Remote controller energy collecting and charging system and method and remote controller

Abstract

The invention discloses a remote controller energy collection and charging system and method and a remote controller, and the system comprises a perovskite solar cell which is used for converting light energy into electric energy through a photoelectric effect; the MPPT module is used for monitoring the output voltage and current of the perovskite solar cell in real time and enabling the perovskite solar cell to be always kept in a maximum power output state by adjusting a power point based on a monitoring result so as to obtain optimized electric energy; and the rechargeable lithium battery is respectively connected to the MPPT module and an external charger and is used for receiving and storing the optimized electric energy provided by the MPPT module and the electric energy provided by the external charger so as to supply power to the remote controller. The energy-saving and environment-friendly requirements are met through the perovskite battery, the efficient and convenient remote controller charging and power storage target is achieved through the MPPT module, the fault risk of a connecting line is reduced through the integrated design, and the stability and reliability are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of energy collection and charging, and in particular to a remote controller energy collection and charging system and method, and a remote controller. BACKGROUND

[0002] In the current rapid development of smart home, as a common terminal for controlling various home appliances, the convenience and stability of power supply of remote controller become key factors affecting user experience. The traditional remote controller mainly relies on dry batteries for power supply, which has many disadvantages.

[0003] From the perspective of use cost, dry batteries have limited power, especially in the case of frequent use, such as alternating use of multiple device remote controllers in a family, the battery consumption speed is fast, and users need to regularly purchase and replace batteries, which undoubtedly increases the use cost of the family. According to statistics, an ordinary family spends tens of yuan or even more on remote controller batteries every year.

[0004] From the perspective of environmental protection, a large number of discarded dry batteries, if not properly disposed of, can cause serious pollution to the soil and water sources. The heavy metal elements such as mercury and cadmium contained in dry batteries, once entering the environment, will accumulate in the ecosystem, endangering the health of animals, plants and humans.

[0005] In addition, when the traditional remote controller is low on power, it may have unstable or even malfunctioning control signals, causing great inconvenience to users. In critical situations, the lack of spare batteries can result in the inability to timely control home appliances, affecting the normal order of life.

[0006] With the increasing demand for energy efficient use and environmental protection, wireless charging technology has emerged. Existing wireless charging technologies are divided into electromagnetic induction wireless charging technology and solar charging technology from the principle. However, the existing electromagnetic induction wireless charging technology relies on external power supply and specific charging base, has low charging efficiency and limited use scenarios. The existing solar charging technology is limited by light conditions and has poor charging effect in indoor low light environments. SUMMARY

[0007] To overcome the defects of the prior art, the present application proposes a remote controller energy collection and charging system and method based on perovskite to meet environmental protection requirements, improve charging efficiency, and realize multi-mode charging compatibility with photoelectric charging and other charging methods, thereby meeting the diverse needs of different users.

[0008] According to a first aspect of the present application, a remote controller energy collection and charging system is provided. The system comprises:

[0009] a perovskite solar cell for converting light energy into electrical energy through photoelectric effect;

[0010] an MPPT module for monitoring output voltage and current of the perovskite solar cell in real time, keeping the perovskite solar cell in a maximum power output state by adjusting a power point based on the monitoring result, and obtaining optimized electric energy;

[0011] a rechargeable lithium battery connected to the MPPT module and an external charger respectively, for receiving and storing the optimized electric energy provided by the MPPT module and the electric energy provided by the external charger, and supplying power to the remote controller.

[0012] Further, the preparation of the perovskite solar cell comprises the following steps:

[0013] dissolving lead iodide, formamidinium iodide and succinate in a mixed solvent of N,N-dimethylformamide and dimethyl sulfoxide, stirring at 60℃ for 6 hours to form a uniform perovskite precursor solution;

[0014] ultrasonically cleaning the indium tin oxide conductive glass with deionized water, acetone and ethanol in sequence to remove surface impurities;

[0015] spinning the perovskite precursor solution on the ultrasonically cleaned indium tin oxide conductive glass at a speed of 2000 rpm for 25-35 seconds to obtain a conductive glass after first spinning;

[0016] spinning the lead iodide solution on the conductive glass after first spinning at a speed of 5000 rpm for 25-35 seconds to obtain a conductive glass with a dense perovskite thin film;

[0017] transferring the conductive glass with the dense perovskite thin film to a hot plate at 90-110℃ for annealing for 15-25 minutes to promote the perovskite crystal to fully crystallize;

[0018] depositing a gold electrode with a thickness of 90-110 nm on the surface of the annealed perovskite thin film by a thermal evaporation method.

[0019] Further, the rechargeable lithium battery uses a silicon-based composite material as an electrode material.

[0020] Further, the rechargeable lithium battery is packaged with a flexible polyimide film outside the battery.

[0021] Further, 2%-5% of vinylene carbonate is added to the electrolyte of the rechargeable lithium battery, and the lithium salt concentration is 1.1-1.3 mol / L.

[0022] Furthermore, the perovskite solar cell is placed on the back surface of the remote control housing; the housing is provided with a transparent polycarbonate window at the position of the perovskite solar cell; the rechargeable lithium battery is embedded in the housing and located below the perovskite solar cell; the MPPT module is embedded in the housing and located below the button portion, and is connected to the perovskite solar cell and the lithium battery respectively through wires.

[0023] According to a second aspect of the present invention, a remote controller is provided, comprising the energy collection and charging system for the remote controller according to the first aspect of the present invention.

[0024] According to a third aspect of the present invention, a remote control energy harvesting and charging method is provided, which is applicable to the perovskite-based remote control energy harvesting and charging system described in the first aspect of the present invention. The method comprises:

[0025] The perovskite solar cell converts light energy into electrical energy through the photoelectric effect;

[0026] The output voltage and current of the perovskite solar cell are monitored in real time by an MPPT module, and the perovskite solar cell is maintained at a maximum power point based on the monitoring results to obtain optimized electric energy; the maximum power point is a combination of output voltage and output current that maximizes the output power of the perovskite solar cell;

[0027] The rechargeable lithium battery receives and stores the optimized electric energy provided by the MPPT module and the electric energy provided by the external charger to power the remote control.

[0028] Furthermore, the output voltage and current of the perovskite solar cell are monitored in real time by the MPPT module, and the perovskite solar cell is maintained at the maximum power point based on the monitoring results to obtain optimized electric energy, including:

[0029] Based on a preset solar photovoltaic power generation equivalent circuit model, the power at the maximum power point corresponding to the perovskite solar cell under different environmental parameters is simulated to form a data set; the maximum power point is the combination of output voltage and output current that maximizes the output power;

[0030] Constructing a photovoltaic prediction neural network with different environmental parameters as input and the output power value at the maximum power point as output, and training it with the data set;

[0031] Acquiring current environmental parameters and a current power point of the perovskite solar cell in real time; the current power point is a combination of a current output voltage and an output current of the perovskite solar cell;

[0032] simulate a current maximum power point under a current environmental parameter based on the equivalent circuit model of the solar photovoltaic power generation, and compare the current maximum power point with a current power point, and adjust according to a comparison result to make the perovskite solar cell continuously operate at the maximum power point;

[0033] According to the current environmental parameter, the power prediction value of the maximum power point of the perovskite solar cell is obtained through the trained photovoltaic prediction neural network, and a prediction error correction value is obtained through fuzzy reasoning on the current environmental parameter based on a preset fuzzy rule base, and the prediction error correction value is superimposed on the power prediction value to obtain an optimized power prediction value.

[0034] Further, the simulation obtains the power of the perovskite solar cell at the maximum power point under different environmental parameters, comprising:

[0035] The working point corresponding to the output voltage and output current of the perovskite solar cell is regarded as a particle, a particle set containing multiple particles is constructed, and the number of particles, particle position and particle speed are initialized;

[0036] The current output power of the perovskite solar cell is calculated as a current fitness through the position of each particle;

[0037] The current fitness value of each particle is compared with the historical optimal fitness value of each particle, if the current fitness value is greater, the individual optimal position of the particle is updated to the current position, and the optimal fitness value of each particle is updated with the fitness value corresponding to the individual optimal position;

[0038] In all the optimal positions of the particles, the individual optimal position with the maximum optimal fitness value is selected as the global optimal position, and the fitness value corresponding to the global optimal position is recorded as the global optimal fitness value;

[0039] The speed and position of each particle are updated according to the individual optimal position and the global optimal position;

[0040] The individual optimal position and the global optimal position of each particle are repeatedly updated, and the speed and position are repeatedly updated, when the change of the power value corresponding to the global optimal position after continuous multiple updates is less than a set threshold or reaches a maximum iteration number, the combination of the voltage value and the current value corresponding to the current global optimal position is output as the maximum power point.

[0041] Further, the photovoltaic prediction neural network constructed with different environmental parameters as input and the output power value of the maximum power point as output, comprising:

[0042] The input layer of the photovoltaic prediction neural network is set to contain 2 nodes to correspond to the light intensity and temperature respectively;

[0043] The hidden layer of the photovoltaic prediction neural network is set to 1 layer, and the activation function of the hidden layer is set to the ReLU function;

[0044] The output layer of the photovoltaic prediction neural network is set to include 1 node, corresponding to the power prediction value at the maximum power point, and the activation function of the output layer is set to a linear function.

[0045] Furthermore, the fuzzy reasoning of the current environmental parameters based on the preset fuzzy rule base to obtain the prediction error correction value includes:

[0046] Define fuzzy input variables related to environmental parameters and fuzzy output variables related to prediction error correction values;

[0047] Determine the membership of different fuzzy input variables to each fuzzy output variable through membership function;

[0048] Based on the membership degree, a prediction error correction value is output according to a preset fuzzy rule base.

[0049] The beneficial effect of the present invention is that, compared with the prior art,

[0050] 1. Power is collected by perovskite solar cells. Due to the characteristics of high photoelectric conversion efficiency, solution processing and low cost, perovskite materials can effectively collect light energy and convert it into electrical energy under common indoor lighting conditions, thereby reducing the use of traditional batteries and achieving energy conservation and environmental protection. At the same time, by setting up an MPPT module to monitor its output voltage and current in real time, and adjusting the power point to always maintain the maximum power output state, ensuring the optimal charging state of the rechargeable lithium battery. In addition, by connecting the lithium battery to an external charger and the perovskite solar cell at the same time, it can have multi-mode charging compatibility of photoelectric charging and charging, thereby meeting the diverse needs of different users.

[0051] 2. By adding succinate to the perovskite precursor solution, the quality of the perovskite film can be further optimized, the film morphology can be improved, defects can be suppressed, and lattice microstress can be relieved. This has led to a photoelectric conversion efficiency of up to 25.4% for rigid cell devices and a certified efficiency of up to 22.5% for flexible devices.

[0052] 3. By adding vinylene carbonate to the electrolyte of rechargeable lithium batteries, a stable solid electrolyte interface (SEI) film can be formed on the electrode surface, reducing the interfacial impedance. Experiments have shown that after adding 2%-5% VC, the battery's charge and discharge efficiency can be increased by 5%-10%, and the cycle life can be extended by 20%-30%. At the same time, by setting the lithium salt concentration to 1.1-1.3 mol / L, the ionic conductivity can be increased, thereby improving the battery's charge and discharge performance.

[0053] 4、Based on the preset solar photovoltaic power generation equivalent circuit model, the speed and position of the particle are updated through iteration, so that the particle can approach the maximum power point at a faster speed, has the advantages of fast convergence speed and strong global search ability, and has good adaptability to the parameter changes and environmental changes of the perovskite solar cell. At the same time, the artificial neural network has strong nonlinear mapping ability and learning ability. When the trained neural network is applied to real-time prediction, compared with the prediction by the equivalent circuit model, the calculation amount required for real-time prediction can be greatly reduced, and the real-time tracking efficiency of the maximum power value is greatly improved. In addition, by superimposing the prediction error correction value obtained by fuzzy logic reasoning on the maximum power value predicted by the photovoltaic prediction neural network, the prediction result can be better optimized, which is closer to the actual situation, so as to provide more accurate output power prediction value and power generation estimation value for users. BRIEF DESCRIPTION OF DRAWINGS

[0054] Figure 1 is the basic structure diagram of the remote control energy collection and charging system of the application;

[0055] Figure 2 is the working principle diagram of the remote control energy collection and charging system of the application;

[0056] Figure 3 is the related algorithm flowchart of the MPPT module in the remote control energy collection and charging system of the application. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical scheme and advantages of the application clearer, the technical scheme of the application will be described clearly and completely below in combination with the drawings in the embodiments of the application. The embodiments described in the application are only a part of the embodiments of the application, not all the embodiments. Based on the spirit of the application, other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the application.

[0058] Embodiment 1

[0059] The embodiment discloses a remote control energy collection and charging system.

[0060] As Figure 1 and 2 In the embodiment, the energy collection and charging system comprises a perovskite solar cell, an MPPT module and a rechargeable lithium battery.

[0061] The perovskite solar cell is used to convert light energy into electric energy through photoelectric effect;

[0062] The MPPT module is used to monitor the output voltage and current of the perovskite solar cell in real time, and based on the monitoring result, the perovskite solar cell is kept at a maximum power point to obtain optimized electric energy; the maximum power point is a combination of the output voltage and output current that makes the output power of the perovskite solar cell maximum.

[0063] The rechargeable lithium battery is connected to the MPPT module and an external charger respectively, and is used to receive and store the optimized electric energy provided by the MPPT module and the electric energy provided by the external charger to supply power to the remote controller.

[0064] In this embodiment, by connecting the MPPT (Maximum Power Point Tracking) module with the rechargeable lithium ion battery and the perovskite solar cell, under the condition of sufficient light, the perovskite solar cell generates electric energy through the photoelectric conversion effect under indoor light conditions, then the MPPT module monitors the output voltage and current in real time, and adjusts the power point to keep it at the maximum power output state at all times, so that the perovskite solar cell can fully utilize the light energy under various light intensities and conditions, and ensure the best charging state for the rechargeable lithium battery. Under the condition of insufficient light, the external charger can directly charge it through the external charging port. When the remote controller is used, the lithium ion battery releases electric energy to supply power to the remote controller.

[0065] The preparation method of the perovskite solar cell has a great influence on the performance of the perovskite solar cell. In this embodiment, in order to obtain a high-quality and high-performance solar cell, the following preparation steps are adopted:

[0066] Step 101, dissolve lead iodide, formamidinium iodide and succinate in a mixed solvent of N,N-dimethylformamide and dimethyl sulfoxide, stir at 55-65℃ for 5.5-6.5 hours to form a uniform perovskite precursor solution.

[0067] More preferably, the stirring temperature in this step can be, for example, 60℃, and the stirring time can be, for example, 6 hours.

[0068] Step 102, ultrasonic clean the indium tin oxide (ITO) conductive glass with deionized water, acetone and ethanol in sequence to remove surface impurities.

[0069] More preferably, the ultrasonic cleaning time in this step can be, for example, 10-20 minutes, and more preferably 15 minutes; the ITO glass can be, for example, an ITO glass with a length of 6cm and a width of 4cm.

[0070] Step 103, spin coating the perovskite precursor solution on the indium tin oxide conductive glass after ultrasonic cleaning at a speed of 1800-2200 rpm for 25-35 seconds to obtain a conductive glass after the first spin coating.

[0071] More preferably, the spin coating speed in this step can be, for example, 2000 rpm, and the duration can be, for example, 30 seconds.

[0072] Step 104, spin coating the lead iodide solution on the conductive glass after the first spin coating at a speed of 4500-5500 rpm for 25-35 seconds to obtain a conductive glass with a dense perovskite film.

[0073] More preferably, the spin coating speed in this step can be, for example, 5000 rpm, and the duration can be, for example, 30 seconds.

[0074] Step 105, transferring the conductive glass with a dense perovskite film to a hot plate at 90-110℃ for annealing for 15-25 minutes to promote the full crystallization of perovskite crystals.

[0075] More preferably, the temperature of the hot plate in this step can be, for example, 100℃, and the annealing time can be, for example, 20 minutes.

[0076] Step 106, depositing a gold electrode with a thickness of 90-110 nm on the surface of the annealed perovskite film by thermal evaporation. More preferably, the thickness of the gold electrode in this step can be, for example, 100 nm.

[0077] Through the above preparation method, a solar cell of the formamidinium lead iodide (FAPbI3) perovskite system can be obtained, which has high photoelectric conversion efficiency and stability.

[0078] Further, the MPPT module in the embodiment is also used to monitor the power of the lithium battery, and when the power of the lithium battery is lower than 10%, an alarm signal is generated to make the charging light display a red light, prompting the user to charge.

[0079] Further, the lithium battery can also be improved to improve its performance, such as endurance time, capacity retention rate, and overall safety, to further improve the overall performance of the remote control. The following will be described in detail:

[0080] First, the rechargeable lithium battery can use silicon-based composite material as electrode material. Compared with the traditional graphite electrode material, the theoretical specific capacity of silicon is as high as 4200 mAh / g, which is much higher than that of graphite (372 mAh / g). Therefore, by compounding nano-silicon particles with carbon materials, such as preparing silicon-carbon nanotube composite material, the volume change of silicon during charging and discharging can be alleviated. Experimental studies have shown that the use of such composite material can increase the energy density of the battery by 30%-50%, and the capacity retention rate can still reach more than 80% after 500 charging and discharging cycles, and the ion transmission performance can still be maintained in the curved state. In addition, the silicon-based composite electrode can fundamentally increase the ability of the battery to store electric charge, so that the battery can store more electric energy under the same volume or weight, meet the demand for high energy density of the battery, and make the endurance reach a higher state. At the same time, the use of silicon-based composite material as electrode material can generally meet the needs of ultra-thin flexible electrode material, so that it can still maintain good ion transmission performance in the curved state.

[0081] Second, a layer of flexible polyimide (PI) film can be packaged outside the battery to provide mechanical protection and ensure that the battery can work stably in different use scenarios of the remote controller.

[0082] Third, specific additives such as vinylene carbonate (VC) can also be added to the electrolyte. VC can form a stable solid electrolyte interface (SEI) film on the electrode surface, reducing the interface impedance. After adding 2%-5% VC, the charging and discharging efficiency of the battery can be increased by 5%-10%, and the cycle life can be extended by 20%-30%. At the same time, the concentration of lithium salt and the composition of the solvent in the electrolyte can be adjusted, such as adjusting the lithium salt concentration to 1.1-1.3 mol / L, and more preferably 1.2 mol / L, which can improve the ionic conductivity and further improve the charging and discharging performance of the rechargeable lithium battery.

[0083] In order to ensure that the perovskite-based remote controller energy collection and charging system can work efficiently and stably, and meet the daily use needs of users, the performance of the perovskite-based remote controller energy collection and charging system of the embodiment can be tested, including testing the energy collection efficiency of the remote controller under different light intensities and angles, including common light conditions such as indoor light and natural light. An electronic load is used to simulate the working state of the remote controller, and the charging and discharging performance, cycle life and power supply stability of the flexible lithium ion battery in the curved state are tested.

[0084] Further, considering the limited internal space of the remote controller, the layout of the components of the remote controller energy harvesting and charging system in the remote controller is optimized as follows: the remote controller housing is provided with a transparent polycarbonate window at the perovskite solar cell position to enable sufficient light to irradiate the perovskite solar cell and provide mechanical protection for the perovskite solar cell; the rechargeable lithium battery is embedded in the interior of the housing of the remote controller and located below the perovskite solar cell; and the MPPT module board is embedded in the interior of the housing of the remote controller and located below the key part, and is connected to the perovskite solar cell and the lithium battery through wires, respectively.

[0085] In this way, by optimizing the layout and wiring and using an efficient battery, the remote controller can be charged for 1-2 hours per day under sufficient light without affecting the appearance and function of the remote controller, meeting the daily use requirements and reducing the frequency of replacing the battery.

[0086] More specifically, household remote controllers usually adopt low-power design because they are in standby state most of the time and only work temporarily when the keys are pressed. Therefore, in the present embodiment, the standby current of the remote controller is usually between 0.1 uA and 1 uA, the working current when the keys are pressed is usually between 1 mA and 5 mA, and the working voltage is usually between 1.2 V and 3 V. Since the working time is only a few milliseconds, the total power consumption is in the range of several microwatts to tens of microwatts. In order to meet the low-power requirement of the remote controller, a rechargeable lithium battery with a capacity of 200 mAh-300 mAh is preferably selected in the present embodiment, and the battery voltage is 3 V, with a service life of up to 5 years. In addition, in order to efficiently and safely utilize the perovskite solar cell, the output voltage of the perovskite solar cell needs to be matched with the voltage of the lithium battery, so the output voltage of the perovskite solar cell needs to be slightly greater than 3 V, and the output current is between tens of mA and 100 mA. Therefore, the size of the perovskite solar cell designed and prepared in the present embodiment is 6 cm x 4 cm, and the output power of the battery is between 0.3 W and 0.5 W, taking into account the size and power requirements of the remote controller.

[0087] Embodiment 2

[0088] The present embodiment provides a remote controller comprising the remote controller energy harvesting and charging system of the present application.

[0089] Further, heat is generated during the charging process, and the internal space of the remote controller is limited. In order to enable the remote controller energy collection and charging system to achieve better heat dissipation, an alloy frame with a thermal conductivity coefficient of 160 W / (m·K) or above, such as an aluminum alloy frame, is arranged inside the shell of the remote controller. In addition, the remote controller energy collection and charging system can also include an overheat protection circuit for real-time monitoring of the temperature inside the remote controller. When the temperature exceeds a certain threshold, the charging power is reduced. Through good heat dissipation design and overheat protection, the system can work at an appropriate temperature, prolong the battery life by 20%-30%, and at the same time improve the stability and reliability of the circuit.

[0090] Embodiment 3

[0091] The embodiment discloses a remote controller energy collection and charging method of the application, which is suitable for the energy collection and charging system of the remote controller of the application. Specifically, the method comprises:

[0092] The perovskite solar cell converts light energy into electrical energy through the photoelectric effect;

[0093] The MPPT module is used to monitor the output voltage and current of the peroviskite solar cell in real time, and based on the monitoring result, the peroviskite solar cell is kept at the maximum power point to obtain optimized electrical energy; the maximum power point is the combination of the output voltage and output current that maximizes the output power of the peroviskite solar cell;

[0094] The rechargeable lithium battery receives and stores the optimized electrical energy provided by the MPPT module and the electrical energy provided by the external charger to supply power to the remote controller.

[0095] Further, in the above method, the MPPT module is used to monitor the output voltage and current of the peroviskite solar cell in real time, and based on the monitoring result, the peroviskite solar cell is kept at the maximum power point to obtain optimized electrical energy, which comprises the following procedures performed by the MPPT module:

[0096] Step 201, based on a preset solar photovoltaic power generation equivalent circuit model, the maximum power point of the peroviskite solar cell corresponding to the power under different environmental parameters is simulated to form a data set; the maximum power point is the combination of the output voltage and output current that maximizes the output power.

[0097] The preset equivalent circuit model of the solar photovoltaic power generation is constructed based on the connection relationship and element parameters of the perovskite solar cell, the MPPT module and the rechargeable lithium battery in the remote controller, and the influence of environmental factors (such as light intensity and temperature parameters) is considered, and is used to describe the electrical characteristics (including voltage, current and power characteristics) of the perovskite solar cell under different environmental parameters (such as light intensity and temperature). The specific form of the equivalent circuit model of the solar photovoltaic power generation can refer to the prior art, which will not be described here.

[0098] When constructing the data set, taking the environmental parameters including light intensity and temperature as an example, the range of light intensity can be 200-1000 W / m, and the range of temperature can be 25-50 DEG C. As an example, the samples in the data set can be constructed as (input: light intensity, temperature; output: power at the maximum power point). It should be understood that the selection of the above environmental parameters is only exemplary, and in other embodiments, other environmental parameters such as humidity can also be introduced as needed to improve accuracy.

[0099] The core idea of the algorithm used in simulation is: by simulating the foraging behavior of bird flocks, the output voltage and output current corresponding to the working point of the perovskite solar cell are taken as the "particle position", the output power is taken as the "fitness function", and then the combination of the output voltage and the output current that maximizes the output power is iteratively searched. The combination is the maximum power point (MPP) of the perovskite solar cell.

[0100] Specifically, the simulation of the power of the perovskite solar cell at the maximum power point corresponding to different environmental parameters includes the following steps:

[0101] Step 2011, the output voltage and output current corresponding to the working point of the perovskite solar cell are regarded as a particle, a particle set containing multiple particles is constructed, and the particle number, particle position and particle velocity are initialized.

[0102] The number, initial position and velocity of the particles can be randomly generated or set based on historical data. Further, the initial position of each particle can be understood as the initial value of the output voltage and output current of the perovskite solar cell; the velocity can be understood as determining the moving direction and step length of the particle in the solution space, that is, the change direction and change step length of the output voltage and output current in the corresponding solution space.

[0103] Step 2012, the current output power of the perovskite solar cell is calculated by the position of each particle as the current fitness.

[0104] Wherein, if the output voltage corresponding to each particle is U, and the output current is I, then the current output power P = U * I.

[0105] Step 2013, compare the current fitness value of each particle with the historical optimal fitness value of each particle, if the current fitness value is larger, update the individual optimal position of the particle as the current position, and update the optimal fitness value of each particle with the fitness value corresponding to the individual optimal position.

[0106] Step 2014, among the optimal positions of all particles, select the individual optimal position with the largest optimal fitness value as the global optimal position, and record the fitness value corresponding to the global optimal position as the global optimal fitness value.

[0107] Step 2014, update the speed and position of each particle according to the individual optimal position and the global optimal position.

[0108] Specifically, the speed update and position update can be performed by the following formula:

[0109]

[0110] wherein, and are the speeds before and after updating, respectively, and are the positions before and after updating, respectively, pbest id is the individual optimal position of the i-th particle, gbest d is the global optimal position, w is the inertia weight, c1 and c2 are the first and second learning factors, and r1 and r2 are the first and second random numbers, used to ensure the randomness and convergence of the search.

[0111] Step 2015, repeatedly update the individual optimal position and the global optimal position of each particle, and repeatedly perform the speed update and the position update, when the change amount of the power value corresponding to the global optimal position after continuous multiple updates is less than a set threshold value or reaches a maximum iteration number, output the combination of the voltage value and the current value corresponding to the current global optimal position as the maximum power point.

[0112] The threshold value of the change amount in this step can be set to be less than or equal to 0.2%, preferably 0.1%. If the output voltage at the maximum power point is denoted as Umpp, and the output current is denoted as Impp, the maximum power point can be denoted as (Umpp, Impp), and the power at the maximum power point can be denoted as Pmpp.

[0113] Step 202, construct a photovoltaic prediction neural network with different environmental parameters as input and the output power value of the maximum power point as output, and train the neural network with the data set.

[0114] In this step, the photovoltaic prediction neural network can be designed to have 2 nodes in the input layer, corresponding to the light intensity and temperature in the environmental parameters respectively; the hidden layer can be designed to have 1 layer, and the number of neurons in the hidden layer can be set according to the complexity of the data. Since the number of variables involved in the present application is small, the number of hidden layers is set to 1; the output layer can be designed to have 1 node, corresponding to the predicted value of the power Pmpp at the maximum power point.

[0115] The activation function of the hidden layer can be selected as the ReLU function, the activation function of the output layer can be selected as the linear function, and the loss function is used to test the mean square error MSE. The network is trained by the gradient descent method to make the predicted value close to the true Pmpp. The calculation formula of the mean square error MSE is as follows:

[0116]

[0117] where N is the number of samples, is the predicted value of the nth sample, P actual,n is the true value of the nth sample.

[0118] Step 203, real-time acquisition of current environmental parameters and current power point of perovskite solar cell; the current power point is the combination of the current output voltage and output current of the perovskite solar cell.

[0119] Step 204, based on the solar photovoltaic power generation equivalent circuit model, simulating the current maximum power point under the current environmental parameters and comparing with the current power point, and adjusting according to the comparison result to make the perovskite solar cell continuously operate at the maximum power point.

[0120] In this step, the step of simulating the current maximum power point under the current environmental parameters is similar to step 203, which will not be repeated here.

[0121] According to the comparison result, the perovskite solar cell is continuously operated at the maximum power point, which includes: if the difference between the output voltage and output current corresponding to the current maximum power point and the output voltage and output current corresponding to the current power point exceeds the predetermined range, the current power point is adjusted to the maximum power point by adjusting the corresponding converter of the perovskite solar cell or by impedance matching; if the difference does not exceed the predetermined range, no adjustment is made.

[0122] Step 205, according to the current environmental parameters, the power prediction value at the maximum power point of the perovskite solar cell is obtained by the trained photovoltaic prediction neural network, and the prediction error correction value is obtained by fuzzy reasoning on the current environmental parameters based on the pre-set fuzzy rule base. The trained photovoltaic prediction neural network is corrected by using the prediction error correction value to obtain the optimized power prediction value.

[0123] Specifically, the predicted error correction value is obtained by fuzzy reasoning on the current environment parameters based on a preset fuzzy rule base, and can specifically include the following steps:

[0124] Step 2061, defining a fuzzy input variable related to the environment parameters and a fuzzy output variable related to the predicted error correction value.

[0125] Among them, the input light intensity parameter value can be fuzzed as low (L), medium (M), high (H), and the input temperature parameter value can be fuzzed as low, medium, high, as fuzzy input variables. At the same time, the output predicted error correction value can be fuzzed as negative large (NB), negative small (NS), zero (Z), positive small (PS), and positive large (PB) as fuzzy output variables.

[0126] Step 2062, determining the membership degree of different fuzzy input variables to each fuzzy output variable by a membership function.

[0127] In this embodiment, the membership function adopts a Gaussian function, and in other embodiments, other membership functions (such as triangular membership function or trapezoidal membership function, etc.) can also be selected according to actual conditions. Among them, the membership function based on the Gaussian function is represented as:

[0128]

[0129] Among them, x is the input variable (such as light intensity, temperature), c i is the center value of the ith fuzzy set, and σ i is the width parameter.

[0130] Step 2063, outputting the predicted error correction value based on the membership degree according to the preset fuzzy rule base.

[0131] In this step, the design principle of the preset fuzzy rule base can include, for example: when the light is high (H) and the temperature is low (L), the neural network prediction value may be low, and the predicted error correction value is positive small (PS); when the light is low (L) and the temperature is high (H), the prediction value may be high, and the predicted error correction value is negative small (NS).

[0132] By correcting the trained photovoltaic prediction neural network with the obtained predicted error correction value, the final optimized maximum power value can be obtained, and the optimization formula involved is as follows:

[0133]

[0134] Among them, E is the power error, a is the fuzzy adjustment coefficient, and Δw fuzzy is the predicted error correction value output by fuzzy logic, and The related weight coefficients of the output layer of the photovoltaic prediction neural network before and after optimization.

[0135] Further, by using the optimized maximum power value, the MPPT module can accurately estimate and store the power generation of the perovskite solar cell, making it convenient for users to understand the power generation of the perovskite solar cell and timely select the appropriate charging method.

[0136] In the embodiment, based on the preset solar photovoltaic power generation equivalent circuit model, the speed and position of the particle are updated by iteration, which can make the particle approach the maximum power point at a faster speed, has the advantages of fast convergence speed and strong global search ability, and has good adaptability to the parameter change and environmental change of the perovskite solar cell. At the same time, the artificial neural network has strong nonlinear mapping ability and learning ability. When the trained neural network is applied to real-time prediction, compared with the prediction by the equivalent circuit model, the calculation amount required for real-time prediction can be greatly reduced, and the real-time tracking efficiency of the maximum power value is greatly improved. In addition, by superimposing the prediction error correction value obtained by fuzzy logic reasoning on the maximum power value predicted by the photovoltaic prediction neural network, the prediction result can be better optimized, which is closer to the actual situation, so as to provide more accurate output power prediction value and power generation estimation value for the user.

[0137] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit it. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that the specific embodiments of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application. Any modification or equivalent replacement without departing from the spirit and scope of the present application should be covered in the protection scope of the claims of the present application.

Claims

1. A remote control energy collection and charging system, characterized in that: include: Perovskite solar cells, which convert light energy into electricity through the photoelectric effect; An MPPT module is used to monitor the output voltage and current of the perovskite solar cell in real time, and to maintain the perovskite solar cell at a maximum power point based on the monitoring results to obtain optimized electrical energy; The maximum power point is a combination of output voltage and output current that maximizes the output power of the perovskite solar cell; A rechargeable lithium battery is connected to the MPPT module and the external charger respectively, and is used to receive and store the optimized electric energy provided by the MPPT module and the electric energy provided by the external charger to power the remote controller.

2. The remote control energy collection and charging system according to claim 1, characterized in that: The preparation method of the perovskite solar cell includes: Dissolving lead iodide, formamidinium iodide, and succinate in a mixed solvent of N,N-dimethylformamide and dimethyl sulfoxide, and stirring at 55-65° C. for 5.5-6.5 hours to form a uniform perovskite precursor solution; The indium tin oxide conductive glass was ultrasonically cleaned with deionized water, acetone, and ethanol in sequence to remove surface impurities. Spin coating the perovskite precursor solution on the ultrasonically cleaned indium tin oxide conductive glass at a speed of 1800 to 2200 rpm for 25 to 35 seconds to obtain a spin-coated conductive glass; Spin coating the lead iodide solution on the conductive glass after the first spin coating at a rotation speed of 4500 to 5500 rpm for 25 to 35 seconds to obtain a conductive glass with a dense perovskite film; The conductive glass with the dense perovskite film is transferred to a hot plate at 90-110°C and annealed for 15-25 minutes to promote full crystallization of the perovskite crystals; A gold electrode with a thickness of 90 to 110 nanometers is deposited on the surface of the annealed perovskite film by thermal evaporation.

3. The remote control energy collection and charging system according to claim 1, characterized in that: The rechargeable lithium battery uses a silicon-based composite material as an electrode material.

4. The remote control energy collection and charging system according to claim 1, characterized in that: The battery exterior of the rechargeable lithium battery is encapsulated with a layer of flexible polyimide film.

5. The remote control energy collection and charging system according to claim 1, characterized in that: 2%-5% vinylene carbonate is added to the electrolyte of the rechargeable lithium battery, and the lithium salt concentration is 1.1-1.3 mol / L.

6. The remote control energy collection and charging system according to claim 1, characterized in that: The perovskite solar cell is placed on the back surface of the remote control housing; the housing is provided with a transparent polycarbonate window at the location of the perovskite solar cell; the rechargeable lithium battery is embedded in the housing and located below the perovskite solar cell; the MPPT module is embedded in the housing and located below the button area, and is connected to the perovskite solar cell and the lithium battery respectively through wires.

7. A remote control comprising the remote control energy collection and charging system according to any one of claims 1 to 6.

8. A remote control energy collection and charging method, applicable to the perovskite-based remote control energy collection and charging system according to any one of claims 1 to 6, characterized in that: The method includes: Convert light energy into electricity through the photoelectric effect via perovskite solar cells; The output voltage and current of the perovskite solar cell are monitored in real time by an MPPT module, and the perovskite solar cell is maintained at a maximum power point based on the monitoring results to obtain optimized electric energy; the maximum power point is the combination of output voltage and output current that maximizes the output power of the perovskite solar cell; The rechargeable lithium battery receives and stores the optimized electric energy provided by the MPPT module and the electric energy provided by the external charger to supply power to the remote controller.

9. The remote control energy collection and charging method according to claim 8, characterized in that: The MPPT module is used to monitor the output voltage and current of the perovskite solar cell in real time, and based on the monitoring results, the perovskite solar cell is kept at the maximum power point to obtain optimized electric energy, including: Based on the preset solar photovoltaic power generation equivalent circuit model, the power at the maximum power point corresponding to the perovskite solar cell under different environmental parameters is simulated to form a data set; Constructing a photovoltaic prediction neural network with different environmental parameters as input and the output power value at the maximum power point as output, and training it with the data set; Acquiring current environmental parameters and a current power point of the perovskite solar cell in real time; the current power point is a combination of a current output voltage and an output current of the perovskite solar cell; Based on the solar photovoltaic power generation equivalent circuit model, simulating the current maximum power point under current environmental parameters and comparing it with the current power point, and adjusting according to the comparison result so that the perovskite solar cell continues to operate at the maximum power point; According to the current environmental parameters, the power prediction value at the maximum power point of the perovskite solar cell is obtained through the trained photovoltaic prediction neural network. At the same time, fuzzy reasoning is performed on the current environmental parameters based on a preset fuzzy rule library to obtain a prediction error correction value. The prediction error correction value is superimposed on the power prediction value to obtain an optimized power prediction value.

10. The remote control energy collection and charging method according to claim 9, characterized in that: The simulation obtains the power at the maximum power point corresponding to the perovskite solar cell under different environmental parameters, including: The operating point corresponding to the output voltage and output current of the perovskite solar cell is regarded as a particle, a particle set containing multiple particles is constructed, and the number of particles, particle positions and particle velocities are initialized; The current output power of the perovskite solar cell is calculated as the current fitness through the position of each particle; Compare the current fitness value of each particle with the historical optimal fitness value of each particle. If the current fitness value is greater, update the individual optimal position of the particle to the current position, and use the fitness value corresponding to the individual optimal position to update the optimal fitness value of each particle; Among the optimal positions of all particles, the individual optimal position with the largest optimal fitness value is selected as the global optimal position, and the fitness value corresponding to the global optimal position is recorded as the global optimal fitness value; Update the speed and position of each particle according to the individual optimal position and the global optimal position; Repeatedly update the individual optimal position and global optimal position of each particle, and repeatedly update the speed and position. When the change in the power value corresponding to the global optimal position after multiple consecutive updates is less than the set threshold or reaches the maximum number of iterations, the combination of voltage and current values ​​corresponding to the current global optimal position is output as the maximum power point.

11. The remote control energy collection and charging method according to claim 9, characterized in that: The photovoltaic prediction neural network constructed by taking different environmental parameters as input and taking the output power value at the maximum power point as output comprises: The input layer of the photovoltaic prediction neural network is set to include two nodes, corresponding to light intensity and temperature respectively; The hidden layer of the photovoltaic prediction neural network is set to 1 layer, and the activation function of the hidden layer is set to the ReLU function; The output layer of the photovoltaic prediction neural network is set to include 1 node, corresponding to the power prediction value at the maximum power point, and the activation function of the output layer is set to a linear function.

12. The remote control energy collection and charging method according to claim 9, characterized in that: The method of performing fuzzy reasoning on the current environmental parameters based on a preset fuzzy rule base to obtain a prediction error correction value includes: Define fuzzy input variables related to environmental parameters and fuzzy output variables related to prediction error correction values; Determine the membership of different fuzzy input variables to each fuzzy output variable through membership function; Based on the membership degree, a prediction error correction value is output according to a preset fuzzy rule base.

Images