Permanent magnet toothless traction machine iron core length and slot area collaborative optimization method based on slot fullness rate improvement

Abstract

The invention discloses a permanent magnet toothless traction machine iron core length and slot area collaborative optimization method based on slot fullness rate improvement, and the method is characterized in that the method comprises the following steps: S1, building a collaborative optimization mathematical model; s2, determining a design feasible region and an evaluation index; s3, evaluation and decision making: calculating and analyzing the influence of different combinations in the design feasible region on the thermal load and manufacturing cost of the motor by using the evaluation indexes defined in the step S2; the change trend of each index is visually displayed through a two-dimensional visual chart and a three-dimensional visual chart, a designer is assisted to select an optimal combination from a feasible region according to a specific cost reduction target and performance tradeoff, and therefore collaborative optimization design is completed. The method has the beneficial effects that (1) quantitative guidance is realized; and (2) performance is guaranteed. And (3) collaborative optimization. And (4) high efficiency and intuition.

Description

Technical Field

[0001] This invention belongs to the field of permanent magnet motor design technology, and particularly relates to a method for synergistically optimizing the core length and stator slot area of ​​a permanent magnet gearless traction machine based on improved slot fill factor, in the context of improved motor manufacturing process and increased slot fill factor, to achieve cost reduction and efficiency improvement. Background Technology

[0002] Permanent magnet gearless traction machines are the core power components of elevator systems, and their performance and cost directly affect the competitiveness and energy efficiency of elevators. In motor design and manufacturing, stator slot fill factor (i.e., the proportion of stator slot area occupied by winding copper wire) is a key process indicator.

[0003] In traditional motor design processes, once design parameters are determined, they are usually not easily changed. However, with advancements in winding technology and insulation materials, slot fill factor has been significantly improved. This means that more copper wire can be filled with the same stator slot area, or a smaller slot area can be used while maintaining the same turns ampere (total current).

[0004] This technological advancement presents an opportunity to reduce costs in motor design. However, current technologies lack a systematic approach to leverage the advantages of increased slot fill factor. Designers often rely on experience or tedious finite element simulations to redesign, failing to quickly and accurately find a new design that reduces material costs (primarily copper and silicon steel sheets) while maintaining the motor's thermal performance (i.e., copper loss). Establishing a quantitative relationship between technological advancements and design parameter optimization is a pressing technical challenge. Summary of the Invention

[0005] This invention aims to address the problems existing in the aforementioned background technology by providing a method for the coordinated optimization of core length and slot area in permanent magnet gearless traction machines based on improved slot fill factor. This method establishes a clear mathematical model that, while ensuring that the key thermal performance (total copper loss) of the motor remains unchanged, quantitatively guides designers on how to reduce manufacturing costs by synergistically decreasing the core length and stator slot area.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A method for co-optimizing the core length and slot area of ​​a permanent magnet toothless traction machine based on slot fill factor improvement, characterized by the following steps:

[0008] Step S1: Establish a collaborative optimization mathematical model: Based on the core constraint that the total copper loss of the motor remains unchanged before and after optimization, a first mathematical equation is established. This first mathematical equation is related to four key variables: the increase ratio of slot fill factor k, the decrease ratio of core length x, the decrease ratio of stator slot area y, and the inherent structural parameters of the motor. By solving this first mathematical equation, the functional relationship between x and y is obtained. This functional relationship constitutes the set of all potential optimization design points.

[0009] Step S2: Determine the feasible region and evaluation indicators: Based on the functional relationship established in Step S1, generate a curve describing the relationship between the reduction ratio x of the core length and the reduction ratio y of the stator slot area. This curve defines the feasible region. Simultaneously, establish a second mathematical model for performance and cost evaluation indicators to assess the merits of each feasible scheme, including the copper loss reference quantity Pcu used to verify the invariance of copper loss. Benchmark The proportion of heat load change that measures the change in heat load (AJ) times And the cost change factor k for evaluating the cost reduction effect Cost ;

[0010] Step S3, Evaluation and Decision: Using the evaluation indicators defined in Step S2, calculate and analyze the impact of different (x,y) combinations in the feasible design domain on the motor's thermal load and manufacturing cost; use two-dimensional and three-dimensional visualization charts to intuitively display the changing trends of each indicator, assisting designers in selecting an optimal (x,y) combination from the feasible domain based on specific cost reduction goals and performance trade-offs, thereby completing the collaborative optimization design.

[0011] In a preferred embodiment of the present invention, the inherent structural parameter of the motor is the initial core length L. ef and the equivalent length L at the end tm .

[0012] In one embodiment of the present invention, the first mathematical equation is as follows:

[0013] ky(L tm0 +L ef0 )x 2 -L ef0 xL tm0 =0

[0014] L tm0 L is the equivalent length of the end winding before optimization. ef0 The effective length of the core before optimization.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0016] (1) Quantitative guidance: Transform the vague "cost reduction" target into a precise mathematical model and design curve, providing designers with a clear and quantitative basis for decision-making and avoiding blind trial and error.

[0017] (2) Performance Guarantee: The core constraint is "constant copper loss", and a copper loss reference value Pcu is introduced. Benchmark Verification was conducted to ensure that the optimized design scheme reduced costs without sacrificing the motor's thermal performance and operational reliability.

[0018] (3) Synergistic optimization: The inherent constraint relationship between the two key design parameters, core length and slot area, was revealed, and synergistic optimization of the two was achieved, which can achieve greater cost reduction potential than adjusting a single parameter.

[0019] (4) High efficiency and intuitiveness: By establishing clear mathematical models for key indicators such as heat load and cost (AJ) times ,k Cost It also provides visual representations, making the design process more efficient and intuitive, and shortening the product development cycle. Attached Figure Description

[0020] Figure 1 This is a graph showing the variation of the reduction ratio x of the core length and the reduction ratio y of the slot area in an embodiment of the present invention, while keeping the copper loss constant.

[0021] Figure 2 In this embodiment of the invention, the heat load change ratio AJ times Curve showing how the percentage x of the core length decreases.

[0022] Figure 3 In this embodiment of the invention, the heat load change ratio AJ times A curve showing how the percentage y of the tank area decreases.

[0023] Figure 4 In this embodiment of the invention, x, y and AJ times A three-dimensional relationship diagram of the three.

[0024] Figure 5 In this embodiment of the invention, the copper loss reference value Pcu Benchmark A 3D visualization analysis chart.

[0025] Figure 6 In this embodiment of the invention, the cost variation factor k Cost A 3D visualization analysis chart.

[0026] Figure 7 This is a two-dimensional projection comparison diagram of the cost change curve and the copper consumption change curve on the xy plane in an embodiment of the present invention. Detailed Implementation

[0027] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings.

[0028] 1. Core Ideas and Variable Definitions

[0029] This invention provides a systematic optimization design method. Its core idea is that when improvements in the motor winding process lead to an increase in stator slot fill factor, we can reduce the core length and stator slot area in a coordinated manner. This allows us to effectively reduce the amount of copper and silicon steel sheets used in the motor while maintaining a constant total copper loss (heat generation), thereby achieving cost reduction.

[0030] To achieve quantitative analysis, all variables used in this invention are first clearly defined:

[0031] Basic parameters:

[0032] L ef0 Effective length of the core before optimization.

[0033] L tm0 Equivalent length of the end winding before optimization.

[0034] S0: Stator slot area before optimization.

[0035] Process and design variables:

[0036] k: Slot fill rate improvement ratio, defined as the ratio of the optimized slot fill rate to the unoptimized slot fill rate, is a dimensionless parameter, k>1.

[0037] y: The reduction ratio of the tank area, defined as the optimized tank area S new The ratio of the area of ​​the groove before optimization to the area S0, i.e., y = S new / S0. This variable is dimensionless, 0 <y≤1。

[0038] x: The reduction ratio of the core length, defined as the optimized core length L. ef_new Compared with the original core length L ef0 The ratio, i.e., x = L ef_new / L ef0 This variable is dimensionless, 0 <x≤1。

[0039] Performance and cost evaluation indicators:

[0040] Pcu Benchmark Copper loss reference value, used to verify whether the optimization scheme meets the constant copper loss constraint.

[0041] AJ times The heat load change ratio is an indicator used to measure the relative change in the heat load of the motor after optimization.

[0042] k Cost Cost variation factor, defined as the ratio of the total material cost after optimization to the total material cost before optimization.

[0043] V Cu : Volume of copper material in the winding.

[0044] V Fe Volume of silicon steel core.

[0045] ρ Cu The density of copper.

[0046] ρ Fe The material density of silicon steel.

[0047] P Cu Price per unit mass of copper materials.

[0048] P Fe Price per unit mass of silicon steel.

[0049] 2. Establishment of the collaborative optimization mathematical model (corresponding to step S1)

[0050] Total copper consumption P Cu It is proportional to the total resistance of the winding. To keep the copper loss constant while maintaining the ampere-turns (product of current and number of turns), the total resistance of the winding before and after optimization must be equal.

[0051] The winding resistance is proportional to its "equivalent length".

[0052] The equivalent length before optimization is a simple superposition of the straight segment (core part) and the end: L tm0 +L ef0 .

[0053] After optimization, due to the increase in slot fill factor by a factor of k and the reduction in slot area by a factor of y, the resistance per unit length becomes the original value. Times. At the same time, the core length is reduced to x·L. ef0 Considering the effect of current density variation on resistance (inversely proportional to the square of the length), the optimized equivalent length is expressed as:

[0054] Setting the two equal, we obtain the core constraint equation of this invention:

[0055]

[0056] Simplifying this equation, we obtain a quadratic equation in one variable x:

[0057] ky(L tm0 +L ef0 )x 2 -L ef0xL tm0 =0

[0058] Solving this equation and taking its physical positive solution yields the final functional relationship between x and y, i.e., the collaborative optimization model:

[0059]

[0060] This formula shows that once the increase in slot fill factor k is determined, the decrease in slot area y will uniquely determine the decrease in core length x.

[0061] 3. Definition of key performance and cost evaluation indicators (corresponding to step S2)

[0062] In order to select the optimal solution from the infinite number of design points (constituting the feasible design region) that satisfy the above formula, this invention also defines the following three key evaluation indicators:

[0063] Reference Copper Consumption (Pcu) Benchmark )

[0064] This indicator is used to verify whether the design scheme strictly meets the constraint of "constant copper loss". Its expression is:

[0065]

[0066] For all (x, y) combinations that satisfy the collaborative optimization model, the calculated Pcu Benchmark All values ​​should be equal to the initial equivalent length L. tm0 +L ef0 .like Figure 5 As shown, the feasible design points (line -o in the figure) all fall on a constant horizontal plane.

[0067] Heat load variation ratio (AJ) times )

[0068] This metric measures the change in heat load (or current density) after the motor size is reduced. Its expression is:

[0069]

[0070] If AJ times A value >1 indicates an increase in heat generation per unit volume or area, placing higher demands on the heat dissipation system.

[0071] Cost variation factor (k) Cost )

[0072] This metric is used to directly evaluate cost reduction effectiveness and is defined as the ratio of the total material cost after optimization to the total material cost before optimization. Its expression is:

[0073]

[0074] Here, the material volume before and after optimization is a function of the design variables, therefore k Cost It is also a function of x and y. k Cost <1 indicates that cost reduction has been achieved; Cost new Cost is the optimized cost. orig V represents the cost before optimization. Cu_new To optimize the volume of the copper wire, V Cu_orig To optimize the cost of the front copper wire.

[0075] 4. Implementation Examples and Decision-Making Process (Corresponding Step S3)

[0076] Assume the initial parameters of a permanent magnet gearless traction machine are L ef0 =45mm, L tm0 =33mm. After the process upgrade, the tank fill rate increased by a ratio k=1.7.

[0077] (1) Generating the feasible region: Substitute the above parameters into the expression for x. Set the range of y (e.g., from 1 to 0.6), and a series of corresponding x values ​​can be calculated. Plot these (x,y) data points as a curve, which is the feasible region. Figure 1 The design feasible region is shown.

[0078] (2) Evaluation Indicator Analysis:

[0079] For each point (x, y) in the feasible region, calculate the corresponding heat load change ratio AJ. times and cost variation factor k Cost .

[0080] Figure 2 and Figure 3 Showcasing AJ times The relationship between x and y shows that under this optimized model, the heat load will increase (AJ). times >1), which requires designers to weigh the pros and cons.

[0081] Figure 6 The cost factor k was shown. Cost Designers can use the surface that varies with x and y to find the area with the lowest cost.

[0082] (3) Comprehensive Decision-Making: Designers can make decisions by integrating all charts. For example, in Figure 7 On the two-dimensional projection diagram, the curves satisfying constant copper loss and the trend of cost change can be observed simultaneously. Designers can set an acceptable maximum heat load increase (e.g., AJ). times <1.2), and then under this constraint, from Figure 1 Find a feasible region that makes k CostThe minimum design point is used to determine the final x and y values, thus completing the optimization. For example, Figure 5 The point marked in the diagram (x≈0.8, y≈0.81), which means that the core length is reduced by about 20% and the slot area is reduced by about 19%, is an excellent design scheme after comprehensive consideration.

[0083] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for synergistic optimization of core length and slot area of ​​a permanent magnet toothless traction machine based on improved slot fill factor, characterized in that, Includes the following steps: Step S1: Establish a collaborative optimization mathematical model: Based on the core constraint that the total copper loss of the motor remains unchanged before and after optimization, a first mathematical equation is established; this mathematical equation relates to four key variables: the increase in slot fill factor. The reduction ratio of core length The proportion of stator slot area reduction And the inherent structural parameters of the motor; by solving this mathematical equation, we can obtain... and The functional relationship between them constitutes the set of all potential optimal design points; Step S2: Determine the design feasibility region and evaluation index: Based on the functional relationship established in Step S1, generate an index describing the reduction ratio of the core length. and the ratio of stator slot area reduction The curve illustrating the changing patterns of the interrelationships between these factors defines the feasible design region. Simultaneously, a second mathematical model is established to evaluate the performance and cost of each feasible option, including a copper loss reference value used to verify the invariance of copper loss. The proportion of heat load change that measures the change in heat load. And cost change factors for evaluating the effectiveness of cost reduction ; Step S3, Evaluation and Decision: Using the evaluation indicators defined in Step S2, calculate and analyze the different... The impact of the combination on motor thermal load and manufacturing cost is investigated; the changing trends of each indicator are intuitively displayed through two-dimensional and three-dimensional visualization charts, assisting designers in selecting an optimal option from the feasible region based on specific cost reduction targets and performance trade-offs. By combining these elements, a collaborative optimization design can be achieved.

2. The method for synergistic optimization of core length and slot area of ​​a permanent magnet toothless traction machine based on slot fill factor improvement, as described in claim 1, is characterized in that... The inherent structural parameter of the motor is the initial core length. and equivalent length of the end .

3. The method for synergistic optimization of core length and slot area of ​​a permanent magnet toothless traction machine based on slot fill factor improvement, as described in claim 1, is characterized in that... The first mathematical equation is as follows: The equivalent length of the end winding before optimization. The effective length of the core before optimization.

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