Method, device, equipment, storage medium and program product for detecting photovoltaic cell

Abstract

This application relates to a method, apparatus, device, storage medium, and program product for testing photovoltaic cells. The method includes: acquiring dark-state volt-ampere data of the cell under test in a dark environment, and acquiring light-state volt-ampere data of the cell under test in a light environment; determining, based on the light-state volt-ampere data, the candidate location range of recombination centers within the functional layers of the cell under test; and determining the distribution detection results of the recombination centers based on the dark-state volt-ampere data and the candidate location range. In the above method, the different volt-ampere data characteristics exhibited by recombination centers distributed in different functional layers of the cell under test are utilized, thereby enabling the detection of recombination centers by analyzing the volt-ampere data of the cell under test. Furthermore, based on a step-by-step approach of first initially determining the candidate location range and then further determining the distribution detection results within the candidate location range, the specific location of the recombination centers within the functional layers of the cell under test is obtained, improving the detection accuracy of recombination centers.

Description

Technical Field

[0001] This application relates to the field of battery testing technology, and in particular to a method, apparatus, equipment, storage medium, and program product for testing photovoltaic cells. Background Technology

[0002] A solar cell is a device that uses the photovoltaic effect to convert sunlight into electrical energy.

[0003] Due to limitations in actual production, the fabricated solar cells often contain heterogeneous structures, forming recombination centers that affect the photovoltaic effect and thus cell performance. To improve cell performance, it is necessary to detect the distribution of recombination centers within the cell. Related technologies typically detect the distribution of recombination centers based on the apparent inhomogeneity of the cell.

[0004] However, the detection accuracy of composite centers in related technologies is relatively poor. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, apparatus, equipment, storage medium, and program product for testing photovoltaic cells to address the aforementioned technical problems.

[0006] In a first aspect, embodiments of this application provide a method for detecting recombination centers in a pool, the method comprising:

[0007] Acquire dark-state volt-ampere data of the battery under test in a dark environment, and acquire optical volt-ampere data of the battery under test in a light environment;

[0008] Based on the photocurrent-voltage data, the candidate location range of recombination centers in the functional layers of the battery under test is determined.

[0009] Based on the dark-state voltammetric data and the range of candidate locations, the distribution detection results of the recombination centers are determined.

[0010] In this embodiment, the different current-voltage data characteristics exhibited by the composite center when it is distributed in different functional layers of the battery under test are utilized to realize the detection of composite center by analyzing the current-voltage data of the battery under test. Based on the step-by-step method of first initially determining the candidate location range and then further determining the distribution detection results within the candidate location range, the specific location of the composite center in the functional layer of the battery under test is obtained, thereby improving the detection accuracy of composite center.

[0011] In one embodiment, acquiring dark-state volt-ampere data of the battery under test in a dark environment includes:

[0012] In a dark environment, acquire the dark-state current sequence of the battery under test under reference voltage conditions;

[0013] Determine the dark-state volt-ampere data based on the reference voltage conditions and the dark-state current sequence.

[0014] In this embodiment of the application, by providing a reference voltage condition for the battery under test to obtain dark-state voltage-current data, the reliability of the obtained dark-state voltage-current data is improved accordingly.

[0015] In one embodiment, in a dark environment, acquiring the dark-state current sequence of the battery under test under a reference voltage condition includes:

[0016] In response to a dark environment where the light source device is turned off, the active power meter is controlled to provide the voltage value in the reference voltage condition to the battery under test; the light source device is used to provide the light source, and the active power meter is connected to the battery under test;

[0017] Obtain the current value of the battery under test at the corresponding voltage to obtain the dark state current sequence.

[0018] In this embodiment, the acquisition of dark-state voltage-current data is achieved by controlling the light source device and the active meter, thereby improving the convenience of acquiring dark-state voltage-current data.

[0019] In one embodiment, the reference voltage conditions include forward scanning voltage conditions and reverse scanning voltage conditions, and the dark-state current sequence includes a first current sequence obtained under the forward scanning voltage conditions and a second current sequence obtained under the reverse scanning voltage conditions; determining dark-state volt-ampere data based on the reference voltage conditions and the dark-state current sequence includes:

[0020] Based on the forward scanning voltage condition and the reverse scanning voltage condition, obtain the first current and the second current corresponding to the same voltage in the first current sequence and the second current sequence.

[0021] For each identical voltage, obtain the average current of the first current and the second current;

[0022] The average value of each voltage and the corresponding current is used as the dark-state volt-ampere data.

[0023] In this embodiment, the dark-state voltage-current-voltage data of the battery under test is determined by combining forward and reverse scanning voltage conditions, which reduces the data error under unidirectional scanning and improves the accuracy of the obtained dark-state voltage-current-voltage data.

[0024] In one embodiment, acquiring the photocurrent-voltage data of the battery under test in a light environment includes:

[0025] Under optical conditions, the optical current sequence of the battery under test is obtained under reference voltage conditions.

[0026] Based on the reference voltage conditions and the photocurrent sequence, determine the photocurrent data.

[0027] In this embodiment of the application, by providing a reference voltage condition for the battery under test to obtain photovoltaic current-voltage data, the reliability of the obtained photovoltaic current-voltage data is improved accordingly.

[0028] In one embodiment, under optical conditions, acquiring the photocurrent sequence of the battery under test under a reference voltage condition includes:

[0029] In response to the light environment of the light source device, the active multimeter is controlled to provide the voltage value in the reference voltage condition to the battery under test; the light source device is used to provide the light source, and the active multimeter is connected to the battery under test;

[0030] Obtain the current value of the battery under test at the corresponding voltage to obtain the photocurrent sequence.

[0031] In this embodiment, the acquisition of optical volt-ampere data is achieved by controlling the light source device and the active meter, thereby improving the convenience of acquiring optical volt-ampere data.

[0032] In one embodiment, based on photocurrent-voltage data, the range of candidate locations of recombination centers within the functional layers of the battery under test is determined, including:

[0033] Based on the optical voltammetry data, the carrier transport bottleneck of the battery under test is determined;

[0034] The range of candidate locations for recombination centers is determined based on the carrier transport bottleneck.

[0035] In this embodiment, the carrier transport bottleneck of the battery under test is determined based on optical voltammetry data, thereby determining the candidate location range of recombination centers in the battery under test. The preliminary location of the recombination center distribution is achieved solely by optical voltammetry data, which improves the convenience of preliminary location and correspondingly improves the efficiency of recombination center detection.

[0036] In one embodiment, determining the carrier transport bottleneck of the battery under test based on opto-current voltage data includes:

[0037] Based on the photovoltaic data, the forward photoelectric conversion efficiency of the battery under test under forward scanning voltage and the reverse photoelectric conversion efficiency of the battery under test under reverse scanning voltage are determined.

[0038] Based on the forward photoelectric conversion efficiency and the reverse photoelectric conversion efficiency, the carrier transport bottleneck is determined.

[0039] In this embodiment, the forward photoelectric conversion efficiency and the reverse photoelectric conversion efficiency are key parameters that determine the carrier transport bottleneck in the battery under test. Determining the carrier transport bottleneck based on the forward photoelectric conversion efficiency and the reverse photoelectric conversion efficiency improves the accuracy of the determined carrier transport bottleneck.

[0040] In one embodiment, the carrier transport bottleneck is determined based on the forward photoelectric conversion efficiency and the reverse photoelectric conversion efficiency, including:

[0041] When the forward photoelectric conversion efficiency is greater than the reverse photoelectric conversion efficiency, hole transport is identified as the bottleneck of carrier transport.

[0042] When the forward photoelectric conversion efficiency is less than the reverse photoelectric conversion efficiency, electron transport is identified as the bottleneck of carrier transport.

[0043] In this embodiment, the carrier transport bottleneck of the battery under test is determined by comparing the relationship between the forward photoelectric conversion efficiency and the reverse photoelectric conversion efficiency. The process is simple and easy to implement, which improves the efficiency of determining the carrier transport bottleneck.

[0044] In one embodiment, determining the range of candidate locations for recombination centers based on carrier transport bottlenecks includes:

[0045] When the bottleneck of charge carrier transport is hole transport, the candidate location range for determining the recombination center includes the hole transport layer and the interface between the hole transport layer and the light-absorbing functional layer in the cell under test.

[0046] When the bottleneck of charge carrier transport is electron transport, the range of candidate locations for recombination centers includes the electron transport layer and the interface between the electron transport layer and the light-absorbing functional layer in the cell under test.

[0047] In this embodiment, a range of candidate locations is determined for different carrier transport bottlenecks, thereby improving the comprehensiveness of recombination center detection.

[0048] In one embodiment, the distribution detection results of recombination centers are determined based on dark-state voltammetry data and the candidate location range, including:

[0049] Based on the negative voltage segment current density data in the dark state current-voltage data, determine the current density curve as a function of voltage.

[0050] Obtain the matching results between the current density curve and the reference current density curve;

[0051] Based on the matching results and the candidate location range, the distribution detection results of the composite center are determined.

[0052] In this embodiment, the matching result between the current density curve obtained from dark-state volt-ampere data and the reference current density curve is used to further determine the distribution detection result of the recombination center within the candidate location range. The secondary positioning of the recombination center distribution location is achieved by relying on dark-state volt-ampere data, which improves the accuracy of the obtained distribution detection result.

[0053] In one embodiment, the reference current density curve includes a first reference curve and a second reference curve; obtaining the matching result between the current density curve and the reference current density curve includes:

[0054] Obtain the first degree of matching between the current density curve and the first reference curve; the first reference curve represents the exponential relationship between current density and voltage.

[0055] The second matching degree between the current density curve and the second reference curve is obtained; the second reference curve represents the linear relationship between current density and voltage.

[0056] The first and second matching degrees are used as the matching results.

[0057] In this embodiment, the relationship between current density and voltage at different locations of the recombination center is used to match the current density curve of the battery under test in a dark environment. The matching result is used to determine the distribution detection result of the recombination center, which improves the data richness of the matching result and can correspondingly improve the reliability of the distribution detection result determined based on the matching result.

[0058] In one embodiment, the distribution detection result of the composite center is determined based on the matching result and the candidate location range, including:

[0059] If the first matching degree is greater than the second matching degree, the distribution detection result of the recombination center is determined to be the charge transport layer in which the recombination center is distributed within the candidate location range;

[0060] If the first matching degree is less than the second matching degree, the distribution detection result of the composite center is determined as the interface position where the composite center is distributed within the candidate position range.

[0061] In this embodiment, the distribution detection results of the composite center in the functional layer of the battery under test are obtained based on the relationship between the first matching degree and the second matching degree, which simplifies the distribution detection process of the composite center and improves the detection accuracy.

[0062] In one embodiment, the method further includes:

[0063] The battery state of the battery under test is determined based on the positive voltage section current-voltage data in the dark state current-voltage data.

[0064] If the battery is faulty, end the test on the battery under test;

[0065] When the battery is in normal condition, perform the step of determining the candidate location range of the recombination center in the functional layer of the battery under test based on the photocurrent-voltage data.

[0066] In this embodiment, the battery state of the battery under test is determined by the positive voltage section current-voltage data in the dark state current-voltage data, so that the detection of recombination centers can continue under normal conditions and the detection of recombination centers can be stopped under fault conditions, thereby improving the applicability of the detection method and simultaneously improving the reliability of the distribution detection results.

[0067] Secondly, embodiments of this application also provide a battery manufacturing method, the method comprising:

[0068] The distribution of recombination centers in the battery under test can be obtained by using any of the above detection methods;

[0069] The functional layers to be adjusted in the battery under test are determined based on the distribution detection results.

[0070] Obtain the adjusted formula of the functional layer to be adjusted, and manufacture the target battery according to the adjusted formula.

[0071] In this embodiment, the distribution detection results of recombination centers in the battery under test provide adjustment guidance for battery manufacturing, so as to use the adjusted formula to manufacture the target battery, thereby improving the distribution of recombination centers in the battery and improving battery performance.

[0072] Thirdly, embodiments of this application also provide a photovoltaic cell testing device, the device comprising:

[0073] The data acquisition module is used to acquire the dark-state volt-ampere data of the battery under test in a dark environment, and to acquire the light-state volt-ampere data of the battery under test in a light environment.

[0074] The candidate determination module is used to determine the range of candidate positions of recombination centers in the functional layers of the battery under test based on photocurrent-voltage data.

[0075] The detection results module is used to determine the distribution detection results of the recombination center based on the dark-state voltammetric data and the candidate location range.

[0076] Fourthly, embodiments of this application also provide a battery testing system, which includes: a light source device, an active power meter, and a control device; the control device is connected to the light source device and the active power meter respectively, and the active power meter is connected to the battery under test; the light source device is used to provide a dark environment or a light environment for the battery under test; the active power meter is used to provide voltage to the battery under test; and the control device is used to implement the steps in the photovoltaic cell testing method provided in any of the embodiments of the first aspect above.

[0077] Fifthly, embodiments of this application also provide a computer device, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps in the photovoltaic cell detection method provided in any of the embodiments of the first aspect above.

[0078] In a sixth aspect, embodiments of this application also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the photovoltaic cell detection method provided in any of the embodiments of the first aspect above.

[0079] In a seventh aspect, embodiments of this application also provide a computer program product, including a computer program that, when executed by a processor, implements the steps in the photovoltaic cell detection method provided in any of the embodiments of the first aspect described above.

[0080] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0081] Figure 1 This is an internal structural diagram of a computer device in one embodiment;

[0082] Figure 2 This is a flowchart illustrating a photovoltaic cell testing method in one embodiment;

[0083] Figure 3 This is a schematic diagram of the internal structure of the battery under test in one embodiment;

[0084] Figure 4 This is a flowchart illustrating the process of determining dark-state voltage-current data in one embodiment;

[0085] Figure 5 This is a schematic diagram of the process for obtaining a dark-state current sequence in one embodiment;

[0086] Figure 6 This is a flowchart illustrating the process of determining dark-state voltage-current data in another embodiment;

[0087] Figure 7 This is a schematic diagram of the process for determining optical voltammetry data in one embodiment;

[0088] Figure 8 This is a schematic diagram of the process for obtaining the photocurrent sequence in one embodiment;

[0089] Figure 9 This is a flowchart illustrating the process of determining a range of candidate locations in one embodiment;

[0090] Figure 10 This is a flowchart illustrating the process of determining a carrier transport bottleneck in one embodiment.

[0091] Figure 11 This is a flowchart illustrating the process of determining the carrier transport bottleneck in another embodiment;

[0092] Figure 12 This is a flowchart illustrating the process of determining the range of candidate locations in another embodiment;

[0093] Figure 13 This is a flowchart illustrating the process of determining the distribution detection result in one embodiment;

[0094] Figure 14 This is a flowchart illustrating the process of obtaining matching results in one embodiment;

[0095] Figure 15 This is a schematic diagram of the matching results of the current density curves in one embodiment;

[0096] Figure 16 This is a schematic diagram of the matching results of the current density curves in another embodiment;

[0097] Figure 17 This is a flowchart illustrating the process of determining the distribution detection result in another embodiment;

[0098] Figure 18 This is a flowchart illustrating a photovoltaic cell detection method in another embodiment;

[0099] Figure 19 This is a flowchart illustrating a photovoltaic cell detection method in another embodiment;

[0100] Figure 20 This is a flowchart illustrating a battery manufacturing method in one embodiment;

[0101] Figure 21 This is a structural block diagram of the detection device for the composite center in the pool in one embodiment;

[0102] Figure 22 This is a schematic diagram of the battery detection system in one embodiment. Detailed Implementation

[0103] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0104] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the term "comprising" and any variations thereof in the specification, claims and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0105] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0106] In the description of the embodiments of this application, the term "and / or" is merely a description of the association relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), unless otherwise explicitly specified.

[0107] A solar cell is a device that uses the photovoltaic effect to convert sunlight into electrical energy.

[0108] The photovoltaic effect refers to the phenomenon where the charge distribution within an object changes when it is exposed to light, generating electromotive force and current. In solar cells, when sunlight or other light shines on the PN junction of the semiconductor in the cell, a voltage appears across the PN junction, forming a photovoltage.

[0109] A PN junction is the basic structure of a solar cell. It is typically formed by creating an N-type region on a P-type silicon wafer, or a P-type region on an N-type silicon wafer, with a PN junction formed at the boundary between the two regions. PN junctions are usually formed using the same material doped with different materials, resulting in a heterogeneous structure within the fabricated solar cell. This heterogeneity can create recombination centers that affect the photovoltaic effect and thus the cell's performance. To improve cell performance, it is necessary to detect the distribution of recombination centers within the cell.

[0110] In related technologies, the distribution of recombination centers is usually detected based on the apparent non-uniformity of the battery, but the distribution of recombination centers inside the battery cannot be detected, resulting in poor detection accuracy of recombination centers in related technologies.

[0111] Based on this, this application provides a method for detecting photovoltaic cells. The method obtains the specific location of the composite center in the functional layer of the cell under test by first initially determining the candidate location range and then further determining the distribution of detection results within the candidate location range in a step-by-step manner, thereby achieving the technical effect of improving the detection accuracy of the composite center.

[0112] In one embodiment, a method for detecting photovoltaic cells is provided, which is applied to... Figure 1 The following description uses a computing device as an example. This computer device includes a processor, memory, communication interface, display screen, and input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a method for detecting photovoltaic cells.

[0113] Those skilled in the art will understand that Figure 1 The structures shown are merely block diagrams of some structures related to the embodiments of this application and do not constitute a limitation on the computer devices on which the embodiments of this application are applied. Specific computer devices may include more or fewer components than those shown in the figures, or combine certain components, or have different component arrangements.

[0114] In one embodiment, this application provides a method for detecting photovoltaic cells, such as... Figure 2 As shown, this embodiment includes the following steps:

[0115] S210. Acquire dark-state volt-ampere data of the battery under test in a dark environment, and acquire light-state volt-ampere data of the battery under test in a light environment.

[0116] The battery to be tested is a photovoltaic cell, i.e., a solar cell, including but not limited to individual cells, battery packs, or battery modules. Optionally, the battery to be tested can be a battery in a photovoltaic power station or a pre-packaged energy storage device. The above method can be applied to online testing of batteries in use.

[0117] For example, the cell under test is a solar cell that uses perovskite-type organometal halide semiconductors as light-absorbing materials, i.e., a perovskite cell. Dark environment refers to a light-free environment, such as at night, while light environment refers to a light-bearing environment, such as during the day.

[0118] Optionally, the computer equipment can acquire the voltage and current of the battery under test in a dark environment as dark-state volt-ampere data of the battery under test in a light environment, and acquire the voltage and current of the battery under test in a light environment as light-state volt-ampere data of the battery under test in a light environment.

[0119] S220. Based on the photocurrent-voltage data, determine the candidate location range of the recombination center in the functional layer of the battery under test.

[0120] The battery under test is a thin-film battery, comprising multiple stacked functional layers. For example, such as... Figure 3 As shown, the battery under test includes a light-absorbing functional layer and a charge transport layer. The light-absorbing functional layer absorbs sunlight to generate electron-hole pairs. The charge transport layer specifically includes an electron transport layer and a hole transport layer located on either side of the light-absorbing functional layer. The electron transport layer transports electrons, and the hole transport layer transports holes, causing electrons and holes to move towards the two ends of the battery, respectively, forming a photogenerated electromotive force, thereby generating a current. In the case of a perovskite battery, the light-absorbing functional layer is the perovskite layer.

[0121] It should be noted that the recombination center is the location of defects, foreign objects, or heterogeneous structures in the battery under test. It causes electrons and holes generated in the battery to recombine and cannot move separately, thus affecting the battery performance.

[0122] Optionally, after obtaining the photocurrent-voltage data of the battery under test, the computer equipment can analyze the photocurrent-voltage data to preliminarily determine the candidate location range of the recombination center in the functional layer of the battery under test.

[0123] For example, a computer device can generate an optical current-voltage curve based on the optical current-voltage data of the battery under test, and match this optical current-voltage curve with multiple preset current-voltage curves under optical conditions to determine the candidate location range of recombination centers in the functional layers of the battery under test based on the matching results. The multiple preset current-voltage curves under optical conditions include current-voltage curves showing recombination centers distributed across various functional layers under optical conditions.

[0124] For example, multiple preset current-voltage curves under optical conditions include a first current-voltage curve when the recombination center is located in the electron transport layer, and a second current-voltage curve when the recombination center is located in the hole transport layer. A computer device can obtain the matching degree between the optical current-voltage curve and the first current-voltage curve, and the matching degree between the optical current-voltage curve and the second current-voltage curve. If the matching degree between the optical current-voltage curve and the first current-voltage curve is greater than the matching degree between the optical current-voltage curve and the second current-voltage curve, the candidate location range of the recombination center in the functional layer of the battery under test is determined to include locations related to the electron transport layer; conversely, if the matching degree between the optical current-voltage curve and the first current-voltage curve is less than the matching degree between the optical current-voltage curve and the second current-voltage curve, the candidate location range of the recombination center in the functional layer of the battery under test is determined to include locations related to the hole transport layer.

[0125] S230. Based on the dark-state voltammetric data and the candidate location range, determine the distribution detection results of the composite center.

[0126] Optionally, after obtaining the candidate location range of the recombination center, the computer equipment can analyze the dark-state voltammetric data to further determine the location of the recombination center within the candidate location range, as the distribution detection result of the recombination center.

[0127] For example, a computer device can generate a dark-state volt-ampere curve based on the dark-state volt-ampere data of the battery under test, and match this dark-state volt-ampere curve with multiple preset volt-ampere curves in a dark environment to further determine the location within a candidate location range based on the matching results, as the distribution detection result of the recombination center. The multiple preset volt-ampere curves in the dark environment include volt-ampere curves showing the recombination center distribution in each functional layer under dark conditions.

[0128] For example, multiple preset volt-ampere curves under optical conditions include a third volt-ampere curve when the recombination center is located in the electron transport layer, and a fourth volt-ampere curve when the recombination center is located in the hole transport layer. The computer equipment can obtain the matching degree between the dark-state volt-ampere curve and the third volt-ampere curve, and the matching degree between the dark-state volt-ampere curve and the fourth volt-ampere curve. If the matching degree between the dark-state volt-ampere curve and the third volt-ampere curve is greater than a matching degree threshold, the candidate location range of the recombination center in the functional layer of the battery under test is determined to include the electron transport layer; otherwise, it includes the interface between the electron transport layer and the light-absorbing functional layer. If the matching degree between the optical volt-ampere curve and the third volt-ampere curve is less than the matching degree between the optical volt-ampere curve and the fourth volt-ampere curve, the candidate location range of the recombination center in the functional layer of the battery under test is determined to include the hole transport layer; otherwise, it includes the interface between the hole transport layer and the light-absorbing functional layer.

[0129] In this embodiment, dark-state volt-ampere data of the battery under test in a dark environment and optical-state volt-ampere data of the battery under test in a light environment are acquired. Based on the optical-state volt-ampere data, the candidate location range of recombination centers in the functional layers of the battery under test is determined. Then, based on the dark-state volt-ampere data and the candidate location range, the distribution detection result of the recombination centers is determined. This method utilizes the different volt-ampere data characteristics exhibited by recombination centers distributed in different functional layers of the battery under test. This allows for the detection of recombination centers by analyzing the volt-ampere data of the battery under test. The specific location of the recombination centers in the functional layers of the battery under test is obtained through a step-by-step approach: first, a preliminary determination of the candidate location range, and then further determination of the distribution detection result within the candidate location range. This improves the detection accuracy of recombination centers. Furthermore, the testing process does not require disassembling the battery, achieving non-destructive testing and correspondingly reducing testing costs.

[0130] In practical applications, a reference voltage condition can be provided for the battery under test to obtain its dark-state volt-ampere data under the reference voltage condition. In one embodiment, such as... Figure 4 As shown, the dark-state volt-ampere data of the battery under test obtained in S210 under dark conditions includes:

[0131] S410. In a dark environment, acquire the dark-state current sequence of the battery under test under the reference voltage condition.

[0132] The reference voltage conditions include at least one voltage value applied to the battery under test. For example, the reference voltage conditions include a forward scan voltage condition where the applied voltage increases sequentially from -0.6V to 1.2V.

[0133] Optionally, the computer device can apply a voltage value under a reference voltage condition to the battery under test in a dark environment without light, so as to obtain the current value of the battery under test under the applied voltage value. Different voltage values ​​correspond to different current values, thereby forming a dark current sequence of the battery under test under the reference voltage condition.

[0134] S420. Determine the dark-state volt-ampere data based on the reference voltage conditions and the dark-state current sequence.

[0135] Optionally, after obtaining the dark-state current sequence of the battery under test, the computer equipment can use the voltage value in the reference voltage condition and the current value corresponding to that voltage value in the dark-state current sequence as dark-state volt-ampere data.

[0136] In this embodiment of the application, under dark conditions, the dark current sequence of the battery under test under reference voltage conditions is obtained, and dark current data is determined based on the reference voltage conditions and the dark current sequence. In the above method, by providing reference voltage conditions for the battery under test to obtain dark current data, the reliability of the obtained dark current data is improved accordingly.

[0137] In the offline testing phase, a light source device can be used to alter the environment of the battery under test, and a voltage can be applied to the battery using an active multimeter. In one embodiment, such as... Figure 5 As shown, S410 above, in a dark environment, acquires the dark-state current sequence of the battery under test under a reference voltage condition, including:

[0138] S510, In response to a dark environment where the light source device is turned off, the active power meter is controlled to provide the voltage value in the reference voltage condition to the battery under test; the light source device is used to provide the light source, and the active power meter is connected to the battery under test.

[0139] In this setup, the light source is positioned opposite the battery under test to illuminate its surface. It should be noted that during offline testing, the battery is placed in a darkroom, and the light source is turned on to provide a light environment; conversely, the light source is turned off to provide a dark environment.

[0140] Optionally, when acquiring a dark-state current sequence, the computer device may turn off the light source device to provide a dark-state environment for the battery under test, and control the active meter to apply the voltage value in the reference voltage condition to the battery under test in response to the dark-state environment of turning off the light source device.

[0141] S520: Obtain the current value of the battery under test at the corresponding voltage to obtain the dark state current sequence.

[0142] Optionally, when applying a voltage value under the reference voltage condition to the battery under test, the computer device synchronously acquires the current value of the battery under test under each applied voltage value, and forms a dark current sequence according to the application sequence of each voltage value.

[0143] In this embodiment, in response to the dark environment of turning off the light source device, the active power meter is controlled to provide the voltage value of the reference voltage condition to the battery under test, so as to obtain the current value of the battery under test under the corresponding voltage and obtain the dark current sequence; the light source device is used to provide the light source, and the active power meter is connected to the battery under test; in the above method, the acquisition of dark current data is realized based on the control of the light source device and the active power meter, which improves the convenience of acquiring dark current data.

[0144] The reference voltage conditions include forward scan voltage conditions and reverse scan voltage conditions. The dark state current sequence includes a first current sequence obtained under the forward scan voltage condition and a second current sequence obtained under the reverse scan voltage condition. Based on this, in one embodiment, such as Figure 6 As shown, S420 determines the dark-state volt-ampere data based on the reference voltage conditions and the dark-state current sequence, including:

[0145] S610. Based on the forward scanning voltage condition and the reverse scanning voltage condition, obtain the first current and the second current corresponding to the same voltage in the first current sequence and the second current sequence.

[0146] The forward scan voltage condition is characterized by applying voltage values ​​from small to large sequentially to the battery under test, while the reverse scan voltage condition is characterized by applying voltage values ​​from large to small sequentially to the battery under test. For example, the forward scan voltage condition includes applying voltage values ​​from -0.6V to 1.2V sequentially, and the reverse scan voltage condition includes applying voltage values ​​from 1.2V to -0.6V sequentially.

[0147] Optionally, after obtaining the first current sequence of the battery under test under forward scanning voltage conditions and the second current sequence under reverse scanning voltage conditions, the computer device can determine the same voltage between the two conditions and obtain the first current and the second current corresponding to the same voltage in the first current sequence and the second current sequence.

[0148] S620. For each identical voltage, obtain the average current of the first current and the second current.

[0149] Optionally, after obtaining the first current and the second current for each identical voltage, the computer device can obtain the average current value of the first current and the second current corresponding to each identical voltage.

[0150] S630. Use the average value of each voltage and the current corresponding to each voltage as dark-state volt-ampere data.

[0151] Optionally, the computer device can use the same voltages between the forward scan voltage condition and the reverse scan voltage condition, and the average current corresponding to the same voltages, as dark-state volt-ampere data.

[0152] In this embodiment, based on the forward scanning voltage condition and the reverse scanning voltage condition, the first current and the second current corresponding to each same voltage in the first current sequence and the second current sequence are obtained. For each same voltage, the average current of the first current and the second current is obtained, and each same voltage and the average current corresponding to each same voltage are used as dark-state volt-ampere data. In the above method, the dark-state volt-ampere data of the battery under test is determined by comprehensively using the forward scanning voltage condition and the reverse scanning voltage condition, which reduces the data error under unidirectional scanning and improves the accuracy of the obtained dark-state volt-ampere data.

[0153] Similar to the dark-state voltammetry data, the same reference voltage conditions are provided to the battery under test in a light-state environment to obtain the light-state voltammetry data of the battery under test under the reference voltage conditions. In one embodiment, such as... Figure 7As shown, the acquisition of the photocurrent-voltage data of the battery under test in the above-mentioned S210 under photosensitive conditions includes:

[0154] S710. Under light conditions, acquire the light current sequence of the battery under test under reference voltage conditions.

[0155] Optionally, the computer device can apply a voltage value under a reference voltage condition to the battery under test in a light-state environment to obtain the current value of the battery under test under the applied voltage value. Different voltage values ​​correspond to different current values, thereby forming a light-state current sequence of the battery under test under the reference voltage condition.

[0156] S720. Determine the photocurrent data based on the reference voltage conditions and the photocurrent sequence.

[0157] Optionally, after obtaining the photocurrent sequence of the battery under test, the computer equipment can use the voltage value in the reference voltage condition and the current value corresponding to that voltage value in the photocurrent sequence as photocurrent data.

[0158] In this embodiment of the application, under optical conditions, the optical current sequence of the battery under test is obtained under reference voltage conditions, and the optical current-voltage data is determined based on the reference voltage conditions and the optical current sequence. In the above method, by providing reference voltage conditions for the battery under test to obtain optical current-voltage data, the reliability of the obtained optical current-voltage data is improved accordingly.

[0159] Turning on the light source device provides an optical environment for the battery under test. In one embodiment, such as... Figure 8 As shown, S710 above, in a light-state environment, acquires the photocurrent sequence of the battery under test under a reference voltage condition, including:

[0160] S810, in response to the light environment of turning on the light source device, controls the active multimeter to provide the voltage value in the reference voltage condition for the battery under test; the light source device is used to provide the light source, and the active multimeter is connected to the battery under test.

[0161] Optionally, when acquiring the photocurrent sequence, the computer device may turn on the light source device to provide a photocurrent environment for the battery under test, so as to control the active meter to apply the voltage value in the reference voltage condition to the battery under test in response to the photocurrent environment of the light source device being turned on.

[0162] S820: Obtain the current value of the battery under test at the corresponding voltage to obtain the photocurrent sequence.

[0163] Optionally, when applying a voltage value under a reference voltage condition to the battery under test, the computer device synchronously acquires the current value of the battery under test under each applied voltage value, and forms a photocurrent sequence according to the application sequence of each voltage value.

[0164] In this embodiment, by responding to the optical environment of the light source device, the active power meter is controlled to provide the voltage value of the reference voltage condition to the battery under test, so as to obtain the current value of the battery under test under the corresponding voltage and obtain the optical current sequence; the light source device is used to provide the light source, and the active power meter is connected to the battery under test; in the above method, the acquisition of optical current data is realized based on the control of the light source device and the active power meter, which improves the convenience of acquiring optical current data.

[0165] The carrier transport bottleneck of the battery under test determines the range of candidate locations for recombination centers. In one embodiment, such as Figure 9 As shown, in step S220 above, based on the photocurrent-voltage data, the candidate location range of the recombination center in the functional layer of the battery under test is determined, including:

[0166] S910. Based on the optical voltammetry data, determine the carrier transport bottleneck of the battery under test.

[0167] Here, charge carriers are electrons or holes generated within the battery under test. A charge carrier transport bottleneck characterizes a transport process within the battery under test that is hindered or restricted; this could be electron transport or hole transport.

[0168] Optionally, the computer equipment can analyze the photocurrent-voltage data of the battery under test to determine the relationship between the electron transport efficiency and the hole transport efficiency in the battery under test, and determine the carrier transport bottleneck of the battery under test based on this relationship. Specifically, if the electron transport efficiency is greater than the hole transport efficiency, the carrier transport bottleneck is determined to be hole transport; conversely, if the electron transport efficiency is less than the hole transport efficiency, the carrier transport bottleneck is determined to be electron transport.

[0169] S920. Determine the range of candidate locations for recombination centers based on carrier transport bottlenecks.

[0170] Optionally, after determining the carrier transport bottleneck of the battery under test, the computer device can use the location in the battery under test that is related to the carrier transport bottleneck as the candidate location range of recombination centers in the battery under test.

[0171] In this embodiment, based on photocurrent-voltage data, the carrier transport bottleneck in the functional layer of the battery under test is determined, and the candidate location range of recombination centers is determined based on the carrier transport bottleneck. In the above method, the carrier transport bottleneck is determined based on photocurrent-voltage data, and then the candidate location range of recombination centers in the battery under test is determined. The preliminary location of the recombination center distribution is achieved solely by photocurrent-voltage data, which improves the convenience of preliminary location and correspondingly improves the efficiency of recombination center detection.

[0172] The photo-current-voltage (PVV) data of the battery under test includes PVV data under forward scanning voltage conditions and PVV data under reverse scanning voltage conditions. Based on this, in one embodiment, such as... Figure 10 As shown, S910 above, based on the optical voltammetry data, determines the carrier transport bottleneck of the battery under test, including:

[0173] S1010. Based on the photovoltaic data, determine the forward photoelectric conversion efficiency of the battery under test under forward scanning voltage conditions, and the reverse photoelectric conversion efficiency of the battery under test under reverse scanning voltage conditions.

[0174] Optionally, the computer device determines the forward photoelectric conversion efficiency of the battery under test under the forward scanning voltage condition based on the photoelectric current-voltage data corresponding to the forward scanning voltage condition, and determines the reverse photoelectric conversion efficiency of the battery under test under the reverse scanning voltage condition based on the photoelectric current-voltage data corresponding to the reverse scanning voltage condition.

[0175] For example, a computer device can obtain the key performance parameters of the battery under test, as shown in Table 1 below, based on the photocurrent-voltage data of the battery under test.

[0176] Table 1 Key performance parameters of the battery under test

[0177]

[0178] Among them, V OC I represents the open-circuit voltage. SC FF represents the short-circuit current, FF represents the fill factor, and PCE represents the photoelectric conversion efficiency.

[0179]

[0180]

[0181] The maximum output power of the battery under test is represented by the maximum value of the product of current I and voltage V in the photocurrent-voltage data. A represents the effective conversion area, which is usually the surface area of ​​the battery under test that is exposed to sunlight.

[0182] S1020. Based on the forward photoelectric conversion efficiency and the reverse photoelectric conversion efficiency, determine the carrier transport bottleneck.

[0183] Optionally, the forward photoelectric conversion efficiency and the reverse photoelectric conversion efficiency of the battery under test are obtained. The computer equipment can determine the carrier transport bottleneck by comparing the forward photoelectric conversion efficiency and the reverse photoelectric conversion efficiency based on the relationship between the two efficiencies.

[0184] In this embodiment, based on photocurrent-voltage data, the forward photoelectric conversion efficiency of the battery under test under forward scanning voltage conditions and the reverse photoelectric conversion efficiency under reverse scanning voltage conditions are determined. Based on these forward and reverse photoelectric conversion efficiencies, the carrier transport bottleneck is identified. In this method, the forward and reverse photoelectric conversion efficiencies are key parameters determining the carrier transport bottleneck in the battery under test. Determining the carrier transport bottleneck based on these efficiencies improves the accuracy of the identified bottleneck.

[0185] The different magnitudes of the forward and reverse photoelectric conversion efficiencies correspond to different carrier transport bottlenecks. In one embodiment, such as... Figure 11 As shown, in S1020 above, based on the forward photoelectric conversion efficiency and the reverse photoelectric conversion efficiency, the carrier transport bottleneck is determined, including:

[0186] S1110. When the forward photoelectric conversion efficiency is greater than the reverse photoelectric conversion efficiency, hole transport is determined to be the bottleneck of carrier transport.

[0187] The fact that the forward photoelectric conversion efficiency is greater than the reverse photoelectric conversion efficiency indicates that the electron transport efficiency is greater than the hole transport efficiency.

[0188] Optionally, the computer equipment can compare the forward photoelectric conversion efficiency and the reverse photoelectric conversion efficiency. If the forward photoelectric conversion efficiency is greater than the reverse photoelectric conversion efficiency, it can determine that the electron transport efficiency of the battery under test is greater than the hole transport efficiency, and simultaneously determine that hole transport is the bottleneck of carrier transport.

[0189] S1120. When the forward photoelectric conversion efficiency is less than the reverse photoelectric conversion efficiency, electron transport is determined to be the bottleneck of carrier transport.

[0190] The fact that the forward photoelectric conversion efficiency is less than the reverse photoelectric conversion efficiency indicates that the electron transport efficiency is less than the hole transport efficiency.

[0191] Optionally, the computer equipment can compare the forward photoelectric conversion efficiency and the reverse photoelectric conversion efficiency. If the forward photoelectric conversion efficiency is less than the reverse photoelectric conversion efficiency, it can determine that the electron transport efficiency of the battery under test is less than the hole transport efficiency, and simultaneously determine that electron transport is the bottleneck of carrier transport.

[0192] In this embodiment, when the forward photoelectric conversion efficiency is greater than the reverse photoelectric conversion efficiency, hole transport is identified as the carrier transport bottleneck; when the forward photoelectric conversion efficiency is less than the reverse photoelectric conversion efficiency, electron transport is identified as the carrier transport bottleneck. In the above method, the carrier transport bottleneck of the battery under test is determined by comparing the magnitude of the forward photoelectric conversion efficiency and the reverse photoelectric conversion efficiency. The process is simple and easy to implement, which improves the efficiency of determining the carrier transport bottleneck.

[0193] Different carrier transport bottlenecks correspond to different ranges of candidate locations. In one embodiment, such as... Figure 12 As shown, S920 above, determining the candidate location range of the recombination center based on the carrier transport bottleneck, includes:

[0194] S1210. When the bottleneck of charge carrier transport is hole transport, the candidate location range of the recombination center is determined to include the hole transport layer and the interface between the hole transport layer and the light-absorbing functional layer in the cell under test.

[0195] Optionally, after the computer equipment identifies the carrier transport bottleneck of the battery under test, it can determine the candidate location range of the recombination center, including the hole transport layer and the interface between the hole transport layer and the light-absorbing functional layer in the battery under test, if the carrier transport bottleneck is hole transport.

[0196] S1220. When the bottleneck of charge carrier transport is electron transport, the candidate location range of the recombination center is determined to include the electron transport layer and the interface between the electron transport layer and the light-absorbing functional layer in the cell under test.

[0197] Optionally, after the computer equipment identifies the carrier transport bottleneck of the battery under test, it can determine the candidate location range of the recombination center, including the electron transport layer and the interface between the electron transport layer and the light-absorbing functional layer in the battery under test, if the carrier transport bottleneck is electron transport.

[0198] In this embodiment, when the carrier transport bottleneck is hole transport, the candidate location range for recombination centers is determined to include the hole transport layer and the interface between the hole transport layer and the light-absorbing functional layer in the battery under test; when the carrier transport bottleneck is electron transport, the candidate location range for recombination centers is determined to include the electron transport layer and the interface between the electron transport layer and the light-absorbing functional layer in the battery under test; in the above method, corresponding candidate location ranges are determined for different carrier transport bottlenecks, improving the comprehensiveness of recombination center detection.

[0199] To further determine the distribution detection results based on the candidate location range, in one embodiment, such as Figure 13As shown, in step S230 above, based on the dark-state voltammetric data and the candidate location range, the distribution detection results of the recombination center are determined, including:

[0200] S1310. Based on the negative voltage segment volt-ampere data in the dark state volt-ampere data, determine the current density curve that varies with voltage.

[0201] Among them, the negative voltage section volt-ampere data are the volt-ampere data under negative voltage conditions in the dark state volt-ampere data.

[0202] Optionally, the computer device can extract the negative voltage segment volt-ampere data from the dark-state volt-ampere data, calculate the current density corresponding to each voltage based on the negative voltage segment volt-ampere data, and fit a current density curve that varies with voltage. The formula for calculating the current density J is as follows:

[0203]

[0204] I represents the current in the volt-ampere data.

[0205] S1320. Obtain the matching result between the current density curve and the reference current density curve.

[0206] The reference current density curve is a pre-obtained current density curve of a reference cell with known recombination center distribution locations under optical conditions. For example, the reference current density curve may include a current density curve corresponding to recombination centers distributed in the electron transport layer, or it may include a current density curve corresponding to recombination centers distributed in the hole transport layer.

[0207] Optionally, after obtaining the current density curve, the computer device can match the current density curve with a reference current density curve to determine the degree of matching between the two, which is taken as the matching result. For example, the degree of matching between the current density curve and the reference current density curve can be curve similarity or overlap.

[0208] S1330. Based on the matching results and the candidate location range, determine the distribution detection results of the composite center.

[0209] Optionally, after obtaining the matching result between the current density curve and the reference current density curve, the location of the recombination center can be further screened and determined within the candidate location range based on the matching result, which serves as the distribution detection result of the recombination center.

[0210] In this embodiment, based on the negative voltage segment current density data in the dark-state current-voltage data, the current density curve that varies with voltage is determined, and the matching result between the current density curve and the reference current density curve is obtained. Based on the matching result and the candidate location range, the distribution detection result of the recombination center is determined. In the above method, the distribution detection result of the recombination center is further determined within the candidate location range based on the matching result between the current density curve obtained from the dark-state current-voltage data and the reference current density curve. Relying on the dark-state current-voltage data, the secondary positioning of the recombination center distribution location is achieved, which improves the accuracy of the obtained distribution detection result.

[0211] The reference current density curve includes a first reference curve and a second reference curve. Based on this, in one embodiment, such as Figure 14 As shown, the above-mentioned S1320, obtaining the matching result between the current density curve and the reference current density curve, includes:

[0212] S1410. Obtain the first matching degree between the current density curve and the first reference curve; the first reference curve represents the exponential relationship between current density and voltage.

[0213] The first reference curve represents the exponential relationship between current density and voltage, which can be expressed by the following formula:

[0214]

[0215] U represents the voltage in the volt-ampere data, and a, b, and c represent the relevant parameters.

[0216] It should be noted that the current density changes exponentially with the voltage, indicating that the current generated in the battery mainly comes from the electron transport layer or the hole transport layer, and the recombination centers are concentrated at the interface positions near the light-absorbing functional layer, such as the interface between the light-absorbing functional layer and the electron transport layer, or the interface between the light-absorbing functional layer and the hole transport layer.

[0217] Optionally, the computer device may use a preset algorithm to obtain a first degree of matching between the obtained current density curve and the first reference curve. For example, the computer device may use the Akaike information criterion (AIC) algorithm to determine the AICc weight ratio between the current density curve and the first reference curve as the first degree of matching.

[0218] S1420. Obtain the second matching degree between the current density curve and the second reference curve; the second reference curve represents the linear relationship between current density and voltage.

[0219] The second reference curve represents the linear relationship between current density and voltage, which can be expressed by the following formula:

[0220]

[0221] It should be noted that the current density and voltage change linearly, indicating that the current generated in the battery mainly comes from the light-absorbing functional layer, and the recombination centers are concentrated in other functional layers adjacent to the light-absorbing functional layer, such as the electron transport layer or the hole transport layer.

[0222] Optionally, the computer device may use a preset algorithm to obtain a second degree of matching between the obtained current density curve and the second reference curve. For example, similar to the first degree of matching, the computer device may use the AIC algorithm to determine the AICc weight ratio between the current density curve and the second reference curve, as the second degree of matching.

[0223] For example, Figure 15 The AICc weighting ratio between the fitted current density curve and the first reference curve, which exhibits an exponential change, is 100%, while the AICc weighting ratio between the fitted current density curve and the second reference curve, which exhibits a linear change, is 0%. Figure 16 The AICc weighting ratio between the fitted current density curve and the first reference curve, which exhibits an exponential change, is 20%, while the AICc weighting ratio between the fitted current density curve and the second reference curve, which exhibits a linear change, is 80%.

[0224] S1430. Use the first matching degree and the second matching degree as the matching result.

[0225] Optionally, after obtaining the first degree of matching between the current density curve and the first reference curve, and the second degree of matching between the current density curve and the second reference curve, the computer device can use the first degree of matching and the second degree of matching together as the matching result.

[0226] In this embodiment, the reference current density curve includes a first reference curve and a second reference curve. A first matching degree between the current density curve and the first reference curve is obtained, and a second matching degree between the current density curve and the second reference curve is obtained. The first matching degree and the second matching degree are used as the matching result. The first reference curve represents an exponential relationship between current density and voltage, and the second reference curve represents a linear relationship between current density and voltage. In the above method, the relationship between current density and voltage exhibited by the recombination center at different locations is used to match the current density curve of the battery under test in a dark environment. The matching result is used to determine the distribution detection result of the recombination center, which improves the data richness of the matching result and can correspondingly improve the reliability of the distribution detection result determined based on the matching result.

[0227] The distribution detection result of the composite center indicates the specific location of the composite center within the functional layer of the battery under test. This specific location is obtained by filtering from a range of candidate locations. In one embodiment, such as... Figure 17 As shown, in S1330 above, based on the matching results and the candidate location range, the distribution detection results of the composite center are determined, including:

[0228] S1710. If the first matching degree is greater than the second matching degree, the distribution detection result of the composite center is determined to be the charge transport layer in which the composite center is distributed in the candidate location range.

[0229] Optionally, after obtaining the matching results, the computer device can compare the magnitude relationship between the first matching degree and the second matching degree in the matching results, so that if the first matching degree is greater than the second matching degree, the distribution detection result of the composite center is determined to be the charge transport layer in which the composite center is distributed in the candidate location range.

[0230] For example, the candidate location range includes the hole transport layer and the interface between the hole transport layer and the light-absorbing functional layer. If the first matching degree is greater than the second matching degree, the computer device determines that the distribution detection result of the recombination center is that the recombination center is distributed in the hole transport layer. The candidate location range includes the electron transport layer and the interface between the electron transport layer and the light-absorbing functional layer. If the first matching degree is greater than the second matching degree, the computer device determines that the distribution detection result of the recombination center is that the recombination center is distributed in the electron transport layer.

[0231] S1720. If the first matching degree is less than the second matching degree, the distribution detection result of the composite center is determined as the interface position where the composite center is distributed in the candidate position range.

[0232] Optionally, after obtaining the matching results, the computer device can compare the magnitude of the first matching degree and the second matching degree in the matching results, so that if the first matching degree is less than the second matching degree, the distribution detection result of the composite center is determined to be the interface position of the composite center in the candidate position range.

[0233] For example, the candidate location range includes the hole transport layer and the interface between the hole transport layer and the light-absorbing functional layer. If the first matching degree is less than the second matching degree, the computer device determines that the detection result of the recombination center distribution is that the recombination center is distributed at the interface between the hole transport layer and the light-absorbing functional layer. The candidate location range includes the electron transport layer and the interface between the electron transport layer and the light-absorbing functional layer. If the first matching degree is less than the second matching degree, the computer device determines that the detection result of the recombination center distribution is that the recombination center is distributed at the interface between the electron transport layer and the light-absorbing functional layer.

[0234] It should be noted that when the forward photoelectric conversion efficiency equals the reverse photoelectric conversion efficiency, the computer equipment determines the distribution detection result of recombination centers based on the dark-state volt-ampere data. Specifically, the current density curve varying with voltage can be determined based on the negative voltage segment volt-ampere data in the dark-state volt-ampere data, and this current density curve is matched with the aforementioned first reference curve and second reference curve to determine the distribution detection result of recombination centers in the battery under test based on the matching result.

[0235] For example, if the first matching degree between the current density curve and the first reference curve is less than the second matching degree between the current density curve and the second reference curve, the detection result of the distribution of recombination centers in the battery under test is determined to be that the recombination centers are located at the interface between the charge transport layer and the light absorption functional layer in the battery under test, that is, the interface between the electron transport layer and the light absorption functional layer, or the interface between the hole transport layer and the light absorption functional layer; if the first matching degree between the current density curve and the first reference curve is greater than the second matching degree between the current density curve and the second reference curve, the detection result of the distribution of recombination centers in the battery under test is determined to be that the recombination centers are located at the charge transport layer in the battery under test, that is, the electron transport layer or the hole transport layer.

[0236] In this embodiment, when the first matching degree is greater than the second matching degree, the distribution detection result of the composite center is determined to be the charge transport layer in which the composite center is distributed within the candidate location range; when the first matching degree is less than the second matching degree, the distribution detection result of the composite center is determined to be the interface position in which the composite center is distributed within the candidate location range. In the above method, the distribution detection result of the composite center in the functional layer of the battery under test is obtained based on the relationship between the first matching degree and the second matching degree, which simplifies the distribution detection process of the composite center and improves the detection accuracy.

[0237] Dark-state volt-ampere data includes volt-ampere data under positive voltage conditions, i.e., positive voltage range volt-ampere data. Based on this, in one embodiment, such as... Figure 18 As shown, the above method also includes:

[0238] S1810. Determine the battery status of the battery under test based on the positive voltage section current-voltage data in the dark state current-voltage data.

[0239] Among them, the battery status is used to characterize whether the battery under test is faulty.

[0240] Optionally, the computer equipment can extract the positive voltage section current-voltage data from the dark state current-voltage data, and analyze and determine the battery state of the battery under test based on the positive voltage section current-voltage data.

[0241] For example, the computer device can fit a positive voltage-range volt-ampere curve based on the positive voltage-range volt-ampere data, and determine the battery status of the battery under test based on whether the changing trend of the positive voltage-range volt-ampere curve meets the changing trend conditions. If the changing trend conditions are met, the battery status is determined to be normal; if the changing trend conditions are not met, the battery status is determined to be faulty. The computer device can also obtain the maximum voltage value / maximum current value from the positive voltage-range volt-ampere data, and determine the battery status based on whether the maximum voltage value / maximum current value meets the corresponding requirements. If the requirements are met, the battery status is normal; if the requirements are not met, the battery status is faulty.

[0242] S1820. If the battery is faulty, end the test of the battery under test.

[0243] Optionally, if the computer equipment determines that the battery is faulty, it can directly end the testing of the battery under test, and can also simultaneously provide feedback on the battery fault.

[0244] S1830. When the battery is in normal condition, perform the step of determining the candidate location range of the recombination center in the functional layer of the battery under test based on the photocurrent-voltage data.

[0245] Optionally, if the computer device determines that the battery is in normal condition, it may continue to perform the test on the battery under test, such as continuing to perform the above-described S220 and related steps.

[0246] In this embodiment, the battery state of the battery under test is determined based on the positive voltage volt-ampere data in the dark-state volt-ampere data. If the battery state is faulty, the testing of the battery under test is terminated. If the battery state is normal, the step of determining the candidate location range of the recombination center in the functional layer of the battery under test based on the optical volt-ampere data is performed. In the above method, the battery state of the battery under test is determined by the positive voltage volt-ampere data in the dark-state volt-ampere data, so that the testing can continue under normal conditions and stop under fault conditions, thereby improving the applicability of the testing method and simultaneously improving the reliability of the distributed testing results.

[0247] To facilitate understanding by those skilled in the art, the detection method for photovoltaic cells provided in this application is described in detail below, such as... Figure 19 As shown, the method may include:

[0248] S1901. Obtain the first current sequence of the battery under test under forward scanning voltage condition and the second current sequence under reverse scanning voltage condition in dark environment to obtain the dark state volt-ampere data of the battery under test.

[0249] S1902. Obtain the third current sequence of the battery under test under forward scanning voltage condition and the fourth current sequence under reverse scanning voltage condition in the light state environment to obtain the photocurrent-voltage data of the battery under test.

[0250] S1903. Determine the battery status of the battery under test based on the positive voltage section current-voltage data in the dark state current-voltage data;

[0251] S1904. If the battery is faulty, terminate the test of the battery under test.

[0252] S1905. Under normal battery conditions, based on the photovoltaic data, determine the forward photoelectric conversion efficiency of the battery under test under forward scanning voltage conditions, and the reverse photoelectric conversion efficiency of the battery under test under reverse scanning voltage conditions.

[0253] S1906. When the forward photoelectric conversion efficiency is greater than the reverse photoelectric conversion efficiency, hole transport is determined to be the carrier transport bottleneck of the battery under test.

[0254] S1907. When the forward photoelectric conversion efficiency is less than the reverse photoelectric conversion efficiency, electron transport is determined to be the carrier transport bottleneck of the battery under test.

[0255] S1908. When the bottleneck of charge carrier transport is hole transport, the candidate location range of the recombination center is determined to include the hole transport layer and the interface between the hole transport layer and the light-absorbing functional layer in the cell under test.

[0256] S1909. When the bottleneck of charge carrier transport is electron transport, the candidate location range of the recombination center is determined to include the electron transport layer and the interface between the electron transport layer and the light-absorbing functional layer in the battery under test.

[0257] S1910. Based on the negative voltage segment current density data in the dark state current-voltage data, determine the current density curve that varies with voltage.

[0258] S1911. Obtain the first degree of matching between the current density curve and the first reference curve; the first reference curve represents the exponential relationship between current density and voltage.

[0259] S1912. Obtain the second matching degree between the current density curve and the second reference curve; the second reference curve represents the linear relationship between current density and voltage.

[0260] S1913. If the first matching degree is greater than the second matching degree, the distribution detection result of the composite center is determined to be the charge transport layer in which the composite center is distributed in the candidate location range.

[0261] S1914. If the first matching degree is less than the second matching degree, the distribution detection result of the composite center is determined as the interface position where the composite center is distributed in the candidate position range.

[0262] It should be noted that the descriptions in S1901-S1914 above can be found in the relevant descriptions in the above embodiments, and their effects are similar, so they will not be repeated here.

[0263] In practical applications, detecting recombination centers in batteries can provide guidance for battery manufacturing. Therefore, this application also provides a battery manufacturing method, such as... Figure 20 As shown, the battery manufacturing method includes:

[0264] S2010. Obtain the distribution detection results of recombination centers in the battery under test by using any of the aforementioned detection methods.

[0265] Optionally, the computer equipment can use the photovoltaic cell detection method provided in any of the foregoing embodiments to detect the distribution detection results of recombination centers in the cell under test. For details of the specific process, please refer to the foregoing embodiments, which will not be repeated here.

[0266] S2020. Determine the functional layer to be adjusted in the battery under test based on the distribution detection results.

[0267] The functional layer to be adjusted refers to the functional layer in the battery under test that requires manufacturing adjustments. For example, the manufacturing adjustment process may include formula adjustments and / or process adjustments.

[0268] Optionally, after obtaining the distribution detection results of the battery under test, the computer equipment can determine the functional layer to be adjusted in the battery under test based on the specific distribution location of the composite center in the distribution detection results.

[0269] For example, if the distribution detection result shows that the composite center is distributed in the functional layer of the battery under test, then the functional layer is taken as the functional layer to be adjusted; if the distribution detection result shows that the composite center is distributed in the interface between adjacent functional layers in the battery under test, then the adjacent functional layer forming the interface is taken as the functional layer to be adjusted, or the interface is taken directly as the functional layer to be adjusted.

[0270] For example, if the detection result of the distribution of recombination centers in the battery under test is that the recombination centers are distributed in the electron transport layer of the battery under test, the computer equipment will determine that the electron transport layer is the functional layer to be adjusted; if the detection result of the distribution of recombination centers in the battery under test is that the recombination centers are distributed at the interface between the electron transport layer and the light-absorbing functional layer of the battery under test, the computer equipment will determine that the electron transport layer and the light-absorbing functional layer forming the interface are the functional layers to be adjusted, or the interface between the electron transport layer and the light-absorbing functional layer will be taken as the functional layer to be adjusted.

[0271] S2030: Obtain the adjusted formula of the functional layer to be adjusted, and produce the target battery according to the adjusted formula.

[0272] Optionally, after identifying the functional layer to be adjusted in the battery under test, the computer device can obtain the adjusted formula corresponding to the functional layer to be adjusted, and use the adjusted formula to form the functional layer to be adjusted during the battery manufacturing process, so as to produce the target battery.

[0273] For example, the computer device stores a correspondence table between the functional layer to be adjusted and the adjusted formula. After obtaining the functional layer to be adjusted, the computer device can determine the adjusted formula corresponding to the functional layer by looking up the correspondence table.

[0274] In this embodiment, the distribution detection results of recombination centers in the battery under test are obtained by any of the aforementioned detection methods. Based on the distribution detection results, the functional layer to be adjusted in the battery under test is determined to obtain the adjusted formula of the functional layer to be adjusted, and the target battery is manufactured according to the adjusted formula. In the above method, the distribution detection results of recombination centers in the battery under test provide adjustment guidance for battery manufacturing, so as to manufacture the target battery using the adjusted formula, thereby improving the distribution of recombination centers in the battery and improving battery performance.

[0275] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0276] In one embodiment, such as Figure 21 As shown, a photovoltaic cell testing device is provided, including: a data acquisition module 2101, a candidate determination module 2102, and a testing result module 2103; wherein:

[0277] The data acquisition module 2101 is used to acquire the dark-state volt-ampere data of the battery under test in a dark environment, and to acquire the light-state volt-ampere data of the battery under test in a light environment.

[0278] The candidate determination module 2102 is used to determine the range of candidate positions of recombination centers in the functional layers of the battery under test based on the photocurrent current data.

[0279] The detection result module 2103 is used to determine the distribution detection results of the composite center based on the dark-state voltammetric data and the candidate location range.

[0280] Each module in the aforementioned photovoltaic cell testing device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0281] In one embodiment, this application also provides a battery detection system, such as... Figure 22 As shown, the battery testing system includes: a light source device 221, an active meter 222, and a control device 223.

[0282] The control device 223 is connected to both the light source device 221 and the active multimeter 222, with the active multimeter 222 connected to the battery under test. The light source device 221 provides a dark or light environment for the battery under test; the active multimeter 222 provides voltage to the battery under test; and the control device 223 implements the steps of the method for detecting the recombination center of any of the aforementioned batteries.

[0283] Optionally, the control device 223 may include a controller and a signal collection device to acquire data through the signal collection device and process data through the controller.

[0284] Optionally, the light source device 221 may be a sunlight simulation device.

[0285] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of any of the above-described photovoltaic cell detection methods.

[0286] Optionally, the processor executing the computer program may also be used to implement the steps of any of the above-described battery manufacturing methods.

[0287] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the photovoltaic cell detection methods described above.

[0288] Optionally, when executed by a processor, the computer program is also used to implement the steps of any of the above-described battery manufacturing methods.

[0289] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of any of the photovoltaic cell detection methods described above.

[0290] Optionally, when executed by a processor, the computer program is also used to implement the steps of any of the above-described battery manufacturing methods.

[0291] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0292] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0293] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for testing photovoltaic cells, characterized in that, The method includes: Acquire dark-state current-voltage data of the battery under test in a dark environment, and acquire optical current-voltage data of the battery under test in a light environment; Based on the optical voltammetry data, determine the range of candidate locations of recombination centers in the functional layers of the battery under test. Based on the dark-state voltammetric data and the candidate location range, the distribution detection result of the recombination center is determined.

2. The method according to claim 1, characterized in that, The acquisition of dark-state volt-ampere data of the battery under test in a dark environment includes: In the dark environment, the dark current sequence of the battery under test under reference voltage conditions is obtained; The dark-state volt-ampere data are determined based on the reference voltage condition and the dark-state current sequence.

3. The method according to claim 2, characterized in that, The step of acquiring the dark-state current sequence of the battery under test under a reference voltage condition in the dark environment includes: In response to a dark environment where the light source device is turned off, the active power meter is controlled to provide the voltage value in the reference voltage condition to the battery under test; the light source device is used to provide a light source, and the active power meter is connected to the battery under test; The current value of the battery under test at the corresponding voltage is obtained to obtain the dark state current sequence.

4. The method according to claim 2, characterized in that, The reference voltage conditions include forward scanning voltage conditions and reverse scanning voltage conditions, and the dark state current sequence includes a first current sequence obtained under the forward scanning voltage conditions and a second current sequence obtained under the reverse scanning voltage conditions. The step of determining the dark-state volt-ampere data based on the reference voltage condition and the dark-state current sequence includes: Based on the forward scanning voltage condition and the reverse scanning voltage condition, obtain the first current and the second current corresponding to the same voltage in the first current sequence and the second current sequence; For each identical voltage, obtain the average current of the first current and the second current; The average value of each of the same voltages and the corresponding currents is taken as the dark-state volt-ampere data.

5. The method according to any one of claims 1-4, characterized in that, The acquisition of the photocurrent-voltage data of the battery under test in a photosensitive environment includes: Under the light environment, the photocurrent sequence of the battery under test under the reference voltage condition is obtained; The optical volt-ampere data are determined based on the reference voltage condition and the optical current sequence.

6. The method according to claim 5, characterized in that, The step of acquiring the photocurrent sequence of the battery under test under a reference voltage condition in the light environment includes: In response to the light environment of turning on the light source device, the active power meter is controlled to provide the voltage value in the reference voltage condition to the battery under test; the light source device is used to provide the light source, and the active power meter is connected to the battery under test; The current value of the battery under test at the corresponding voltage is obtained to obtain the photocurrent sequence.

7. The method according to any one of claims 1-4, characterized in that, The step of determining the candidate location range of recombination centers in the functional layer of the battery under test based on the photocurrent-voltage data includes: Based on the optical voltammetry data, the carrier transport bottleneck of the battery under test is determined; The candidate location range of the recombination center is determined based on the carrier transport bottleneck.

8. The method according to claim 7, characterized in that, The step of determining the carrier transport bottleneck of the battery under test based on the optical voltammetry data includes: Based on the optical voltammetry data, the forward photoelectric conversion efficiency of the battery under test under forward scanning voltage conditions and the reverse photoelectric conversion efficiency of the battery under test under reverse scanning voltage conditions are determined. The carrier transport bottleneck is determined based on the forward photoelectric conversion efficiency and the reverse photoelectric conversion efficiency.

9. The method according to claim 8, characterized in that, The step of determining the carrier transport bottleneck based on the forward photoelectric conversion efficiency and the reverse photoelectric conversion efficiency includes: When the forward photoelectric conversion efficiency is greater than the reverse photoelectric conversion efficiency, hole transport is determined to be the bottleneck of carrier transport. When the forward photoelectric conversion efficiency is less than the reverse photoelectric conversion efficiency, electron transport is determined to be the bottleneck of carrier transport.

10. The method according to claim 7, characterized in that, Determining the candidate location range of the recombination center based on the carrier transport bottleneck includes: When the carrier transport bottleneck is hole transport, the candidate location range of the recombination center is determined to include the hole transport layer and the interface between the hole transport layer and the light-absorbing functional layer in the battery under test. When the carrier transport bottleneck is electron transport, the candidate location range for the recombination center is determined to include the electron transport layer and the interface between the electron transport layer and the light-absorbing functional layer in the battery under test.

11. The method according to any one of claims 1-4, characterized in that, The step of determining the distribution detection result of the recombination center based on the dark-state voltammetry data and the candidate location range includes: Based on the negative voltage segment current density data in the dark state current-voltage data, determine the current density curve as a function of voltage; Obtain the matching result between the current density curve and the reference current density curve; Based on the matching results and the candidate location range, the distribution detection results of the composite center are determined.

12. The method according to claim 11, characterized in that, The reference current density curve includes a first reference curve and a second reference curve; obtaining the matching result between the current density curve and the reference current density curve includes: Obtain the first degree of matching between the current density curve and the first reference curve; the first reference curve represents the exponential relationship between current density and voltage. A second degree of matching is obtained between the current density curve and the second reference curve; the second reference curve represents a linear relationship between current density and voltage. The first matching degree and the second matching degree are used as the matching result.

13. The method according to claim 12, characterized in that, The step of determining the distribution detection result of the composite center based on the matching result and the candidate location range includes: If the first matching degree is greater than the second matching degree, the distribution detection result of the composite center is determined to be that the composite center is distributed in the charge transport layer within the candidate location range; If the first matching degree is less than the second matching degree, the distribution detection result of the composite center is determined to be the interface position of the composite center within the candidate position range.

14. The method according to any one of claims 1-4, characterized in that, The method further includes: The battery state of the battery under test is determined based on the positive voltage section current-voltage data in the dark state current-voltage data. If the battery is in a faulty state, the testing of the battery under test shall be terminated. When the battery is in a normal state, the step of determining the candidate location range of the recombination center in the functional layer of the battery under test based on the photocurrent-voltage data is performed.

15. A method for manufacturing a battery, characterized in that, The method includes: The distribution detection results of recombination centers in the battery under test are obtained by using any one of the detection methods of claims 1-14; The functional layer to be adjusted in the battery under test is determined based on the distribution detection results. Obtain the adjusted formula of the functional layer to be adjusted, and manufacture the target battery according to the adjusted formula.

16. A testing device for photovoltaic cells, characterized in that, The device includes: The data acquisition module is used to acquire dark-state current-voltage data of the battery under test in a dark environment, and to acquire optical current-voltage data of the battery under test in a light environment. The candidate determination module is used to determine the range of candidate positions of the recombination center in the functional layer of the battery under test based on the photocurrent current data. The detection result module is used to determine the distribution detection result of the recombination center based on the dark-state voltammetric data and the candidate location range.

17. A battery testing system, characterized in that, The battery testing system includes: a light source device, an active power meter, and a control device; the control device is connected to the light source device and the active power meter respectively, and the active power meter is connected to the battery under test; the light source device is used to provide a dark environment or a light environment for the battery under test; the active power meter is used to provide voltage to the battery under test; the control device is used to implement the steps of the method according to any one of claims 1 to 14.

18. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 15.

19. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 15.

20. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 15.

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