Underwater IPT system parameter identification method based on hybrid particle swarm optimization

Abstract

The invention discloses an underwater IPT system parameter identification method based on hybrid particle swarm optimization, and the method comprises the steps: constructing an equivalent circuit model of an underwater IPT system, and enabling the eddy current loss in water to be equivalent to the power loss caused by the eddy current equivalent resistance of a transmitting coil and a receiving coil; under the condition that a system structure and water body parameters are kept unchanged and only excitation frequency is changed, a constraint relation that eddy current equivalent resistance is in direct proportion to frequency square is introduced; acquiring input impedance Zin1 and Zin2 of the system under different test conditions, and respectively establishing expressions of the Zin1 and Zin2 about to-be-identified parameters; constructing an equation set based on the Zin1 and Zin2 of the plurality of frequency points and the constraint relation; and constructing particle individuals by taking the eddy-current loss equivalent resistance, the mutual inductance and the load resistance as to-be-identified parameters, and solving the equation set by adopting a hybrid particle swarm optimization algorithm to obtain an identification value. According to the method, the parameter identification precision and efficiency of the underwater IPT system can be improved, and the practical requirements of the underwater environment are met.

Description

Technical Field

[0001] This invention belongs to the field of wireless power transmission technology, and more specifically, relates to a method for parameter identification of underwater IPT systems based on hybrid particle swarm optimization. Background Technology

[0002] Autonomous underwater vehicles (AUVs) possess high maneuverability and autonomy, making them crucial equipment for water exploration, monitoring, and development. Currently, AUVs primarily rely on traditional methods such as battery replacement or wired charging for power replenishment, neither of which can meet the demands of long-term continuous operation. However, inductive power transfer (IPT) technology, with its safety and reliability, can effectively meet the continuous underwater power supply requirements of AUVs.

[0003] Figure 1 This is a model of an underwater IPT system. In the figure, 1 represents the AUV, 2 the receiving coil, 3 the transmitting coil, 4 the battery, and 5 the support frame. Compared to air, the underwater operating environment is more complex: the conductivity underwater is significantly higher than that in air, leading to a more pronounced eddy current effect in the underwater IPT system, which significantly reduces the system's transmission efficiency. To achieve efficient operation of the underwater IPT system under parameter disturbances, it is necessary to identify key parameters.

[0004] Currently, most related studies employ frequency scanning methods to obtain unknown parameters. However, in underwater environments, the impact of eddy current losses on the system can be considered equivalent to the impact of the equivalent resistance of eddy current losses on the system. This equivalent resistance changes continuously with frequency. If the identification equation is established simply by switching frequencies while ignoring the influence of frequency on the equivalent resistance, the identification accuracy will be affected. Furthermore, existing research on underwater IPT systems mostly focuses on SS-type compensated topologies. In contrast, bilateral LCC-type topologies offer better filtering and better meet the power supply requirements of AUVs, but current research lacks parameter identification methods for bilateral LCC-type IPT systems. Summary of the Invention

[0005] To address the aforementioned deficiencies or improvement needs of existing technologies, this invention provides a parameter identification method for underwater IPT systems based on hybrid particle swarm optimization, which can improve the accuracy and efficiency of parameter identification and meet the practical needs of underwater environments.

[0006] To achieve the above objectives, this invention provides a parameter identification method for an underwater IPT system based on hybrid particle swarm optimization. The underwater IPT system includes an inverter, a primary-side compensation network, a primary-side coil, a secondary-side coil, a secondary-side compensation network, and a rectifier. The primary-side compensation network includes a primary-side parallel compensation capacitor. The underwater IPT system parameter identification method includes the following steps: An equivalent circuit model of the underwater IPT system is constructed. In this model, eddy current losses in water are equated to the power losses generated by the equivalent resistance of eddy currents on the transmitting and receiving coils of the underwater IPT system. Furthermore, under the condition that the system structure and water parameters remain unchanged, and only the system frequency is changed, the equivalent resistance of the eddy currents is proportional to the square of the system frequency. Based on the equivalent circuit model, the following parameters are determined: Z in1 Z in2 The expression for the parameter to be identified, where, Z in1 Z is the system input equivalent impedance. in2 This is the equivalent impedance of the back end of the primary-side parallel compensation capacitor; Multiple different frequencies Z in1 Z in2 The expressions for the parameters to be identified and the ratios of the eddy current equivalent resistances at different frequencies are used as equations to construct a system of equations. The parameters to be identified are treated as individual particles, and the system of equations is solved using a hybrid particle swarm optimization algorithm. The optimal solution of the system of equations is used as the identification value of the parameters to be identified. The parameters to be identified include the equivalent resistance of eddy current loss, the mutual inductance of the system, and the equivalent resistance of the system load.

[0007] Preferably, solving the system of equations using a hybrid particle swarm optimization algorithm includes the following steps: Calculating the fitness function includes the following steps: Measure system input voltage at multiple different frequencies System input current The voltage at the end of the parallel compensation capacitor on the primary side The current flowing through the parallel compensation capacitor on the primary side According to each frequency , , , Calculation of measured values Z in1 Z in2 Measured impedance magnitude at each frequency; Based on the updated value of the parameter to be identified, Z in1 Z in2Calculation of the expression for the parameter to be identified Z in1 Z in2 The theoretical impedance magnitude at each frequency; At each frequency Z in1 The absolute value of the difference between the theoretical impedance modulus and the measured impedance modulus, Z in2 The absolute values ​​of the differences between the theoretical impedance magnitude and the measured impedance magnitude are summed, and this sum is used as the objective function of the hybrid particle swarm optimization algorithm. The fitness function is then calculated based on the objective function.

[0008] Preferably, the primary-side compensation network is a primary-side LCC compensation network, the secondary-side compensation network is a secondary-side LCC compensation network, and the primary-side LCC compensation network includes a primary-side series compensation inductor. Primary-side series compensation capacitor Primary-side parallel compensation capacitor The secondary-side LCC compensation network includes a secondary-side series compensation inductor. Secondary-side series compensation capacitor Parallel compensation capacitor with secondary side ; The parameters to be identified include system mutual inductance. The equivalent resistance of all loads at the back end of the secondary-side LCC compensation network. , primary side eddy current equivalent resistance and secondary side eddy current equivalent resistance ; The Z in1 Z in2 The expression for the parameter to be identified is: ; ; in, The reflected impedance of the secondary side circuit. The system angular frequency, For the imaginary part of a complex number, The inductance of the secondary coil, The internal resistance of the secondary coil is... The inductance of the primary coil is... is the internal resistance of the primary coil.

[0009] Preferably, set = = .

[0010] Preferably, two different frequencies are selected. f 1. f2. The system of equations is as follows: ; in, For frequency f x hour The value, j It can take the value 1 or 2. x It can take the value 1 or 2. For frequency f x hour The value, For frequency f x hour The value, For frequency f x hour The value of .

[0011] Preferably, the frequency f x hour , , , The measured values ​​are respectively denoted as U in1_rms_fx , U in2_rms_fx , I in1_rms_fx , I in2_rms_fx ; Z in1 In frequency f x The measured impedance modulus at time is denoted as , will Z in2 In frequency f x The measured impedance modulus at time is denoted as The calculation formula is: .

[0012] Preferably, the objective function is calculated using the following formula: ; The fitness function is calculated using the following formula: ,in This represents the fitness value.

[0013] Preferably, the step of solving the system of equations using a hybrid particle swarm optimization algorithm further includes the following steps: Initialize the particle population; Update the velocity and position of the particles. After each update, calculate the current theoretical impedance modulus based on the current particle update value, and calculate the current fitness value based on the current theoretical impedance modulus. Based on the current fitness value, the particles are selected and crossovered to obtain the optimal solution of the equation system.

[0014] In summary, this invention addresses the issues of unknown parameters and the continuous change in equivalent resistance of eddy current losses with frequency in underwater environments. It achieves parameter identification by constructing a system of equations and employing a hybrid particle swarm optimization algorithm to solve these equations. This method improves the accuracy and efficiency of parameter identification, meeting the practical needs of underwater environments. Specifically, it is manifested in the following aspects: (1) This invention addresses the relationship between four equations constructed based on the circuit model diagram and the equivalent resistance of eddy current loss and frequency, and then collects data... Z in1 Z in2 The voltage and current at both ends are measured, and then a hybrid particle swarm optimization algorithm is used for identification, transforming the problem of unknown parameters into an optimization problem, thereby accurately identifying the equivalent resistance of system mutual inductance, load, and eddy current losses. Compared with existing methods, this invention does not require re-performing finite element simulations when system parameters change; nor does it require reconstructing an accurate analytical expression for the equivalent resistance of eddy current losses when the docking position or coupling state changes. (2) The present invention uses the ratio of the equivalent resistance of eddy current at different frequencies as an equation in the equation set, that is, it incorporates the frequency-varying characteristics into the identification framework and combines them with the hybrid optimization strategy, which can further improve the accuracy of parameter identification. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of an underwater IPT system model; Figure 2 This is a schematic diagram of an IPT coil model considering eddy current losses according to an embodiment of the present invention; Figure 3 This is an equivalent circuit diagram of an underwater bilateral LCC type IPT system according to an embodiment of the present invention; Figure 4 This is a flowchart of the parameter identification algorithm based on hybrid particle swarm optimization according to an embodiment of the present invention; Figure 5 This is a simulation model of the underwater IPT system according to an embodiment of the present invention; Figure 6 This is the fitness convergence curve of an embodiment of the present invention; Figure 7 This is the convergence process of the equivalent resistance of the eddy current loss parameter in an embodiment of the present invention; Figure 8 This is the parameter mutual inductance convergence process of an embodiment of the present invention; Figure 9 This is the parameter equivalent resistance convergence process in an embodiment of the present invention; Figure 10 This refers to the relative error of each parameter in the embodiments of the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0017] In the description of embodiments of this application, the term "multiple" means two or more. The terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, apparatus, product, or device that includes a series of steps or modules is not necessarily limited to those steps or modules that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such processes, methods, products, or devices.

[0018] The naming or numbering of steps in the embodiments of the present invention does not mean that the steps in the method flow must be executed in the time / logical order indicated by the naming or numbering. The execution order of the named or numbered process steps can be changed according to the technical purpose to be achieved, as long as the same or similar technical effect can be achieved.

[0019] In this invention, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this invention can be combined with other embodiments.

[0020] This invention provides a parameter identification method for an underwater IPT system based on hybrid particle swarm optimization. The underwater IPT system includes an inverter, a primary-side compensation network, a primary-side coil, a secondary-side coil, a secondary-side compensation network, a rectifier, and a parameter identification module. The primary-side compensation network includes a primary-side parallel compensation capacitor. The underwater IPT system parameter identification method includes the following steps: constructing an equivalent circuit model of the underwater IPT system, equating the eddy current loss in the water to the power loss caused by the equivalent resistance of the eddy currents in the transmitting and receiving coils; under the condition that the system structure and water parameters remain unchanged and only the system frequency is changed, introducing a constraint relationship that the equivalent resistance of the eddy currents is proportional to the square of the frequency; and determining the parameters based on the equivalent circuit model. Z in1 Z in2 The expression for the parameter to be identified, where, Z in1 Z is the system input equivalent impedance. in2 The equivalent impedance of the primary-side parallel compensation capacitor; based on multiple frequency points. Z in1 Z in2 The system of equations is constructed based on the constraints mentioned above; the equivalent resistance of eddy current loss, mutual inductance, and load resistance are used as parameters to be identified to construct individual particles, and the system of equations is solved using a hybrid particle swarm optimization algorithm to obtain the optimal identification value of the parameters to be identified.

[0021] The preferred implementation method for each step is explained in detail below.

[0022] (1) Modeling and parameter relationships of underwater IPT system considering eddy current losses In circuit models, eddy current losses are often equivalent to the combined effect of resistance and inductance. However, since the equivalent inductance is small, it is often ignored in practical modeling; therefore, eddy current losses are generally simplified to an equivalent resistance. Furthermore, once the dimensions, materials, and other characteristics of the transmitting and receiving coils are determined, the equivalent resistance of the eddy current losses... R eddy and The eddy current loss is proportional to the square of the frequency, meaning that the equivalent resistance of the eddy current loss increases significantly with increasing frequency. Therefore, we can obtain Formula 1.

[0023] ; in, For frequency The corresponding eddy current loss equivalent resistance, For frequency The corresponding eddy current loss equivalent resistance, 1 represents frequency The corresponding angular frequency, 2 represents frequency The corresponding angular frequency.

[0024] Figure 2 The equivalent circuit model of the IPT coil considering eddy current losses is shown. U dc Input DC voltage U inv This is the AC voltage after inversion. R p and R S These are the coil internal resistances on the primary and secondary sides, respectively. R eddy1 and R eddy2 These are the equivalent resistances of eddy current losses on the primary and secondary sides, respectively. M For the mutual inductance between coils, L P and L S These are the inductances of the primary and secondary coils, respectively. T 1 to T 4 represents a field-effect transistor. D 1 to D 4 is a diode. R eq To compensate for the equivalent resistance of all loads at the back end of the secondary side compensation network, R L For load, R eq and R L The relationship between them is shown in Equation 2. Equation 1 and 2 represent the compensation networks on the primary and secondary sides, respectively. This study adopts a two-sided LCC type compensation network. Figure 3 This is the equivalent circuit diagram of a bilateral LCC type IPT system. The IPT system includes a primary-side LCC compensation network, a primary-side coil, a secondary-side coil, and a secondary-side LCC compensation network. The primary-side LCC compensation network includes a primary-side series compensation inductor. Primary-side series compensation capacitor Primary-side parallel compensation capacitor The secondary-side LCC compensation network includes a secondary-side series compensation inductor. Secondary-side series compensation capacitor Parallel compensation capacitor with secondary side .

[0025] ; According to Kirchhoff's voltage law, for Figure 3 The equations for the primary and secondary circuits of the equivalent circuit model are written respectively, and the relationship shown in equation (3) is obtained.

[0026] ; in, Input current to the system, The current flowing through the primary coil, The current flowing through the secondary coil, The current flowing through the equivalent load, The system angular frequency, For the imaginary part of a complex number, The primary and secondary coils of the system are mutually inducted.

[0027] Therefore, by combining the circuit equations shown in Equations 3 and 4, the input impedance of the system can be obtained. Z in1 Z in2 Expression. Wherein, Z in1 With Z in2 As shown in Equations 6 and 7 respectively.

[0028] ; in, The input voltage of the primary side system. This is the voltage at the downstream end of the parallel compensation capacitor on the primary side. Input current to the system, The current flowing through the parallel compensation capacitor on the primary side, This is the reflected impedance of the secondary circuit.

[0029] Inductance in the circuit ( L 1. L P , L S , L 2,) Capacitor ( C 1. C P , C S , C 2), and resistance ( R S , R P Parameters such as mutual inductance can be directly measured using an impedance analyzer. However, in aquatic environments, mutual inductance... M eddy current loss equivalent resistance R eddy1 and R eddy2 (Assuming the primary and secondary side test circuits are symmetrical, therefore) R eddy2 = R eddy1 = R eddy ) and load Req It cannot be measured directly. Due to the input impedance. Z in2 and Z in1 There is a definite functional relationship between these three unknown parameters and the primary and secondary voltages ( ). U in2 , U in1 ) and current ( I in2 , I in1 Indirect identification was used. Considering that two sets of equations could not solve for the three unknown parameters, this study constructed five sets of equations by measuring the above electrical parameters at two different frequencies to complete the solution, as shown in Equation 8. It should be noted that the equivalent resistance of eddy current loss changes with frequency, and its value is proportional to the square of the frequency. Based on this characteristic, an additional correlation expression needs to be established.

[0030] ; in, For frequency f x hour The value, j It can take the value 1 or 2. x It can take the value 1 or 2. For frequency f x hour The value, For frequency f x hour The value, For frequency f x hour The value, For frequency f x hour The value of .

[0031] (2) Multi-parameter identification method based on hybrid particle swarm optimization algorithm Based on the system model constructed above, in order to solve R eq , M and R eddyThis chapter proposes a hybrid particle swarm optimization algorithm to address the problem of identifying three unknown parameters. This method encodes the three parameters to be identified as individual particles, randomly initializes the population within a set range, and constructs a fitness function based on the error between observed data and model output to evaluate the quality of individual particles. During the iteration process, the algorithm updates particle velocity and position using the particle swarm optimization algorithm to find the optimal solution for the fitness function, while simultaneously introducing elite retention and crossover operations to enhance population diversity and avoid getting trapped in local optima. This hybrid strategy maintains the fast convergence characteristic of the original particle swarm optimization algorithm while significantly improving its ability to escape local optima, making it more robust to complex multimodal optimization problems. The specific implementation steps of this multi-parameter identification method are shown below.

[0032] A. Initialize the population Step 1: Algorithm parameters are set as follows: Population size is set to... n Learning factors c 1 and c 2. The inertia weight is set to w The maximum number of iterations is k Maximum speed Vmax Configure crossover probability ( p_c and the number of elite particles ( elite_num ).

[0033] Step 2: In this embodiment of the invention, two sets of frequencies are selected and denoted as follows: f 1 (80) kHz )and f 2 (85) kHz Data was sampled from the simulation model at the two sets of frequencies mentioned above, and the frequencies were... f x hour , , , The measured values ​​are respectively denoted as U in1_rms_fx , U in2_rms_fx , I in1_rms_fx , I in2_rms_fx , will obtain |U in1_rms_fx | , |I in1_rms_fx | , |U in2_rms_fx | and |I in2_rms_fx | Valid values ​​are input into the identification program.

[0034] Step 3: Set up a vector to store the location information. Initially, the vector is set to zero. After each iteration, the vector is updated.

[0035] B. Calculate the fitness function To calculate the fitness function, this study encapsulates it as an independent function, which is called by the main function during each run to achieve efficient fitness calculation. The specific calculation process of this function consists of the following steps.

[0036] Step 1: Substitute the voltage and current values ​​obtained in Step 2 of A at different frequencies into Formulas 9 and 10 respectively to obtain four measured impedance magnitudes. These four values ​​are expressed as follows: |Z in1_rms_f1 | , |Z in2_rms_f1 | , |Z in1_rms_f2 | and |Z in2_rms_f2 | .

[0037] ; Step 2: Calculate the absolute difference between the measured impedance magnitude and the theoretical impedance magnitude at the same frequency, and then sum the absolute differences at different frequencies to obtain the objective function. f(x) Its expression is shown in Formula 11. To improve the accuracy of subsequent parameter identification, the measured impedance modulus should be as close as possible to the theoretical value. Ideally, when the two are completely equal, the objective function result is 0. Based on the updated values ​​of the parameters to be identified, Z in1 Z in2 Calculation of the expression for the parameter to be identified Z in1 Z in2 The theoretical impedance magnitude at each frequency is denoted as . ; ; Step 3: To further improve the accuracy of the identification results, the objective function is improved to construct the fitness function e, the expression of which is shown in Formula 12; in an ideal state, the result of this fitness function is 1.

[0038] ; After each update, the current theoretical impedance modulus is calculated based on the current particle update value, and the current fitness is calculated based on the current theoretical impedance modulus.

[0039] C. Update speed Step 1: In this embodiment of the invention, velocity update is the key mechanism guiding the particle search direction. The velocity update of the i-th particle in the d-th iteration can be expressed as a linear combination of three vectors, as shown in Equation 13.

[0040] ; in vid+1 Indicates the first i The particle in the first d+1 The velocity vector of the next iteration vid At the current speed, xid+1 The current position vector, pbest_i For particles i The best historical position gbest To be the globally optimal position w This is the inertia weighting coefficient. c1 and c2 These are the individual cognition coefficient and the social cognition coefficient, respectively. r1 and r2 It is a random number.

[0041] Step 2: To ensure the convergence and stability of the algorithm, upper and lower bound constraints need to be set for the speed. The speed needs to always be within [...]. Vmax, -Vmax ]between.

[0042] D. Update position For each position update, the velocity update must be completed first. The new position of the particle is obtained by adding the current position and the velocity vector, as shown in Equation 14.

[0043] ; Step 2: To ensure the convergence and stability of the algorithm, upper and lower bound constraints need to be set for the position. The position needs to always be within [ x_lb, x_ub ]between.

[0044] E. Select Operation The selection operation aims to further refine the selection of the best-fit individuals from those with relatively good fitness obtained after position and velocity iterations. Each parameter to be identified is sorted and selected individually. The specific steps are as follows: First, the selected best-fit individuals are sorted according to their fitness values, forming an ordered sequence of individuals; then, the top individuals in the sequence are selected... elite_num Individuals with high fitness form an elite set. By comparing the fitness values ​​of individuals within this set, individuals with even better fitness are selected and added to the parent population. This process is repeated until a sufficient number of parent individuals are selected.

[0045] F. Cross-operation Crossover is a process that generates new candidate solutions by mixing the genetic information of multiple parent individuals in E. The specific process is as follows: Step 1: Pair up the parent individuals obtained from E, ensuring that each pair has... Pc The probability is used to determine whether to perform a crossover operation.

[0046] Step 2: If selected for crossover operation, the formula for generating new offspring is shown in Formula 15. ; For individuals that are not selected, their offspring can simply copy their parents, as shown in Formula 16.

[0047] ; Step 3: Handle boundary cases.

[0048] The overall process of the hybrid particle swarm optimization algorithm is as follows: Figure 4 As shown.

[0049] (3) Simulation verification and result analysis To verify the practical feasibility and identification effectiveness of the proposed identification method, the embodiments of the present invention are based on Figure 3 The structure, in Matlab of Simulink The module constructs a simulation model of an underwater bilateral LCC topology IPT system (see...). Figure 5 The specific parameters of each component in the model are listed in Table 1.

[0050] Table 1 System Parameter Settings ; like Figure 5 As shown, an underwater IPT system model was built to conduct simulation experiments on the system at different frequencies. Each time a frequency was switched, the effective values ​​of the relevant voltage and current at that frequency needed to be recorded in real time. To identify the three unknown parameters, two frequency switches were required. The effective values ​​of voltage and current obtained after the switches were input into an M-file. The number of iterations was set, and the program was run to obtain the results shown below. Figures 6 to 10 The following diagram shows the parameter convergence curve, fitness convergence curve, and error magnitude for each parameter. All results were obtained after 200 iterations; observation shows that the algorithm essentially converges after 100 iterations.

[0051] To more intuitively measure the performance of the algorithm, calculations can be performed using Formula 17, which allows for a comparison between the actual and simulated values ​​of the parameters.

[0052] ; observe Figure 10 It can be seen that the overall error of parameter identification using the hybrid particle swarm optimization algorithm can be controlled within 2%, of which...R eq and R eddy The identification error can be reduced to below 0.3%, and the method has high identification accuracy for parameter identification.

[0053] In summary, this invention proposes a parameter identification method for underwater IPT systems based on hybrid particle swarm optimization. First, by analyzing the equivalent model of eddy current loss in the underwater IPT system, a method is derived... Z in2 , Z in1 and M , R eq , R eddy The functional relationship between them and R eddy and f The constraints were defined, specifying the measured values ​​of voltage and current required for parameter identification. Based on this, the proposed hybrid particle swarm optimization algorithm was used to... M , R eq and R eddy Three target parameters are identified. This algorithm combines the local fine-grained search capability of particle swarm optimization with the global exploration advantage of genetic operators, enabling efficient solution of unknown parameters. To verify the performance of the method, a system was built... Simulink Experiments were conducted using a simulation model. Simulation results show that the algorithm's identification error for all key parameters is less than 2%, among which... R eq and R eddy Its identification accuracy is less than 0.3%, demonstrating excellent accuracy and efficiency, and meeting the practical needs of underwater environments.

[0054] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for parameter identification of underwater IPT systems based on hybrid particle swarm optimization, characterized in that, The underwater IPT system includes an inverter, a primary-side compensation network, a primary-side coil, a secondary-side coil, a secondary-side compensation network, and a rectifier. The primary-side compensation network includes a primary-side parallel compensation capacitor. The underwater IPT system parameter identification method includes the following steps: An equivalent circuit model of the underwater IPT system is constructed. In this model, eddy current losses in water are equated to the power losses generated by the equivalent resistance of eddy currents on the transmitting and receiving coils of the underwater IPT system. Furthermore, under the condition that the system structure and water parameters remain unchanged, and only the system frequency is changed, the equivalent resistance of the eddy currents is proportional to the square of the system's angular frequency. Based on the equivalent circuit model, the following parameters are determined: Z in1 Z in2 The expression for the parameter to be identified, where, Z in1 Z is the system input equivalent impedance. in2 This is the equivalent impedance of the back end of the primary-side parallel compensation capacitor; Multiple different frequencies Z in1 Z in2 The expressions for the parameters to be identified and the ratios of the eddy current equivalent resistances at different frequencies are used as equations to construct a system of equations. The parameters to be identified are treated as individual particles, and the system of equations is solved using a hybrid particle swarm optimization algorithm. The optimal solution of the system of equations is used as the identification value of the parameters to be identified. The parameters to be identified include the equivalent resistance of eddy current loss, the mutual inductance of the system, and the equivalent resistance of the system load.

2. The underwater IPT system parameter identification method based on hybrid particle swarm optimization as described in claim 1, characterized in that, The process of solving the system of equations using a hybrid particle swarm optimization algorithm includes the following steps: Calculating the fitness function includes the following steps: Measure system input voltage at multiple different frequencies System input current The voltage at the end of the parallel compensation capacitor on the primary side The current flowing through the parallel compensation capacitor on the primary side According to each frequency , , , Calculation of measured values Z in1 Z in2 Measured impedance magnitude at each frequency; Based on the particle update value of the parameter to be identified, Z in1 Z in2 Calculation of the expression for the parameter to be identified Z in1 Z in2 The theoretical impedance magnitude at each frequency; At each frequency Z in1 The absolute value of the difference between the theoretical impedance modulus and the measured impedance modulus, Z in2 The absolute values ​​of the differences between the theoretical impedance magnitude and the measured impedance magnitude are summed, and this sum is used as the objective function of the hybrid particle swarm optimization algorithm. The fitness function is then calculated based on the objective function.

3. The underwater IPT system parameter identification method based on hybrid particle swarm optimization as described in claim 2, characterized in that, The primary-side compensation network is a primary-side LCC compensation network, and the secondary-side compensation network is a secondary-side LCC compensation network. The primary-side parallel compensation capacitor is denoted as... The primary-side LCC compensation network also includes a primary-side series compensation inductor. series compensation capacitor with primary side The secondary-side LCC compensation network includes a secondary-side series compensation inductor. Secondary-side series compensation capacitor Parallel compensation capacitor with secondary side ; The parameters to be identified include system mutual inductance. The equivalent resistance of all loads at the back end of the secondary-side LCC compensation network. , primary side eddy current equivalent resistance and secondary side eddy current equivalent resistance ; The Z in1 Z in2 The expression for the parameter to be identified is: ； ； in, The reflected impedance of the secondary side circuit. The system angular frequency, For the imaginary part of a complex number, The inductance of the secondary coil, The internal resistance of the secondary coil is... The inductance of the primary coil is... is the internal resistance of the primary coil.

4. The underwater IPT system parameter identification method based on hybrid particle swarm optimization as described in claim 3, characterized in that, set up = = .

5. The underwater IPT system parameter identification method based on hybrid particle swarm optimization as described in claim 4, characterized in that, Select two different frequencies f 1. f 2. The system of equations is as follows: ； in, For frequency f x hour The value, j It can take the value 1 or 2. x It can take the value 1 or 2. For frequency f x hour The value, For frequency f x hour The value, For frequency f x hour The value of .

6. The underwater IPT system parameter identification method based on hybrid particle swarm optimization as described in claim 5, characterized in that, frequency f x hour , , , The measured values ​​are respectively denoted as U in1_rms_fx , U in2_rms_fx , I in1_rms_fx , I in2_rms_fx ; Z in1 In frequency f x The measured impedance modulus at time is denoted as , will Z in2 In frequency f x The measured impedance modulus at time is denoted as The calculation formula is: 。 7. The underwater IPT system parameter identification method based on hybrid particle swarm optimization as described in claim 6, characterized in that, The formula for calculating the objective function is as follows: ； The fitness function is calculated using the following formula: ,in This represents the fitness value.

8. The underwater IPT system parameter identification method based on hybrid particle swarm optimization as described in claim 2, characterized in that, The method of solving the system of equations using a hybrid particle swarm optimization algorithm further includes the following steps: Initialize the particle population; Update the velocity and position of the particles. After each update, calculate the current theoretical impedance modulus based on the current particle update value, and calculate the current fitness value based on the current theoretical impedance modulus. Based on the current fitness value, the particles are selected and crossovered to obtain the optimal solution of the equation system.

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