Multi-objective optimization design method for electromagnetic force proportionality characteristic of cdc solenoid valve

Abstract

The application provides a multi-objective optimization design method for electromagnetic force proportionality characteristics of a CDC electromagnetic valve, and belongs to the field of CDC electromagnetic valve design. The method comprises the following steps: establishing an equivalent model of the CDC electromagnetic valve; establishing a mathematical model for measuring the electromagnetic force proportionality characteristics; selecting target optimization structure parameters; performing finite element simulation analysis to establish an experimental database; using an artificial neural network to construct a fitting function between input variables and output variables; and using a cuckoo multi-objective search algorithm for multi-objective optimization. The application provides a mathematical model for electromagnetic force proportionality characteristics that can be used for a multi-objective optimization algorithm, thereby solving the problem that the electromagnetic force proportionality characteristics cannot be solved according to numerical values as characteristic curves. The application uses the cuckoo multi-objective search algorithm, which can better solve continuous optimization problems of structure parameters of the same type, and has higher efficiency and precision compared with an orthogonal experiment method for solving such problems.

Description

Technical Field

[0001] This invention belongs to the field of CDC solenoid valve design and relates to a multi-objective optimization design method for the electromagnetic force proportional characteristics of CDC solenoid valves. Background Technology

[0002] Semi-active shock absorbers are used in some high-end vehicles to provide higher levels of vehicle stability and ride comfort. The CDC (Controlled Droplet Dispensing) solenoid valve, as a core component of the semi-active shock absorber system, directly affects the control accuracy and damping adjustment capability of the shock absorber. Currently, the application of CDC solenoid valves in semi-active shock absorber systems is relatively mature, and how to further improve product performance has become a research hotspot. Traditional design methods mainly rely on experience and trial and error, lacking systematic analysis and optimization methods. When performance is already quite good, further optimization becomes significantly more costly and inefficient. In particular, the core performance parameters of the CDC solenoid valve are the proportional characteristics and force values ​​of the electromagnetic force, which are difficult to measure directly numerically as characteristic curves. Therefore, multi-objective algorithms cannot be directly applied to solve for the optimal combination of structural parameters, resulting in a persistent lack of efficient and high-precision structural optimization methods.

[0003] Based on the above background and challenges, developing a multi-objective optimization design method for the electromagnetic force characteristics and structural parameters of CDC solenoid valves specifically for semi-active dampers has become a current research hotspot, and it has important application value in improving the performance and driving experience of semi-active damper systems.

[0004] Patent CN 110263463A proposes an electromagnetic induction characteristic analysis method based on Ansys Maxwell software, analyzing the interaction between the structure and performance of a solenoid valve. However, it can only identify macroscopic structural issues and has limited applicability to CDC solenoid valves. Patent CN202210972274.1 proposes a method for structural optimization of solenoid valves using a multi-objective algorithm. While the core performance parameters of ordinary solenoid valves can be measured numerically and optimized directly using algorithms, the core performance of CDC solenoid valves is a characteristic curve, which cannot be directly optimized using multi-objective algorithms. Patent CN 114048705A proposes a fuzzy control method and control system for adjustable damping shock absorbers. It primarily optimizes CDC solenoid valve feedback control to improve product performance, rather than focusing on the internal structural parameters of the product.

[0005] To address the aforementioned issues, this patent aims to establish a mathematical model of the electromagnetic force proportional characteristics for CDC solenoid valves that can be used for multi-objective optimization algorithms. This model enables the structural parameters to be optimized through multi-objective optimization algorithms, seeking a combination of structural parameters that allows the CDC solenoid valve to achieve optimal overall performance. Summary of the Invention

[0006] To provide a multi-objective optimization method applicable to the electromagnetic force characteristic curve of a CDC proportional solenoid valve, this invention aims to propose a multi-objective optimization design method for the electromagnetic force proportional characteristics of a CDC solenoid valve.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0008] A multi-objective optimization design method for the proportional electromagnetic force characteristic of a CDC solenoid valve, comprising the following steps:

[0009] A. Establish an equivalent model of the CDC proportional solenoid valve.

[0010] The CDC proportional solenoid valve includes an adjusting mechanism 1, an adjusting spring 2, a valve core 3, a moving iron core magnetic isolation ring 4, a magnetic isolation ring 5, a support spring 6, a valve cover 7, a coil 8, and a valve seat 9. The moving iron core 4 and the magnetic sleeve are clearance-fitted, while the valve core 3 and the moving iron core 4 are interference-fitted. During operation, the moving iron core 4 is subjected to electromagnetic force, and the valve core 3 is subjected to the combined action of the support spring and the adjusting spring. Therefore, the entire assembly of the moving iron core 4 and the valve core 3 is subjected to the combined action of electromagnetic force and spring force. Based on the technical characteristics of finite element simulation, the CDC proportional solenoid valve undergoes structural equivalence simplification, and a two-dimensional axisymmetric simulation model is established.

[0011] B. Establishing a mathematical model applicable to the electromagnetic force proportional characteristic algorithm.

[0012] To enable numerical measurement of the electromagnetic force characteristic curve and thus its applicability to algorithms, a mathematical model is proposed. This model can measure the product's (CDC proportional solenoid valve's) ability to balance the magnitude of the electromagnetic force and maintain a constant electromagnetic force within the operating range. The formula is as follows:

[0013]

[0014] In the formula, S is the electromagnetic force proportionality characteristic metric, used as the algorithm optimization index; n is the number of experimental points selected; x i Let be the electromagnetic force value at the i-th experimental point.

[0015] C. Select target optimization structural parameters

[0016] Based on electromagnetic principles and simulation experiments, the parameters that have a significant impact on the electromagnetic force characteristics of the CDC proportional solenoid valve include the length of the magnetic isolation ring 5, the position of the magnetic isolation ring 5, and the shape of the pole shoe base 9. Therefore, in order to obtain better proportional characteristics and greater electromagnetic force without changing the original volume of the product, the length of the magnetic isolation ring 5, the position of the magnetic isolation ring 5, and the shape of the pole shoe base 9 are used as optimized structural parameters, and the proportional characteristics of electromagnetic force are used as the optimization target.

[0017] Based on the response surface methodology, m sets of experimental parameters are established. For example, in this invention, three structural parameters are selected as input variables. Then, the range of each structural parameter is input into the response surface methodology experimental data generation software. The software will generate m sets of experimental parameters based on the number of variables and their ranges. The generated experimental parameters are then standardized using the following formula:

[0018]

[0019] Y′=Y

[0020]

[0021] In the formula, X is the length of the magnetic shielding ring 5, Y is the position of the magnetic shielding ring 5, Z is the width of the pole shoe base boss 9, X′ is the standardized length value of the magnetic shielding ring 5, Y′ is the standardized position value of the magnetic shielding ring 5, Z′ is the standardized width value of the pole shoe base boss 9, and X... max X represents the upper limit of the length of the magnetic shielding ring 5. min Y is the lower limit of the length of the magnetic shielding ring 5. max Y represents the upper limit of the value at position 5 of the magnetic shielding ring. min Z is the lower limit of the value of the magnetic shielding ring 5 position. max X represents the upper limit of the width of the extreme shoe base boss 9. min The lower limit of the width of the extreme shoe base boss 9;

[0022] D. Perform finite element simulation analysis and establish a simulation database.

[0023] Using Maxwell electromagnetic simulation software, the structural parameters corresponding to the equivalent model established in step A were modified sequentially according to the experimental group established in step C. After modification, a solution analysis was performed to simulate and solve the electromagnetic force proportionality characteristics of the equivalent model. The electromagnetic force proportionality characteristics include: the ability of the moving iron core to have an electromagnetic force proportional to the driving current within the working range, and the magnitude of the average electromagnetic force. After the solution is completed, a simulation database is established based on the simulation results.

[0024] D1. Import the equivalent model into the 2D magnetostatic solver. Modify the equivalent model according to the structural parameter values ​​designed in the experimental group. After modification, define the material parameters of each component. Set the moving iron core 4 as a soft magnetic material, the magnetic shielding ring 5, valve cover 7, and valve seat 9 as high magnetic permeability materials, the valve core 3 and magnetic shielding ring 5 as non-magnetic metals, and the other components that do not affect the magnetic field distribution as non-magnetic materials. Click the Assign Boundary option to create the Balloon solution domain and set it as the balloon boundary to complete the setting of the boundary conditions of the solution domain.

[0025] D2. Based on the Hooke's coefficient and effective extension / contraction of the adjusting spring 2 and the supporting spring 6, define the motion range and direction of motion of the moving iron core 4, and set the spring force curve of the moving iron core 4, as follows:

[0026] force=(k1+k2)x

[0027] In the formula, force is the magnitude of the load force on the moving iron core 4, k1 is the Hooke coefficient of the adjusting spring, k2 is the Hooke coefficient of the supporting spring, and x is the displacement of the moving iron core 4.

[0028] D3. Perform finite element mesh generation on the established equivalent model. Specifically: select the moving iron core 4, magnetic isolation ring 5, valve cover 7, and valve seat 9 in the equivalent model, use the software mesh generation tool to set it, click mesh operation→lengthbased, enter the volume of the mesh generation, and complete the finite element mesh generation.

[0029] D4. Design the excitation source as an external circuit drive. Configure the circuit system in the Maxwell circuit editor. The circuit system includes the excitation source, resistors, and coils, and should be consistent with the actual drive circuit of the product. After configuration, import the configuration into the 2D magnetostatic solver.

[0030] D5. After setting up, perform simulation analysis on the established finite element model. The analysis results will generate data on the key performance parameters of the CDC proportional solenoid valve, including the electromagnetic force characteristic curve. According to the experimental data set designed in step C, change the structural parameters and return to step D1 until the simulation analysis results of all experimental data sets are obtained. Save and record the results, and substitute the electromagnetic force characteristic curve into the mathematical model established in step B to obtain the output target value.

[0031] E. Constructing a fitting function between input and output variables using artificial neural networks.

[0032] Based on the experimental data set constructed in step C, the standardized combination of structural parameter values ​​is selected as the input variable and substituted into the electromagnetic force characteristic curve in the mathematical model established in step B as the output variable. 75%-80% of the data in the experimental data set is selected as the training set, and the remaining 20%-25% of the data is selected as the test set to train the network. The network is trained multiple times on the database until the functional correlation in the training results is greater than 0.9, thus completing the construction of the fitting function between the structural parameters of the CDC solenoid valve and the electromagnetic force proportional characteristics.

[0033] F. Utilize the Dujuan multi-objective search algorithm to perform multi-objective optimization of the structural parameters of the CDC solenoid valve.

[0034] F1. Construct the fitness function for the multi-objective optimization algorithm; using the fitting function obtained in step E, construct the fitness function for the genetic algorithm as follows:

[0035]

[0036] Where, index i H′ represents the fitness of the i-th individual in the population. i and F′ i These are the mean electromagnetic force and mean heat output of the i-th individual in the population, respectively. The higher the individual's fitness, the higher the index. i The smaller the value, the stronger the average electromagnetic force and the stronger the ability of the electromagnetic force to maintain constant characteristics within the working range, and the higher the performance of the CDC proportional solenoid valve.

[0037] F2. Use the Cuckoo Multi-Objective Search algorithm to find the optimal solution set; within the range of structural parameters, initialize the population, randomly generate the initial nest positions, and set the maximum number of iterations T; calculate the fitness value of the current nests; update the nest positions using Wright's fly-through algorithm, and calculate the index of all nests after the update. i Value, comparing the index of the new Bird's Nest with that of the old Bird's Nest. i The value size, retaining the index. i For nests with smaller values, repeat this process until the number of iterations equals the maximum number of iterations T, and then use the final population output as the optimal solution set.

[0038] Furthermore, the number of groups n in step B is calculated based on the different ranges of experimental parameter values.

[0039] The beneficial results of this invention are as follows:

[0040] 1) This invention proposes a mathematical model for measuring the proportional characteristics of the electromagnetic force of a CDC solenoid valve, which can solve the problem that the proportional characteristics of electromagnetic force as a characteristic curve cannot be used for algorithm evaluation.

[0041] 2) This invention applies electromagnetic simulation technology, which is more efficient and accurate than traditional design methods.

[0042] 3) This invention proposes a method for optimizing the structure of CDC proportional solenoid valves by applying the Azalea multi-objective search algorithm, which can achieve efficient and high-performance optimization of product structure. Attached Figure Description

[0043] Figure 1 This is a flowchart of the present invention.

[0044] Figure 2 The equivalent model diagram of the CDC proportional solenoid valve constructed for this invention is shown.

[0045] Figure 3 This is a comparison of the electromagnetic force characteristic curves of the CDC proportional solenoid valve before and after optimization in an embodiment of the present invention.

[0046] In the diagram: 1 Adjustment mechanism, 2 Adjustment spring, 3 Valve core, 4 Moving iron core, 5 Magnetic isolation ring, 6 Support spring, 7 Valve cover, 8 Coil, 9 Valve seat. Detailed Implementation

[0047] The present application will now be described in full and in detail with reference to the accompanying drawings of the embodiments of the present invention. It should be understood that the specific embodiments described herein are only used to explain the details of the present invention and are only a part of the embodiments of the present invention, not all of them.

[0048] Figure 1 The flowchart of the multi-objective optimization design method for the electromagnetic force proportional characteristics of the CDC solenoid valve described in this application includes the following steps:

[0049] A. Establish an equivalent model of the CDC proportional solenoid valve:

[0050] The CDC proportional solenoid valve includes an adjusting mechanism 1, an adjusting spring 2, a valve core 3, a moving iron core magnetic shielding ring 5, a magnetic shielding ring 5, a support spring 6, a valve cover 7, a coil 8, and a valve seat 9. The moving iron core 4 and the magnetic sleeve are clearance-fitted, while the valve core 3 and the moving iron core 4 are interference-fitted. During operation, the moving iron core 4 is subjected to electromagnetic force, and the valve core 3 is subjected to the combined action of the support spring and the adjusting spring. Therefore, the entire assembly of the moving iron core 4 and the valve core 3 is subjected to the combined action of electromagnetic force and spring force. Based on the technical characteristics of finite element simulation, the CDC proportional solenoid valve undergoes structural equivalence simplification, and a two-dimensional axisymmetric simulation model is established.

[0051] B. Establishing a mathematical model applicable to the electromagnetic force proportional characteristic algorithm.

[0052] Taking the product in this embodiment as an example, the effective working range is 2mm. In order to make the electromagnetic force characteristic curve measurable numerically and thus applicable to the algorithm, a mathematical model is established to make the product's ability to balance the magnitude of the electromagnetic force and its constant electromagnetic force within the working range measurable numerically. The formula is as follows:

[0053]

[0054] In the formula, S is the electromagnetic force proportionality index, used as the algorithm optimization index, n is the number of experimental points selected, and x i Let n be the electromagnetic force value at the i-th experimental point. The value of n is selected based on the actual working interval length. For example, in this example, the working interval is 2 mm, and a sample point is selected every 0.1 mm, so the value of n is 20.

[0055] C. Select target optimization structural parameters

[0056] The parameters of the magnetic shielding ring length, pole shoe base boss width, and magnetic shielding ring position were combined, and 17-30 sets of simulation experiments were established based on the response surface methodology. The experimental parameters were standardized using the following formulas:

[0057]

[0058] Y′=Y

[0059]

[0060] In the formula, X is the length of the magnetic shielding ring 5, Y is the position of the magnetic shielding ring 5, Z is the width of the pole shoe base boss 9, X′ is the standardized length value of the magnetic shielding ring 5, Y′ is the standardized position value of the magnetic shielding ring 5, Z′ is the standardized width value of the pole shoe base boss 9, and X... max X represents the upper limit of the length of the magnetic shielding ring 5. min Y is the lower limit of the length of the magnetic shielding ring 5. max Y represents the upper limit of the value at position 5 of the magnetic shielding ring. min Z is the lower limit of the value of the magnetic shielding ring 5 position. max Z represents the upper limit of the width of the extreme shoe base boss 9. min The lower limit of the width of the extreme shoe base boss 9;

[0061] The table below shows a comparison of the data before and after standardization:

[0062] -1 0.5 0 0 0.5 0.5 -1 1 2.5 0 1 1 -1 0.5 5 0 0.5 0.5 -1 0 2.5 0 0 0 3 0.5 2.5 0.67 0.5 0.5 3 1 5 0.67 1 1 3 0 5 0.67 0 0 3 0.5 2.5 0.67 0.5 0.5 3 0.5 2.5 0.67 0.5 0.5 3 0.5 2.5 0.67 0.5 0.5 3 0 0 0.67 0 0 3 1 0 0.67 1 1 3 0.5 2.5 0.67 0.5 0.5 5 0.5 5 1 0.5 0.5 5 0 2.5 1 0 0 5 1 2.5 1 1 1 5 0.5 0 1 0.5 0.5

[0063] D. Perform finite element analysis and establish a simulation database:

[0064] Step 1: Set the moving iron core 4 to steel-1010, the magnetic sleeve, valve cover 7, and valve seat 9 to steel-1008, the magnetic shielding ring 5 and valve core 3 to copper, and set the other components that do not affect the magnetic field distribution to non-magnetic materials. Click the AssignBoundary option to create the Balloon solution domain and set it as the balloon boundary to complete the setting of the solution domain boundary conditions.

[0065] Step 2: Define the load force curve of the moving iron core based on the Hooke coefficient of the adjusting spring being 10 N / mm and the effective extension being 2 mm; and the Hooke coefficient of the supporting spring being 15 N / mm. Define the maximum displacement of the moving iron core as 2 mm and the direction of movement as positive.

[0066] Step 3: Perform finite element mesh generation. Since the moving iron core is made of soft magnetic material, its volume is small and the driving voltage is also small, so the eddy current loss and skin effect are not obvious. The mesh generation is based on the volume of the component. The magnetic sleeve and moving iron core are set to 0.1mm, and the other parts are set to 1mm.

[0067] Step 4: Add a drive circuit. The target model CDC proportional solenoid valve has a 6V excitation source, 100 turns of coil, and an internal resistance of 50Ω. It is driven by an external circuit with a DC voltage source connected in series with an equivalent internal resistance.

[0068] Step 5: After setting up, perform simulation analysis on the established finite element model. The analysis results will generate data on the key performance parameters of the CDC proportional solenoid valve, including the electromagnetic force characteristic curve. According to the experimental data set designed in step C, change the structural parameters and return to step 1 until the simulation analysis results of all experimental data sets are obtained. Save and record the results, and substitute the electromagnetic force characteristic curve into the mathematical model established in step B to obtain the output target value.

[0069] E. Constructing a fitting function between input and output variables using artificial neural networks:

[0070] Based on the experimental data set constructed in step C, standardized combinations of structural parameter values ​​are selected as input variables and substituted into the electromagnetic force characteristic curve in the mathematical model established in step B as the output variable. 75%-80% of the data in the experimental data set is selected as the training set, and the remaining 20%-25% is used as the test set to train the network. The network is trained multiple times until the correlation of the function in the training results is greater than 0.9, thus completing the construction of the fitting function between the structural parameters of the CDC solenoid valve and the proportional electromagnetic force characteristic. F. The CDC proportional solenoid valve structural parameters are optimized using the Dujuan multi-objective search algorithm:

[0071] Step 1: Construct the fitness function for the multi-objective optimization algorithm. Using the relational model obtained in step E, construct the fitness function for the genetic algorithm as follows:

[0072]

[0073] Where, index i H' represents the fitness of the i-th individual in the population. i and F′ i θ represents the mean electromagnetic force and mean heat loss of the i-th individual in the population. The higher the individual's fitness, the greater θ becomes. i The smaller the value, the greater the electromagnetic force and the lower the heat loss, resulting in higher performance of the CDC proportional solenoid valve.

[0074] Step 2: Use the Cuckoo Multi-Objective Search algorithm to find the optimal solution set. Within the range of structural parameters, initialize the population with a size of 50, randomly generate initial nest locations, and set the maximum number of iterations to 100. Calculate the fitness value of the current nest. Update the nest locations using Wright's fly-through method, calculate the fitness values ​​of all nests after the update, compare the fitness values ​​of the new nests with those of the old nests, and retain the nests with higher fitness. Repeat this process until the maximum number of iterations is reached, and use the final population output as the optimal solution set.

[0075] The mean electromagnetic force under the final optimal structural parameters is 3.0785 N, and the sample variance is 0.03641 N. Compared with the results before optimization, as shown... Figure 3 As shown, the mean electromagnetic force increased by 10.97%, and the sample variance decreased by 39.93, demonstrating a significant performance improvement. The traditional design, prototyping, and testing process takes approximately 1-2 months, while this method only takes 7-8 days, resulting in a significant efficiency improvement. Traditional empirical formulas involve an error of approximately 30%, while this method can control the error within 10%, making it more accurate.

[0076] The above-described embodiments are merely illustrative of the implementation methods of the present invention, but should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the protection scope of the present invention.

Claims

1. A multi-objective optimization design method for the electromagnetic force proportional characteristics of a CDC proportional solenoid valve, characterized in that, Includes the following steps: First, an equivalent model of the CDC proportional solenoid valve and a mathematical model applicable to the electromagnetic force proportional characteristic algorithm are established. Second, structural parameters are optimized by selecting objectives, and a simulation database is established through finite element simulation analysis. Third, an artificial neural network is used to construct a fitting function between input and output variables. Finally, the Dujuan multi-objective search algorithm is used to optimize the structural parameters of the CDC proportional solenoid valve using multiple objectives. The details are as follows: Step A: Establish an equivalent model of the CDC proportional solenoid valve; The moving iron core (4) and the magnetic sleeve in the CDC proportional solenoid valve are in clearance fit, and the valve core (3) and the moving iron core (4) are in interference fit. The moving iron core (4) is subjected to electromagnetic force when working, and the valve core (3) is subjected to the combined action of the support spring and the adjusting spring. Therefore, the whole composed of the moving iron core (4) and the valve core (3) is subjected to the combined action of electromagnetic force and spring force. The CDC proportional solenoid valve is simplified by structural equivalence using the finite element simulation method, and a two-dimensional axisymmetric simulation model is established. Step B: Establish a mathematical model applicable to the electromagnetic force proportional characteristic algorithm, used to measure the ability of the CDC proportional solenoid valve to balance the magnitude of the electromagnetic force and maintain a constant electromagnetic force within the operating range. The formula is as follows: In the formula, S is the electromagnetic force proportionality characteristic measurement index, which is used as the algorithm optimization index; n is the number of experimental points selected. Let i be the electromagnetic force value at the i-th experimental point; Step C: Select target optimization structural parameters The length of the magnetic isolation ring (5), the position of the magnetic isolation ring (5), and the width of the valve seat (9) of the CDC proportional solenoid valve are used as optimized structural parameters, and the proportional characteristics of the electromagnetic force are used as the optimization target. Based on the response surface methodology, m sets of experimental parameters are established. Specifically, three structural parameters are selected as input variables. Then, the range of each structural parameter is input into the response surface methodology experimental data generation software. Based on the number of variables and the range of variable ranges, m sets of experimental parameters are generated. The generated experimental parameters are then standardized. Step D: Perform finite element simulation analysis and establish a simulation database. Using Maxwell electromagnetic simulation software, the structural parameters corresponding to the equivalent model established in step A were modified sequentially according to the experimental group established in step C. The solution analysis was performed, and the electromagnetic force proportionality characteristics of the equivalent model were simulated and solved. These characteristics include the ability of the moving iron core to experience an electromagnetic force proportional only to the driving current within the working range, and the magnitude of the average electromagnetic force. After the solution was completed, a simulation database was established based on the simulation results. Step E: Construct a fitting function between the input and output variables using an artificial neural network. Based on the experimental data set constructed in step C, the standardized combination of structural parameter values ​​is selected as the input variable. The electromagnetic force characteristic curve is substituted into the mathematical model established in step B to obtain the output variable. 75%-80% of the data in the experimental data set is selected as the training set, and the remaining data is selected as the test set to train the network. The database is trained multiple times until the functional correlation in the training result is greater than 0.9, thus completing the construction of the fitting function between the structural parameters of the CDC proportional solenoid valve and the electromagnetic force proportional characteristic. Step F: Use the Dujuan multi-objective search algorithm to optimize the structural parameters of the CDC proportional solenoid valve for multiple objectives.

2. The multi-objective optimization design method for the electromagnetic force proportional characteristics of a CDC proportional solenoid valve according to claim 1, characterized in that, In step C, the formula for standardization is as follows: In the formula, X is the length of the magnetic shielding ring (5), Y is the position of the magnetic shielding ring (5), and Z is the width of the valve seat (9). The length value of the standardized magnetic shielding ring (5) is shown. To standardize the position value of the magnetic isolation ring (5), The width value of the valve seat (9) after standardization. This is the upper limit of the length of the magnetic shielding ring (5). The lower limit of the length of the magnetic shielding ring (5) is given. The upper limit of the position value of the magnetic shielding ring (5) is given. The lower limit of the position value of the magnetic shielding ring (5) is given. The upper limit of the width of valve seat (9) is given. The lower limit of the width of valve seat (9) is given.

3. The multi-objective optimization design method for the electromagnetic force proportional characteristics of a CDC proportional solenoid valve according to claim 2, characterized in that, Step D is described in detail below: D1. Import the equivalent model into the 2Dmagnetostatic solver. Modify the equivalent model according to the structural parameter values ​​designed in the experimental group. After modification, define the material parameters of each component. Set the moving iron core (4) as a soft magnetic material, the valve cover (7) and valve seat (9) as high permeability materials, the magnetic shielding ring (5) and valve core (3) as non-magnetic metals, and the other components that do not affect the magnetic field distribution as non-magnetic materials. Click the Assign Boundary option to create the Balloon solution domain and set it as the balloon boundary to complete the setting of the boundary conditions of the solution domain. D2. Based on the Hooke coefficient and effective extension of the adjusting spring and the supporting spring, define the motion range and direction of motion of the moving iron core (4), and set the spring force curve of the moving iron core (4) as follows: In the formula, The magnitude of the load force on the moving iron core (4) To adjust the Hooke's constant of the spring, To support the Hooke's coefficient of the spring, x is the displacement of the moving iron core (4); D3. Perform finite element mesh generation on the established equivalent model. Specifically: select the moving iron core (4), magnetic isolation ring (5), valve cover (7), and valve seat (9) in the equivalent model, and use the software mesh generation tool to set it. Click mesh operation→length based, input the volume of the mesh generation, and complete the finite element mesh generation. D4. Design the excitation source as an external circuit drive. Set the circuit system in the Maxwell circuit editor. The circuit system includes an excitation source, resistor and coil (8), which is consistent with the actual drive circuit of the product. After setting it up, import it into the 2D magnetostatic solver; D5. After setting up, perform simulation analysis on the established finite element model. The analysis results can generate data on the key performance parameters of the CDC proportional solenoid valve, including the electromagnetic force characteristic curve. According to the experimental data set designed in step C, change the structural parameters and return to step D1 until the simulation analysis results of all experimental data sets are obtained. Save and record the results. Substitute the electromagnetic force characteristic curve into the mathematical model established in step B to obtain the output target value.

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