Method and device for designing an energy-saving motor

Abstract

The application discloses a design method and device of an energy-saving motor, and the method comprises the following steps: determining a target parameter to be optimized and an initial value of the target parameter; constructing a target function of motor energy loss and a constraint condition according to the target parameter; constructing a water circulation algorithm optimization model according to the target parameter, the target function and the constraint condition, and calculating the optimal solution of the target parameter by using the water circulation algorithm optimization model. Compared with a design method which depends on the design experience of a designer and repeated debugging and calculation, the application is beneficial to improving the design efficiency of the energy-saving motor. In addition, compared with a probability heuristic algorithm such as a genetic algorithm, the water circulation algorithm optimization model has stronger robustness, can avoid falling into a local optimum, and improves the global optimal search capability.

Description

Technical Field

[0001] This application relates to the field of energy-saving motor design technology, specifically to a design method and device for an energy-saving motor. Background Technology

[0002] With the advancement of the national carbon neutrality process, energy conservation has become an important standard for national industrial production development. Induction motors, as the main power source for most industries, consume more than half of the world's electricity annually, making the development of energy-efficient motors of great significance.

[0003] Energy-saving motors refer to motors that reduce the loss of electromagnetic, thermal, and mechanical energy through new materials, new processes, and new motor designs, thereby reducing overall motor energy loss and improving output efficiency. New motor designs involve optimizing existing motor materials and structural parameters. Introducing new materials for optimization incurs additional production costs, while optimizing motor structural parameters can improve output efficiency without increasing production costs.

[0004] However, current optimization of motor structural parameters often relies on the designer's experience and repetitive debugging and calculations, which is somewhat arbitrary. Secondly, optimizing motor structural parameters is a complex nonlinear constraint optimization problem, and the relationship between adjustable parameters (including stator diameter, magnetic flux density, current density, etc.) and induction motor performance parameters is quite complex. These difficulties make multi-parameter, multi-objective induction motor structural optimization design inefficient and lengthy.

[0005] In recent years, researchers have been working to solve induction motor design problems by employing various mathematical programming and optimization techniques, combined with different types of constraints or multiple objectives. Genetic algorithms, a probabilistic heuristic algorithm, have been applied to efficiently optimize motor structures. Although genetic algorithms have been successfully used to solve complex nonlinear optimization problems, some shortcomings remain in practical applications. For example, when the parameters being optimized are highly correlated and the premature convergence of the genetic algorithm model reduces its search capability, its efficiency in finding the optimal solution will be significantly reduced. Summary of the Invention

[0006] The purpose of this application is to provide a design method, apparatus, electronic device and computer-readable storage medium for an energy-saving motor, which can solve at least one of the above-mentioned technical problems.

[0007] To achieve the above objectives, this application provides a design method for an energy-saving motor, characterized by comprising:

[0008] Determine the target parameters to be optimized and their initial values;

[0009] Construct the objective function and constraints for motor energy loss based on the target parameters;

[0010] A water cycle algorithm optimization model is constructed based on the target parameters, the objective function, and the constraints, and the optimal solution for the target parameters is calculated using the water cycle algorithm optimization model.

[0011] Optionally, the method further includes:

[0012] Construct a parameterized model of the motor based on the target parameters;

[0013] The optimal solution of the target parameters is input into the motor parameterization model for verification.

[0014] Optionally, the initial value of the target parameter is the value of the target parameter of the selected model of motor.

[0015] Optionally, the target parameter includes: stator diameter D s Magnetic flux density B, stator current density J s air gap length l g Stator slot depth d s stator slot width w s Stator core length l s Rotor slot depth d r Rotor slot width w r .

[0016] Optionally, the motor is a three-phase squirrel-cage motor;

[0017] The objective function is:

[0018] Loss = Loss i +Loss c +Loss f

[0019] Where Loss represents the total energy loss, Loss i Indicates iron loss, Loss c Indicates copper loss, Loss f Indicates frictional loss;

[0020] Iron loss is expressed as:

[0021]

[0022] Among them, D s B represents the stator diameter, and l represents the magnetic flux density. s d represents the length of the stator core. s Indicates the stator slot depth, l g w represents the air gap length. s Indicates the width of the stator slots;

[0023] Copper loss is represented as:

[0024] Loss c =K3J s 2 d s w s (K4+K6D s w r +K7)+C2(d r -C3)

[0025] Among them, J s d represents the stator current density. r Indicates the rotor slot depth, w r Indicates the rotor slot width;

[0026] Friction loss is expressed as:

[0027]

[0028] Above K i (i = 0, 1.., 9) and C j (j=1,2,3) are physical constants.

[0029] Optionally, the constraints include:

[0030]

[0031]

[0032]

[0033]

[0034] Where G1(x) represents the ratio of maximum torque to full-load torque, G2(x) represents the ratio of starting torque to full-load torque, G3(x) represents the full-load power factor, G4(x) represents the ratio of starting current to full-load current, and S f R represents the slip ratio under full load. A R represents the resistance per phase of the rotor. B X represents the equivalent resistance of the induction motor. A X represents the magnetizing reactance per phase of the rotor. B This represents the equivalent reactance of an induction motor.

[0035] Optionally, the step of constructing a water cycle algorithm optimization model based on the target parameters, the objective function, and the constraints, and obtaining the optimal solution for the target parameters based on the water cycle algorithm optimization model, includes:

[0036] S31. Set the initial parameters of the water cycle algorithm optimization model. The initial parameters include the initial number of water droplets, the number of target parameters, the sum of the number of rivers and oceans, the number of streams, the evaporation control parameters, the maximum number of iterations, and generate the initial population individuals.

[0037] S32. Calculate the fitness of each individual in the population using the objective function, and sort and classify all individuals in the population according to their fitness to obtain the ocean, river and stream.

[0038] S33. Calculate the number of streams flowing into the ocean and the number of streams flowing into rivers in the current population;

[0039] S34. Update the positions of streams flowing into rivers, streams flowing into the ocean, and rivers flowing into the ocean;

[0040] S35. After the location is updated, calculate the fitness of the ocean, river and stream respectively;

[0041] S36. If a stream is more fit than the river or ocean it is connected to, then the stream's position is swapped with the river's or ocean's position; otherwise, the position is not swapped. If a river is more fit than the ocean it is connected to, then the river's position is swapped with the ocean's position; otherwise, the position is not swapped.

[0042] S37. Determine whether the river and stream meet the evaporation conditions according to the evaporation condition parameters. If the result is no, proceed to step S38. If the result is yes, proceed to step S38 after the rainfall process is executed.

[0043] S38. Update the evaporation condition parameters;

[0044] S39. Determine whether the maximum number of iterations has been reached. If the result is yes, end the iteration and output the optimal solution of the target parameters. If the result is no, return to step S32.

[0045] To achieve the above objectives, this application also provides a design device for an energy-saving motor, comprising:

[0046] A determination module is used to determine the target parameters to be optimized and the initial values ​​of the target parameters;

[0047] The module is used to construct the objective function and constraints for motor energy loss based on the target parameters;

[0048] The construction and calculation module is used to construct a water cycle algorithm optimization model based on the target parameters, the objective function, and the constraints, and to calculate the optimal solution of the target parameters based on the water cycle algorithm optimization model.

[0049] To achieve the above objectives, this application also provides an electronic device, comprising:

[0050] processor;

[0051] A memory in which executable instructions of the processor are stored;

[0052] The processor is configured to execute the energy-saving motor design method described above by executing the executable instructions.

[0053] To achieve the above objectives, this application also provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the energy-saving motor design method as described above.

[0054] This application also provides a computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the energy-saving motor design method described above.

[0055] This application constructs a water-cycle algorithm optimization model based on the target parameters to be optimized and the objective function of motor energy loss constructed based on the target parameters. The optimal solution for the target parameters, i.e., the optimal structural parameters of the induction motor, can then be calculated using this model. Compared to design methods that rely on the designer's experience and repetitive debugging and calculations, this application improves the design efficiency of energy-saving motors. Furthermore, because this application uses a water-cycle algorithm optimization model, it exhibits stronger robustness compared to current probabilistic heuristic algorithms such as genetic algorithms, and can avoid getting trapped in local optima, thus improving the global optimum search capability. Attached Figure Description

[0056] Figure 1 This is a flowchart of the design method for an energy-saving motor according to an embodiment of this application.

[0057] Figure 2 This is a flowchart illustrating parameter optimization using a water cycle algorithm optimization model in an embodiment of this application.

[0058] Figure 3 This is a schematic block diagram of the design device for the energy-saving motor according to an embodiment of this application.

[0059] Figure 4 This is a schematic block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0060] To explain in detail the technical content, structural features, objectives and effects of this application, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0061] Example 1

[0062] Please see Figure 1 This application discloses a design method for an energy-saving motor, comprising:

[0063] S1. Determine the target parameters to be optimized and their initial values.

[0064] Specifically, the initial value of the target parameter is the value of the target parameter for the selected motor model. For example, if the selected motor is model A, then the value of the target parameter for model A is the initial value of the target parameter.

[0065] Specifically, the target parameters include the stator diameter D. s Magnetic flux density B, stator current density J s air gap length l g Stator slot depth d s stator slot width w s Stator core length l s Rotor slot depth d r Rotor slot width w r .

[0066] S2. Construct an objective function and constraints for motor energy loss based on the target parameters, with the aim of reducing energy loss in energy-saving motors. The constraints define a range of values ​​for the target parameters, i.e., their upper and lower bounds.

[0067] Specifically, the motor is a three-phase squirrel-cage motor;

[0068] The objective function is:

[0069] Loss = Loss i +Loss c +Loss f

[0070] Among them, Loss i Indicates iron loss, Loss c Indicates copper loss, Loss f The value represents friction loss, while Loss represents the total energy loss (i.e., the total energy loss from iron loss, copper loss, and friction loss).

[0071] Iron loss is expressed as:

[0072]

[0073] Among them, D s B represents the stator diameter, and l represents the magnetic flux density. s d represents the length of the stator core. s Indicates the stator slot depth, lg w represents the air gap length. s Indicates the width of the stator slots;

[0074] Copper loss is represented as:

[0075] Loss c =K3J s 2 d s w s (K4+K6D s w r +K7)+C2(d r -C3)

[0076] Among them, J s d represents the stator current density. r Indicates the rotor slot depth, w r Indicates the rotor slot width;

[0077] Friction loss is expressed as:

[0078]

[0079] Above K i (i = 0, 1.., 9) and C j (j = 1, 2, 3) are physical constants. These physical constants are related to the specific materials and are obtained by looking up tables based on material selection; they will not be discussed in detail in this application.

[0080] Specifically, the constraints include:

[0081]

[0082]

[0083]

[0084]

[0085] Where G1(x) represents the ratio of maximum torque to full-load torque, G2(x) represents the ratio of starting torque to full-load torque, G3(x) represents the full-load power factor, G4(x) represents the ratio of starting current to full-load current, and S f R represents the slip ratio under full load. A R represents the resistance per phase of the rotor. B X represents the equivalent resistance of the induction motor. A X represents the magnetizing reactance per phase of the rotor. B This represents the equivalent reactance of an induction motor.

[0086] It should be noted that the rotor phase resistance R A Equivalent resistance R of induction motorB Rotor phase magnetizing reactance X A Equivalent reactance X of induction motor B It is derived based on the above target parameters, but not solely based on them; specific materials and other factors also need to be considered. Based on this constraint, the range of values ​​for the target parameters can be obtained (i.e., defining the upper and lower bounds of the target parameters).

[0087] By setting the above constraints, we can ensure that the motor performance is not lower than the standard level.

[0088] S3. Construct a water cycle algorithm optimization model based on the objective parameters, objective function, and constraints, and calculate the optimal solution for the objective parameters based on the water cycle algorithm optimization model.

[0089] The water cycle algorithm is a metaheuristic algorithm proposed to simulate the natural water cycle process. This algorithm combines the search for optimal solutions to multi-parameter nonlinear equations with the natural water cycle process. Guided by a fitness function, it merges streams and rivers, using evaporation and rainfall as aids to escape local optima, ultimately converging into a pattern to find an approximate optimal solution to the problem. Here, the ocean represents the optimal solution, rivers the suboptimal solution, and streams the even suboptimal solution; evaporation refers to evaporating streams and rivers with too slow a flow rate or unable to reach the ocean, and updating their positions with new rainfall.

[0090] Please see Figure 2 Specifically, an optimization model for the water cycle algorithm is constructed based on the objective parameters, objective function, and constraints. The optimal solution for the objective parameters is then obtained based on this optimization model, including:

[0091] S31. Set the initial parameters for the water cycle algorithm optimization model. The initial parameters include the initial number of water droplets N. pop The number of target parameters D, and the sum of the number of rivers and oceans N. sr Number of streams N s Evaporation control parameter d max Maximum number of iterations i max And generate the initial population of individuals:

[0092] x i =LB+rand×(UB-LB), i=1, 2, 3,...,D

[0093] Where LB represents the lower bound of the independent variable, UB represents the upper bound of the independent variable, and rand is a uniform random number between (0,1). LB and UB are generated based on the above constraints.

[0094] S32. Calculate the fitness of individuals in each population using the objective function, and sort and classify all individuals in the population according to their fitness to obtain the ocean, river and stream (which together form the total population).

[0095] Specifically, the formula for calculating fitness can be expressed as:

[0096]

[0097] Where f(xi) represents the fitness function, which is the objective function for the energy loss of the motor mentioned above.

[0098] The matrix of the total population is:

[0099]

[0100] S33. Calculate the number of streams flowing into the ocean and the number of streams flowing into rivers in the current population.

[0101] Specifically, the calculation formula is as follows:

[0102]

[0103] in, N represents the number of streams that flow into a specific river or sea. steams The number of streams is represented by "round".

[0104] S34. Update the positions of streams flowing into rivers, streams flowing into the ocean, and rivers flowing into the ocean.

[0105] Specifically, the position update formula is:

[0106]

[0107]

[0108]

[0109] The three formulas above are the position update formulas for streams flowing into rivers, streams flowing into the ocean, and rivers flowing into the ocean, respectively. Here, t represents the iteration number, C is a constant greater than or equal to 1 and less than or equal to 2, and X (where i is sea, river, or stream) represents the current position of the ocean, river, and stream, respectively.

[0110] S35. After the location is updated, calculate the fitness of the ocean, river and stream respectively.

[0111] S36. If a stream has better fitness than the river or ocean it is connected to, then the stream's position is swapped with the river's or ocean's position; otherwise, the position is not swapped. If a river has better fitness than the ocean it is connected to, then the river's position is swapped with the ocean's position; otherwise, the position is not swapped.

[0112] S37. Determine whether the river and stream meet the evaporation conditions based on the evaporation condition parameters. If the result is no, proceed to step S38. If the result is yes, proceed to step S38 after the rainfall process is executed.

[0113] The following are the criteria for determining whether rivers and streams meet the conditions for evaporation:

[0114]

[0115]

[0116] Where m = 1, 2, ..., N sr -1, n = 1, 2, ..., N pop -N sr d max It is a parameter that is close to 0 and decreases with the number of iterations, as shown in the following formula:

[0117]

[0118] Among them, t max It is the maximum number of iterations.

[0119] Specifically, when a river meets the evaporation conditions, the new location of the stream after a rainfall process is as follows:

[0120]

[0121] When a stream flowing towards the ocean meets the evaporation conditions, its new location after a rainfall event near the ocean will be:

[0122]

[0123] Where μ is a constant that determines the degree of dispersion of the new stream, and randn is a 1*N matrix vector that conforms to a standard normal distribution.

[0124] By controlling the evaporation and rainfall processes of rivers and streams, we can prevent them from falling into localized convergence.

[0125] S38. Update evaporation condition parameters.

[0126] S39. Determine whether the maximum number of iterations has been reached. If the result is yes, proceed to step S40. If the result is no, return to step S32.

[0127] S40. End the iteration and output the optimal solution for the objective parameters.

[0128] This application constructs a water-cycle algorithm optimization model based on the target parameters to be optimized and the objective function of motor energy loss constructed based on the target parameters. The optimal solution for the target parameters, i.e., the optimal structural parameters of the induction motor, can then be calculated using this model. Compared to design methods that rely on the designer's experience and repetitive debugging and calculations, this application improves the design efficiency of energy-saving motors. Furthermore, because this application uses a water-cycle algorithm optimization model, it exhibits stronger robustness compared to current probabilistic heuristic algorithms such as genetic algorithms, and can avoid getting trapped in local optima, thus improving the global optimum search capability.

[0129] Specifically, the design method for the energy-saving motor in this application also includes:

[0130] Construct a parameterized model of the motor based on the target parameters;

[0131] The optimal solution of the objective parameters is input into the parameterized model of the motor for verification.

[0132] By inputting the optimal solution of the target parameters into the parameterized model of the motor for verification, it is possible to verify whether the optimized result meets the requirements.

[0133] Specifically, a parametric model of the motor can be built using Maxwell software.

[0134] Example 2

[0135] Please see Figure 3 This application discloses a design device for an energy-saving motor, comprising:

[0136] The determination module 201 is used to determine the target parameters to be optimized and the initial values ​​of the target parameters;

[0137] Module 202 is used to construct the objective function and constraints for motor energy loss based on the target parameters;

[0138] The construction and calculation module 203 is used to construct a water cycle algorithm optimization model based on the target parameters, objective function and constraints, and to calculate the optimal solution of the target parameters based on the water cycle algorithm optimization model.

[0139] This application constructs a water-cycle algorithm optimization model based on the target parameters to be optimized and the objective function of motor energy loss constructed based on the target parameters. The optimal solution for the target parameters, i.e., the optimal structural parameters of the induction motor, can then be calculated using this model. Compared to design methods that rely on the designer's experience and repetitive debugging and calculations, this application improves the design efficiency of energy-saving motors. Furthermore, because this application uses a water-cycle algorithm optimization model, it exhibits stronger robustness compared to current probabilistic heuristic algorithms such as genetic algorithms, and can avoid getting trapped in local optima, thus improving the global optimum search capability.

[0140] Example 3

[0141] Please combine Figure 3 This application discloses an electronic device, comprising:

[0142] Processor 30;

[0143] Memory 40, which stores executable instructions of processor 30;

[0144] The processor 30 is configured to execute the energy-saving motor design method as described in Embodiment 1 by executing executable instructions.

[0145] Example 4

[0146] This application discloses a computer-readable storage medium storing a program thereon, which, when executed by a processor, implements the energy-saving motor design method as described in Embodiment 1.

[0147] Example 5

[0148] This application discloses a computer program product or computer program that includes computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the energy-saving motor design method as described in Embodiment 1.

[0149] It should be understood that, in the embodiments of this application, the processor may be a central processing unit (CPU), but it may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0150] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by hardware related to computer program instructions. The program for implementing the above methods can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0151] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0152] The above-disclosed examples are merely preferred embodiments of this application and should not be construed as limiting the scope of this application. Therefore, any equivalent variations made in accordance with the claims of this application shall fall within the scope of this application.

Claims

1. A method of designing an energy efficient motor, characterized by, The method comprises the following steps: determining a target parameter to be optimized and an initial value of the target parameter; constructing a target function of motor energy loss and a constraint condition according to the target parameter; constructing a water cycle algorithm optimization model according to the target parameter, the target function and the constraint condition, and calculating an optimal solution of the target parameter by using the water cycle algorithm optimization model; The target parameters include: stator diameter D s , magnetic flux density B, stator current density J s , air gap length l g , stator slot depth d s , stator slot width w s , stator core length l s , rotor slot depth d r , rotor slot width w r ; the motor is a three-phase squirrel cage motor; the target function is: wherein Loss represents the total energy loss, Loss i represents the iron loss, Loss c represents the copper loss, Loss f represents the friction loss; wherein the iron loss is represented as: wherein D s represents the stator diameter, B represents the magnetic flux density, l s represents the stator core length, d s represents the stator slot depth, l g represents the air gap length, w s represents the stator slot width; the copper loss is represented as: Loss c = K3J s 2 d s w s (K4+K6D s w r +K7) + C2(d r -C3) wherein J s represents the stator current density, d r represents the rotor slot depth, w r represents the rotor slot width; the friction loss is represented as: K i (i = 0, 1.., 9) and C j (j = 1, 2, 3) are physical constants.

2. The method of designing an energy efficient electric machine as recited in claim 1, wherein, The method further comprises the following steps: constructing a motor parameterization model according to the target parameter; inputting the optimal solution of the target parameter into the motor parameterization model for verification.

3. The method for designing an energy-saving motor according to claim 1, wherein: the initial value of the target parameter is the value of the target parameter of a selected motor model.

4. The method for designing an energy-saving motor according to any one of claims 1 to 3, wherein: the constraint condition comprises: wherein G1(x) represents the ratio of maximum torque to full load torque, G2(x) represents the ratio of starting torque to full load torque, G3(x) represents the full load power factor, G4(x) represents the ratio of starting current to full load current, S f represents the full load slip, R A represents the rotor per phase resistance, R B represents the induction motor equivalent resistance, X A represents the rotor per phase magnetizing reactance, X B represents the induction motor equivalent reactance.

5. The method for designing an energy-saving motor according to claim 1, wherein: the step of constructing a water cycle algorithm optimization model according to the target parameter, the target function and the constraint condition, and calculating an optimal solution of the target parameter by using the water cycle algorithm optimization model comprises: S31, setting initial parameters of the water cycle algorithm optimization model, the initial parameters including initial water droplet population number N pop , target parameter number N, sum of river number and ocean number N sr , stream number N s , evaporation control parameter d max , maximum iteration number i max , and generating initial population individuals; S32, calculating the fitness of each population individual by using the target function, and sorting and classifying all population individuals according to the fitness to obtain the sea, river and stream; S33, calculating the number of streams flowing into the sea and the number of streams flowing into the river in the current population; S34, updating the positions of the streams flowing into the river, the streams flowing into the sea and the rivers flowing into the sea; S35, calculating the fitness of the sea, river and stream after the position updating; S36, if the fitness of the stream is better than that of the river or the sea connected thereto, the position of the stream is exchanged with the position of the river or the sea, otherwise the position exchange is not performed; if the fitness of the river is better than that of the sea connected thereto, the position of the river is exchanged with the position of the sea, otherwise the position exchange is not performed; S37, judging whether the river and the stream meet the evaporation condition according to the evaporation condition parameter, if the result is no, the process proceeds to step S38, and if the result is yes, the rainfall process is performed and then the process proceeds to step S38; S38, updating the evaporation condition parameter; S39, judging whether the maximum iteration number is reached, if the result is yes, the iteration is ended and the optimal solution of the target parameter is output, and if the result is no, the process returns to step S32.

6. An apparatus for designing an energy saving motor, characterized by The method comprises the following steps: a determining module for determining a target parameter to be optimized and an initial value of the target parameter; a constructing module for constructing a target function of motor energy loss and a constraint condition according to the target parameter; a constructing and calculating module for constructing a water cycle algorithm optimization model according to the target parameter, the target function and the constraint condition, and calculating an optimal solution of the target parameter by using the water cycle algorithm optimization model; The target parameters include: stator diameter D s , magnetic flux density B, stator current density J s , air gap length l g , stator slot depth d s , stator slot width w s , stator core length l s , rotor slot depth d r , rotor slot width w r ; the motor is a three-phase squirrel cage motor; the target function is: wherein Loss represents the total energy loss, Loss i represents the iron loss, Loss c represents the copper loss, Loss f represents the friction loss; wherein the iron loss is represented as: wherein D s represents the stator diameter, B represents the magnetic flux density, l s represents the stator core length, d s represents the stator slot depth, l g represents the air gap length, w s represents the stator slot width; the copper loss is represented as: Loss c = K3J s 2 d s w s (K4+K6D s w r +K7) + C2(d r -C3) wherein J s represents the stator current density, d r represents the rotor slot depth, w r represents the rotor slot width; the friction loss is represented as: K i (i = 0, 1.., 9) and C j (j = 1, 2, 3) are physical constants.

7. An electronic device, comprising: The method comprises the following steps: a processor; a memory having executable instructions of the processor stored therein; wherein the processor is configured to perform the design method of the energy saving motor of any one of claims 1 to 5 via execution of the executable instructions.

8. A computer-readable storage medium having stored thereon a program, characterized in that, The program, when executed by a processor, implements the design method of the energy saving motor of any one of claims 1 to 5. The program, when executed by a processor, implements the design method of the energy saving motor of any one of claims 1 to 5.

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