A wireless charging optimization method and device based on Hippo optimization algorithm

Abstract

The present invention discloses a wireless charging optimization method and device based on the Hippopotamus optimization algorithm, which is applied to wireless charging of lithium batteries. The method includes: obtaining lithium battery parameter data, determining the optimization target, optimization variables, and constraints for wireless charging of lithium batteries; initializing a hippopotamus population and calculating the fitness function value of each hippopotamus individual to determine the optimal position and optimal fitness value; entering a main loop and setting an iteration termination condition; updating the individual position using the position update strategy of the hippopotamus exploration phase; updating the individual position using the position update strategy of the hippopotamus defense phase; and updating the individual position using the position update strategy of the hippopotamus escape phase; exiting the loop when the number of iterations reaches a preset maximum number, outputting the obtained multi-stage current results; and using a wireless energy transmission control system to output the obtained multi-stage current in a time-sharing manner to charge the lithium battery. The present invention accelerates the charging speed of lithium batteries.

Description

Technical Field

[0001] The present invention relates to the field of wireless charging technology, and in particular to a wireless charging optimization method and device based on a Hippo optimization algorithm. Background Art

[0002] In recent years, wireless charging technology has been widely used in electric vehicle lithium battery chargers due to its convenience, speed and safety, and has attracted the attention of many scholars.

[0003] However, the constant current and constant voltage charging method traditionally used in current wireless charging systems has limitations. During the constant voltage stage, the charging current will continue to decrease, resulting in a slowdown in charging speed.

[0004] Secondly, for the traditional constant current and constant voltage charging method, once the topological parameters of the wireless energy transmission system are determined, the output current and voltage cannot be adjusted, which limits the freedom of charging and makes it impossible to flexibly respond to charging needs in different situations.

[0005] Moreover, the existing meta-heuristic algorithms for multi-stage constant current charging curve optimization have their own strengths, but also their own limitations, and it is often difficult to balance the solution quality and solution cost.

[0006] Therefore, how to design a multi-stage constant current charging optimization method for wireless charging of electric vehicle lithium batteries with high flexibility and fast charging speed, and at the same time optimize the charging curve using an excellent meta-heuristic algorithm, is a key problem that technicians in this field urgently need to solve.

[0007] The Hippo optimization algorithm is a novel metaheuristic algorithm proposed recently. Similar to many excellent metaheuristic algorithms such as the particle swarm optimization algorithm and the gray wolf optimization algorithm, it has a streamlined structure, high parallelism and robustness. At the same time, its three-stage update strategy has the advantages of a wide search range and large population diversity. Summary of the Invention

[0008] In response to the above technical problems, the present invention provides a wireless charging optimization method and device based on the Hippo optimization algorithm, and proposes a corresponding control circuit, so that the lithium battery of electric vehicles can be charged stably and efficiently.

[0009] To achieve the above object, the present invention provides the following solutions:

[0010] A wireless charging optimization method based on the Hippo optimization algorithm, the optimization method comprising:

[0011] Obtain lithium battery parameter data, determine the optimization objectives, optimization variables and constraints of lithium battery wireless charging; initialize the hippo population and calculate the fitness function value of each hippo individual to determine the optimal position and optimal fitness value; enter the main loop and set the iteration termination condition; use the position update strategy of the hippo exploration phase to update the individual position; use the position update strategy of the hippo defense phase against predators to update the individual position; use the position update strategy of the hippo escape phase to update the individual position; when the number of iterations reaches the preset maximum number, exit the loop and output the obtained multi-stage current results; use the wireless energy transmission control system to output the obtained multi-stage current in time-sharing to charge the lithium battery.

[0012] The method of using the location update strategy of the Hippopotamus exploration phase to update the individual location includes the following steps:

[0013] S11. The position of the male hippopotamus is determined using formula (4), specifically:

[0014]

[0015] in, represents the position of the male hippopotamus, Dhipho represents the position of the best hippopotamus in the current population, y1 is a random number between [0,1], and I1 is an integer 1 or 2;

[0016] S12. The position of the female hippopotamus or the young hippopotamus is determined using formula (5), specifically:

[0017]

[0018] in, represents the position of a female hippopotamus or a young hippopotamus, I2 is an integer 1 or 2, and the value of h1 is determined by formula (6), specifically:

[0019]

[0020] Among them, r1, r2, r3, and r4 are vectors in the range [0, 1], r5 ​​is a random number in the range [0, 1], and Q1 and Q2 are integers 0 or 1;

[0021] The value of Ξ is determined by formula (7), specifically:

[0022]

[0023] Among them, r7 is a random number in the range of [0,1], r6 represents the degree of outlier of the young hippopotamus, and the range is between [0,1];

[0024] S13. The first update of the hippo population is determined by formulas (8) and (9), specifically:

[0025]

[0026] Among them, F i Mhipho Represents the fitness of a male hippopotamus in the current cycle, F i FBhipho Represents the fitness of a female hippopotamus or a young hippopotamus individual in the current cycle.

[0027] The method of using the position updating strategy of the hippopotamus in the predator defense phase to update the individual position includes the following steps:

[0028] S21: Predator position location, determined by formula (10), specifically:

[0029]

[0030] Among them, r8 is a vector in the range [0,1];

[0031] The distance between the predator and the hippopotamus is determined by formula (11), specifically:

[0032]

[0033] S22: The hippopotamus defends against predators. The hippopotamus's movement distance and position when facing a predator are obtained, which are determined by formula (12):

[0034]

[0035] Where, i = N / 2+1, N / 2+2,…, N, j = 1, 2,…, m, represents the position matrix of the hippopotamus when facing a predator, f is a random number in the range [2, 4], c is a random number in the range [1, 1.5], D is a random number in the range [2, 3], g is a random number in the range [-1, 1], r9 is a 1×m-dimensional random vector, and RL is a random vector that obeys the levy distribution, which is determined by the following formulas (13) and (14):

[0036]

[0037] Where w and v are random numbers between the range [0,1], θ is a constant 1.5, and Γ is a factorial function;

[0038] S23: The second update of the hippo population is determined by formula (15), specifically:

[0039]

[0040] Among them, F iHiphoR Represents the fitness of a hippopotamus individual in the current cycle.

[0041] The position updating strategy used in the hippopotamus escape predator stage to update the individual position includes the following steps:

[0042] S31: The hippopotamus randomly updates its position and calculates its fitness. Its random position is determined by formula (16), specifically:

[0043]

[0044] Where i = 1, 2, ..., N, j = 1, 2, ..., m, r 10 is a random number between [0,1];

[0045] lb j leaf and ub j leaf The value of is determined by formula (17), specifically:

[0046]

[0047] Where t = 1, 2, ..., τ, τ represents the maximum number of iterations;

[0048] The value of τ is determined by formula (18), specifically:

[0049]

[0050] Among them, r 11 is a random vector between the range [0,1], r 13 is a random number between the range [0,1], r 12 is a random number that follows a normal distribution;

[0051] S32: Update the hippo population for the third time and output the final result. The individual update is determined by formula (19), specifically:

[0052]

[0053] Among them, F i HiphoE Represents the fitness of a hippopotamus individual in the current cycle;

[0054] Finally, the optimal hippopotamus individual is obtained, and its position result is used as the five-stage optimal current output result I 1.best , I 2.best , I 3.best , I 4.best , I 5.best .

[0055] A wireless charging device based on the Hippo optimization algorithm, the wireless charging device comprising:

[0056] The wireless energy transmission control system is used to output multi-stage current in time-sharing mode to charge the lithium battery. The wireless energy transmission control system includes a primary side, a secondary side and a control side. The primary side includes an inverter N, a variable inductance unit L1, a first fixed capacitor C1, a variable capacitor unit C p , DC power supply V in , primary side inductor L t The secondary side includes a second fixed capacitor C2, a rectifier Z, and a secondary side inductor L r , a third fixed capacitor C3; the control side includes a voltage sensor, a current sensor, a pulse square wave generator group, and a main control circuit;

[0057] The output current of the wireless energy transfer control system is determined by the following formula (20):

[0058]

[0059] Among them, Z E is the equivalent load of lithium battery, w r is the fundamental frequency of AC power, and n is the number of harmonics taken.

[0060] The output currents of the five stages are determined by formula (21), specifically:

[0061]

[0062] It can be seen from the above technical solution that the implementation of the present invention has the following benefits:

[0063] Compared with the constant current and constant voltage charging method of traditional wireless charging systems, the wireless charging optimization method and device based on the Hippo optimization algorithm of the present invention achieves stable and optimal multi-stage current charging through curve optimization design, provides a charging strategy for lithium battery charging, improves the charging speed of lithium batteries in electric vehicles, and reduces the temperature rise of lithium batteries during charging. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the prior art and the drawings required for use in the embodiments. The following drawings are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0065] Figure 1 Optimize the overall process and control timing diagram for wireless charging based on the Hippo optimization algorithm;

[0066] Figure 2 A flow chart of a charging optimization control method for a wireless charging system;

[0067] Figure 3 This is a schematic diagram of the overall architecture of a wireless energy transmission control system;

[0068] Figure 4 This is the equivalent topology diagram of the wireless charging system. DETAILED DESCRIPTION

[0069] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0070] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application pertains. The terms used herein in the specification of this application are for the purpose of describing specific embodiments only and are not intended to limit this application.

[0071] The circuit equivalent structure transformations mentioned in this article are all derived from the two-port equivalent impedance derived from Kirchhoff's voltage theorem and Kirchhoff's current theorem. The inductors and capacitors involved in this article all have normal circuit characteristics.

[0072] A wireless charging optimization method based on the Hippo optimization algorithm comprises the following steps:

[0073] S1. Obtain lithium battery parameter data and determine the optimization objectives, optimization variables and constraints for lithium battery wireless charging. The optimization objectives include minimizing the charging time t, and the optimization variables include the lithium battery capacity C f , lithium battery state of charge SOC, lithium battery charging energy efficiency η, lithium battery charging time t, the constraints include that the lithium battery charging time, state of charge, charging energy efficiency, and capacity are all within a certain range. In this example, the range satisfies the following formula (1):

[0074]

[0075] S2. Initialize the hippo population and calculate the fitness function value of each hippo individual to determine the optimal position and optimal fitness value, as follows:

[0076] S21. Use the following formula (2) to obtain the initial population:

[0077] X i:x ij =lb j +r·(ub j -lb j ) (2)

[0078] where x ij represents the position of the i-th individual in the population in the j-th dimension, i = 1, 2, ..., N, j = 1, 2, ..., m; ub j and lb j They represent the upper and lower bounds of the j-th dimension of the search space respectively; r represents a random number between [0,1];

[0079] S22. Calculate the fitness function value of each individual using the following formula (3):

[0080] F max =α×F1+β×F2+γ×F3 (3)

[0081] Among them, F max is the fitness function value, F1 is the standardized state of charge of the lithium battery, F2 is the standardized charging time of the lithium battery, F3 is the standardized charging efficiency of the lithium battery, α is the weight of the state of charge, β is the weight of the charging time, and γ is the weight of the charging efficiency;

[0082] S3. Enter the main loop and set the iteration termination condition, specifically:

[0083] Define the population size as N and the maximum number of iterations as T max , dimension m.

[0084] S4. Use the position update strategy of the Hippo exploration phase to update the individual position for the first time, specifically:

[0085] S41. The position of the male hippopotamus is determined using the following formula (4):

[0086]

[0087] Among them, X i Mhipho represents the position of the male hippopotamus, Dhipho represents the position of the best hippopotamus in the current population, y1 is a random number between [0,1], and I1 is an integer 1 or 2;

[0088] S42. The position of a female hippopotamus or a young hippopotamus is determined using formula (5), specifically:

[0089]

[0090] Among them, X i FBhiphorepresents the position of a female hippopotamus or a young hippopotamus, I2 is an integer 1 or 2, and the value of h1 is determined by formula (6), specifically:

[0091]

[0092] Among them, r1, r2, r3, and r4 are vectors in the range [0, 1], r5 ​​is a random number in the range [0, 1], and Q1 and Q2 are integers 0 or 1;

[0093] The specific value of Ξ is:

[0094]

[0095] Among them, r7 is a random number in the range of [0,1], r6 represents the degree of outlier of the young hippopotamus, and the range is between [0,1];

[0096] S43. The first update of the hippo population is determined by formulas (8) and (9), specifically:

[0097]

[0098] Among them, F i Mhipho Represents the fitness of a male hippopotamus in the current cycle, F i FBhipho Represents the fitness of a female hippopotamus or a young hippopotamus individual in the current cycle.

[0099] S5. Update individual positions for the second time using the position update strategy of the hippopotamus in the predator defense phase.

[0100] S51: Predator position determination, determined by formula (10), specifically:

[0101]

[0102] Among them, r8 is a vector in the range [0,1];

[0103] The distance between the predator and the hippopotamus is determined by formula (11), specifically:

[0104]

[0105] S52: The hippopotamus defends against predators. The hippopotamus's movement distance and position when facing a predator are obtained, which are determined by formula (12):

[0106]

[0107] Where i = N / 2+1, N / 2+2,…, N, j = 1, 2,…, m, X iHiphoR represents the position matrix of the hippopotamus when facing a predator, f is a random number in the range [2, 4], c is a random number in the range [1, 1.5], D is a random number in the range [2, 3], g is a random number in the range [-1, 1], r9 is a 1×m-dimensional random vector, and RL is a random vector that obeys the levy distribution, which is determined by the following formulas (13) and (14):

[0108]

[0109] Where w and v are random numbers between the range [0,1], θ is a constant 1.5, and Γ is a factorial function;

[0110] S53: The hippopotamus population is updated for the second time, which is determined by formula (15):

[0111]

[0112] Among them, F i HiphoR Represents the fitness of a hippopotamus individual in the current cycle.

[0113] S6. Update the individual position for the third time using the position update strategy used in the hippopotamus escape from predators phase.

[0114] S61: The hippopotamus randomly updates its position and calculates its fitness. Its random position is determined by formula (16), specifically:

[0115]

[0116] Where i = 1, 2, ..., N, j = 1, 2, ..., m, r 10 is a random number between [0,1];

[0117] lb j leaf and ub j leaf The value of is determined by formula (17), specifically:

[0118]

[0119] Where t = 1, 2, ..., τ, τ represents the maximum number of iterations;

[0120] The value of τ is determined by formula (18), specifically:

[0121]

[0122] Among them, r 11 is a random vector between the range [0,1], r 13is a random number between the range [0,1], r 12 is a random number that follows a normal distribution;

[0123] S62: Update the hippo population for the third time and output the final result. The individual update is determined by formula (19), specifically:

[0124]

[0125] Among them, F i HiphoE Represents the fitness of a hippopotamus individual in the current cycle;

[0126] S7. When the number of iterations reaches the preset maximum number, exit the loop and output the multi-stage current results, specifically:

[0127] Finally, the optimal hippopotamus individual is obtained, and its position result is used as the optimal preset output current I in the five stages. 1.best , I 2.best , I 3.best , I 4.best , I 5.best .

[0128] S8. Using the wireless energy transmission control system, the multi-stage current obtained by time-sharing output is used to charge the lithium battery. Specifically:

[0129] S81. Establish a wireless energy transmission control system. Specifically, for the wireless energy transmission network, based on Kirchhoff's law, the circuit equivalent transformation can be obtained. Figure 4 The topology shown in the figure, the output current of the wireless energy transmission network satisfies the following formula (20):

[0130]

[0131] Among them, w r is the fundamental angular frequency of the wireless energy transmission network, n is the harmonic order taken, and I0 is the current output current.

[0132] S82. When the first stage of wireless charging of the lithium battery begins, the terminal voltage V0 and terminal current I0 of the lithium battery are collected in real time using the Hall voltage sensor and the Hall current sensor.

[0133] S83. The wireless energy transmission control circuit controls the wireless energy transmission network to make the network output the optimal charging current for the current stage. The optimal preset currents for the five stages are determined by formula (21), specifically:

[0134]

[0135] S84. When the terminal voltage V0 is greater than the set maximum terminal voltage value V 0.maxWhen , switch to the next charging stage;

[0136] S85. Collect the terminal voltage V0 and terminal current I0 of the lithium battery in real time again.

[0137] S86. Loop steps S83 to S85 until the fifth charging stage, and end charging.

[0138] Specifically, for easier understanding, please refer to Figure 1 The figure shows the overall process and control timing diagram of wireless charging optimization based on the Hippo optimization algorithm.

[0139] Specific control circuit and control method such as Figure 2 、 Figure 3 As shown:

[0140] Module 1 voltage sensor includes:

[0141] Hall voltage sensor for collecting lithium battery voltage value;

[0142] Specifically, the Hall voltage sensor feeds back the voltage value V0 collected on the load side to the main control circuit of module three for comparison with a preset threshold voltage.

[0143] Module 2 current sensor includes:

[0144] A Hall current sensor for collecting current values ​​at both ends of the lithium battery;

[0145] Specifically, the Hall current sensor feeds back the current value I0 collected on the load side to the main control circuit of module four for comparison with the preset current value.

[0146] The modular three-pulse square wave generator set includes:

[0147] A first pulse square wave generator A1, a second pulse square wave generator B1, a third pulse square wave generator A2, and a fourth pulse square wave generator B2;

[0148] The pulse square wave generator group receives the digital signals from the four main control circuits of the module and changes the switching frequency and phase angle of the corresponding power switch.

[0149] Module 4 main control circuit includes:

[0150] The output current I0 collected by the current sensor of module 1 in the first time sequence stage is compared with the preset optimal charging current I 1.best For comparison, if the output current I0 is less than the preset optimal charging current I 1.best , then adjust the variable capacitance unit C p and the value of the variable inductor unit L1, adjust the output current to the optimal current I 1.bestAt the same time, the output voltage V0 collected by the first timing stage of the Hall voltage sensor of module 1 is compared with the threshold voltage V 0.max Comparison, if the output voltage V0 is greater than the threshold voltage V 0.max , then adjust the output frequency and phase of the module's three-pulse square wave generator group, change the switching frequency and phase of the corresponding switch tube, and switch the charging into the next stage; the next four stages are similar, until the charging process is completed.

[0151] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any way. Any simple modification, change and equivalent structural change made to the above embodiment based on the technical essence of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A wireless charging optimization method based on the Hippo optimization algorithm, which is applied to multi-stage constant current charging of lithium batteries, characterized in that: Obtain lithium battery parameter data and determine the optimization objectives, optimization variables, and constraints for lithium battery wireless charging; initialize the hippo population and calculate the fitness function value of each hippo individual to determine the optimal position and optimal fitness value; Enter the main loop and set the iteration termination condition; Use the position update strategy of the Hippo exploration phase to update the individual position; Use the position update strategy of the hippopotamus in the defense phase against predators to update the individual position; Use the position update strategy of the hippopotamus escaping from the predator stage to update the individual position; When the number of iterations reaches the preset maximum number, the loop is exited and the multi-stage current results are output; Using wireless energy transmission control system, multi-stage current is output in time-sharing mode to charge lithium batteries; The wireless energy transmission control system includes a primary side, a secondary side and a control side. The control side includes a voltage sensor, a current sensor, a pulse square wave generator group and a main control circuit. The voltage sensor feeds back the voltage value V0 collected on the load side to the main control circuit for comparison with a preset threshold voltage. The current sensor feeds back the current value I0 collected on the load side to the main control circuit for comparison with a preset current value. The pulse square wave generator group receives the digital signal of the main control circuit and changes the switching frequency and phase angle of the corresponding power switch. The main control circuit compares the output current I0 collected by the current sensor in the first timing stage with the preset optimal charging current I 1.best For comparison, if the output current I0 is less than the preset optimal charging current I 1.best , then adjust the system parameters to adjust the output current to the optimal current I 1.best At the same time, the output voltage V0 collected by the voltage sensor in the first timing stage is compared with the lithium battery threshold voltage V 0.max Comparison, if the output voltage V0 is greater than the threshold voltage V 0.max , then adjust the output frequency and phase of the pulse square wave generator group, change the switching frequency and phase of the corresponding switch tube, and switch the charging into the next stage; The following four stages are repeated in this way until the charging process is completed; The optimization goal, optimization variables and constraints of lithium battery wireless charging are determined, the optimization goal includes the shortest charging time, and the optimization variables include the lithium battery capacity C f , lithium battery state of charge SOC, lithium battery charging energy efficiency η, lithium battery charging time T1, the constraints include the lithium battery charging time, state of charge, charging energy efficiency, and capacity are within a certain range, satisfying the following equation: Among them, SOC min and SOC max are the minimum and maximum charge capacities of lithium batteries, T min and T max are the minimum and maximum charging time of lithium batteries, η min and η max are the minimum and maximum charging efficiencies of lithium batteries, respectively; The fitness function value of each hippopotamus individual is calculated to satisfy the following equation: F max =α×F1+β×F2+γ×F3 Among them, F max is the fitness function value, F1 is the standardized state of charge of the lithium battery, F2 is the standardized charging time of the lithium battery, F3 is the standardized charging efficiency of the lithium battery, α is the weight of the state of charge, β is the weight of the charging time, and γ is the weight of the charging efficiency.

2. The wireless charging optimization method based on the Hippo optimization algorithm according to claim 1, characterized in that: The initialization of the hippo population satisfies the following equation: X i :x ij =lb j +r·(ub j -lb j ),i=1,2,...,N,j=1,2,...,m where X i represents the i-th individual in the population, x ij represents the position of the i-th individual in the population in the j-th dimension, i = 1, 2, ..., N, j = 1, 2, ..., m; ub j and lb j They represent the upper and lower bounds of the j-th dimension of the search space respectively; r represents a random number between [0,1]; The method of using the location update strategy of the Hippopotamus exploration phase to update the individual location includes the following steps: S11. The position of the male hippopotamus satisfies the following equation: in, represents the position of the male hippopotamus, x ij Mhipho represents the position of the i-th male hippopotamus in the j-th dimension, Dhipho represents the position of the best hippopotamus in the current population, y1 is a random number between [0,1], and I1 is an integer 1 or 2; S12. The position of the female hippopotamus or the young hippopotamus satisfies the following equation: Among them, X i FBhipho Represents the location of a female hippopotamus or a juvenile hippopotamus individual, represents the position of the i-th female hippopotamus or young hippopotamus in the j-th dimension, I2 is an integer 1 or 2, MG i is the average position of some randomly selected hippopotamus individuals, T is the critical criterion value, and the value of h1 satisfies the following equation: Where r1, r2, r3, and r4 are vectors in the range [0, 1], r5 ​​is a random number in the range [0, 1], and ~Q1 and Q2 are integers 0 or 1; The value of Ξ satisfies the following equation: Among them, r7 is a random number in the range of [0,1], r6 represents the degree of outlier of the young hippopotamus, which ranges between [0,1], and h2 is a random number in the range of [0,1]; S13. Update the hippo population for the first time, satisfying the following equation: Among them, F i Mhipho Represents the fitness of a male hippopotamus in the current cycle, F i FBhipho Represents the fitness of a female hippopotamus or a young hippopotamus in the current cycle, F i is the fitness threshold; The method of using the position updating strategy of the hippopotamus in the predator defense phase to update the individual position includes the following steps: S21: Predator position location, satisfying the following equation: Predator:Predator j =lb j +r8·(u b -lb j ),j=1,2,...,m Among them, r8 is a vector between the range [0,1], Predator is a row vector representing the predator, Predator j represents the location of the predator; Distance of predators from individual hippos Satisfies the following equation: S22: Hippo defends against predators. Obtain the movement distance and position of the hippo when facing a predator, satisfying the following equation: Where i = N / 2+1, N / 2+2,…, N, j = 1, 2,…, m, X i HiphoR represents the position matrix of the hippopotamus when facing the predator, x ij HiphoR represents the position of the i-th individual in the j-th dimension when the hippopotamus faces a predator, f is a random number in the range [2,4], c is a random number in the range [1,1.5], D is a random number in the range [2,3], g is a random number in the range [-1,1], r9 is a random vector of 1×m dimensions, F o is the fitness critical value, d is a random number between the range [0,1], F predatorj is the fitness value of the j-th predator, RL is a random vector obeying the levy distribution, satisfying the following equation: Where w and v are random numbers between the range [0,1], θ is a constant 1.5, and Γ is a factorial function; S23: Update the hippo population for the second time, satisfying the following equation: Among them, F i HiphoR Represents the fitness of a hippopotamus individual in the current cycle.

3. The wireless charging optimization method based on the Hippo optimization algorithm according to claim 1, characterized in that: The position updating strategy used in the hippopotamus escape predator stage to update the individual position includes the following steps: S31: The hippopotamus randomly updates its position and calculates its fitness. Its random position satisfies the following equation: Among them, X i Hiphoε Represents the position matrix when the hippopotamus randomly updates its position, x ij Hiphoε represents the position of the i-th individual in the j-th dimension when the hippopotamus randomly updates its position, i = 1, 2, ..., N, j = 1, 2, ..., m, r 10 is a random number between [0,1]; lb j leaf and ub j leaf The value of satisfies the following equation: Where t = 1, 2, ..., τ, τ represents the maximum number of iterations; The value of τ satisfies the following equation: Among them, r 11 is a random vector between the range [0,1], r 13 is a random number between the range [0,1], r 12 is a random number that follows a normal distribution; S32: Update the hippo population for the third time and output the final result. The individual update satisfies the following equation: Among them, F i Hiphoε Represents the fitness of a hippopotamus individual in the current cycle; Finally, the optimal hippopotamus individual is obtained, and its position result is used as the five-stage optimal current output result I 1.best , I 2.best , I 3.best , I 4.best , I 5.best .

4. A wireless charging device based on the Hippo optimization algorithm, the device being used to execute the wireless charging optimization method based on the Hippo optimization algorithm according to any one of claims 1 to 3, characterized in that: The wireless charging device includes a wireless energy transmission control system, the primary side of which includes an inverter N, a variable inductance unit L1, a first fixed capacitor C1, a variable capacitor unit C p1 , DC power supply V in , primary side inductor L t The secondary side of the wireless energy transfer control system includes a second fixed capacitor C2, a rectifier Z, and a secondary side inductor L r , a third fixed capacitor C3; The output current of the wireless energy transmission control system satisfies the following equation: Among them, Z E is the equivalent load of lithium battery, ω r is the fundamental frequency of AC power, and n is the number of harmonics taken; The output currents of the five stages satisfy the following equations: Among them, I 1.best , I 2.best , I 3.best , I 4.best , I 5.best These are the optimal charging current values ​​for the five stages under the multi-level constant current mode.

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