Method for optimizing interface layer and enhancing stability of perovskite solar cell

Abstract

The invention relates to the field of perovskite photovoltaic technology, and discloses a perovskite solar cell interface layer optimization and stability enhancement method. The method comprises the following steps: firstly, acquiring real-time operation data of a battery, and screening a to-be-optimized interface layer set; based on an interface material attribute and a target stability threshold value, taking a battery overall structure as a center node, taking a to-be-optimized interface layer as a starting node and taking the target stability threshold value as a termination node, establishing an interface performance weighting network, and exporting a weight of an edge by a matching degree of the material attribute and the threshold value; in the network, aiming at the path of each starting node from a central node to a termination node, applying a self-adaptive optimization strategy to search an optimal solution, and identifying a plurality of optimization tracks; and generating an interface layer optimization scheme according to the optimal solution, and after a selection instruction is received, presenting an interface layer detailed material parameter record corresponding to the selected scheme. According to the method, interface layer optimization and stability enhancement are realized through network construction and optimization trajectory analysis.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of perovskite photovoltaic, in particular to a method for optimizing and enhancing the stability of the interface layer of a perovskite solar cell. BACKGROUND

[0002] Perovskite solar cells have attracted widespread attention in the field of new energy due to their excellent photoelectric conversion characteristics. In the structural design of such cells, the interface layer, as the connecting part between different functional layers, directly affects key processes such as charge transport and interface recombination. Currently, the selection and matching of interface layer materials still face many challenges. Different materials are easily affected by environmental factors such as humidity and temperature changes during long-term operation, leading to degradation of interface characteristics and affecting the overall operation of the cell.

[0003] In the prior art, interface layer optimization relies on empirical material selection or single performance index control, lacking systematic consideration of the correlation between the overall structure of the cell and each interface layer. In actual operation, cell performance degradation is the result of the combined action of multiple interface layers, and local optimization of a single interface often fails to effectively improve overall stability. At the same time, traditional optimization methods fail to fully utilize real-time operation data, making it difficult to dynamically respond to performance degradation processes, resulting in a deviation between the optimization scheme and actual needs, and failing to timely adapt to changes in the state of the cell in different operating stages.

[0004] In addition, the matching between the properties of the interface material and the stability requirements lacks quantitative analysis means, and most schemes remain at the qualitative description level, making it difficult to accurately identify key interface factors affecting stability. In this case, the interface layer optimization process is often blind, increasing research and development costs and limiting the sustained application of perovskite solar cells in actual scenarios. SUMMARY

[0005] The present application aims to provide a method for optimizing and enhancing the stability of the interface layer of a perovskite solar cell to solve the problems raised in the background art.

[0006] To achieve the above-mentioned purpose, the present application provides a method for optimizing and enhancing the stability of the interface layer of a perovskite solar cell, which comprises:

[0007] Obtaining real-time operation data of a perovskite solar cell, the real-time operation data including performance degradation indicators and target stability thresholds, and selecting a set of interface layers to be optimized from a plurality of candidate interface layers of the perovskite solar cell;

[0008] based on the interface material properties in the real-time operation data and the target stability threshold, taking the overall structure of the perovskite solar cell as a central node, each interface layer in the set of interface layers to be optimized as a starting node, and the target stability threshold as a terminal node, an interface performance weighted network is established, wherein a weight value of a connecting edge in the interface performance weighted network is derived by a matching degree of the interface material properties and the target stability threshold;

[0009] In the interface performance weighted network, for each starting node, a path set from the starting node to the terminal node through the central node is applied with an adaptive optimization strategy to search for a path weight optimal solution, and a plurality of optimization trajectories of the set of interface layers to be optimized associated with the performance degradation index via the central node are identified;

[0010] According to the path weight optimal solution in the plurality of optimization trajectories, an interface layer optimization scheme for the real-time operation data is generated, and when a selection instruction for the interface layer optimization scheme is received, a detailed material parameter record of the set of interface layers to be optimized corresponding to the selected interface layer optimization scheme is presented.

[0011] Preferably, based on the interface material properties in the real-time operation data and the target stability threshold, taking the overall structure of the perovskite solar cell as a central node, each interface layer in the set of interface layers to be optimized as a starting node, and the target stability threshold as a terminal node, an interface performance weighted network is established, comprising the following sub-steps:

[0012] The historical aging rate of each interface layer in the set of interface layers to be optimized is extracted, and a durability factor of each interface layer is calculated based on the historical aging rate;

[0013] The degree of influence of each interface layer on the photoelectric conversion efficiency of the perovskite solar cell is analyzed, and an efficiency-sensitive connecting weight from each starting node to the central node in the interface performance weighted network is derived in combination with an expected efficiency level in the real-time operation data;

[0014] In combination with a thermal expansion coefficient record of each interface layer, historical failure statistics of the set of interface layers to be optimized, and a design life requirement in the real-time operation data, a thermal stability-sensitive connecting weight from each starting node to the central node in the interface performance weighted network is calculated;

[0015] The durability factor, the efficiency-sensitive connecting weight, and the thermal stability-sensitive connecting weight are integrated, and multiplied by a minimum matching value of the interface material properties corresponding to each interface layer and the target stability threshold, so as to determine a connecting edge weight value from each starting node to the central node in the interface performance weighted network.

[0016] The connection edge weight values from the terminal nodes to the center node in the interface performance weighted network are set using the target stability threshold in the real-time operation data, and the construction process of the interface performance weighted network is completed by summarizing all the connection edge weight values from the starting nodes to the center node and the connection edge weight values from the terminal nodes to the center node.

[0017] Preferably, the analysis of the degree of action of each interface layer on the photoelectric conversion efficiency of the perovskite solar cell, combined with the expected efficiency level in the real-time operation data, derives the efficiency-sensitive connection weight from each starting node to the center node in the interface performance weighted network, including the following operations:

[0018] The difference between the maximum efficiency improvement value and the minimum efficiency improvement value of each interface layer in the perovskite solar cell is measured, and the efficiency improvement interval is defined accordingly;

[0019] The historical efficiency response data set of each interface layer is collected, and the average efficiency response reference is calculated in combination with the time decay factor of each historical efficiency response data set;

[0020] The absolute deviation of the historical efficiency response data of each interface layer from the average efficiency response reference is summarized to generate the total efficiency response deviation of each interface layer;

[0021] The total efficiency response deviation of each interface layer is divided by the efficiency improvement interval to obtain the deviation proportion, and the deviation proportion is mapped to the preset influence degree interval to obtain the corresponding weight correction coefficient;

[0022] The deviation proportion is multiplied by the weight correction coefficient to finally generate the efficiency-sensitive connection weight from each starting node to the center node in the interface performance weighted network.

[0023] Preferably, the historical efficiency response data set of each interface layer is collected, and the average efficiency response reference is calculated in combination with the time decay factor of each historical efficiency response data set, including the following steps:

[0024] Based on the preset material degradation rate and the average value and dispersion of the efficiency response data of each interface layer in the recent historical period, an adaptive change rate parameter is derived;

[0025] The time stamp offset of the efficiency response data of each interface layer and the adaptive change rate parameter are used to calculate the time decay weight of each interface layer;

[0026] The time decay weight of each interface layer is multiplied by the corresponding historical efficiency response data to generate the weighted efficiency response value of each interface layer;

[0027] accumulating the weighted efficiency response values of all interface layers to obtain a total weighted efficiency response, and accumulating the time decay weights of all interface layers to obtain a total decay weight;

[0028] deriving the average efficiency response benchmark by dividing the total weighted efficiency response by the total decay weight.

[0029] Preferably, the coefficient of thermal expansion record of each interface layer, the historical failure statistics of the interface layer set to be optimized, and the design life requirement in the real-time operation data are combined to calculate the thermal stability sensitive connection weight from each starting node to the center node in the interface performance weighted network, including the following processes:

[0030] extracting the failure frequency of each interface layer from the historical failure statistics, and calculating the failure probability coefficient of each interface layer based on the failure frequency;

[0031] combining the failure probability coefficient of each interface layer with the period difference value of the coefficient of thermal expansion record and the design life requirement, and calculating the thermal stability sensitive connection weight from each starting node to the center node in the interface performance weighted network by linear combination.

[0032] Preferably, the process of applying an adaptive optimization strategy to search for the optimal solution of path weight in the interface performance weighted network for each starting node to reach the terminal node through the center node identifies multiple optimization trajectories of the interface layer set to be optimized associated with the performance degradation index through the center node, including the following steps:

[0033] Based on the path combination from each starting node to the terminal node in the interface performance weighted network, an optimization solution space is created;

[0034] A heuristic search algorithm or a random optimization method is used to evaluate the gradient change or fitness score of all connection edges in the optimization solution space;

[0035] For the connection edge weight in each path, the single-path optimal weight that a single interface layer independently satisfies the target stability threshold or the combined-path optimal weight configuration that multiple interface layers cooperatively satisfy the target stability threshold is calculated by minimizing the path cost function;

[0036] According to each single-path optimal weight and combined-path optimal weight configuration, the gradient change or fitness score in the optimization solution space is updated, and when the path cost function cannot be further reduced, multiple single paths and combined paths are output.

[0037] Integrate the plurality of single paths and combined paths to determine a plurality of optimization trajectories of the interface layer set to be optimized associated with the performance degradation index via the central node.

[0038] Preferably, the method further comprises the following steps of: obtaining real-time operation data of the perovskite solar cell, the real-time operation data comprising a performance degradation index and a target stability threshold; and screening an interface layer set to be optimized from a plurality of candidate interface layers of the perovskite solar cell, comprising the following operations:

[0039] Obtaining the real-time operation data, and querying interface layer records associated with the performance degradation index in a perovskite solar cell database according to the performance degradation index;

[0040] Counting a historical optimization frequency and an optimization number of each interface layer record in the perovskite solar cell database;

[0041] Based on the historical optimization frequency and the optimization number, dynamically screening the interface layer set to be optimized from the plurality of candidate interface layers.

[0042] Preferably, the method further comprises the following steps of: after generating the interface layer optimization scheme, feeding back a path weight optimal solution in the interface layer optimization scheme to a perovskite solar cell control system to adjust a material deposition parameter of the interface layer.

[0043] Preferably, the adjusting the material deposition parameter of the interface layer comprises: modifying a thickness distribution and a component ratio of the interface layer according to the path weight optimal solution.

[0044] Preferably, the method further comprises a verification mechanism:

[0045] After generating the interface layer optimization scheme, performing a simulated environment accelerated aging test process to collect a performance attenuation trajectory of the interface layer in a test process;

[0046] Real-time comparing the performance attenuation trajectory with a performance improvement parameter corresponding to the interface layer optimization scheme;

[0047] When detecting that an actual performance attenuation rate exceeds a scheme prediction value by a preset deviation threshold, automatically triggering an operation of re-screening the interface layer set to be optimized.

[0048] Compared with the prior art, the present application has the following beneficial effects:

[0049] From the real-time operation data of perovskite solar cells, the interface layer set to be optimized is screened, making the determination of the optimization object more in line with the actual operation state of the cell, and avoiding ineffective analysis of irrelevant interface layers. By establishing an interface performance weighted network, the overall structure of the cell, the interface layer to be optimized, and the target stability threshold are included in a unified analysis framework, showing the relationship between each interface layer and the overall structure and stability requirements, breaking the limitations of traditional local optimization, and allowing the role of each interface layer to be clearly presented in the overall system.

[0050] In the interface performance weighted network, the adaptive optimization strategy is used to search for the optimal solution of the path weight, which can identify the optimization trajectory related to the performance degradation index from multiple possible optimization paths. This approach fully considers the mutual influence between interface layers, making the identification of the optimization trajectory more systematic and comprehensive, rather than limited to independent adjustment of a single interface.

[0051] The interface layer optimization scheme generated based on the optimization trajectory is closely related to real-time operation data and can be dynamically adjusted according to the actual operation state of the cell, making the scheme more in line with actual needs. When receiving a selection instruction, the corresponding detailed material parameter record is presented, providing clear reference information for specific implementation and facilitating accurate application of the optimization scheme in actual operation, making the interface layer optimization process more operable.

[0052] Overall, this method forms a coherent interface layer optimization and stability enhancement process by integrating real-time data, constructing network models, and analyzing optimization trajectories, organically combining each link from data acquisition to scheme implementation to form a complete closed loop, adapting to the optimization needs of the complex interface system of perovskite solar cells. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 A working principle diagram of the perovskite solar cell interface layer optimization and stability enhancement method described in the present application;

[0054] Figure 2 A flowchart for establishing an interface performance weighted network;

[0055] Figure 3 A flowchart for deriving efficiency-sensitive connection weights;

[0056] Figure 4 A flowchart for identifying an optimization trajectory;

[0057] Figure 5 A flowchart for a verification mechanism. DETAILED DESCRIPTION

[0058] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0059] Please refer to Figure 1 The present application provides a method for optimizing and enhancing the stability of the interface layer of a perovskite solar cell, which comprises:

[0060] Step 1: Obtain real-time running data of the perovskite solar cell, which contains performance degradation indicators and target stability threshold, and screen a set of interface layers to be optimized from a plurality of candidate interface layers of the perovskite solar cell. The real-time running data can be collected in real time by a battery running monitoring system. The performance degradation indicators include, but are not limited to, the output voltage decay rate of the battery, the short-circuit current drop amplitude, etc. The target stability threshold is pre-set according to the battery design standard and actual application requirements. The candidate interface layers include various interface layers in the battery structure such as the electron transport layer, the hole transport layer, and the buffer layer. Through analysis of the real-time running data, the interface layers with performance close to or lower than the target stability threshold are screened to form the set of interface layers to be optimized.

[0061] Step 2: Based on the interface material properties in the real-time running data and the target stability threshold, the overall structure of the perovskite solar cell is taken as the center node, each interface layer in the set of interface layers to be optimized is taken as the starting node, and the target stability threshold is taken as the terminal node. An interface performance weighted network is established, wherein the weight value of the connecting edge in the interface performance weighted network is derived by the matching degree of the interface material properties and the target stability threshold. The interface material properties include the chemical composition, electrical properties, thermal properties, etc. of the material. By analyzing the degree of fit between these properties and the target stability threshold, the weight of the connecting edge between the nodes in the network is determined. The higher the weight value, the greater the potential of the corresponding interface layer in improving the stability of the battery.

[0062] Step 3: In the interface performance weighted network, for each starting node, a process of applying an adaptive optimization strategy to search for the weight optimal solution of the path set from the center node to the terminal node is performed, and a plurality of optimization trajectories of the set of interface layers to be optimized associated with the performance degradation indicators via the center node are identified. The adaptive optimization strategy can dynamically adjust the search direction and step size according to the feedback information in the path search process. By continuously iterating the calculation, the weight-optimal path is found from a large number of possible paths, and these paths correspond to the optimization trajectories that can effectively improve the performance degradation problem of the battery.

[0063] Step 4: According to the path weight optimal solution in the plurality of optimized trajectories, an interface layer optimization scheme for real-time operation data is generated, and when a selection instruction for the interface layer optimization scheme is received, a detailed material parameter record of the selected interface layer set to be optimized corresponding to the interface layer optimization scheme is presented. The detailed material parameter record includes the purity, thickness, preparation process parameters, etc. of the material, providing specific parameter guidance for actual interface layer optimization operations.

[0064] Embodiment 1: refer to Figure 2 The specific sub-steps of establishing the interface performance weighted network in step 2 are as follows: the historical aging rate of each interface layer in each interface layer set to be optimized is extracted, which can be obtained by analyzing the performance monitoring data of the battery at different running cycles. These data include information such as the physical structure change and chemical component evolution of the interface layer during continuous operation. Based on the historical aging rate, the durability factor of each interface layer is calculated. The numerical value of the durability factor is related to the aging speed of the interface layer. The slower the aging speed, the higher the numerical value of the durability factor, and vice versa.

[0065] The degree of influence of each interface layer on the photovoltaic conversion efficiency of the perovskite solar cell is analyzed, and the efficiency sensitive connection weight from each starting node to the center node in the interface performance weighted network is derived combined with the expected efficiency level in the real-time operation data. Different types of interface layers have different roles in the battery working process. For example, the hole transport layer is responsible for the effective extraction and transmission of holes, and the carrier mobility of its material will directly affect the transmission efficiency of holes, thereby changing the photovoltaic conversion efficiency of the battery. The conductivity of the electron transport layer is related to whether the electrons can be quickly transferred from the light absorbing layer, reducing the occurrence of charge recombination. By quantitatively analyzing these influences, combined with the expected efficiency level in the real-time operation data, the specific numerical value of the efficiency sensitive connection weight is determined.

[0066] Combining the thermal expansion coefficient records of each interface layer, the historical failure statistics of the interface layer set to be optimized, and the design life requirements in the real-time operation data, the thermal stability sensitive connection weights from each starting node to the central node in the interface performance weighted network are calculated. Among them, the failure frequency of each interface layer is extracted from the historical failure statistics. The failure frequency is calculated based on the ratio of the number of functional failures of the interface layer to the total operating time in the past operation records of the battery. The failure probability coefficient of each interface layer is calculated based on the failure frequency, and the magnitude of the failure probability coefficient increases with the increase of the failure frequency. The failure probability coefficient of each interface layer is combined with the cycle difference value of the thermal expansion coefficient record and the design life requirement, and the thermal stability sensitive connection weight is calculated through linear combination. The coefficients used in the linear combination process are determined based on the actual situation such as the position of the interface layer in the overall structure of the battery and the strength of the interaction with other material layers, so as to reflect the proportion of different factors in the thermal stability assessment.

[0067] The durability factor, efficiency-sensitive connection weight, and thermal stability-sensitive connection weight are integrated and multiplied by the minimum matching value of the interface material properties corresponding to each interface layer and the target stability threshold to determine the edge weight of the connection from each starting node to the center node in the interface performance weighted network. The minimum matching value is obtained by comparing each parameter of the interface material properties (such as electrical performance parameters, thermal performance parameters, chemical stability parameters, etc.) with the corresponding parameters of the target stability threshold one by one. The value corresponding to the parameter with the lowest matching degree is found. This value is the minimum matching value. The higher the matching degree, the larger the minimum matching value.

[0068] Using the target stability threshold in the real-time operation data, the weight value of the connection edge from the end node to the central node in the interface performance weighted network is set. The setting of this weight value needs to comprehensively consider the priority of the target stability threshold in the battery performance evaluation system and the degree of impact on the long-term reliable operation of the battery. The construction process of the interface performance weighted network is completed by summarizing the weight values ​​of the connection edges from all starting nodes to the central node and the weight values ​​of the connection edges from the end node to the central node. During the summarization process, it is necessary to ensure that the calculation units of all weight values ​​are unified and the data format is consistent to ensure that the constructed interface performance weighted network can accurately reflect the correlation strength and performance impact relationship between each node.

[0069] In the process of constructing the interface performance weighted network, strict verification of each data is required to ensure the accuracy and integrity of the extracted historical aging rate, thermal expansion coefficient record, and historical failure statistics. For data with missing or abnormal values, interpolation method, mean substitution method, and other data processing methods are used for supplement and correction to avoid deviation in network construction due to data problems. At the same time, when calculating each weight value, the physical meaning and calculation logic of each parameter need to be clearly defined to ensure that each step of the calculation process is traceable and repeatable, thereby improving the reliability and practicality of the interface performance weighted network.

[0070] Example 2: refer to Figure 3 , analyze the degree of each interface layer affecting the photovoltaic conversion efficiency of perovskite solar cells, and combine the expected efficiency level in real-time operation data to derive the efficiency-sensitive connection weight from each starting node to the center node in the interface performance weighted network. The specific operation is as follows:

[0071] Measure the difference between the maximum efficiency improvement value and the minimum efficiency improvement value of each interface layer in the perovskite solar cell, and define the efficiency improvement interval based on this. The measurement process needs to be carried out in a standardized experimental environment, with consistent external conditions such as light intensity, environmental temperature, and humidity. Only the parameters of the target interface layer, such as material purity and thickness, are changed. Through repeated testing, the corresponding cell photovoltaic conversion efficiency under different parameters of the interface layer is obtained. The maximum and minimum efficiency improvement values are selected from them, and the difference between the two is the efficiency improvement interval.

[0072] The historical efficiency response data set of each interface layer is collected, and the average efficiency response reference is calculated by combining the time attenuation factors of the historical efficiency response data sets. The specific steps are as follows: based on the preset material degradation rate and the average value and dispersion of the efficiency response data of each interface layer in the recent historical period, the adaptive change rate parameter is derived. The material degradation rate is determined according to the inherent characteristics of the interface layer material and the past experimental observation results, the average value is calculated by arithmetic mean of the efficiency response data in the recent historical period, and the dispersion is measured by the standard deviation of the data dispersion. The adaptive change rate parameter comprehensively reflects the material degradation trend and the fluctuation of recent data. The time stamp offset of the efficiency response data of each interface layer and the adaptive change rate parameter are used to calculate the time attenuation weight of each interface layer. The time stamp offset is the difference between the data recording time and the current time, the larger the difference, the more obsolete the data, and the smaller the time attenuation weight, so as to reflect the higher reference value of new data in the calculation. The time attenuation weight of each interface layer is multiplied by the corresponding historical efficiency response data to generate the weighted efficiency response value of each interface layer, which not only retains the information of historical data, but also highlights the influence of recent data through weight adjustment. The weighted efficiency response sum of all interface layers is obtained by accumulating the weighted efficiency response values of all interface layers, and the total attenuation weight is obtained by accumulating the time attenuation weights of all interface layers. The average efficiency response reference is derived by dividing the weighted efficiency response sum by the total attenuation weight, which reflects the average efficiency response level of all interface layers considering the time factor.

[0073] The absolute deviation amount of the historical efficiency response data of each interface layer from the average efficiency response reference is summarized to generate the efficiency response deviation total amount of each interface layer. For each interface layer, the difference between each historical efficiency response data and the average efficiency response reference is calculated one by one, and the absolute value of the difference is taken as the absolute deviation amount. All absolute deviation amounts are added to obtain the efficiency response deviation total amount of the interface layer, which reflects the deviation degree of the efficiency response of the interface layer from the overall average level.

[0074] The efficiency response deviation total amount of each interface layer is divided by the efficiency improvement interval to obtain the deviation proportion, which reflects the proportion of the deviation total amount in the efficiency improvement interval. The deviation proportion is mapped to the preset influence degree interval to obtain the corresponding weight correction coefficient. The influence degree interval is divided according to different levels of influence on efficiency in actual application scenarios, different deviation proportions fall into different intervals, each interval corresponds to a weight correction coefficient, and the correction coefficient is used to adjust the influence degree of the deviation proportion on the maximum weight.

[0075] The deviation ratio is multiplied by the weight correction coefficient to finally generate the efficiency-sensitive connection weight of each starting node to the center node in the interface performance weighted network. The weight value comprehensively considers the deviation degree, efficiency improvement potential and influence degree of the interface layer efficiency response, and can quantitatively reflect the sensitivity of each interface layer to the battery photoelectric conversion efficiency, providing key parameters for the subsequent construction of the interface performance weighted network.

[0076] Throughout the process, it is necessary to ensure that the collected data is complete and accurate, and to identify and process abnormal data to avoid interference with the calculation results. At the same time, the setting of various parameters and the calculation method should be consistent to ensure the comparability of the efficiency-sensitive connection weight of different interface layers, thereby providing a reliable basis for the optimization of the interface layer.

[0077] Example 3: see Figure 4 In the interface performance weighted network, for each starting node, the adaptive optimization strategy is applied to search for the optimal solution of the path weight, and the multiple optimization trajectories of the interface layer set to be optimized are identified through the center node associated performance degradation index. The specific steps are as follows:

[0078] Based on the path combination of each starting node to the termination node in the interface performance weighted network, an optimization solution space is created. The path combination includes all possible paths from the starting node (interface layer to be optimized) to the termination node (target stability threshold) through the center node (battery overall structure), and each path is composed of a series of connection edges. Different paths represent different interface layer optimization directions and combination methods. The optimization solution space covers all possible weight configurations of these paths, providing a range for subsequent optimization search.

[0079] A heuristic search algorithm or a random optimization method is used to evaluate the gradient change or fitness score of all connection edges in the optimization solution space. The heuristic search algorithm efficiently explores the optimal solution in the solution space by simulating the laws in natural evolution or physical processes, such as selecting, crossing, and mutating to gradually approach the optimal solution. The random optimization method generates solutions randomly and evaluates their advantages and disadvantages, and constantly adjusts the search direction. The gradient change is used to describe the influence of the small change of the connection edge weight on the total weight of the path. A positive value indicates that the increase in weight will cause the total weight to rise, and a negative value indicates the opposite. The fitness score is obtained by scoring the path according to the pre-set evaluation standard, and the score reflects the degree of optimization of the corresponding optimization scheme.

[0080] For the connection edge weight in each path, the single-path optimal weight of a single interface layer independently satisfying the target stability threshold or the combined-path optimal weight configuration of multiple interface layers cooperatively satisfying the target stability threshold is calculated by minimizing the path cost function. The expression of the path cost function is:

[0081]

[0082] wherein, represents a path cost function value, and is a preset coefficient, represents the weight of the th connecting edge in the path, represents the total number of connecting edges in the path, represents the standard deviation of the weights of all connecting edges in the path.

[0083] When calculating the optimal weight of a single path, only the path corresponding to a single interface layer is considered, and the weight of each connecting edge in the path is adjusted to minimize the cost function value. When calculating the optimal weight configuration of a combined path, the paths corresponding to multiple interface layers are considered simultaneously, and the weight relationship of the connecting edges in each path is coordinated to ensure that the overall cost function value is minimized, thereby achieving the synergistic optimization effect of multiple interface layers.

[0084] According to the optimal weight of each single path and the optimal weight configuration of the combined path, the gradient change or fitness score in the optimization solution space is updated. During the updating process, the newly obtained weight configuration is substituted into the original evaluation model, and the gradient change or fitness score is recalculated to match the evaluation result with the current optimal weight configuration. After continuous iterations, the value of the path cost function no longer decreases, i.e., it reaches a stable state, and the multiple single paths and combined paths are output, which are the paths with the lowest cost in the current optimization solution space.

[0085] Integrate multiple single paths and combined paths to determine multiple optimization trajectories of the performance degradation index associated with the central node of the interface layer set to be optimized. During the integration process, the commonalities and differences of each path are analyzed, and the repeated or conflicting paths are removed, while the paths with practical optimization significance are retained to form optimization trajectories that clearly show the specific path from the current interface layer state to gradually reach the target stability threshold by adjusting relevant parameters, providing a direct basis for the generation of subsequent interface layer optimization schemes.

[0086] Obtain real-time running data of the perovskite solar cell, which includes performance degradation indicators and target stability thresholds. From multiple candidate interface layers of the perovskite solar cell, a set of interface layers to be optimized is selected, and the specific operations are as follows:

[0087] Real-time running data is acquired, which is collected in real time through sensors installed on the battery, including voltage, current, temperature and other parameters. Performance degradation indicators such as output power attenuation rate and fill factor drop amplitude are extracted through data processing, and target stability thresholds such as power attenuation not exceeding a certain proportion within a certain running period are determined. According to the performance degradation indicators, the interface layer records associated with the performance degradation indicators in the perovskite solar cell database are queried. The database stores historical data of various interface layers under different performance degradation conditions, and the possible problematic interface layers can be preliminarily located through the association query.

[0088] The historical optimization frequency and optimization times of each interface layer record in the perovskite solar cell database are counted. The historical optimization frequency refers to the ratio of the number of times the interface layer is included in the optimization range in the past period of time to the total monitoring time, and the optimization times refer to the number of times the interface layer is actually optimized. These data can be directly counted through the operation log in the database.

[0089] Based on the historical optimization frequency and optimization times, a set of interface layers to be optimized is dynamically selected from multiple candidate interface layers. When screening, the historical optimization frequency and optimization times are comprehensively considered. Generally, the interface layer with high historical optimization frequency and many optimization times is more likely to have performance fluctuations or degradation, and these interface layers are preferentially included in the set to be optimized. For interface layers that show signs of performance degradation, even if their historical optimization frequency and times are low, they will be included according to the actual situation to ensure that the set to be optimized can fully cover the interface layers that need to be optimized.

[0090] During the whole process, the real-time and accuracy of data acquisition need to be ensured, and the records in the database should be updated in time to avoid deviation of the screening results caused by data lag or errors. At the same time, the identification of the optimization trajectory and the screening of the interface layer set should be dynamically adjusted according to the actual running state of the battery to adapt to the optimization needs under different working conditions.

[0091] In some embodiments, the method further comprises: after generating the interface layer optimization scheme, feeding the path weight optimal solution in the interface layer optimization scheme into a perovskite solar cell control system to adjust the material deposition parameters of the interface layer. The perovskite solar cell control system is composed of hardware devices and software programs. The hardware devices include a control module of the deposition device, a sensor group, and an actuator, and the software programs are responsible for receiving the path weight optimal solution and converting it into specific control instructions. The path weight optimal solution contains the performance parameters that each interface layer should achieve after optimization, such as the conductivity of the material and the interface contact resistance. These parameters are imported into the control program through the interface of the control system. The control program analyzes these parameters to determine the type and direction of the material deposition parameters that need to be adjusted. For example, when the path weight optimal solution shows that the conductivity of an electron transport layer needs to be improved, the control program will analyze the deposition conditions that affect this parameter and generate corresponding adjustment instructions. The sensor group monitors various indicators in the deposition process in real time, such as deposition rate and film thickness, and feeds the monitoring data back to the control program. The control program continuously fine-tunes the control instructions based on the deviation between the feedback data and the path weight optimal solution to ensure that the material deposition process follows the optimization scheme.

[0092] Adjusting the material deposition parameters of the interface layer includes modifying the thickness distribution and the composition ratio of the interface layer according to the path weight optimal solution. Taking the hole transport layer of a perovskite solar cell as an example, if the path weight optimal solution indicates that the hole mobility of this layer needs to be improved, and the hole mobility is related to the film thickness and the doping ratio in the composition, then the thickness distribution and the composition ratio need to be adjusted. The modification of the thickness distribution needs to be combined with the structural design of the battery. The thickness of the hole transport layer can be appropriately increased in areas with strong light absorption to ensure that holes can be fully extracted, and the thickness can be appropriately reduced in the edge area to reduce material waste. During the adjustment process, the movement speed and the spraying amount of the deposition device nozzle are precisely controlled. The faster the nozzle moves, the less material is deposited per unit area, and the thinner the film layer. Conversely, the thicker the film layer. By pre-setting the movement path and speed parameters, a gradient distribution of the thickness is achieved. The adjustment of the composition ratio involves the mixing ratio of different raw materials. For example, in the material of the hole transport layer, the ratio of the host material and the dopant will affect the hole mobility. According to the path weight optimal solution, the control system will adjust the flow rate of the feed pump to change the mixing ratio of the two materials. If the doping ratio needs to be increased, the flow rate of the dopant feed pump will be increased, and the flow rate of the host material will be correspondingly reduced. Conversely, the flow rate of the dopant feed pump will be reduced, and the flow rate of the host material will be correspondingly increased.

[0093] For the electron transport layer, if the path weight optimal solution requires its electron conduction ability to be enhanced, it can be achieved by adjusting the thickness distribution and the proportion of metal oxides in the composition. At the contact interface between the electron transport layer and the light absorbing layer, the thickness can be appropriately increased to reduce the interface resistance, while in the area away from the contact interface, it can be thinned to reduce the series resistance. In terms of composition, if a mixed material of titanium dioxide and zinc oxide is used, the proportion of zinc oxide can be increased because zinc oxide has a higher electron mobility. When adjusting, the evaporation rate of the two materials is controlled to change the composition ratio in the deposited film layer.

[0094] The adjustment of the buffer layer also follows the above logic. If the path weight optimal solution shows that its blocking ability for ion migration is insufficient, the thickness distribution needs to be adjusted to enhance the blocking effect. The thickness in the area where ion aggregation is likely to occur is increased, and the proportion of insulating materials in the composition is adjusted. By increasing the proportion of insulating materials, the diffusion of ions is suppressed.

[0095] During the entire adjustment process, the control system collects the thickness data and composition analysis data of the film layer in real time, and continuously corrects the deposition parameters by comparing them with the target values in the path weight optimal solution. For example, the film thickness is monitored using an ellipsometer. If the deviation of the actual thickness from the target thickness distribution exceeds the allowed range, the control system will immediately adjust the moving speed of the nozzle or the amount of feed; the composition ratio is analyzed by X-ray photoelectron spectroscopy. If the actual proportion of dopant is lower than the target value, the flow parameter of the feed pump will be reset to ensure that the composition ratio meets the optimization requirements.

[0096] The adjustment of different interface layers needs to be coordinated to avoid mutual influence. For example, when adjusting the thickness of the electron transport layer and the perovskite absorbing layer at the same time, it is necessary to ensure that the contact interface between the two layers is flat, and the change of the thickness of the electron transport layer will not cause the film quality of the perovskite absorbing layer to decrease. The control system realizes the matching between the interface layers by synchronously controlling the parameters of multiple deposition modules, so that the overall structure meets the requirements of the path weight optimal solution.

[0097] Example 5: see Figure 5After generating the interface layer optimization scheme, the accelerated environmental aging test process is performed. The accelerated environmental aging test is realized by building a closed test chamber, which can accurately control the temperature, humidity, light intensity, and gas atmosphere parameters. According to the actual application scenario of perovskite solar cells, the accelerated aging conditions in the test chamber are set, for example, in the high temperature and high humidity environment test, the temperature is set to be higher than the actual use environment temperature value, the humidity is set to be higher relative humidity, and the constant light intensity is maintained to accelerate the aging process of the interface layer. During the test process, high-precision monitoring equipment is used to collect the performance parameters of the interface layer in real time, such as conductivity, carrier mobility, and interface contact resistance. The change of these parameters with time forms the performance degradation trajectory, and the trajectory data is continuously recorded and stored in the database of the test system.

[0098] The performance degradation trajectory is compared with the performance improvement parameters corresponding to the interface layer optimization scheme in real time. The performance improvement parameters are the performance change curve of the interface layer in the aging process predicted according to the optimization scheme, which includes performance parameter thresholds at different time nodes. The comparison process is completed by a special data processing software, which corresponds the real-time collected performance degradation trajectory data with the predicted data of the performance improvement parameters one by one, calculates the performance difference at each time node, for example, the difference between the actual measured interface layer conductivity and the predicted conductivity threshold at 100 hours of testing, and generates a curve of the difference with time, which intuitively shows the deviation between the actual performance and the predicted performance.

[0099] When it is detected that the actual performance degradation rate exceeds the predicted value by a preset deviation threshold, the operation of reselecting the interface layer set to be optimized is automatically triggered. The preset deviation threshold is set according to the importance of the interface layer and the application requirements of the battery, for example, for the electron transport layer which directly affects the photoelectric conversion efficiency, the deviation threshold can be set to a smaller value, while for the buffer layer with auxiliary function, the deviation threshold can be appropriately relaxed. The detection process is automatically performed by the software system, which continuously monitors the deviation between the performance degradation rate and the predicted value. When the deviation exceeds the preset threshold for continuous multiple time nodes, or the single deviation reaches a certain multiple of the threshold, the system determines that the current optimization scheme fails to achieve the expected effect, and immediately sends a trigger signal. When reselecting the interface layer set to be optimized, the system will re-call the historical data in the perovskite solar cell database, combined with the latest performance degradation trajectory, to expand the selection range, including not only the previously optimized interface layer, but also other interface layers that may affect the current performance, for example, when the performance degradation of the electron transport layer exceeds the expectation, the perovskite layer and the buffer layer in contact with it may need to be considered at the same time, the performance degradation indicators of these interface layers are re-evaluated, and the interface layer set that needs to be further optimized is selected, entering a new optimization cycle.

[0100] During the entire verification mechanism operation, the environmental parameters of the test cabin remain stable, avoiding the distortion of performance data caused by environmental fluctuations. The frequency of data collection is set according to the aging rate, and the collection frequency is increased in the rapid performance decay stage to capture subtle performance changes, and the frequency is reduced in the stable stage to reduce data redundancy. After the re-screening operation is started, the original interface layer optimization scheme will be marked as to be improved, and the new optimization process is executed based on the updated to-be-optimized interface layer set until the generated optimization scheme passes the test of the verification mechanism, ensuring that the performance and stability of the interface layer meet the design requirements.

[0101] It should be noted that, in this document, the terms such as first and second are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between such entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or apparatus including a series of elements includes not only those elements, but also other elements not explicitly listed, or inherent to such a process, method, article or apparatus.

[0102] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing and enhancing the stability of the interface layer of a perovskite solar cell, characterized in that: The method comprises the following steps: Acquiring real-time operating data of the perovskite solar cell, the real-time operating data including a performance degradation index and a target stability threshold, and screening a set of interface layers to be optimized from a plurality of candidate interface layers of the perovskite solar cell; Based on the interface material properties in the real-time operation data and the target stability threshold, an interface performance weighted network is established with the overall structure of the perovskite solar cell as a central node, each interface layer in the set of interface layers to be optimized as a starting node, and the target stability threshold as an ending node, wherein the weight values ​​of the connecting edges in the interface performance weighted network are derived by the degree of matching between the interface material properties and the target stability threshold; In the interface performance weighted network, for each set of paths from the starting node to the ending node via the central node, an adaptive optimization strategy is applied to search for an optimal solution for the path weight, and multiple optimization trajectories are identified in which the set of interface layers to be optimized is associated with the performance degradation indicator via the central node; Based on the optimal solution of the path weights in the multiple optimization trajectories, an interface layer optimization scheme for the real-time operation data is generated, and when a selection instruction for the interface layer optimization scheme is received, a detailed material parameter record of the interface layer set to be optimized corresponding to the selected interface layer optimization scheme is presented.

2. The method for optimizing and enhancing the stability of the interface layer of a perovskite solar cell according to claim 1, characterized in that: The method of establishing an interface performance weighted network based on the interface material properties in the real-time operation data and the target stability threshold, taking the overall structure of the perovskite solar cell as the central node, each interface layer in the set of interface layers to be optimized as the starting node, and the target stability threshold as the ending node, comprises the following sub-steps: Extracting a historical aging rate of each interface layer in each set of interface layers to be optimized, and calculating a durability factor of each interface layer based on the historical aging rate; Analyzing the effect of each interface layer on the photoelectric conversion efficiency of the perovskite solar cell, and combining the expected efficiency level in the real-time operation data to derive the efficiency-sensitive connection weight from each starting node to the central node in the interface performance weighted network; Calculating the thermal stability sensitive connection weight from each starting node to the central node in the interface performance weighted network by combining the thermal expansion coefficient record of each interface layer, the historical failure statistics of the interface layer set to be optimized, and the design life requirements in the real-time operation data; Integrating the durability factor, the efficiency-sensitive connection weight, and the thermal stability-sensitive connection weight, and multiplying the result with the minimum matching value of the interface material property corresponding to each interface layer and the target stability threshold, thereby determining a connection edge weight value from each starting node to the central node in the interface performance weighted network; The target stability threshold in the real-time operation data is used to set the weight value of the connection edge from the termination node to the central node in the interface performance weighted network, and the construction process of the interface performance weighted network is completed by summarizing the weight values ​​of the connection edges from all the starting nodes to the central node and the weight values ​​of the connection edges from the termination nodes to the central node.

3. The method for optimizing and enhancing the stability of the interface layer of a perovskite solar cell according to claim 2, characterized in that: The analyzing the effect of each interface layer on the photoelectric conversion efficiency of the perovskite solar cell, combining the expected efficiency level in the real-time operation data, and deriving the efficiency-sensitive connection weight from each starting node to the central node in the interface performance weighted network, includes the following operations: Measure the difference between the maximum and minimum efficiency improvement of each interface layer in the perovskite solar cell and define the efficiency improvement range based on this; Collecting a historical efficiency response data set of each interface layer, and calculating an average efficiency response benchmark by combining a time decay factor of each of the historical efficiency response data sets; Summarizing the absolute deviations of the historical efficiency response data of each interface layer from the average efficiency response benchmark to generate a total efficiency response deviation of each interface layer; Dividing the total efficiency response deviation of each interface layer by the efficiency improvement interval to obtain a deviation ratio, and mapping the deviation ratio to a preset impact degree interval to obtain a corresponding weight correction coefficient; The deviation ratio is multiplied by the weight correction coefficient to finally generate the efficiency-sensitive connection weight from each starting node to the central node in the interface performance weighted network.

4. The method for optimizing and enhancing the stability of the interface layer of a perovskite solar cell according to claim 3, characterized in that: The collecting of the historical efficiency response data set of each interface layer and the calculation of the average efficiency response benchmark in combination with the time decay factor of each historical efficiency response data set include the following steps: Based on the preset material degradation rate and the average and dispersion of the efficiency response data of each interface layer in the recent historical period, the adaptive change rate parameter is derived; Calculating the time attenuation weight of each interface layer using the timestamp offset of the efficiency response data of each interface layer and the adaptive change rate parameter; Multiplying the time decay weight of each interface layer by the corresponding historical efficiency response data to generate a weighted efficiency response value for each interface layer; The weighted efficiency response values ​​of all interface layers are accumulated to obtain the sum of weighted efficiency responses, and the time attenuation weights of all interface layers are accumulated to obtain the total attenuation weight; The average efficiency response reference is derived by dividing the weighted efficiency response sum by the total decay weight.

5. The method for optimizing and enhancing the stability of the interface layer of a perovskite solar cell according to claim 2, wherein: The thermal stability sensitive connection weight from each starting node to the central node in the interface performance weighted network is calculated by combining the thermal expansion coefficient record of each interface layer, the historical failure statistics of the interface layer set to be optimized, and the design life requirements in the real-time operation data. The following processes are included: Extracting the fault occurrence frequency of each interface layer from the historical fault statistics, and calculating the failure probability coefficient of each interface layer based on the fault occurrence frequency; The failure probability coefficient of each interface layer is combined with the thermal expansion coefficient record and the cycle difference value of the design life requirement, and the thermal stability sensitive connection weight from each starting node to the central node in the interface performance weighted network is calculated through linear combination.

6. The method for optimizing and enhancing the stability of the interface layer of a perovskite solar cell according to claim 1, characterized in that: The process of applying an adaptive optimization strategy to search for an optimal solution for the path weights for each set of paths from the starting node to the ending node via the central node in the interface performance weighted network, and identifying multiple optimization trajectories of the interface layer set to be optimized associated with the performance degradation indicator via the central node, includes the following steps: Creating an optimization solution space based on a path combination from each of the starting nodes to the ending nodes in the interface performance weighted network; Using a heuristic search algorithm or a random optimization method to evaluate the gradient changes or fitness scores of all connected edges in the optimization solution space; For each path, by minimizing the path cost function, the optimal weight of a single path that satisfies the target stability threshold independently on a single interface layer, or the optimal weight configuration of a combined path that satisfies the target stability threshold collaboratively on multiple interface layers, is calculated. updating the gradient change or fitness score in the optimization solution space according to the optimal weight of each single path and the optimal weight configuration of the combined path, and outputting multiple single paths and combined paths when the path cost function cannot be further reduced; The plurality of single paths and combined paths are integrated to determine a plurality of optimization trajectories of the interface layer set to be optimized associated with the performance degradation indicator via the central node.

7. The method for optimizing and enhancing the stability of the interface layer of a perovskite solar cell according to claim 1, characterized in that: The step of obtaining real-time operating data of the perovskite solar cell, wherein the real-time operating data includes a performance degradation index and a target stability threshold, and selecting a set of interface layers to be optimized from a plurality of candidate interface layers of the perovskite solar cell, includes the following operations: Acquire the real-time operation data, and query a perovskite solar cell database for interface layer records associated with the performance degradation indicator according to the performance degradation indicator; Counting the historical optimization frequency and optimization times of each interface layer recorded in the perovskite solar cell database; Based on the historical optimization frequency and optimization times, the set of interface layers to be optimized is dynamically screened from the multiple candidate interface layers.

8. The method for optimizing and enhancing the stability of the interface layer of a perovskite solar cell according to claim 1, wherein: The method further includes: after generating the interface layer optimization scheme, feeding back the path weight optimal solution in the interface layer optimization scheme to the perovskite solar cell control system to adjust the material deposition parameters of the interface layer.

9. The method for optimizing and enhancing the stability of the interface layer of a perovskite solar cell according to claim 8, characterized in that: The adjusting the material deposition parameters of the interface layer includes: modifying the thickness distribution and component ratio of the interface layer according to the path weight optimal solution.

10. The method for optimizing and enhancing the stability of the interface layer of a perovskite solar cell according to claim 1, wherein: The method further comprises a verification mechanism: After generating the interface layer optimization plan, executing a simulated environment accelerated aging test process to collect the performance degradation trajectory of the interface layer during the test; Comparing the performance degradation trajectory with the performance improvement parameters corresponding to the interface layer optimization solution in real time; When it is detected that the actual performance decay rate exceeds the solution prediction value and reaches the preset deviation threshold, the operation of re-screening the set of interface layers to be optimized is automatically triggered.

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