro-drive system according to claim 1, characterized in that, The gear strength constraint mentioned in step (3) includes: (a) Not following the number of cutting teeth constraint: ; (b) Single-pole transmission ratio constraint: ; (c) Tooth surface contact strength constraint: , σ H Calculate the contact stress at the node, in N / mm. 2 , σ HG To calculate the contact limit stress of a gear, in N / mm², the contact strength check formula can be expanded as follows: (1) (d) Tooth root bending strength constraint: , σ F Calculate the contact stress at the node, in N / mm. 2 , σ FG To calculate the contact limit stress of the gear, the unit is N / mm. 2 The bending strength check formula can be further expanded as follows: (1.1)。 4. The method for coordinated optimization of electromechanical control parameters of a dual-switch reluctance electro-drive system according to claim 1, characterized in that, The system dynamic constraints mentioned in step (4) include: (a) The maximum torque of the electric drive system is greater than the peak value of the torque required by the electric drive system; (b) The maximum power of the electric drive system is greater than the peak power required by the electric drive system.

5. The method for coordinated optimization of electromechanical control parameters of a dual-switch reluctance electric drive system according to claim 1, characterized in that, The finite element model mentioned in step (5) is established using JMAG software, and the value range of the stator current is 0~300A.

6. The method for coordinated optimization of electromechanical control parameters of a dual-switch reluctance electro-drive system according to claim 1, characterized in that, The SA simulated annealing algorithm in step (8) includes the following steps: 1) Generate an initial solution x0 and calculate the energy function E0 = f(x0); set the initial temperature T0 and the final temperature T. f Cooling coefficient α, iteration number L for each temperature; 2) External circulation: Simulates the annealing process, when the temperature T is greater than the termination temperature T f At that time, perform the following operations L times: ① The perturbation generates a new solution x'; ② Calculate ΔE = f(x') – f(x), where ΔE is the energy difference; ③ Decide whether to accept x' according to the Metropolis criterion; 3) Cool down, T = αT, use the new temperature T to return to step (2) to generate x', where α is the cooling coefficient; 4) The temperature drops to the termination temperature T f When x is equal to or less than the given value, return the final solution, i.e., the optimal solution x. best .

7. The method for coordinated optimization of electromechanical control parameters of a dual-switch reluctance electric drive system according to claim 1, characterized in that, The operating range of "traversing all operating points" in step (9) is the CLTC-P cyclic operating point.

8. The method for collaborative optimization of electromechanical control parameters of a dual-switch reluctance electric drive system according to claim 1, characterized in that, The particle swarm optimization algorithm described in step (11) is a swarm intelligence-based optimization algorithm that efficiently searches for the optimal solution in the solution space by simulating cooperation and competition among individuals. The particles modulate their own motion by observing two "optimal solutions," namely the individual optimal solution and the swarm optimal solution, and finally reach the final optimal solution.

9. The method for coordinated optimization of electromechanical control parameters of a dual-switch reluctance electric drive system according to claim 1, characterized in that, The motor loss calculation model in step (7) is a fast calculation model for switched reluctance motor losses based on analytical solutions and numerical interpolation. The specific calculation process includes: 1) Derive the A-phase flux linkage waveform Ψ using the SRM dynamic torque interpolation model. A (t), and calculate the B-phase flux linkage waveform Ψ. B (t), C-phase flux linkage waveform Ψ C (t); 2) Based on the magnetic flux data and structural data of each part of the SRM stator and rotor, through... Calculate the magnetic flux density data, where B i (t) represents the magnetic flux density waveform, φ i (t) represents the magnetic flux waveform, S i Let be the cross-sectional area through which the magnetic flux passes; 3) Through Calculate the hysteresis loss Ph and eddy current loss Pe, where K h K is the hysteresis correction factor. e C is the eddy current correction factor. h C is the hysteresis loss coefficient. e f is the eddy current loss coefficient. c B is the magnetization frequency. m α is the amplitude of the magnetic flux density waveform, and α is the Strinmetz factor, ranging from 1.6 to 2.

0. 4) Calculate the hysteresis loss and eddy current loss of the stator pole, stator yoke, rotor pole, and rotor yoke using the superposition principle, and add them together to obtain the overall iron loss P of the SRM. Fe ; (3) In the formula P hsp P hS P hrp P hr These are the hysteresis losses of the stator pole, stator yoke, rotor pole, and rotor yoke, respectively; P esp P eS P erp P er These are the eddy current losses of the stator pole, stator yoke, rotor pole, and rotor yoke, respectively.

10. The method for coordinated optimization of electromechanical control parameters of a dual-switch reluctance electro-drive system according to claim 1, characterized in that, The system dynamics model described in step (4) is built using Simulink and Matlab, and the simulation of the total system loss and total efficiency described in step (9) is performed using Isight software for automated iterative calculation.

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