Method for optimizing structure of high-power electromagnetic energy harvester based on genetic algorithm

Abstract

The application discloses a high-power electromagnetic energy harvester structure optimization method based on a genetic algorithm and belongs to the field of energy harvester. The energy harvester is composed of fixed magnets, suspended magnets, coils, shells and load resistors, wherein the fixed magnets are arranged at the top and the bottom and are composed of large and small stators. The fixed magnets and the suspended magnets are arranged in a mutually repelling manner, and the coils are wound on the upper and lower sections of the shells of the fixed magnets. Since when the excitation frequency is close to the resonance frequency of the energy harvester, the energy harvester can generate greater power. Therefore, the genetic algorithm is used to code the parameters such as the outer diameters and heights of the large and small stators, the spacing between the small stators, the height of the suspended magnet, the spacing between the coils, the number of turns and the thickness of the coils, dynamic response simulation of electricity-magnet-solid is carried out, the method of assigning fitness is used to realize the constraint condition that the resonance frequency of the energy harvester falls within the excitation frequency band and the total height of the energy harvester is not greater than a certain size, and the structure scheme of the energy harvester that can capture greater power is obtained after multiple generations of iteration.

Description

Technical Field

[0001] This invention relates to a method for optimizing the structure of a high-power electromagnetic energy trap based on a genetic algorithm, and belongs to the field of energy traps. Background Technology

[0002] With the development of technology, various methods have emerged for supplying battery power. For wireless sensors that operate for extended periods in harsh environments where batteries cannot be replaced, traditional battery power methods are no longer sufficient, thus requiring a method that can provide a stable power supply over a long period.

[0003] Energy harvesting technology offers an effective solution to this problem. Based on its principles, energy harvesters can extract energy from the environment and convert it into electrical energy needed by sensors. Mechanical vibration energy is a green and renewable energy source that can be continuously harvested from the environment, with the advantage of being unaffected by weather, location, or other factors. Vibration energy harvesting devices can collect mechanical vibration energy from the environment and then convert this harvested energy into electrical energy, providing power to sensors and other equipment around the clock.

[0004] Vibrational energy harvesting systems utilize piezoelectric, electromagnetic, or electrostatic technologies to convert the kinetic energy contained in these oscillations into usable electrical energy. Although vibrational energy is abundant, the utilization rate of vibrational energy harvesters is still very low. This is because the resonant frequency of the harvester itself does not match the frequency of the vibration source; secondly, the structure has a significant impact on the resonant frequency of the harvester. Inappropriate parameter selection can limit the power of the harvester, as it can generate significant power when the excitation frequency matches the resonant frequency. However, the power output of the harvester is drastically reduced in non-resonant situations. Magnetic springs are one of the commonly used techniques in nonlinear energy harvesting systems. A magnetic spring consists of two fixed magnets with a third magnet suspended between them. The three magnets are arranged in a mutually repulsive manner. The nonlinear magnetic force generated between the suspended and stationary magnets is the cause of the nonlinearity in energy harvesting by the harvester.

[0005] How to optimize the structure of a high-power electromagnetic energy trap to obtain the optimal parameter solution efficiently is a technical problem that urgently needs to be solved. Summary of the Invention

[0006] The purpose of this invention is to provide a method for optimizing the structural parameters of an electromagnetic energy trap under constraints on its structure and performance (e.g., resonant frequency within a range greater than 150Hz and less than 170Hz, and overall height less than 0.1m). Optimization allows the energy trap to provide maximum power to meet usage requirements. This invention provides a high-power electromagnetic energy trap structure optimization method based on a genetic algorithm. Specifically, it encodes key parameters (e.g., large stator outer diameter b2 and height h2, small stator outer diameter b1 and height h1, small stator spacing d, levitation magnet height h3, coil spacing x, coil turns N, and coil thickness L) using a genetic algorithm, transforming optimization variables into genes for individuals. The energy trap's output power is used as the evaluation factor for individual fitness, selecting more suitable individuals. Individuals with higher fitness are more competitive and more likely to be inherited as parents; repeating this process multiple times yields the optimal parameter solution.

[0007] The present invention specifically adopts the following technical solution:

[0008] A method for optimizing the structure of a high-power electromagnetic energy trap based on a genetic algorithm, comprising:

[0009] S1. Obtain the set of parameters to be optimized for the electromagnetic energy trap and the constraints on the structure and performance of the electromagnetic energy trap. Each structural parameter in the set of parameters to be optimized has its own preset optimizable range.

[0010] S2. Randomly sample multiple sets of initial values ​​for all structural parameters in the set of parameters to be optimized within their respective optimizable ranges. Each set of initial values ​​for all structural parameters is compiled into a population of individuals, and all population individuals constitute the initial parameter population.

[0011] S3. Based on the combination of structural parameters corresponding to each individual in the initial parameter population, perform frequency sweep calculation on each individual. At each excitation frequency, solve the motion equation of the suspended magnet of the electromagnetic energy harvester under external excitation to obtain the steady-state amplitude of the calculated time-domain response. Combine all frequencies to obtain the frequency domain response curve of the individual, and then obtain the output power objective function value corresponding to each individual. Then, set the fitness of the individuals in the initial parameter population that do not meet the constraints to zero, and set the fitness of the individuals that meet the constraints to the output power objective function value corresponding to the individual.

[0012] S4. Based on the fitness of the latest initial parameter population, the initial parameter population is screened to form a genetic parameter population; this genetic parameter population is paired to generate a genetic pairing parameter population; the genetic pairing parameter population is crossbred to obtain a genetic crossover parameter population; the genetic crossover parameter population is mutated to obtain a secondary genetic parameter population; based on the combination of structural parameters corresponding to each individual in the secondary genetic parameter population, the output power objective function value corresponding to each individual is obtained by solving the motion equation of the levitating magnet of the electromagnetic energy trap under external excitation; then, the fitness of individuals in the secondary genetic parameter population that do not meet the constraints is set to zero, while the fitness of individuals that meet the constraints is set to the output power objective function value corresponding to that individual; finally, this genetic parameter population is used as the initial parameter population for the next iteration.

[0013] S5. Continuously iterate through S4 until the convergence condition of the genetic algorithm is met, select the optimal parameter solution from the parameter population, and thus achieve the optimization goal of maximizing the output power of the energy harvester.

[0014] Preferably, the electromagnetic energy trap includes a fixed magnet, a suspended magnet, and a coil. The fixed magnet is divided into a top magnet and a bottom magnet with the same structural parameters, both of which are composed of a large stator and a small stator. The two fixed magnets and the suspended magnet are arranged in a mutually repulsive manner, and the suspended magnet is suspended between the upper and lower small stators. There are also two identical coils, with the upper and lower coils coaxially wrapped around the outside of the upper and lower stators, respectively.

[0015] Preferably, the structural parameters in the set of parameters to be optimized for the electromagnetic energy trap include the outer diameter and height of the large stator, the outer diameter and height of the small stator, the distance between the upper and lower small stators, the height of the levitation magnet, the distance between the upper and lower coils, the number of turns of a single coil, and the height of a single coil.

[0016] Preferably, the constraints on the structure and performance of the electromagnetic energy trap are: the resonant frequency of the electromagnetic energy trap is in the range of 150 to 170 Hz, and the total height is less than 0.1 m.

[0017] Preferably, the equation of motion for the suspended magnet of the electromagnetic energy trap under external excitation is as follows:

[0018] In the case of a closed circuit and under the action of external excitation, the suspended magnet is subjected to a mechanical damping force F. d The total magnetic damping force F exerted by the induced current in all coils on the levitation magnet e Gravity F g The magnetic spring force F exerted by the stationary magnet on the levitation magnet m The equation of motion is:

[0019]

[0020] In the formula: ω represents the relative acceleration of the levitating magnet, m represents the mass of the levitating magnet, A represents the acceleration amplitude of the external excitation source, and ω represents the excitation frequency.

[0021] Magnetic spring force F m The calculation formula is:

[0022]

[0023] In the formula: z represents the relative position of the levitating magnet with respect to the center of the energy trap, β1 represents the residual magnetic flux of the levitating magnet, V represents the volume of the levitating magnet, μ0 represents the permeability of free space, B represents the total magnetic field, and θ z This represents the partial derivative with respect to the relative position z of the levitating magnet;

[0024] Mechanical damping force F d Total magnetic damping force F e The calculation formula is:

[0025]

[0026]

[0027]

[0028] In the formula: c represents the mechanical damping coefficient, C(z) represents the functional relationship between the equivalent magnetic damping coefficient and the displacement of the levitation magnet; N represents the number of coil turns, β1 represents the residual magnetic flux of the levitation magnet, and V represents the volume of the levitation magnet. The relative velocity of the levitating magnet is represented by L, the coil thickness by Z, the relative position of the levitating magnet by ζ, the distance from the top and bottom coils to the middle position of the energy trap by D, and the diameter of the coil by D.

[0029] As a preferred method, the objective function value of the output power for each individual in the population is obtained by solving the motion equation of the suspending magnet of the electromagnetic energy harvester under external excitation, as follows:

[0030] A stepwise numerical simulation was performed on the motion of the suspended magnet under external excitation of the electromagnetic energy trap. In each simulation step, the relative acceleration of the suspended magnet during the external excitation was obtained by solving the aforementioned equations of motion. relative speed And the relative position z, thus obtaining the amplitude-frequency response curve of the electromagnetic energy trap, and at the same time, based on the velocity Calculate the voltage between the upper and lower coils based on their relative position z:

[0031]

[0032]

[0033] The total voltage ε0 = ε1 - ε2 generated by the coils is calculated based on the voltage difference between the upper and lower coils, and then the objective function value of the output power of the electromagnetic energy harvester is calculated:

[0034]

[0035]

[0036] Where: ε c R1 represents the voltage across the load resistor, and R represents the load resistance. C P represents the resistance of a coil consisting of two coils, and P represents the power generated through the load resistor.

[0037] As a preferred method, the fourth-order Runge-Kutta algorithm is used to solve the equations of motion.

[0038] Preferably, the convergence condition of the genetic algorithm is that the number of individuals in the parameter population tends to stabilize.

[0039] Compared with existing technologies, this invention has the following advantages: This invention employs a genetic algorithm to optimize the structure of a high-power electromagnetic energy trap, thereby significantly improving the energy trapping power. During the optimization process, the electromechanical response can be used to determine whether the optimal output power is achieved under the constraints of a resonant frequency greater than 150Hz and less than 170Hz, and an overall height less than 0.1m. This results in an energy trap structure that meets practical application requirements. Attached Figure Description

[0040] Figure 1 This is a schematic diagram showing the structure and key parameters of the electromagnetic energy trap used in this invention;

[0041] Figure 2 This is a flowchart illustrating the steps of the energy trap parameter optimization method based on genetic algorithm of this invention;

[0042] Figure 3 This is the iterative graph of the genetic algorithm of this invention;

[0043] Figure 4 This is a comparison diagram of the magnetic spring force experienced by the energy trap of the present invention before and after optimization;

[0044] Figure 5 This is a comparison chart of the output power of the energy harvester of the present invention before and after optimization.

[0045] In the diagram, 1-top large stator magnet, 2-top small stator magnet, 3-coil, 4-load resistor, 5-floating magnet, 6-bottom small stator magnet, and 7-bottom large stator magnet. Detailed Implementation

[0046] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0047] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0048] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the meaning consistent with their meaning in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined.

[0049] Figure 1 This is a structural diagram of a typical high-power electromagnetic energy trapping device optimized according to the present invention. The main components of the electromagnetic energy trapping device are a magnetic spring (i.e., a three-magnet arrangement), an energy trapping device housing, and a coil for power extraction. The electromagnetic energy trapping device includes fixed magnets, a levitation magnet, and a coil. The fixed magnets are divided into a top magnet and a bottom magnet with identical structural parameters, both composed of a large stator and a small stator. The two fixed magnets and the levitation magnet are arranged in a mutually repulsive manner, with the levitation magnet suspended between the two small stators. There are also two identical coils, with the upper and lower coils coaxially wrapped around the outside of the upper and lower stators, respectively. Here, the stator is the fixed magnet, and the levitation magnet is the mover. In this embodiment, the magnetic spring consists of two fixed cylindrical magnets, namely an upper cylindrical magnet and a lower cylindrical magnet, and a solid levitation magnet. The upper cylindrical magnet includes a top large stator magnet 1 and a top small stator magnet 2, and the lower cylindrical magnet includes a bottom small stator magnet 6 and a bottom large stator magnet 7. In the initial state without external excitation, the levitation magnet 5 is positioned in the middle of the two coils 3. Under external excitation, the levitation magnet 5 moves vertically within the housing of the electromagnetic energy trap, and its kinetic energy is converted into useful electrical energy through the coil 3. The upper and lower coils are connected in series and connected to an external load resistor 4.

[0050] The structural parameters and corresponding constraints of the electromagnetic energy trap to be optimized can be selected according to the actual optimization objectives. In this embodiment, the constraints on the structure and performance of the electromagnetic energy trap are set as follows: the resonant frequency of the electromagnetic energy trap is in the range of 150-170Hz, and the total height is less than 0.1m. Since the resonant frequency of the energy trap is related to the magnetic spring force F... m Therefore, in this embodiment, a total of 9 key parameters can be selected: the outer diameter b2 and height h2 of the large stator, the outer diameter b1 and height h1 of the small stator, the distance d between the upper and lower small stators, the height h3 of the levitation magnet, the distance x between the upper and lower coils, the number of turns N of a single coil, and the thickness L of a single coil. Figure 1 The above nine key structural parameters that need to be optimized are also marked in the text.

[0051] Genetic algorithms are stochastic search algorithms that draw inspiration from natural selection and genetic mechanisms in the biological world. Unlike traditional algorithms, genetic algorithms do not rely on gradient information and can reduce the risk of getting trapped in local optima. This invention uses a genetic algorithm to optimize the structure of a high-power electromagnetic energy trap to obtain the optimal parameter solution with high efficiency. Since the energy trap can generate greater power when the excitation frequency is close to the resonant frequency of the energy trap, a genetic algorithm is used to encode the nine key structural parameters to be optimized. Electro-magnetic-solid dynamic response simulations are conducted, and the constraints of the energy trap's resonant frequency falling within the excitation frequency band and the total height of the energy trap not exceeding a certain size are achieved by assigning fitness. After multiple generations of iteration, a structural scheme for the energy trap that captures greater power is obtained.

[0052] based on Figure 1 To optimize the parameters of the electromagnetic energy trap shown, a corresponding numerical simulation model needs to be established to calculate the performance of the electromagnetic energy trap under different structural parameters. Therefore, an analytical model of nonlinear magnetic force and magnetic damping force can be established first. Then, the formulas for magnetic force and magnetic damping can be directly applied to the motion equations of the electromagnetic energy trap. Numerical simulation can be performed by solving the motion equations, providing a basis for optimizing the structural parameters.

[0053] In addition, before using genetic algorithms to perform electromagnetic energy harvester performance optimization calculations, it is necessary to design appropriate gene coding rules and fitness functions based on the characteristics of the output power optimization problem, determine the initial population generation algorithm to improve the quality of the initial population, and clarify the electromagnetic energy harvester production voltage objective function model to be used in the calculation.

[0054] The analytical formulas for magnetic force and magnetic damping derived in this invention establish the equations of motion for the energy trap system. The equations of motion for the suspended magnet of the electromagnetic energy trap under external excitation in this invention are as follows:

[0055] Analysis shows that, under the conditions of a closed circuit and external excitation, the suspended magnet experiences a mechanical damping force F. d The total magnetic damping force F exerted by the induced current in all coils on the levitation magnet e Gravity F g The magnetic spring force F exerted by the stationary magnet on the levitation magnet m Therefore, the equation of motion for the levitated magnet can be expressed as:

[0056]

[0057] In the formula: ω represents the relative acceleration of the levitating magnet, m represents the mass of the levitating magnet, A represents the acceleration amplitude of the excitation source, and ω represents the excitation frequency.

[0058] The key element is the magnetic spring force F. m The calculation formula is:

[0059]

[0060] In the formula: z represents the relative position of the levitating magnet with respect to the center of the energy trap, β1 represents the residual magnetic flux of the levitating magnet, V represents the volume of the levitating magnet, μ0 represents the permeability of free space, B represents the total magnetic field, and θ z This represents the partial derivative with respect to the relative position z of the levitating magnet;

[0061] In addition, the gravity F in the equation of motion g It is a known quantity, while the mechanical damping force F d Total magnetic damping force F e The following formula can be used to calculate it:

[0062]

[0063]

[0064]

[0065] In the formula: c represents the mechanical damping coefficient, C(z) represents the functional relationship between the equivalent magnetic damping coefficient and the displacement of the levitation magnet; N represents the number of coil turns, β1 represents the residual magnetic flux of the levitation magnet, and V represents the volume of the levitation magnet. The relative velocity of the levitating magnet is represented by L, the coil thickness by Z, the relative position of the levitating magnet by ζ, the distance from the top and bottom coils to the middle position of the energy trap by D, and the diameter of the coil by D.

[0066] Based on the above-constructed equations of motion for the levitated magnet, and given the external excitation, the output power objective function value for each individual in the population can be obtained by solving the equations of motion of the levitated magnet under the external excitation in a genetic algorithm. The specific method is as follows:

[0067] The motion of the suspended magnet under external excitation of the electromagnetic energy trap was simulated in both the frequency and time domains. For each excitation frequency, the relative acceleration of the suspended magnet during the external excitation process was obtained by solving the time-domain motion equations. relative speed And the relative position z, thus obtaining the amplitude-frequency response curve of the electromagnetic energy trap. Simultaneously, based on the structural parameters of the individuals in the population, the output voltage generated by the electromagnetic energy trap and the resistance in the coil can be calculated. When the coil's internal resistance is the same as the load resistance, the output power reaches its maximum; therefore, the output power can be obtained by calculating the voltage and resistance in the coil. Specifically, firstly, based on the velocity... Calculate the voltage between the upper and lower coils based on their relative position z:

[0068]

[0069]

[0070] Since the upper and lower coils are connected to each other and the winding directions are opposite, the total voltage ε0 = ε1 - ε2 generated by the coils can be calculated based on the voltage difference between the upper and lower coils, and thus the target function value of the output power of the electromagnetic energy harvester can be calculated:

[0071]

[0072]

[0073] Where: ε c R1 represents the voltage across the load resistor, and R represents the load resistance. C P represents the resistance of a coil consisting of two coils, and P represents the power generated through the load resistor.

[0074] Therefore, the above-mentioned equations of motion and the power objective function value can be obtained by solving the equations of motion. Any feasible method in the prior art can be used for the solution. In this embodiment, the Runge-Kutta method can be used to numerically solve the established equations of motion for the energy harvester system. In this embodiment, the fitness of individual populations can be directly replaced by the power objective function value, but it is necessary to consider whether the individual populations satisfy the constraints.

[0075] See Figure 2The diagram shown is a flowchart of the energy trap parameter optimization method based on genetic algorithm of the present invention. The specific steps of the optimization method are described in detail below, including steps S1 to S5.

[0076] S1. Obtain the set of parameters to be optimized for the electromagnetic energy trap, as well as the constraints on the structure and performance of the electromagnetic energy trap. Each structural parameter in the set of parameters to be optimized has its own preset optimizable range. Subsequent genetic algorithm optimization needs to perform parameter optimization within the respective optimizable ranges of these structural parameters.

[0077] S2. Randomly sample multiple sets of initial values ​​for all structural parameters in the set of parameters to be optimized within their respective optimizable ranges. Each set of initial values ​​for all structural parameters is compiled into a population individual by encoding rules. All population individuals constitute the initial parameter population.

[0078] Specifically, the gene coding rules used in this embodiment are designed as follows:

[0079] Based on the key structural parameters of the electromagnetic energy trap, individuals representing the energy trap's performance are constructed, thereby determining the encoding rules. In this embodiment, the control variables for the electromagnetic energy trap's performance include nine key parameters: the outer diameter b2 and height h2 of the large stator, the outer diameter b1 and height h1 of the small stator, the distance d between the two small stators, the height h3 of the levitation magnet, the distance x between the two coils, the number of turns N of a single coil, and the thickness L of a single coil. A population of individuals is formed by a set of values ​​for these nine key structural parameters. The parameter information in each individual contains combinations of the nine structural parameter values, hereinafter referred to as structural parameter combinations. During population evolution, the genetic algorithm continuously optimizes the individual individuals according to the genetic strategy, i.e., simultaneously optimizing the structural parameters, thereby achieving the goal of simultaneously optimizing the electromagnetic energy trap. Binary encoding can be used, and an encoded individual is represented as follows:

[0080] X = [X0…X9…X] 18 X 19 …X 28 X 29 …X 89 ]

[0081] In the formula, every 10 components represent a structural parameter. For example, X0…X9 represent the first structural parameter, the outer diameter b2 of the large stator. 10 …X 19 This represents the second structural parameter, the height of the large stator, h2. And so on.

[0082] S3. Based on the combination of structural parameters corresponding to each individual in the initial parameter population, frequency sweep calculation is performed on each individual. At each excitation frequency, the motion equation of the suspended magnet of the electromagnetic energy harvester under external excitation is solved to obtain the steady-state amplitude of the calculated time-domain response. By combining all frequencies, the frequency domain response curve of the individual can be obtained, and then the output power objective function value corresponding to each individual can be obtained. Then, the fitness of the individuals in the initial parameter population that do not meet the constraints is set to zero, while the fitness of the individuals that meet the constraints is set to the output power objective function value corresponding to the individual.

[0083] S4. Based on the fitness of the latest initial parameter population, the initial parameter population is screened to form a genetic parameter population; this genetic parameter population is paired to generate a genetic pairing parameter population; the genetic pairing parameter population is crossbred to obtain a genetic crossover parameter population; the genetic crossover parameter population is mutated to obtain a secondary genetic parameter population; based on the combination of structural parameters corresponding to each individual in the secondary genetic parameter population, the motion equation of the suspended magnet of the electromagnetic energy trap under external excitation is solved using the Runge-Kutta method to obtain the output power objective function value corresponding to each individual in the population; then, the fitness of individuals in the secondary genetic parameter population that do not meet the constraints is set to zero, while the fitness of individuals that meet the constraints is set to the output power objective function value corresponding to that individual in the population; finally, this genetic parameter population is used as the initial parameter population for the next iteration.

[0084] S5. Continuously iterate through S4 until the convergence condition of the genetic algorithm is met, select the optimal parameter solution from the parameter population, and thus achieve the optimization goal of maximizing the output power of the energy harvester.

[0085] The convergence condition of the genetic algorithm can be selected according to the actual situation. In this embodiment, the convergence condition of the genetic algorithm is that the number of individuals in the parameter population tends to stabilize. When the number of individuals in the parameter population tends to stabilize, it means that the optimal solutions in the parameter population are basically the same. Therefore, the optimal parameter solution can be selected to achieve the optimization goal, that is, to maximize the output power of the energy harvester.

[0086] After applying the above optimization method in this embodiment, the result is as follows: Figures 3-5 As shown:

[0087] like Figure 3 As shown, with continuous iteration of the initial parameter population, individuals with high fitness will be retained, and the average output power of individuals in the population will continuously increase. Because the individual with the largest output power objective function value is set to a negative value, through multiple iterations, the individual with the smallest negative output power objective function value is the optimal parameter solution. Therefore, the output power objective function value decreases curvilinearly with population iteration, and the output power reaches its maximum when the curve tends to horizontal.

[0088] like Figure 4 As shown, the magnetic spring force experienced by the levitation magnet of the electromagnetic energy trap changes almost linearly when it is displaced within a small range, and the resonant frequency of the electromagnetic energy trap can be calculated from this.

[0089] like Figure 5 As shown, the output power generated by the electromagnetic energy trap changes with the excitation frequency. The optimized electromagnetic energy trap generates the maximum voltage when the external excitation is around 151Hz, indicating that the resonant frequency of the electromagnetic energy trap is 151Hz.

[0090] This demonstrates that the high-power electromagnetic energy trap structure optimization method based on genetic algorithms of this invention utilizes genetic algorithms to optimize the influence of parameters such as stator outer diameter and height, stator spacing, levitation magnet height, coil spacing, number of coil turns, and coil thickness on the energy trap's energy harvesting power. This enables the energy trap to achieve optimized output power under the constraints of a resonant frequency range greater than 150Hz and less than 170Hz, and an overall height of less than 0.1m, thus meeting practical application requirements.

[0091] Matters not covered in this invention are well-known technologies. For example, the processes of population pairing, crossover, and mutation in genetic algorithms can be referred to the well-known principles of genetic algorithms themselves, or can be directly implemented using related programs packaged in existing technologies. These will not be described in detail.

[0092] The above embodiments are only for illustrating the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement it accordingly. They should not be construed as limiting the scope of protection of the present invention. All equivalent changes or modifications made in accordance with the spirit and essence of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for optimizing the structure of a high-power electromagnetic energy harvester based on a genetic algorithm, characterized by, The method comprises the following steps: S1, obtaining a set of to-be-optimized parameters of an electromagnetic energy harvester and constraint conditions of structure and performance of the electromagnetic energy harvester, each structural parameter in the set of to-be-optimized parameters being provided with a respective optimizable range; S2, randomly sampling a plurality of groups of initial values of all structural parameters in the set of to-be-optimized parameters in the respective optimizable ranges, each group of initial values of all structural parameters being compiled into a population individual, and all population individuals forming an initial parameter population; S3, based on a structural parameter combination corresponding to each population individual in the initial parameter population, performing sweep calculation on each population individual, solving a suspended magnet motion equation of the electromagnetic energy harvester under external excitation at each excitation frequency, obtaining a time domain response steady-state amplitude, obtaining a frequency domain response curve of the population individual in combination with all frequencies, further obtaining an output power objective function value corresponding to each population individual, setting the fitness of a population individual in the initial parameter population that does not satisfy the constraint condition to zero, and setting the fitness of a population individual that satisfies the constraint condition to the output power objective function value corresponding to the population individual; S4, screening the initial parameter population according to the fitness of the latest initial parameter population to form a genetic parameter population, pairing the genetic parameter population to form a genetic paired parameter population, crossing the genetic paired parameter population to obtain a genetic crossed parameter population, mutating the genetic crossed parameter population to obtain a secondary genetic parameter population, obtaining an output power objective function value corresponding to each population individual in the secondary genetic parameter population by solving a suspended magnet motion equation of the electromagnetic energy harvester under external excitation, setting the fitness of a population individual in the secondary genetic parameter population that does not satisfy the constraint condition to zero, and setting the fitness of a population individual that satisfies the constraint condition to the output power objective function value corresponding to the population individual, and finally taking the genetic parameter population as the initial parameter population for the next round of iteration; S5, continuously iterating S4 until a convergence condition of the genetic algorithm is reached, selecting an optimal parameter solution from the parameter population, and thus achieving the optimization target of maximizing the output power of the energy harvester.

2. The genetic algorithm based structure optimization method of high power electromagnetic energy harvester as claimed in claim 1 wherein, The electromagnetic energy harvester comprises fixed magnets, a suspended magnet and coils, wherein the fixed magnets are divided into top magnets and bottom magnets with the same structure parameters, and are both composed of large stators and small stators, the two fixed magnets and the suspended magnet are arranged in a repelling manner, and the suspended magnet is suspended between the upper and lower small stators; the coils also have the same two, and the upper and lower coils are coaxially arranged outside the upper and lower stators.

3. The genetic algorithm based structure optimization method of high power electromagnetic energy harvester as claimed in claim 1 wherein, The structural parameters in the set of to-be-optimized parameters of the electromagnetic energy harvester include the outer diameter and height of the large stator, the outer diameter and height of the small stator, the spacing between the upper and lower small stators, the height of the suspended magnet, the spacing between the upper and lower coils, the number of turns of a single coil and the height of a single coil.

4. The genetic algorithm based structure optimization method of high power electromagnetic energy harvester as claimed in claim 1 wherein, The constraint conditions of structure and performance of the electromagnetic energy harvester are that the resonance frequency of the electromagnetic energy harvester is within the range of 150-170 Hz, and the total height is less than 0.1 m.

5. The genetic algorithm based structure optimization method of high power electromagnetic energy harvester as claimed in claim 1 wherein, The suspended magnet motion equation of the electromagnetic energy harvester under external excitation is as follows: In the case of a closed circuit and under the action of an external excitation, the levitation magnet is subjected to a mechanical damping force F d , the total magnetic damping force F acting on the levitation magnet by the induced currents in all the coils e , the weight force F g , the magnetic spring force F acting on the levitation magnet by the stationary magnet m , the equation of motion being: wherein: represents the relative acceleration of the levitated magnet, m represents the mass of the levitated magnet, A represents the acceleration amplitude of the external excitation source, and ω represents the excitation frequency. Magnetic spring force F m The calculation formula is: where z represents the relative position of the levitated magnet with respect to the center of the flux concentrator, β1represents the residual magnetic flux of the levitated magnet, V represents the volume of the levitated magnet, μ0represents the permeability of free space, B represents the total magnetic field, represents the partial derivative with respect to the relative position z of the levitated magnet. Mechanical damping force F d Total magnetic damping force F e The calculation formula is: wherein c represents a mechanical damping coefficient, C(z) represents a function of the equivalent magnetic damping coefficient and the displacement of the levitation magnet; N represents the number of turns of the coil, β1 represents the residual magnetic flux of the levitation magnet, V represents the volume of the levitation magnet, wherein Vrel represents the relative velocity of the levitation magnet, L represents the thickness of the coil, z represents the relative position of the levitation magnet, ζ represents the distance from the top coil and the bottom coil to the middle position of the energy harvester, and D represents the diameter of the coil.

6. The genetic algorithm based structure optimization method of high power electromagnetic energy harvester as claimed in claim 1 wherein, The method for obtaining the output power objective function value corresponding to each population individual by solving the levitation magnet motion equation of the electromagnetic energy harvester under external excitation is as follows: solving the motion equation in each simulation step, the relative acceleration of the levitated magnet during the external excitation is obtained relative velocity and relative position z, and then the amplitude-frequency response curve of the electromagnetic energy harvester is obtained, and the voltage of the upper and lower coils is calculated according to the velocity and relative position z. The total voltage ε0=ε1-ε2 generated by the coils is calculated according to the voltage difference between the upper and lower coils, so as to calculate the output power objective function value of the electromagnetic energy harvester: wherein: ε c represents the voltage across the load resistor, R1represents the load resistor, R C represents the resistance of a coil consisting of two coils, P represents the power generated through the load resistor.

7. The genetic algorithm based structure optimization method of high power electromagnetic energy harvester as claimed in claim 1 wherein, The fourth-order Runge-Kutta algorithm is used to solve the motion equation.

8. The genetic algorithm based structure optimization method of high power electromagnetic energy harvester as claimed in claim 1 wherein, The convergence condition of the genetic algorithm is that the population individual number in the parameter population tends to be stable.

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