Electromagnetic wave energy collection method and device

Abstract

The invention discloses an electromagnetic wave energy collection method and device, and relates to the technical field of wireless energy collection, and the method comprises the steps: receiving a wireless electromagnetic wave signal in an environment, and converting the wireless electromagnetic wave signal into an AC signal; converting the output alternating current signal into a direct current signal, and performing voltage regulation on the direct current signal; optimizing the perturbation and observation method by using a particle swarm optimization algorithm, and searching the working voltage corresponding to the maximum power of the collection device in the voltage regulation process based on the optimized perturbation and observation method; collecting the corresponding direct current electric energy based on the maximum power; according to the method, accurate modeling and optimization of the energy conversion process are achieved, and therefore the problems that in the prior art, under the dynamic environment, the convergence speed is low, oscillation is prone to occurring, and the maximum power point is difficult to track accurately are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless energy harvesting, and particularly relates to a method and device for electromagnetic wave energy harvesting. Background Art

[0002] With the rapid development of wireless communication technologies and the popularization of emerging technologies such as the Internet of Things (IoT), wireless sensor networks (WSNs), and 5G communications, radio signals ubiquitous in society (such as Wi-Fi, cellular communications, radio and television broadcasts, satellite signals, etc.) have gradually formed an electromagnetic environment in the form of a "background noise". Although the energy density of each individual signal in these environmental electromagnetic waves is extremely low, due to their wide distribution and continuous existence, by using advanced antennas and energy conversion technologies, collecting this weak electromagnetic wave energy, converting it into direct current electrical energy and storing it for driving low-power devices, sensors, or as an emergency power source has become an important direction in the research of energy conservation, environmental protection, and self-powered systems. In addition, with the continuous pursuit of green energy and sustainable development, wireless energy harvesting technology is also regarded as a potential solution for powering future IoT nodes and portable devices. By wirelessly collecting the electromagnetic wave energy in the environment, system self-power supply can be achieved, reducing the dependence on traditional battery replacement, thereby reducing maintenance costs and environmental pollution.

[0003] Wireless Energy Harvesting technology has received increasing attention in recent years. Its main goal is to capture the electromagnetic waves scattered in the environment through an antenna or coil, convert them into voltage and current, and then through rectification, matching, and electrical energy conversion, finally store them in a power storage device. This technology not only conforms to the concept of green energy and sustainable development but also helps to reduce the maintenance and replacement costs of IoT devices.

[0004] The currently widely used dynamic maximum power point tracking (DMPPT) technology based on impedance matching is a typical example. Its implementation process is mainly divided into three stages: First, the electromagnetic wave signal intensity parameters are collected in real time through a coupling coil, and a mathematical model of the source impedance is established; Second, the gradient descent algorithm is used to iteratively adjust the capacitance / inductance value of the matching network; Finally, the maximum power output point is locked through a hysteresis comparator. However, it is found in actual deployment that when the environmental electromagnetic field changes suddenly, the step size setting of the gradient algorithm and the system response delay will produce coupling interference, resulting in the tracking trajectory oscillating repeatedly on the power curve; especially in the scenario where Wi-Fi signals in the 2.4GHz band and microwaves in the 5.8GHz band are mixed, the multi-frequency coupling effect will further increase the system convergence time. Experimental data shows that its stabilization time can reach more than 120ms, making it difficult to meet the real-time power supply requirements in a dynamic environment. Existing technologies have proven the feasibility of using the electromagnetic waves distributed in the environment for energy harvesting in the theoretical and laboratory stages. However, since the energy density of electromagnetic waves in the environment is usually extremely low (microwatts or even lower), even with the use of efficient antennas and rectifier circuits, the ultimately converted electrical energy is very limited. This makes it often difficult for existing technologies to meet the continuous power supply requirements of devices in practical applications.

[0005] In summary, the existing energy harvesting methods have problems such as slow convergence speed, easy oscillation, and difficulty in accurately tracking the maximum power point in a dynamic environment, which directly affect the stability and overall efficiency of the electromagnetic wave energy harvesting system in energy harvesting when the intensity of electromagnetic waves in the environment fluctuates. Summary of the Invention

[0006] Aiming at the deficiencies of the existing technologies in a dynamic environment, such as slow convergence speed, easy oscillation, and difficulty in accurately tracking the maximum power point, the present invention proposes an electromagnetic wave energy harvesting method and device, which realizes the efficient conversion of wireless energy in a dynamic environment by combining the P&O algorithm optimized by PSO, thereby solving the problems existing in the existing technologies.

[0007] An electromagnetic wave energy harvesting method includes the following steps: Receive wireless electromagnetic wave signals in the environment; Convert the received wireless electromagnetic wave signals into DC signals, and use a DC-DC converter to adjust the voltage of the DC signals; Optimize the perturbation observation method using the particle swarm optimization algorithm, and find the operating voltage corresponding to the maximum power of the collection device during the voltage regulation process of the DC signal based on the optimized perturbation observation method. Specifically, it includes: randomly initializing the positions and velocities of the particles within the set voltage range, calculating the output power corresponding to the position of each updated particle by updating the velocity and position of each particle, and setting a fitness function with the maximum power as the target. Among them, in each iterative update process, find the voltage position when the output power of each current particle reaches the maximum as the individual optimal solution; take the voltage position corresponding to the maximum power among all the individual optimal solutions of the particles as the global optimal solution; apply a perturbation step size to the global optimal solution, compare the output power corresponding to the global optimal solution before and after the perturbation. If the output power after the perturbation is greater than the output power before the perturbation, accept the perturbation and output the voltage position after the perturbation as the global optimal solution; otherwise, perform a reverse perturbation and compare again until the global optimal solution is output. Among them, the position of the particle is the candidate operating voltage, and the velocity represents the direction and distance of the particle's movement in the next iteration. Obtain and collect DC electrical energy according to the operating voltage corresponding to the maximum power.

[0008] Furthermore, the process of calculating the output power corresponding to the position of each updated particle by updating the velocity and position of each particle specifically includes the following steps: Each updated particle i Velocity Is expressed as: ; Among them, Represents the inertia weight; c 1, c 2 represents the learning factor; r 1, r 2 represents a random number; i = 1, 2,..., N, where N is the number of particles; V best Represents the current optimal position of each particle; V i Represents the particle i Individual position; v i Represents the velocity of the particle; t Represents the number of iterations; G best Is the voltage value corresponding to the maximum power among all the particles in the current iterative process; The position of each updated particle Is expressed as: ; At the same time, Is restricted to the interval inside; After updating, the output power of each particle at the latest position is: ; wherein, represents the current measured at voltage in A.

[0009] Furthermore, the conversion of the received wireless electromagnetic wave signal into a DC signal specifically includes: converting the wireless electromagnetic wave signal into an AC signal; converting the output AC signal into a DC signal through a rectifier circuit; wherein, the rectifier circuit is a Schottky diode rectifier bridge or a voltage multiplier rectifier circuit.

[0010] Furthermore, the wireless electromagnetic wave signal includes radio waves or magnetic field signals.

[0011] Furthermore, the radio waves are received by using a dipole antenna, a patch antenna, and a loop antenna.

[0012] Furthermore, the magnetic field signal is obtained by collecting the low-frequency magnetic field signal in the environment by using an inductance coil.

[0013] The present invention further includes an electromagnetic wave energy harvesting device, comprising: an energy harvesting module for receiving wireless electromagnetic wave signals in the environment; a signal conversion module for converting the received wireless electromagnetic wave signal into a DC signal and regulating the voltage of the DC signal by using a DC-DC converter; a maximum power point tracking module for optimizing the perturbation observation method by using the particle swarm optimization algorithm and finding the operating voltage corresponding to the maximum power of the harvesting device during the voltage regulation of the DC signal based on the optimized perturbation observation method, specifically including: randomly initializing the positions and velocities of particles within a set voltage range, calculating the output power of each updated particle at the corresponding position by updating the velocities and positions of each particle, and setting a fitness function with the maximum power as the target; wherein, in each iterative update process, finding the voltage position when the output power of each current particle reaches the maximum as the individual optimal solution; taking the voltage position corresponding to the maximum power among all the individual optimal solutions of the particles as the global optimal solution; applying a perturbation step size to the global optimal solution, comparing the output powers corresponding to the global optimal solution before and after the perturbation, if the output power after the perturbation is greater than the output power before the perturbation, then accepting the perturbation and outputting the voltage position after the perturbation as the global optimal solution; otherwise, performing a reverse perturbation and re-comparing until the global optimal solution is output; wherein, the position of the particle is the candidate operating voltage, and the velocity represents the direction and distance of the particle's movement in the next iteration; An energy storage module for obtaining and collecting DC electrical energy according to the operating voltage corresponding to the maximum power.

[0014] The present invention provides an electromagnetic wave energy collection method, which has the following beneficial effects: By combining the perturbation observation method with particle swarm optimization, the present invention realizes the accurate modeling and optimization of the energy conversion process; when the environment changes (such as the change of the electromagnetic wave signal intensity), the particle swarm optimization algorithm can guide each particle to continuously approach the optimal voltage on its own exploration path during each iteration process, find the voltage position that can make the output power reach the highest, and at the same time use the perturbation observation method to apply a perturbation step to the current voltage position for local fine-tuning, and find the voltage position corresponding to the maximum power by comparing the magnitudes of the output power before and after fine-tuning; by combining the particle swarm optimization algorithm with the perturbation observation method, it can converge faster and quickly find the maximum power point, reduce power fluctuations; solve the problems of slow convergence speed, easy oscillation and difficulty in accurately tracking the maximum power point in the existing methods in a dynamic environment; improve the stability and overall efficiency of the electromagnetic wave energy collection system in energy collection in a dynamic environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flowchart of an electromagnetic wave energy collection method in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments.

[0017] The present invention proposes an electromagnetic wave energy collection device, which mainly includes an energy collection module, a signal conversion module, an MPPT control module, and an energy storage and management module. Each module works together to achieve the efficient collection, conversion, tracking, and storage of wireless electromagnetic energy.

[0018] 1. The energy collection module includes: Antenna / coil: used to receive radio waves or magnetic field signals in the environment, convert them into AC signals, and capture multi-band wireless electromagnetic waves in the environment. When designing, the requirements of broadband, high gain, and miniaturization are taken into account.

[0019] Type selection: If mainly collecting radio waves, dipole antennas, patch antennas, loop antennas, etc. can be used. If mainly collecting low-frequency magnetic field signals, inductance coils can be used. If it is desired to cover multiple frequency bands, multi-band or broadband antennas can be used. Effective area: denoted as A eff (unit: m 2 ), representing the receiving ability of the antenna within a specific frequency band.

[0020] 2. Signal conversion module, including: converting AC signals into DC signals through a rectifier circuit (such as a Schottky diode rectifier bridge), and the conversion efficiency is denoted as n rect (dimensionless), with typical values between 0.6 and 0.9. The output voltage is increased through a boost conversion circuit (such as a DC-DC boost circuit, a switching power supply converter) to meet the requirements of the energy storage device. DC-DC converter: adjusts the voltage of the DC signal to match the subsequent energy storage device.

[0021] 3. MPPT control module: Utilizes the maximum power point tracking (MPPT) algorithm (such as the perturbation and observation method P&O or an improved algorithm combining particle swarm optimization PSO) to adjust the working voltage in real time, so that the system always operates in the maximum power output state. The objective function is the output power P = V × I, where V is the DC voltage (unit: V) and I is the DC current (unit: A).

[0022] 4. Energy storage and management module, including an energy storage device: such as a supercapacitor or a lithium battery, used to store the converted DC electrical energy. Management circuit: responsible for the stable output and scheduling of energy to ensure stable power supply to the load.

[0023] Based on the above electromagnetic wave energy harvesting device, the present invention also proposes an electromagnetic wave energy harvesting method, specifically including the following steps: S1. Acquisition of wireless electromagnetic wave signals and signal conversion: Electromagnetic wave power density model, the power density of wireless electromagnetic waves in the environment (unit: W / m 2 ) can be estimated by the following formula: ; where E : Electric field strength in the environment (unit: V / m); η0: Free space wave impedance, approximately 377 Ω.

[0024] S1.1. Environmental signal detection: Use a spectrum analyzer or software-defined radio (SDR) to detect the available radio frequency bands and power distribution in the environment.

[0025] S1.2. Antenna / coil selection and optimization: Select a suitable antenna for mainstream wireless frequency bands (such as Wi-Fi, cellular communication, broadcast signals, etc.), and use electromagnetic simulation software (such as HFSS, CST) to optimize the antenna structure. Antenna received power: The power received by the antenna within the effective area A eff is: P ant For: ; where A effis the effective area of the antenna (unit: m 2 ).

[0026] S1.3. Signal rectification: Using a Schottky diode rectifier bridge or a voltage multiplier rectifier circuit to convert the AC signal into a DC signal.

[0027] After the rectified DC power is converted by the rectifier circuit, the DC power P dc is:[[]] ; η rect : The conversion efficiency of the rectifier circuit (dimensionless).

[0028] S1.4. Signal conversion; DC-DC conversion output power: The final output power adjusted by the DC-DC converter P out is:[[]] ; η dc : The efficiency of the DC-DC converter (dimensionless).

[0029] S2. MPPT working model: In the MPPT part, the goal is to adjust the system working voltage V to maximize the output power P(V): ; where, V: DC voltage (unit: V); I(V): Output current at voltage V (unit: A).

[0030] A traditional method based on the Perturb and Observe (P&O) method and an improved method combined with Particle Swarm Optimization (PSO) are proposed for MPPT. The Maximum Power Point Tracking (MPPT) algorithm is used to optimize the energy harvesting system so that it can obtain the maximum power output under different environmental conditions. In the electromagnetic wave energy harvesting system, due to the possible change of the environmental electromagnetic field intensity, MPPT dynamically adjusts the load matching conditions to maximize the energy conversion efficiency. The Perturb and Observe (P&O) method is the most common implementation of MPPT. It slightly adjusts the operating point (such as the load resistance or the duty cycle of the boost converter), observes the power change trend, and adjusts the operating state of the harvesting system accordingly to stay near the maximum power point.

[0031] S2.1. Core idea of the Perturbation and Observation (P&O) algorithm: "Perturb" by slightly adjusting the load impedance R or the duty cycle D of the boost conversion circuit. Observe the change in power P: if P increases, continue to adjust in this direction; if P decreases, adjust in the opposite direction. Iterate repeatedly to stabilize the system near the maximum power point (MPP, Maximum Power Point). The specific steps are as follows: (1) Initialization: Set the initial operating voltage Measure the initial current Calculate the initial power: ; Set the voltage perturbation step size ΔV (unit: V) and define the power tolerance є (unit: W) as the convergence judgment criterion.

[0032] (2) Perturbation and measurement: By adjusting the load impedance R or the duty cycle D of the boost conversion circuit, obtain the new voltage V new and current I new . Update the operating voltage: ; Measure the new current I new and calculate the new power: ; (3) Judgment and adjustment: If P new > P old , it means that the perturbation in this direction is effective, continue to adjust in this direction and keep the sign of ΔV; if P new < P old , it means that the perturbation in the current direction is ineffective, then reverse the adjustment step size, that is, make ΔV = -ΔV.

[0033] (4) Update and repeat: Update the variables: ; Repeat steps (2) - (3) until ; When the perturbation amplitude is small enough and the power change is not obvious, it is considered that the maximum power point has been reached (that is, when the power change is less than the predetermined threshold, it is considered that the maximum power point has been reached).

[0034] The MPPT perturbation observation method makes the system always operate at the maximum power point by dynamically adjusting the load voltage or duty cycle, improving the energy harvesting efficiency. This method has simple calculations and is easy to implement, and is suitable for low-power energy management applications such as wireless energy harvesting, photovoltaic systems, and radio frequency energy harvesting.

[0035] S2.2. MPPT algorithm improved by Particle Swarm Optimization (PSO).

[0036] Since the Perturb and Observe (P&O) method is widely used in the Maximum Power Point Tracking (MPPT) problem, it has some problems, such as: Fixed step size: If the step size is too large, it may cause oscillations; if the step size is too small, the convergence speed is slow; Prone to local optimum: In a dynamic environment, P&O may not be able to follow the change of the maximum power point in time. To solve these problems, the Particle Swarm Optimization (PSO) algorithm is used to optimize P&O, making it converge faster, reducing oscillations, and improving the energy conversion efficiency.

[0037] Particle Swarm Optimization (PSO) is an optimization algorithm based on swarm intelligence. It finds the optimal solution by simulating the behavior of bird flocks or fish schools. Basic idea: Each particle represents a possible solution (i.e., the load voltage V). The particle continuously adjusts its position based on its own experience (personal best value P best ) and global experience (global best value G best ) to reach the optimal solution. To improve the convergence speed and avoid local oscillations, the PSO algorithm can be used to perform a global search on MPPT; the specific steps include: (1) Parameter and variable definition: Number of particles: N (usually 10 - 20); Voltage boundary: Allowed voltage range [V min , V max ]; The state of each particle i ( i = 1, 2,..., N) includes: Position V i (representing the candidate operating voltage, unit: V) and velocity v i (unit: V / s); Inertia weight: (dimensionless); Learning factors: c1 and c2 (dimensionless); Random number: .

[0038] The output power formula of the acquisition system is: ; Objective: Find the optimal operating voltage V to obtain the maximum power P max .

[0039] (2) Initialization: Randomly initialize the initial position and velocity of the particles within the allowed voltage range: ; ; where rand() is a random number generated between [0, 1], v min represents the minimum boundary of the velocity, v maxRepresents the maximum boundary of speed.

[0040] Calculate the initial power for each particle: ; Set the individual best of each particle to the current state: ; Global best G best Is the voltage value corresponding to the maximum power among all particles.

[0041] (3) Iterative update: In each iteration (assuming the maximum number of iterations is T), perform the following steps: For each iteration t = 0, 1, 2,..., T max : Update the particle velocity, and the velocity update formula: ; Where, : Inertia weight; C 1, C 2: Learning factor; r 1, r 2: Random number.

[0042] Update the particle position, and the position update formula: ; At the same time, limit within the interval inside.

[0043] Power evaluation: Calculate the power of each particle at the new position: ; (4) Update individual and global best: During the particle update process, introduce the idea of the perturbation observation method: If P i （t+1） > P i （t） , it means that the current perturbation direction is beneficial, and keep the current velocity direction. If P i （t+1） < P i （t） , then perform local adjustment on the current particle, that is, reverse part of the perturbation: V i （t+1） = V i （t） - ΔV; And appropriately reduce the perturbation step size △V (e.g., △V = 0.9×△V) to improve the accuracy.

[0044] Update the individual and global best: If P i （t+1） > P best , then update the individual best: V best , i = V i （t+1） , P best = P i （t +1） . Check all particles. If the new individual best of a particle is greater than the current global best, then update the global best G best .

[0045] If then update: ; If a certain P best,i is greater than the current global optimum, then update the global optimum: ; where: V i : The voltage position of the i -th particle (unit: V); v i : The velocity of the i -th particle (unit: V / s); I (V): The current measured at voltage V (unit: A); P i : The power corresponding to the i -th particle (unit: W).

[0046] (5) When the maximum iteration number Tmax is reached or the power change amplitude of all particles is less than the set threshold, it is considered convergent, and the global optimal solution is output.

[0047] Advantages of PSO optimizing P&O: 1. Faster convergence: Through global search, PSO can find the maximum power point faster, avoiding the inefficiency of the gradual search of P&O.

[0048] 2. Reduced oscillation: When approaching the maximum power point, PSO gradually reduces the step size through the convergence mechanism, reducing power fluctuations.

[0049] 3. Adapt to dynamic changes: When the environment changes (such as the change in the intensity of electromagnetic wave signals), PSO can adjust adaptively and quickly find the new maximum power point.

[0050] The present invention realizes the accurate modeling and optimization of the energy conversion process by establishing a full-process model from environmental electromagnetic waves to the final DC output and combining MPPT algorithms (traditional P&O and improved PSO methods). It ensures that the system can be efficiently designed and implemented both theoretically and in practice, thereby overcoming the problems of low energy density, poor matching, low conversion efficiency, and slow MPPT convergence in the prior art. Based on the scheme of collecting and storing electromagnetic wave energy in the air, by adopting an efficient antenna design, optimizing the rectification and matching circuits, and introducing an adaptive MPPT control (such as the P&O algorithm optimized by combining PSO), it can realize the intelligent collection and efficient conversion of wireless energy, and further provide a self-powered solution for low-power devices. The beneficial effects of this scheme are reflected in reducing the maintenance cost, enhancing the self-powered ability of the system, and meeting the requirements of environmental protection.

[0051] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. A method for collecting electromagnetic wave energy, characterized in that, It includes the following steps: Receiving wireless electromagnetic wave signals in the environment; Converting the received wireless electromagnetic wave signals into DC signals and regulating the voltage of the DC signals using a DC-DC converter; Optimizing the perturbation observation method using the particle swarm optimization algorithm, and finding the operating voltage corresponding to the maximum power of the collection device during the voltage regulation process of the DC signal based on the optimized perturbation observation method. Specifically, it includes: randomly initializing the positions and velocities of particles within a set voltage range, calculating the output power at the positions corresponding to each updated particle by updating the velocity and position of each particle, and setting a fitness function with the maximum power as the target. Among them, in each iteration update process, finding the voltage position when the output power of each current particle reaches the maximum as the individual optimal solution; taking the voltage position corresponding to the maximum power among all particle individual optimal solutions as the global optimal solution; applying a perturbation step size to the global optimal solution, comparing the output powers corresponding to the global optimal solution before and after perturbation. If the output power after perturbation is greater than the output power before perturbation, accept the perturbation and output the global optimal solution with the perturbed voltage position; otherwise, perform a reverse perturbation and compare again until the global optimal solution is output. Among them, the position of the particle is the candidate operating voltage, and the velocity represents the direction and distance of the particle's movement in the next iteration; Obtaining and collecting DC electrical energy according to the operating voltage corresponding to the maximum power.

2. The method for collecting electromagnetic wave energy according to claim 1, characterized in that, The process of calculating the output power at the positions corresponding to each updated particle by updating the velocity and position of each particle specifically includes the following steps: Each updated particle i velocity is expressed as: ； Among them, represents the inertia weight; c 1, c 2 represents the learning factor; r 1, r 2 represents a random number; i = 1, 2,..., N, where N is the number of particles; V best represents the current optimal position of each particle; V i represents the particle i 's individual position; v i represents the velocity of the particle; t represents the number of iterations; G best is the voltage value corresponding to the maximum power among all particles in the current iteration process; The updated position of each particle Indicates: ； At the same time, is restricted to the interval ; The output power of each updated particle at the latest position is: ； Among them, represents the current measured at voltage with the unit of A.

3. A method for collecting electromagnetic wave energy according to claim 1, characterized in that, The conversion of the received wireless electromagnetic wave signals into DC signals specifically includes: Converting the wireless electromagnetic wave signals into AC signals; Converting the output AC signals into DC signals through a rectification circuit. Among them, the rectification circuit is a Schottky diode rectifier bridge or a voltage multiplier rectification circuit.

4. A method for collecting electromagnetic wave energy according to claim 1, characterized in that, The wireless electromagnetic wave signals include radio waves or magnetic field signals.

5. A method for collecting electromagnetic wave energy according to claim 4, characterized in that, The radio waves are received by using a dipole antenna, a patch antenna, and a loop antenna.

6. A method for collecting electromagnetic wave energy according to claim 4, characterized in that, The magnetic field signals are obtained by collecting the low-frequency magnetic field signals in the environment using an inductance coil.

7. An electromagnetic wave energy harvesting device, characterized in that, It includes: An energy harvesting module for receiving wireless electromagnetic wave signals in the environment; A signal conversion module for converting the received wireless electromagnetic wave signals into DC signals and regulating the voltage of the DC signals using a DC-DC converter; The maximum power point determination module is used to optimize the perturbation observation method by using the particle swarm optimization algorithm, and find the operating voltage corresponding to the maximum power of the collection device during the voltage regulation process of the DC signal based on the optimized perturbation observation method. Specifically, it includes: randomly initializing the positions and velocities of particles within the set voltage range, calculating the output power at the positions corresponding to each updated particle by updating the velocity and position of each particle, and setting a fitness function with the maximum power as the target. Among them, in each iterative update process, the voltage position when the output power of each current particle reaches the maximum is found as the individual optimal solution; the voltage position corresponding to the maximum power among all the individual optimal solutions of the particles is used as the global optimal solution; a perturbation step size is applied to the global optimal solution, and the output powers corresponding to the global optimal solution before and after the perturbation are compared. If the output power after the perturbation is greater than the output power before the perturbation, the perturbation is accepted, and the voltage position after the perturbation is output as the global optimal solution; otherwise, a reverse perturbation is performed and the comparison is made again until the global optimal solution is output. Among them, the position of the particle is the candidate operating voltage, and the velocity represents the direction and distance of the particle's movement in the next iteration. The energy storage module is used to obtain and collect DC electrical energy according to the operating voltage corresponding to the maximum power.

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