Method, device and system for detecting wettability of electrolyte

Abstract

The invention discloses a method, a device and a system for detecting the wettability of an electrolyte, and relates to the technical field of secondary batteries. A specific implementation mode of the method comprises the following steps: scanning the lithium ion battery by utilizing near infrared spectrum equipment, and collecting near infrared spectrum data aiming at the lithium ion battery; and determining a characteristic wavelength and a characteristic value of the characteristic wavelength in a preset characteristic wave band by using the near infrared spectrum data and a plurality of preset characteristic wave bands, generating a corresponding characteristic vector for the characteristic value, and inputting the characteristic vector into a preset wettability detection model to obtain the wettability of the electrolyte of the lithium ion battery. According to the embodiment, the problems that a traditional mode for detecting the wettability of the electrolyte is high in cost and low in speed, the structure of the battery may be damaged, and the effectiveness and the accuracy of detection of the performance and the safety state of the battery are low are solved.

Description

Technical Field

[0001] This invention relates to the field of secondary battery technology, and in particular to a method, apparatus and system for detecting the wettability of electrolyte. Background Technology

[0002] Battery electrolyte is a crucial medium for ion conduction in a battery. Therefore, by detecting changes in electrolyte composition and the quality of electrode materials, battery performance and safety status can be assessed. For example, electrolyte wettability is one of the important indicators used for assessment. Traditional methods such as CT (computed tomography) and ultrasonic testing are commonly used to detect electrolyte wettability. However, these methods are not only costly and slow, but may also damage the battery structure, resulting in low effectiveness and accuracy in detecting battery performance and safety status. Summary of the Invention

[0003] In view of this, embodiments of the present invention provide a method, apparatus and system for detecting the wettability of electrolyte, which can solve the problems that traditional methods for detecting the wettability of electrolyte are not only costly and slow, but may also damage the battery structure, resulting in low effectiveness and accuracy in detecting battery performance and safety status.

[0004] To achieve the above objectives, according to one aspect of the present invention, a method for detecting the wettability of an electrolyte is provided.

[0005] An embodiment of the present invention provides a method for detecting the wettability of an electrolyte, comprising: scanning a lithium-ion battery using a near-infrared spectroscopy device and acquiring near-infrared spectral data for the lithium-ion battery; using the near-infrared spectral data and multiple preset characteristic bands, determining characteristic wavelengths and characteristic values ​​of the characteristic wavelengths within the preset characteristic bands, generating corresponding feature vectors for the characteristic values, and inputting the feature vectors into a preset wettability detection model to obtain the wettability of the electrolyte in the lithium-ion battery.

[0006] Optionally, determining the characteristic wavelength within the preset characteristic band includes: For each wavelength within the preset characteristic band, the peak area ratio corresponding to the wavelength is calculated based on the near-infrared spectral data, the preset wetting standard ratio corresponding to the wavelength is obtained, and the coefficient of variation of the wavelength is calculated based on the peak area ratio and the preset wetting standard ratio. The characteristic wavelength within the preset characteristic band is determined based on the magnitude of the coefficient of variation.

[0007] Optionally, determining the characteristic wavelength within the preset characteristic band includes: Based on the absorbance value corresponding to each wavelength within the preset characteristic band included in the near-infrared spectral data, the characteristic wavelength within the preset characteristic band is determined.

[0008] Optionally, determining the characteristic wavelength within the preset characteristic band includes: Based on the near-infrared spectral data, the second derivative value corresponding to each characteristic wavelength in the preset characteristic band is calculated, the preset characteristic value in the second derivative value is identified, and the wavelength corresponding to the preset characteristic value is determined as the characteristic wavelength in the preset characteristic band.

[0009] Optionally, the method further includes: the near-infrared spectroscopy device scanning the spectral range of the lithium-ion battery from 900 nm to 2500 nm; And / or, The method further includes: for cases where the near-infrared spectroscopy device scans multiple regions of the lithium-ion battery multiple times, fusing the local electrolyte wetting of each region to obtain the overall electrolyte wetting of the lithium-ion battery.

[0010] Optional, also includes: When the near-infrared spectroscopy device scans multiple regions of a lithium-ion battery multiple times, if the local electrolyte wetting degree in any region is less than the corresponding regional wetting degree threshold, an early warning message is generated for that region.

[0011] Optional, also includes: Near-infrared spectral data samples of battery samples with different wetting levels were collected using a near-infrared spectroscopy device; Using the near-infrared spectral data samples and multiple preset feature bands, the training feature values ​​corresponding to the feature wavelengths within the preset feature bands are determined, and corresponding training feature vectors are generated for the training feature values. The infiltration detection model is trained using the aforementioned training feature vectors.

[0012] Optional, also includes: The acquired near-infrared spectral data are preprocessed, wherein the preprocessing method includes one or more of the following: standard normal transformation, curve smoothing, and second derivative.

[0013] To achieve the above objectives, according to another aspect of the present invention, an electrolyte wettability detection device is provided.

[0014] An electrolyte wettability detection device according to an embodiment of the present invention includes: an acquisition unit for acquiring near-infrared spectral data for a lithium-ion battery collected by a near-infrared spectroscopy device; and a data processing unit for using the near-infrared spectral data and multiple preset characteristic bands to determine characteristic wavelengths and characteristic values ​​of the characteristic wavelengths within the preset characteristic bands, generating corresponding feature vectors for the characteristic values, and inputting the feature vectors into a preset wettability detection model to obtain the wettability of the electrolyte in the lithium-ion battery.

[0015] To achieve the above objectives, according to another aspect of the present invention, an electrolyte wettability detection system is provided.

[0016] An electrolyte wettability detection system according to an embodiment of the present invention includes: a near-infrared spectroscopy device and an electrolyte wettability detection device as described above.

[0017] To achieve the above objectives, according to another aspect of the present invention, an electronic device is provided.

[0018] An electronic device according to an embodiment of the present invention includes: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the electrolyte wetting detection method provided in the embodiment of the present invention.

[0019] To achieve the above objectives, according to another aspect of the present invention, a computer-readable medium is provided.

[0020] An embodiment of the present invention provides a computer-readable medium having a computer program stored thereon, which, when executed by a processor, implements the electrolyte wettability detection method provided in the embodiment of the present invention.

[0021] To achieve the above objectives, according to another aspect of the present invention, a computer program product is provided.

[0022] A computer program product according to an embodiment of the present invention includes a computer program that, when executed by a processor, implements the electrolyte wettability detection method provided in this embodiment of the present invention.

[0023] One embodiment of the above invention has the following advantages or beneficial effects: In this embodiment of the invention, a wettability detection model can be pre-set. After scanning the lithium-ion battery with a near-infrared spectroscopy device, near-infrared spectral data for the lithium-ion battery can be collected. Then, using the near-infrared spectral data and multiple preset characteristic bands, the characteristic wavelengths and characteristic values ​​of the characteristic wavelengths within the preset characteristic bands are determined, and corresponding characteristic vectors are generated for the characteristic values. These vectors are then input into the preset wettability detection model to obtain the wettability of the electrolyte in the lithium-ion battery. Thus, in this embodiment of the invention, near-infrared spectral data is obtained by scanning the lithium-ion battery with near-infrared spectroscopy and wettability analysis is performed using the wettability detection model. This method is simple and convenient, does not damage the battery structure, improves the effectiveness and accuracy of battery performance and safety status detection, and is applicable to electrolyte detection for various battery types. Furthermore, in this embodiment of the invention, wettability analysis using the wettability detection model can yield quantified wettability data, reducing detection errors and improving the efficiency of wettability detection.

[0024] The further effects of the aforementioned unconventional alternative methods will be explained below in conjunction with specific implementation methods. Attached Figure Description

[0025] The accompanying drawings are provided to better understand the invention and are not intended to unduly limit the scope of the invention. Wherein: Figure 1 This is a schematic diagram of the main process of an electrolyte wettability detection method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of another main process of the electrolyte wettability detection method according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the main units of the electrolyte wettability detection device according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of a computer system suitable for implementing embodiments of the present invention. Detailed Implementation

[0026] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of the present invention, including various details to aid understanding. These details should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0027] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0028] Where there is no conflict, the embodiments and features in the embodiments of this invention can be combined with each other. The acquisition, transmission, storage, use, and processing of data in the technical solutions of this invention comply with the relevant provisions of national laws and regulations, are used for legal and reasonable purposes, and are not shared, disclosed, or sold outside of these legal uses, and are subject to supervision and management by regulatory authorities.

[0029] Regarding user information, necessary measures should be taken to prevent unauthorized access to such personal information data, ensure that personnel authorized to access such data comply with relevant laws and regulations, and safeguard the security of user personal information. Once this user personal information data is no longer needed, risks should be minimized by restricting or even prohibiting data collection and / or deleting the data. Where applicable, including in certain relevant applications, user privacy should be protected through data de-identification, such as by removing specific identifiers (e.g., date of birth), controlling the amount or specificity of stored data (e.g., collecting location data at the city level rather than the specific address level), controlling how data is stored, and / or other de-identification methods.

[0030] It should be noted that the technical solutions provided in the embodiments of the present invention can be implemented based on robots driving vehicles, or through electronic devices controlling vehicles, or through the autonomous driving system of autonomous vehicles.

[0031] Additionally, it should be noted that the terms "first," "second," etc., used in the embodiments of the present invention are not limitations on quantity, number, or order, but are used to distinguish different vehicles or different relative positional relationships.

[0032] Because poor electrolyte wetting can lead to decreased battery performance and safety risks, such as thermal runaway, electrolyte wetting detection is one of the indicators used to assess battery performance and safety status. Therefore, electrolyte wetting detection is an important method for evaluating battery performance and safety. The electrolyte wetting detection method in this embodiment of the invention can specifically be a battery electrolyte wetting detection method, and the battery can include various types, such as lithium-ion batteries.

[0033] Studies have found that for different wavelengths, the functional groups of various solvents in the battery electrolyte exhibit characteristic absorption peaks in the near-infrared band. Furthermore, the characteristic absorption peaks of these functional groups differ in electrolytes with varying wettability; for example, electrolytes with different wettability levels show frequency shifts and intensity changes in their characteristic absorption peaks. Therefore, the battery electrolyte can be scanned using a near-infrared spectroscopy device to obtain the corresponding near-infrared spectral data, and the wettability of the electrolyte can be determined through analysis of this data. Specifically, Table 1 shows the characteristic absorption peaks of solvents such as EC (ethylene carbonate) / DMC (dimethyl carbonate) in the near-infrared band (1200~2400 nm) in the electrolyte.

[0034] Table 1

[0035] Figure 1 This is a schematic diagram illustrating the main steps of an electrolyte wettability detection method according to an embodiment of the present invention. Specifically, as shown... Figure 1 As shown, the method for detecting the wettability of electrolyte mainly includes the following steps.

[0036] Step S101: Scan the lithium-ion battery using a near-infrared spectroscopy device and collect near-infrared spectral data for the lithium-ion battery.

[0037] Near-infrared spectroscopy equipment refers to devices that can provide near-infrared spectra, such as fiber optic probe arrays. These devices scan lithium-ion batteries using the near-infrared spectrum provided by the equipment to obtain the scanned near-infrared spectral data. The wavelength range of the near-infrared spectrum provided by the equipment can be set according to requirements, such as 1200~2400nm, 900-2500nm, etc., and the resolution of the near-infrared spectrum can be less than 5nm.

[0038] Specifically, near-infrared spectral data can be waveform data. In this embodiment of the invention, the wettability of the lithium-ion battery is obtained by analyzing this waveform data. Lithium-ion batteries can include various forms, such as pouch cells and prismatic cells, which improves the compatibility of wettability detection.

[0039] In one application scenario of this invention, a stage for placing lithium-ion batteries can be pre-set. After placing the lithium-ion batteries to be tested on the stage, a near-infrared spectroscopy device is used to scan them, thereby acquiring near-infrared spectral data for the lithium-ion batteries after scanning.

[0040] Specifically, the fiber optic probe array can cover the length and width of the battery cell in a lithium-ion battery. The distance between the light probe and the surface of the battery cell and the scanning point spacing can be set according to requirements. For example, the distance between the light probe and the surface of the battery cell can be set to 5nm, and the scanning point spacing can be set to 10nm.

[0041] It should be noted that lithium-ion batteries inject electrolyte into the cell. Whether the electrolyte can uniformly wet the electrode material can be reflected by the wettability of the electrolyte. Therefore, the lithium-ion batteries tested are usually cells with injected electrolyte. The scanning of lithium-ion batteries in this step refers to the scanning of the electrolyte in the lithium-ion battery.

[0042] In one embodiment, the near-infrared spectral data of this invention may be preprocessed. Preprocessing methods may include one or more of the following: Standard Normal Transform (SNV), curve smoothing, and second derivative. Preprocessing the near-infrared spectral data improves data accuracy, thereby enhancing the accuracy of wettability detection. SNV represents an infrared spectral preprocessing technique primarily used to eliminate spectral baseline drift and interference caused by physical properties of the electrolyte (such as particle size and surface scattering).

[0043] In another implementation, in some scenarios, to accurately understand the wetting degree of different regions of the lithium-ion battery, the lithium-ion battery can be divided into regions, and the wetting degree of each region can be detected separately. Therefore, the near-infrared spectroscopy device used in this case scans the region of the lithium-ion battery for which the wetting degree is to be detected. For example, the lithium-ion battery can be divided into a top region, a middle region, and a bottom region, and then the embodiments of the present invention can be performed on each region separately to obtain the wetting degree corresponding to each region.

[0044] It should be noted that this step can be performed by first storing the obtained near-infrared spectral data and establishing a correspondence with the information of the lithium-ion battery. When it is necessary to perform wettability analysis on the lithium-ion battery in the future, the stored near-infrared spectral data can be extracted according to the correspondence. Alternatively, wettability analysis can be performed in real time after obtaining the near-infrared spectral data, that is, the subsequent steps can be executed directly.

[0045] Step S102: Using near-infrared spectral data and multiple preset characteristic bands, determine the characteristic wavelengths and characteristic values ​​of the characteristic wavelengths within the preset characteristic bands, generate corresponding characteristic vectors for the characteristic values, input the characteristic vectors into the preset wettability detection model, and obtain the wettability of the electrolyte in the lithium-ion battery.

[0046] In this invention, after obtaining near-infrared spectral data, it can be input into a preset wetting degree detection model. The wetting degree of the electrolyte, i.e., the wetting degree of the scanned lithium-ion battery, is obtained through analysis of the near-infrared spectral data. In this embodiment, to reduce the amount of data processing, the near-infrared spectral data can be processed first to identify data that better reflects the wetting effect of the lithium-ion battery, such as data that better reflects the uniformity of electrolyte wetting. Analyzing the wetting degree based on this data not only ensures the accuracy of the detection results but also reduces the amount of data to be analyzed, improving efficiency. Therefore, in this embodiment, characteristic wavelengths within a preset characteristic band can be determined first, then the characteristic values ​​of each characteristic wavelength can be obtained and corresponding characteristic vectors can be generated. Finally, the characteristic vectors of each characteristic wavelength are input into the wetting degree detection model.

[0047] The characteristic wavelength represents the wavelength that best reflects the wetting effect, and the characteristic value represents the parameter value of the preset feature corresponding to the characteristic wavelength. The preset feature can be set according to requirements, such as peak area, peak area ratio, absorbance, second derivative value, etc.

[0048] In this embodiment of the invention, based on the absorption characteristics of near-infrared spectra by lithium-ion batteries, characteristic wavelengths typically appear within a certain wavelength range. To simplify the determination process of characteristic wavelengths, multiple characteristic wavelength ranges can be preset, i.e., preset characteristic wavelength ranges, to facilitate the determination of characteristic wavelengths from these preset characteristic wavelength ranges. Specifically, preset characteristic wavelength ranges may include 1680 nm - 1720 nm, 1445 nm - 1465 nm, 2140 nm - 2160 nm, 1550 nm - 1570 nm, etc.

[0049] In another embodiment, the present invention can determine the characteristic wavelength based on the peak area ratio of each wavelength within each preset characteristic band in near-infrared spectral data. Specifically, this can be performed as follows: for each wavelength within the preset characteristic band, calculate the peak area ratio corresponding to the wavelength based on the near-infrared spectral data, obtain the preset wetting standard ratio corresponding to the wavelength, calculate the coefficient of variation of the wavelength based on the peak area ratio and the preset wetting standard ratio, and determine the characteristic wavelength within the preset characteristic band based on the magnitude of the coefficient of variation.

[0050] Near-infrared spectral data is typically a waveform. For lithium-ion batteries with normal wetting effects, the peak area ratio corresponding to each wavelength can be pre-determined based on the corresponding near-infrared spectral data, which can then be used as the preset wetting standard ratio in this embodiment of the invention. In this step, the peak area ratio corresponding to each wavelength within the preset characteristic band can be calculated based on the near-infrared spectral data, and then the preset wetting standard ratio corresponding to each wavelength can be obtained. The two data corresponding to each wavelength are then compared to determine the difference between them. If the difference is large, it indicates that the wetting effect of the lithium-ion battery differs significantly from the normal wetting effect. Therefore, the characteristic value corresponding to this wavelength better reflects the wetting abnormality of the lithium-ion battery, and this wavelength can be determined as the characteristic wavelength. In this embodiment of the invention, the difference between the peak area ratio corresponding to each wavelength and the preset wetting standard ratio can be represented by the coefficient of variation. Specifically, the ratio between the peak area ratio and the preset ratio can be determined as the coefficient of variation. After obtaining the coefficient of variation, it can be compared with a preset coefficient threshold. If the coefficient of variation corresponding to a certain wavelength is greater than the preset threshold, it can be determined as the characteristic wavelength. The preset threshold can be set according to the scenario, such as 15%.

[0051] In another embodiment, the present invention can also determine the characteristic wavelength based on the absorbance corresponding to each wavelength. Specifically, this can be performed as follows: based on the absorbance value corresponding to each wavelength within a preset characteristic band included in the near-infrared spectral data, determine the characteristic wavelength within the preset characteristic band.

[0052] The absorbance value reflects the change in the intensity of transmitted light after the near-infrared spectrum passes through a lithium-ion battery. Therefore, in this step, the characteristic wavelength that better reflects the wetting effect can be determined based on the absorbance value. This step can pre-calculate the absorbance threshold corresponding to each wavelength under normal wetting conditions. Then, by comparing the absorbance value corresponding to each wavelength with the absorbance threshold, a large difference indicates a significant difference between the wetting effect of the lithium-ion battery and the normal wetting effect. That is, the characteristic value corresponding to this wavelength better reflects the wetting abnormality of the lithium-ion battery, so this wavelength can be determined as the characteristic wavelength. The difference in this step can include the difference between the absorbance value corresponding to the wavelength and the absorbance threshold.

[0053] In another embodiment of the invention, the characteristic wavelength can be determined based on the second derivative of the spectrum. Certain wavelengths of the second derivative of the spectrum can reflect the wetting effect of the electrolyte, such as the wavelengths corresponding to the minimum values ​​and inflection points of the second derivative. Specifically, this can be performed as follows: calculating the second derivative value corresponding to each characteristic wavelength within a preset characteristic band based on near-infrared spectral data, identifying preset characteristic values ​​among the second derivative values, and determining the wavelength corresponding to the preset characteristic value as the characteristic wavelength within the preset characteristic band.

[0054] Preset eigenvalues ​​represent the values ​​of characteristic points such as minimum values ​​and inflection points in the second derivative values. These eigenvalues ​​are determined from the obtained second derivative values, and the corresponding characteristic wavelengths can then be derived. For example, the wavelength corresponding to the minimum value in the second derivative value could be 1700 nm, representing the change in the solvent aggregation state (free or bound) in the electrolyte; the wavelength corresponding to the inflection point in the spectral second derivative value could be 2150 nm, representing the intensity of the solvent-electrode cross-section interaction in the electrolyte.

[0055] It should be noted that the above methods for determining characteristic wavelengths can be performed individually or in combination, and no limitation is made in the embodiments of the present invention.

[0056] In this embodiment of the invention, the immersion detection model is pre-set and can be trained through various model training methods. The immersion detection model can include various types, specifically including machine learning algorithm models, such as partial least squares regression models.

[0057] In another embodiment, the training method of the wettability detection model can be specifically performed as follows: near-infrared spectral data samples of battery samples with different wettability are collected using a near-infrared spectral device; using the near-infrared spectral data samples and multiple preset feature bands, the training feature values ​​corresponding to the feature wavelengths within the preset feature bands are determined, and corresponding training feature vectors are generated for the training feature values; the wettability detection model is trained using the training feature vectors.

[0058] To improve the accuracy of model training, battery samples with different wetting levels can be used, such as 200 lithium-ion batteries with wetting levels ranging from 10% to 100%. To reduce the training workload, after obtaining the near-infrared spectral data samples, characteristic wavelengths within a preset characteristic band can be determined, and then the corresponding training feature values ​​can be determined. The principle for determining the characteristic wavelengths and training feature values ​​in this training stage is the same as that for determining the characteristic wavelengths and feature values ​​in this step. In this embodiment of the invention, the model training method is not limited; for example, machine learning model training methods can be used.

[0059] In this embodiment of the invention, the lithium-ion battery scanned in step S101 can be the entire lithium-ion battery or a region of the lithium-ion battery. If the scanned lithium-ion battery is the entire lithium-ion battery, this step can determine the overall wettability of the lithium-ion battery; if the scanned lithium-ion battery is a region of the lithium-ion battery, this step can determine the wettability of the corresponding region of the lithium-ion battery. For cases where the near-infrared spectroscopy device scans multiple regions of the lithium-ion battery multiple times, the wettability of the local electrolyte corresponding to each region can be fused to determine the overall electrolyte wettability of the lithium-ion battery.

[0060] In this embodiment of the invention, the wettability fusion method is not limited; for example, it can be fused using a weighted summation method. Specifically, the overall wettability A of the electrolyte in a lithium-ion battery can be expressed as: A = Σ (regional weights) (Local electrolyte wetting). Since the degree of influence of electrolyte wetting at different locations in a lithium-ion battery on the overall electrolyte wetting of the lithium-ion battery varies, regional weights can be pre-set for each different region. The regional weights represent the degree of influence of the corresponding local electrolyte wetting on the overall electrolyte wetting of the lithium-ion battery.

[0061] It should be noted that when a certain area of ​​a lithium-ion battery corresponds to multiple wettabilities of the local electrolyte, the wettability with the most recent detection time can be selected for fusion.

[0062] In one implementation, when a near-infrared spectroscopy device scans multiple regions of a lithium-ion battery multiple times, if the local electrolyte wetting degree in any region is less than the corresponding regional wetting degree threshold, an early warning message is generated for that region.

[0063] Different wetting standards, or regional wetting thresholds, can be set for the electrolyte in different regions of a lithium-ion battery. Therefore, if the local electrolyte wetting in any region is less than the corresponding regional wetting threshold, it means that the wetting in that region does not meet the wetting standard, and an early warning message for that region can be generated and sent.

[0064] It should be noted that a corresponding overall wettability threshold can also be set for the overall electrolyte wettability of lithium-ion batteries. This threshold can then be used to determine whether the overall wettability is less than the threshold. If it is less, an early warning message will be generated and sent.

[0065] In this embodiment of the invention, near-infrared spectral data of a lithium-ion battery is obtained by scanning it with near-infrared spectroscopy, and wettability analysis is performed using a wettability detection model. The operation is simple and convenient, does not damage the battery structure, and improves the effectiveness and accuracy of battery performance and safety status detection. It is applicable to electrolyte detection for various battery types. Furthermore, in this embodiment of the invention, wettability analysis using a wettability detection model can yield quantified wettability data, reducing wettability detection errors and improving the efficiency of wettability detection.

[0066] It should be noted that the wettability detection method in this embodiment of the invention requires less than or equal to 1 minute to detect the wettability of a single battery, which is less than or equal to the time required by traditional methods (usually greater than or equal to 2 hours), thus improving the time and efficiency required for battery wettability detection. Furthermore, the wettability detection method in this embodiment of the invention can reduce the wettability detection error, such as reducing it to below 5%, and can also avoid scrapping due to the disassembly of the detection equipment, thereby reducing the scrap rate.

[0067] Furthermore, Figure 2 This is a schematic diagram of the main steps of another electrolyte wettability detection method according to an embodiment of the present invention. Specifically, as shown... Figure 2 As shown, taking the electrolyte wettability test of an 18650 cylindrical battery as an example, the electrolyte wettability test method mainly includes the following steps.

[0068] Step S201: Place the battery after liquid injection on the stage, with the fiber optic probe 5 nm away from the cell surface, scan the 18650 cylindrical battery with a near-infrared spectroscopy device, and collect near-infrared spectral data for the 18650 cylindrical battery.

[0069] In this embodiment of the invention, the near-infrared spectroscopy device takes an optical fiber probe matrix as an example. The optical fiber probe matrix provides near-infrared spectroscopy with a wavelength range of 1000-2200nm and a scanning point spacing of 10nm to scan the battery on the stage. It can support dynamic scanning at a transmission speed of 0.5m / min, thereby acquiring the near-infrared spectral data corresponding to the needle.

[0070] Step S202: Perform SNV normalization and second derivative transformation preprocessing on the acquired near-infrared spectral data.

[0071] Step S203: Based on the preprocessed near-infrared spectral data, determine the characteristic wavelength and characteristic value of the characteristic wavelength in the preset characteristic band, generate the corresponding feature vector, and input it into the trained wettability detection model.

[0072] The characteristic wavelengths can be specifically: 1450±10nm (OH), 1700±10nm (CH), 2150±10nm (C=O).

[0073] Step S204: Obtain the electrolyte wettability of the 18650 cylindrical battery output by the wettability detection model.

[0074] In this step, if step S201 scans the entire electrolyte of the 18650 cylindrical battery, the overall wettability of the 18650 cylindrical battery is obtained, such as a result of 87.3%. If step S201 scans the electrolyte of a specific region of the 18650 cylindrical battery, the local wettability of that region is obtained. For example, if step S201 scans the electrolyte in the bottom region of the 18650 cylindrical battery, the local wettability of the bottom region can be obtained, such as a result of 62%. If the wettability threshold for the bottom region is 70%, a warning message for the bottom region can be generated.

[0075] It should be noted that the data processing principle in the embodiments of the present invention is the same as... Figure 1 The data processing principles in the illustrated embodiments are the same and will not be repeated here.

[0076] Furthermore, Figure 3 This diagram shows a partial structural schematic of the electrolyte wettability detection device provided in an embodiment of the present invention. Figure 3 As shown, the electrolyte wettability detection device 300 may include: an acquisition unit 301 and a data processing unit 302, wherein, Acquisition unit 301 is used to acquire near-infrared spectral data for lithium-ion batteries collected by near-infrared spectroscopy equipment; The data processing unit 302 is used to determine the characteristic wavelength and characteristic value of the characteristic wavelength within the preset characteristic band using the near-infrared spectral data and multiple preset characteristic bands, generate a corresponding characteristic vector for the characteristic value, and input the characteristic vector into a preset wettability detection model to obtain the wettability of the electrolyte of the lithium-ion battery.

[0077] Furthermore, the data processing unit 302 is further configured to calculate the peak area ratio corresponding to the wavelength based on the near-infrared spectral data for each wavelength within the preset characteristic band, obtain the preset wetting standard ratio corresponding to the wavelength, and calculate the coefficient of variation of the wavelength based on the peak area ratio and the preset wetting standard ratio. The characteristic wavelength within the preset characteristic band is determined based on the magnitude of the coefficient of variation.

[0078] Furthermore, the data processing unit 302 is further configured to determine the characteristic wavelength within the preset characteristic band based on the absorbance value corresponding to each wavelength within the preset characteristic band included in the near-infrared spectral data.

[0079] Furthermore, the data processing unit 302 is further configured to calculate the second derivative value corresponding to each characteristic wavelength in the preset characteristic band based on the near-infrared spectral data, identify the preset characteristic value in the second derivative value, and determine the wavelength corresponding to the preset characteristic value as the characteristic wavelength in the preset characteristic band.

[0080] Furthermore, the acquisition unit 301 is further used to scan the spectral range of the lithium-ion battery (900nm~2500nm) using the near-infrared spectroscopy device. And / or, The data processing unit 302 is further configured to, in the case of multiple scans of multiple regions of the lithium-ion battery by the near-infrared spectroscopy device, fuse the local electrolyte wetting of each region to obtain the overall electrolyte wetting of the lithium-ion battery.

[0081] Furthermore, the data processing unit 302 is further configured to generate early warning information for a region when the local electrolyte wetting degree in any region is less than the corresponding regional wetting degree threshold, in the case of multiple scans of multiple regions of the lithium-ion battery by the near-infrared spectroscopy device.

[0082] Furthermore, the acquisition unit 302 is further used to acquire near-infrared spectral data samples of battery samples with different immersion depths using a near-infrared spectroscopy device; The data processing unit 302 is further configured to use the near-infrared spectral data sample and multiple preset feature bands to determine the training feature value corresponding to the feature wavelength within the preset feature band, and generate a corresponding training feature vector for the training feature value. The infiltration detection model is trained using the aforementioned training feature vectors.

[0083] Furthermore, the data processing unit 302 is further used to preprocess the acquired near-infrared spectral data, wherein the preprocessing method includes one or more of standard normal transformation, curve smoothing, and second derivative.

[0084] Furthermore, embodiments of the present invention also provide an electrolyte wettability detection system. This electrolyte wettability detection system may include: a near-infrared spectroscopy device and, for example... Figure 3 The electrolyte wettability detection device.

[0085] Furthermore, embodiments of the present invention also provide an electronic device. This electronic device may include: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the electrolyte wetting detection method provided in the above embodiments.

[0086] Furthermore, embodiments of the present invention also provide a computer-readable medium having a computer program stored thereon that implements a method for detecting the wettability of an electrolyte. When the computer program is executed by the vehicle-mounted processor, it implements the electrolyte wetting detection method provided in the above embodiments.

[0087] Furthermore, embodiments of the present invention also provide a vehicle. This vehicle implements the electrolyte wetting detection method provided in the first aspect embodiment above, or includes the electrolyte wetting detection device provided in the above embodiment.

[0088] The following is for reference. Figure 4 The diagram shows a schematic of the structure of a computer system 400 suitable for implementing the electrolyte wettability detection method of the embodiments of the present invention. Figure 4 The computer system shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0089] like Figure 4 As shown, the computer system 400 includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 402 or programs loaded from storage section 408 into random access memory (RAM) 403. The RAM 403 also stores various programs and data required for the operation of the system 400. The CPU 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0090] The following components are connected to I / O interface 405: an input section 406; an output section 407 including devices such as cathode ray tubes (CRTs), liquid crystal displays (LCDs), and speakers; a storage section 408 including devices such as hard disks; and a communication section 409 including network interface cards such as LAN cards and modems. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to I / O interface 405 as needed. A removable medium 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 410 as needed so that computer programs read from it can be installed into storage section 408 as needed.

[0091] In particular, according to the embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by central processing unit (CPU) 401, it performs the functions defined above in the system of this invention.

[0092] It should be noted that the computer-readable medium shown in this invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0093] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

Claims

1. A method for detecting the wettability of an electrolyte, characterized in that, include: The lithium-ion battery was scanned using a near-infrared spectroscopy device, and near-infrared spectral data for the lithium-ion battery were collected. Using the near-infrared spectral data and multiple preset characteristic bands, the characteristic wavelengths and characteristic values ​​of the characteristic wavelengths within the preset characteristic bands are determined, and corresponding characteristic vectors are generated for the characteristic values. The characteristic vectors are then input into a preset wettability detection model to obtain the wettability of the electrolyte in the lithium-ion battery.

2. The method according to claim 1, characterized in that, Determining the characteristic wavelength within the preset characteristic band includes: For each wavelength within the preset characteristic band, the peak area ratio corresponding to the wavelength is calculated based on the near-infrared spectral data, the preset wetting standard ratio corresponding to the wavelength is obtained, and the coefficient of variation of the wavelength is calculated based on the peak area ratio and the preset wetting standard ratio. The characteristic wavelength within the preset characteristic band is determined based on the magnitude of the coefficient of variation.

3. The method according to claim 1, characterized in that, Determining the characteristic wavelength within the preset characteristic band includes: Based on the absorbance value corresponding to each wavelength within the preset characteristic band included in the near-infrared spectral data, the characteristic wavelength within the preset characteristic band is determined.

4. The method according to claim 1, characterized in that, Determining the characteristic wavelength within the preset characteristic band includes: Based on the near-infrared spectral data, the second derivative value corresponding to each characteristic wavelength in the preset characteristic band is calculated, the preset characteristic value in the second derivative value is identified, and the wavelength corresponding to the preset characteristic value is determined as the characteristic wavelength in the preset characteristic band.

5. The method according to claim 1, characterized in that, The method further includes: the near-infrared spectroscopy device scanning the spectral range of the lithium-ion battery from 900 nm to 2500 nm; And / or, The method further includes: for cases where the near-infrared spectroscopy device scans multiple regions of the lithium-ion battery multiple times, fusing the local electrolyte wetting of each region to obtain the overall electrolyte wetting of the lithium-ion battery.

6. The method according to claim 5, characterized in that, Also includes: When the near-infrared spectroscopy device scans multiple regions of a lithium-ion battery multiple times, if the local electrolyte wetting degree in any region is less than the corresponding regional wetting degree threshold, an early warning message is generated for that region.

7. The method according to claim 1, characterized in that, Also includes: Near-infrared spectral data samples of battery samples with different wetting levels were collected using a near-infrared spectroscopy device; Using the near-infrared spectral data samples and multiple preset feature bands, the training feature values ​​corresponding to the feature wavelengths within the preset feature bands are determined, and corresponding training feature vectors are generated for the training feature values. The infiltration detection model is trained using the aforementioned training feature vectors.

8. The method according to claim 1, characterized in that, Also includes: The acquired near-infrared spectral data are preprocessed, wherein the preprocessing method includes one or more of the following: standard normal transformation, curve smoothing, and second derivative.

9. An electrolyte wettability detection device, characterized in that, include: The acquisition unit is used to acquire near-infrared spectral data for lithium-ion batteries collected by a near-infrared spectroscopy device. The data processing unit is used to determine the characteristic wavelength and characteristic value of the characteristic wavelength within the preset characteristic band using the near-infrared spectral data and multiple preset characteristic bands, generate a corresponding characteristic vector for the characteristic value, and input the characteristic vector into a preset wettability detection model to obtain the wettability of the electrolyte of the lithium-ion battery.

10. A wettability detection system for an electrolyte, characterized in that, include: Near-infrared spectroscopy equipment and electrolyte wettability detection device as described in claim 9.

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