Lithium ion battery automatic test system and method thereof

Abstract

The invention discloses an automatic test system and method for a lithium ion battery, and relates to the field of intelligent testing, and the method comprises the steps: collecting the voltage and capacity data of a negative electrode of the lithium ion battery during a charging test, carrying out the differential processing of the data, generating a capacity-voltage differential curve, carrying out the dense sampling of the differential curve, and obtaining a capacity-voltage differential curve; a series of detailed capacity-voltage data points are obtained, and finally, the dense data points are used for judging whether the lithium precipitation phenomenon exists in the battery or not. Therefore, the internal state of the lithium ion battery in the charging process, especially the lithium precipitation phenomenon, can be accurately monitored and evaluated, which is crucial for battery performance evaluation, safety detection and life prediction.

Description

Technical Field

[0001] The present application relates to the field of intelligent testing, and more specifically, to a lithium-ion battery automatic testing system and method thereof. Background Art

[0002] With the rapid development of electric vehicles (EVs) and renewable energy storage systems, lithium-ion batteries are increasingly being used as efficient energy storage devices. However, lithium-ion batteries may experience lithium plating during use, which is the deposition of metallic lithium on the surface of the negative electrode. This phenomenon not only reduces the performance and life of the battery, but may also cause safety hazards such as thermal runaway and short circuit. Therefore, accurately detecting and predicting the occurrence of lithium plating is crucial to ensure the safety and reliability of the battery.

[0003] Patent CN118980945A discloses a test method and system for the critical point of lithium deposition in lithium-ion batteries. It uses lithium iron phosphate (LFP) as the positive electrode material, accurately collects the negative electrode voltage and capacity data through constant current charging, and performs differential processing to generate a capacity-voltage differential curve, so as to clearly identify the changes in the characteristics of lithium deposition. Finally, the starting point of lithium deposition is accurately determined based on the specific inflection point in the differential curve, ensuring the high accuracy and reliability of the test results, which is suitable for long-term monitoring of battery status at different aging stages.

[0004] This patent determines the starting point (critical point) of lithium precipitation manually or by embedding a judgment algorithm in the system. However, manual analysis takes a lot of time to review data and charts, especially when dealing with large-scale data sets, which is extremely inefficient, and this method is highly dependent on the experience and technical level of the operator. Different people may come to different conclusions, lacking consistency and objectivity. Although the judgment algorithm embedded in the system improves efficiency, it is usually based on preset thresholds or simple mathematical models. These settings may not be flexible enough to capture complex changes in electrochemical dynamic behavior. As the battery state changes, fixed algorithms may not accurately reflect the actual situation and require frequent adjustments to maintain accuracy.

[0005] Therefore, an optimized automatic testing solution for lithium-ion batteries is desired. Summary of the invention

[0006] In order to solve the above technical problems, this application is proposed.

[0007] According to one aspect of the present application, a lithium-ion battery automatic testing method is provided, which comprises: S1: collecting voltage and capacity data of a negative electrode of a tested lithium-ion battery during a charging test; S2: performing differential processing on the collected voltage and capacity data of the negative electrode to obtain a capacity-voltage differential curve; S3: densely sampling the capacity-voltage differential curve to obtain a dense sequence of capacity-voltage points; S4: judging the internal lithium deposition state of the tested lithium-ion battery based on the dense sequence of capacity-voltage points; Wherein, said S4 includes: Embedding and encoding each capacity-voltage point in the dense sequence of capacity-voltage points to obtain a dense sequence of capacity-voltage embedded features; Performing multi-scale semantic implicit coding on the dense sequence of capacity-voltage embedded features to obtain a dense sequence of capacity-voltage semantic multi-scale implicit coding features; The dense sequence of the capacity-voltage semantic multi-scale implicit coding features is subjected to capacity-voltage semantic dynamic feature significant aggregation to obtain the capacity-voltage semantic significant aggregation feature, including: calculating the aggregation center representation of the dense sequence of the capacity-voltage semantic multi-scale implicit coding features; based on the aggregation center representation, the dense sequence of the capacity-voltage semantic multi-scale implicit coding features is subjected to dynamic aggregation to obtain the capacity-voltage semantic significant aggregation feature; According to the capacity-voltage semantically significant aggregation feature, a lithium deposition critical point at which lithium deposition of the tested lithium-ion battery begins to occur is obtained.

[0008] According to another aspect of the present application, a lithium-ion battery automatic testing system is provided, which includes: a charging data acquisition module, which is used to collect the voltage and capacity data of the negative electrode of the tested lithium-ion battery during the charging test process; a voltage-capacity differential processing module, which is used to perform differential processing on the collected voltage and capacity data of the negative electrode to obtain a capacity-voltage differential curve; a differential curve dense sampling module, which is used to perform dense sampling on the capacity-voltage differential curve to obtain a dense sequence of capacity-voltage points; a battery internal lithium deposition state judgment module, which is used to judge the battery internal lithium deposition state of the tested lithium-ion battery based on the dense sequence of capacity-voltage points; wherein the battery internal lithium deposition state judgment module includes: A capacity-voltage embedding coding unit, used for embedding and coding each capacity-voltage point in the dense sequence of capacity-voltage points to obtain a dense sequence of capacity-voltage embedding features; A capacity-voltage semantic multi-scale encoding unit, used for performing multi-scale semantic implicit encoding on the dense sequence of the capacity-voltage embedded features to obtain a dense sequence of capacity-voltage semantic multi-scale implicit encoding features; A capacity-voltage semantic aggregation unit, used to perform capacity-voltage semantic dynamic feature significant aggregation on the dense sequence of the capacity-voltage semantic multi-scale implicit coding features to obtain capacity-voltage semantic significant aggregation features, the capacity-voltage semantic aggregation unit comprising: a capacity-voltage semantic feature aggregation center representation subunit, used to calculate the aggregation center representation of the dense sequence of the capacity-voltage semantic multi-scale implicit coding features; a capacity-voltage semantic dynamic aggregation subunit, used to perform dynamic aggregation on the dense sequence of the capacity-voltage semantic multi-scale implicit coding features based on the aggregation center representation to obtain the capacity-voltage semantic significant aggregation features; The lithium deposition critical point judgment unit is used to obtain the lithium deposition critical point at which lithium deposition begins to occur in the tested lithium-ion battery according to the capacity-voltage semantic significant aggregation.

[0009] Compared with the prior art, the present application provides a lithium-ion battery automatic testing system and method thereof, which utilizes data analysis and coding technology based on deep learning to embed and encode dense sequences of various capacity-voltage points, and then performs multi-scale implicit coding on each capacity-voltage embedded feature, so as to intelligently identify the lithium deposition critical point where lithium deposition of the tested lithium-ion battery begins to occur based on the dynamic pressure semantic significant aggregation representation between each capacity-voltage semantic multi-scale implicit coding feature. In this way, full automation of data processing and critical point identification is achieved without manual intervention, ensuring the consistency and objectivity of the results. And it can capture complex electrochemical dynamic behavior changes, rather than just relying on preset thresholds or simple mathematical models, making the model more flexible and able to accurately reflect the true state inside the battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0011] Figure 1 Flow chart of a lithium-ion battery automatic testing method according to an embodiment of the present application.

[0012] Figure 2 Schematic diagram of data flow in step S4 of the lithium-ion battery automatic testing method according to an embodiment of the present application.

[0013] Figure 3 4 is a flow chart of step S43 in the lithium-ion battery automatic testing method according to an embodiment of the present application.

[0014] Figure 4 4 is a flow chart of step S431 in the lithium-ion battery automatic testing method according to an embodiment of the present application.

[0015] Figure 5 is a block diagram of a lithium-ion battery automatic testing system according to an embodiment of the present application. DETAILED DESCRIPTION

[0016] The embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, but rather these embodiments are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0017] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0018] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0019] Therefore, in view of the problems in the above background technology, the present application proposes a lithium-ion battery automatic testing method. Figure 1 Flow chart of the automatic testing method of lithium-ion batteries according to an embodiment of the present application. Figure 1 As shown, according to the automatic testing method of lithium-ion batteries in the embodiment of the present application, it includes: S1: collecting voltage and capacity data of the negative electrode of the tested lithium-ion battery during the charging test; S2: performing differential processing on the collected voltage and capacity data of the negative electrode to obtain a capacity-voltage differential curve; S3: densely sampling the capacity-voltage differential curve to obtain a dense sequence of capacity-voltage points; S4: judging the internal lithium deposition state of the tested lithium-ion battery based on the dense sequence of capacity-voltage points.

[0020] Specifically, in step S1, the voltage and capacity data of the negative electrode of the tested lithium-ion battery during the charging test are collected. It should be understood that the voltage data of the negative electrode refers to the potential change of the negative electrode relative to the positive electrode or relative to a reference point (for example, in some cases it can be the outer shell of the battery) during the charging process. As lithium ions are extracted from the positive electrode and embedded in the negative electrode material, the potential of the negative electrode will change accordingly. These changes can provide clues as to whether lithium deposition (i.e., lithium plating) on ​​the surface of the negative electrode has begun to occur. Capacity refers to the amount of electricity that a battery can store and release under specific conditions, usually expressed in milliampere hours (mAh). During the charging process, the capacity data reflects how many lithium ions are successfully embedded in the negative electrode material. When lithium plating occurs, part of the lithium is no longer reversibly embedded in the negative electrode material but is deposited in the form of metallic lithium, which results in a reduction in available capacity. It is worth mentioning that lithium plating usually occurs at the negative electrode. When the battery is overcharged, at low temperature or charged quickly, lithium ions may not be able to be embedded in the negative electrode material (such as graphite) in time, but instead are deposited as metallic lithium on the negative electrode surface. This phenomenon can be identified by monitoring the voltage changes of the negative electrode, because lithium plating will cause a voltage platform or abnormal changes in a specific voltage range.

[0021] Specifically, before any actual data acquisition is performed, it is necessary to ensure that the equipment and environmental conditions used meet strict experimental standards. This includes the selection of high-precision and high-stability measuring instruments, such as digital multimeters or multi-channel data loggers, which need to be able to capture voltage and current changes in real time at a high enough frequency (usually milliseconds) and have sufficient resolution to reflect subtle differences. In addition, a controlled experimental environment needs to be established to eliminate the influence of external factors on the measurement results, such as temperature fluctuations or electromagnetic interference.

[0022] Next, start collecting. In order to obtain reliable negative electrode voltage data, a probe is usually set between the negative and positive electrodes of the battery or at the negative electrode relative to a reference point (such as the battery casing). As lithium ions are extracted from the positive electrode and embedded in the negative electrode material, the potential of the negative electrode changes accordingly. These changes can provide clues as to whether lithium deposition (i.e., lithium plating) on ​​the negative electrode surface has begun to occur. At the same time, in order to obtain capacity data, it is necessary to continuously monitor the current and time flowing into the battery during the charging process, and obtain the total power input by integral calculation, thereby determining the amount of power that the battery can store and release under specific conditions.

[0023] During the entire charging test, the system continuously collects the voltage readings and corresponding capacity values ​​of the negative electrode. In order to improve data quality, on the one hand, the sampling interval needs to be shortened as much as possible to record the status of each moment more carefully; on the other hand, the stability and integrity of data transmission must be ensured to avoid data loss or errors caused by network failures or other reasons. To this end, redundant designs can be used, such as dual-channel synchronous recording or local caching mechanisms, to ensure that key information can be fully saved even in the event of an emergency.

[0024] Finally, all collected data will be stored in a specially constructed database for subsequent analysis. Considering the importance of lithium-ion battery performance evaluation, this database not only needs to have efficient data storage capabilities, but also should support complex query operations, allowing researchers to easily retrieve the entire history of a group of batteries within a specific time period. In this way, it can provide solid data support for the safety and reliability of lithium-ion batteries.

[0025] Specifically, in step S2, the voltage and capacity data of the collected negative electrode are differentiated to obtain a capacity-voltage differential curve. Accordingly, considering that during the charging process, when lithium precipitation begins to occur, the electrochemical behavior of the negative electrode surface will change significantly. This change may not be obvious on the original voltage-capacity curve, but these subtle changes can be amplified by calculating the rate of change of capacity with voltage (ie, dQ / dV). Therefore, on the capacity-voltage differential curve, the inflection point where lithium precipitation occurs will be more clearly visible, which helps to more accurately determine the critical point at which lithium precipitation begins. In particular, the traditional reference electrode-based method is easily affected by the internal polarization of the battery, resulting in errors in the test results. The dQ / dV curve obtained by differential processing has an identification criterion based on the rate of change of voltage rather than the absolute voltage value, so it can better eliminate the interference caused by polarization and provide more reliable test results.

[0026] In actual operation, once the negative electrode voltage and capacity data during charging are obtained, a numerical differentiation algorithm is used to calculate the ratio of capacity change to voltage change at each time point. The capacity here refers to the amount of electricity that the battery can store and release under specific conditions, usually expressed in milliampere hours (mAh); while the voltage refers to the change in potential of the negative electrode relative to the positive electrode or a reference point. When lithium plating begins to occur, the electrochemical behavior of the negative electrode surface will change significantly. This change may not be obvious on the original voltage-capacity curve, but after differentiation, these inflection points will be more clearly visible on the capacity-voltage differential curve, which will help to more accurately determine the critical point where lithium plating begins.

[0027] To achieve this, a series of mathematical tools and techniques are used. For example, the difference quotient method can be applied to approximate the derivative, or a more complex finite difference formula can be used to improve the accuracy. In addition, considering that noise may interfere with the results, smoothing techniques such as moving average filters can be introduced to reduce the impact of random fluctuations and ensure that the final differential curve is smooth and undistorted. At the same time, since the traditional reference electrode-based method is easily affected by the internal polarization of the battery, resulting in errors in the test results, the dQ / dV curve obtained by differential processing is based on the voltage change rate rather than the absolute voltage value, which better eliminates the interference caused by polarization and provides more reliable test results.

[0028] In this way, by performing differential processing on the collected negative electrode voltage and capacity data, not only can the subtle changes that may exist in the charging process be amplified, but also a reliable measurement method that is not affected by polarization can be provided, laying a solid foundation for the subsequent judgment of the critical point of lithium deposition.

[0029] Specifically, in step S3, the capacity-voltage differential curve is densely sampled to obtain a dense sequence of capacity-voltage points. It should be understood that, considering that there are different change points between the data points in the capacity-voltage differential curve, in order to be able to understand and analyze the numerical information in each sampling point more carefully and accurately, so as to better understand the change points on the curve, in the technical solution of the present application, the capacity-voltage differential curve is densely sampled to obtain a dense sequence of capacity-voltage points. That is, by dense sampling, the number of data points can be significantly increased, thereby increasing the resolution of the curve, which helps to capture the subtle changes or characteristic points that may exist on the capacity-voltage differential curve, such as spikes, valleys or other discontinuities, which are essential for identifying specific electrochemical events (such as lithium precipitation), and by dense sampling, more data points can be provided for analysis, making it more accurate in determining the starting point or end point of important electrochemical reactions. For example, when detecting the lithium precipitation phenomenon, dense data points can help more accurately locate the critical point where lithium precipitation begins.

[0030] The core of dense sampling lies in choosing the right sampling interval and strategy. The ideal sampling scheme should ensure that all important features in the entire voltage range are covered without causing excessive computational burden due to too many data points. To this end, the sampling parameters are set according to the specific characteristics of the battery and the expected analysis objectives. For example, in areas where the voltage changes more drastically, a shorter sampling interval may be used to capture rapid changes; in relatively flat areas, the interval can be appropriately relaxed to reduce redundant information. In addition, considering the important electrochemical reactions that may occur near certain key voltage points, special attention will be paid to these locations to ensure that there is sufficient data density for subsequent analysis.

[0031] After completing dense sampling, what is obtained is a dense sequence consisting of a large number of discrete points, each of which contains information about the capacity change at a specific voltage. This sequence provides rich material for subsequent deep learning models and feature extraction. Especially when it comes to complex electrochemical behaviors, such as the occurrence of lithium deposition, dense data points can provide more details and help more accurately locate the critical point where lithium deposition begins.

[0032] Specifically, in step S4, based on the dense sequence of the capacity-voltage points, the internal lithium deposition state of the tested lithium-ion battery is judged. Accordingly, the technical concept of the present application is to use data analysis and coding technology based on deep learning to embed and encode the dense sequence of each capacity-voltage point, and then perform multi-scale implicit coding on each capacity-voltage embedded feature, so as to intelligently identify the lithium deposition critical point where lithium deposition of the tested lithium-ion battery begins to occur according to the dynamic pressure semantic significant aggregation representation between each capacity-voltage semantic multi-scale implicit coding feature. In this way, full automation of data processing and critical point identification is achieved without manual intervention, ensuring the consistency and objectivity of the results. And it can capture complex electrochemical dynamic behavior changes, rather than just relying on preset thresholds or simple mathematical models, making the model more flexible and able to accurately reflect the true state inside the battery.

[0033] Figure 2 Flow chart of step S4 in the automatic testing method for lithium-ion batteries according to an embodiment of the present application. Specifically, in the embodiment of the present application, Figure 2 As shown, the S4 includes: S41, embedding and encoding each capacity-voltage point in the dense sequence of the capacity-voltage points to obtain a dense sequence of capacity-voltage embedded features; S42, multi-scale semantic implicit encoding of the dense sequence of the capacity-voltage embedded features to obtain a dense sequence of capacity-voltage semantic multi-scale implicit encoding features; S43, capacity-voltage semantic dynamic feature significant aggregation of the dense sequence of the capacity-voltage semantic multi-scale implicit encoding features to obtain capacity-voltage semantic significant aggregation features; S44, according to the capacity-voltage semantic significant aggregation features, obtaining the critical point of lithium deposition at which lithium deposition of the tested lithium-ion battery begins to occur.

[0034] In step S41, each capacity-voltage point in the dense sequence of the capacity-voltage points is embedded and encoded to obtain a dense sequence of capacity-voltage embedded features. Specifically, in an embodiment of the present application, each capacity-voltage point in the dense sequence of the capacity-voltage points is embedded and encoded to obtain a dense sequence of capacity-voltage embedded features, including: using a capacity-voltage embedded coding matrix to embed the each capacity-voltage point in the dense sequence of the capacity-voltage points to obtain a dense sequence of capacity-voltage embedded coding vectors as the dense sequence of the capacity-voltage embedded features.

[0035] It should be understood that, considering that each capacity-voltage point contains the semantic association between different capacities and voltages, in order to better represent its inherent characteristic information, in the technical solution of the present application, the capacity-voltage embedding coding matrix is ​​used to embed the capacity-voltage points in the dense sequence of the capacity-voltage points to reduce the originally sparse or redundant data dimensions to a smaller space that can still capture key information, and obtain a dense sequence of capacity-voltage embedded coding vectors. It is worth mentioning that the capacity-voltage embedding coding matrix converts the original capacity-voltage point into a representation in a low-dimensional vector space. Embedding coding through the capacity-voltage embedding coding matrix can capture the complex patterns and inherent structures in different capacities and voltages, so that the battery characteristics under different states can be effectively distinguished in a low-dimensional space. In particular, the capacity-voltage embedding coding matrix is ​​constructed by collecting a large amount of historical voltage and capacity data.

[0036] The following is a specific process of embedding and encoding each capacity-voltage point in the dense sequence of the capacity-voltage points using the capacity-voltage embedding encoding matrix: First, before implementing embedded coding, a capacity-voltage embedded coding matrix needs to be constructed. This matrix is ​​obtained by collecting a large amount of historical voltage and capacity data, and it converts the original capacity-voltage points into a representation in a low-dimensional vector space. Constructing such a coding matrix requires rich domain knowledge and strong machine learning capabilities to ensure that the final model can accurately reflect the characteristics of existing data and has good generalization performance, so that it can be applied to new data that has never been seen.

[0037] The next step is to apply it to the actual dense sequence of capacity-voltage points. At this stage, each capacity-voltage point is regarded as a multidimensional vector, which contains the voltage value and cumulative capacity value at a specific moment. The process of embedded coding is essentially to transform these multidimensional vectors so that they are mapped to a low-dimensional space defined by the encoding matrix. Specifically, for each capacity-voltage point, the corresponding row vector (if matrix multiplication is used) or weight coefficient (if weighted summation method is used) is found and applied according to its corresponding voltage and capacity values ​​to generate a new low-dimensional vector. This new vector is the capacity-voltage embedded coding vector, which not only retains the key information of the original data, but also enhances the comparability and correlation between different data points.

[0038] It is worth noting that embedding coding is not a simple one-to-one mapping, but attempts to reveal the true relationship hidden behind high-dimensional data. For example, two seemingly different capacity-voltage points may be very close in the low-dimensional embedding space, indicating that they actually represent similar electrochemical states. On the contrary, some data points that appear to be close on the surface are far apart in the embedding space, suggesting that there are important differences between them. Therefore, through embedding coding, researchers can obtain a more intuitive and insightful way to understand the complex dynamic behavior inside the battery.

[0039] In step S42, multi-scale semantic implicit coding is performed on the dense sequence of the capacity-voltage embedded features to obtain a dense sequence of capacity-voltage semantic multi-scale implicit coding features. Specifically, in an embodiment of the present application, multi-scale semantic implicit coding is performed on the dense sequence of the capacity-voltage embedded features to obtain a dense sequence of capacity-voltage semantic multi-scale implicit coding features, including: inputting the dense sequence of the capacity-voltage embedded coding vectors into a sequence encoder based on the LSTM-RNN hybrid model to obtain a dense sequence of the first capacity-voltage semantic implicit coding vector and a dense sequence of the second capacity-voltage semantic implicit coding vector; cascading each group of corresponding first capacity-voltage semantic implicit coding vectors and second capacity-voltage semantic implicit coding vectors in the dense sequence of the first capacity-voltage semantic implicit coding vector and the dense sequence of the second capacity-voltage semantic implicit coding vector to obtain a dense sequence of capacity-voltage semantic multi-scale implicit coding vectors as the dense sequence of the capacity-voltage semantic multi-scale implicit coding features.

[0040] Accordingly, it is considered that the dense sequence of the capacity-voltage embedded coding vector has characteristic information of different time scales during the charging test, such as capacity-voltage fluctuations in a short period of time and periodic trends and patterns during the entire charging cycle. Considering that RNN can process sequence data, it is easy to encounter gradient vanishing or explosion problems when processing long sequences, which makes it difficult to capture long-term dependencies, but it has better perception and capture capabilities for data between shorter time distances. LSTM can selectively remember or forget information by introducing a special gating mechanism (input gate, forget gate and output gate), thereby effectively solving the gradient problem of traditional RNN, and is very suitable for processing time series data with long time interval dependencies. Based on this, in the technical solution of the present application, the dense sequence of the capacity-voltage embedded coding vector is input into a sequence encoder based on the LSTM-RNN hybrid model to use the RNN part to focus on capturing local features and short-term dynamics, helping to identify rapid changes or abnormal events that occur in a short period of time. At the same time, LSTM is used to capture long-term dependencies in the sequence to ensure that the model can understand the trends and changes in the entire charging and discharging cycle, and obtain a dense sequence of the first capacity-voltage semantic implicit coding vector and a dense sequence of the second capacity-voltage semantic implicit coding vector.

[0041] It should be understood that the first capacity-voltage semantic implicit coding feature represents a local feature, such as a rapid change during the charging process, a transient phenomenon, or an event occurring in a short period of time. The second capacity-voltage semantic implicit coding feature represents a global feature, such as a trend throughout the charging and discharging cycle, a long-term change pattern, or a behavior in a stable state. Therefore, in order to integrate feature information at different levels or angles, thereby obtaining a richer, more comprehensive and representative data representation, in the technical solution of the present application, each group of corresponding first capacity-voltage semantic implicit coding vectors and second capacity-voltage semantic implicit coding vectors in the dense sequence of the first capacity-voltage semantic implicit coding vector and the dense sequence of the second capacity-voltage semantic implicit coding vector are cascaded to integrate feature information at different levels, thereby obtaining a dense sequence of capacity-voltage semantic multi-scale implicit coding vectors.

[0042] In step S43, the dense sequence of the capacity-voltage semantic multi-scale implicit coding features is significantly aggregated with capacity-voltage semantic dynamic features to obtain capacity-voltage semantic significant aggregation features. Accordingly, considering that the internal electrochemical reactions at different stages (such as the initial, middle and final stages of charging) will have different characteristic manifestations, that is, each capacity-voltage semantic multi-scale implicit coding feature contains internal complex behavior patterns and key significant feature information, and these features have dynamic behaviors that continue to change with the advancement of charging, which is very important for identifying specific events (such as the occurrence of lithium precipitation) because these events are often accompanied by specific dynamic features. Therefore, in order to capture the dynamic change patterns in the data and generate feature representations with stronger expressiveness and significance, in the technical solution of the present application, a capacity-voltage semantic dynamic feature significant aggregation mechanism is used to process the dense sequence of the capacity-voltage semantic multi-scale implicit coding features to obtain capacity-voltage semantic significant aggregation features.

[0043] Figure 3 4 is a flow chart of step S43 in the lithium-ion battery automatic testing method according to an embodiment of the present application. Specifically, in the embodiment of the present application, Figure 3 As shown, in step S43, the dense sequence of the capacity-voltage semantic multi-scale implicit coding features is subjected to capacity-voltage semantic dynamic feature significant aggregation to obtain capacity-voltage semantic significant aggregation features, including: S431, calculating the aggregation center representation of the dense sequence of the capacity-voltage semantic multi-scale implicit coding features; S432, based on the aggregation center representation, the dense sequence of the capacity-voltage semantic multi-scale implicit coding features is subjected to dynamic aggregation to obtain the capacity-voltage semantic significant aggregation features.

[0044] Figure 4 FIG. 4 is a flow chart of step S431 in the automatic testing method for lithium-ion batteries according to an embodiment of the present application. More specifically, in the embodiment of the present application, Figure 4 As shown, in step S431, the aggregation center representation of the dense sequence of the capacity-voltage semantic multi-scale implicit coding features is calculated, including: S4311, calculating the static potential weight factor of each capacity-voltage semantic multi-scale implicit coding vector in the dense sequence of the capacity-voltage semantic multi-scale implicit coding vector to obtain a dense sequence of capacity-voltage semantic multi-scale feature static potential weight factors; S4312, taking the dense sequence of the capacity-voltage semantic multi-scale feature static potential weight factors as the weighted sequence, calculating the position-weighted sum of the dense sequence of the capacity-voltage semantic multi-scale implicit coding vector to obtain the capacity-voltage semantic multi-scale pseudo-anchor aggregation center representation vector as the aggregation center representation.

[0045] Specifically, in the embodiment of the present application, in step S4311, the static potential weight factor of each capacity-voltage semantic multi-scale implicit coding vector in the dense sequence of the capacity-voltage semantic multi-scale implicit coding vector is calculated to obtain a dense sequence of capacity-voltage semantic multi-scale feature static potential weight factors, including: Each capacity-voltage semantic multi-scale implicit coding vector in the dense sequence of the capacity-voltage semantic multi-scale implicit coding vector is subjected to characteristic static potential energy measurement to obtain a dense sequence of capacity-voltage semantic multi-scale characteristic static potential energy measurement coefficients. The process can be expressed by the formula: ;in, is a dense sequence of the capacity-voltage semantic multi-scale implicit encoding vectors, and are the first, second, and third dense sequences of the capacity-voltage semantic multi-scale implicit encoding vectors. and capacity-voltage semantic multi-scale implicit encoding vector, It is The eigenvalues ​​of each position in the capacity-voltage semantic multi-scale implicit encoding vector, It is The mean of the capacity-voltage semantic multi-scale implicit encoding vectors, It is The standard deviation of the capacity-voltage semantic multi-scale implicit encoding vector, It is The number of eigenvalues ​​in the capacity-voltage semantic multi-scale implicit encoding vector, is the first in the dense sequence of static potential energy measurement coefficients of capacity-voltage semantic multi-scale features Capacity-voltage semantic multi-scale feature static potential energy measurement coefficient; The dense sequence of the capacity-voltage semantic multi-scale feature static potential energy measurement coefficients is subjected to feature energy level gating screening to obtain a dense sequence of the capacity-voltage semantic multi-scale feature static potential energy weight factors.

[0046] It should be understood that in order to quantify the feature stability and its potential energy state at each time point, so as to reveal the intrinsic connection between the features and their correlation with the target results, each capacity-voltage semantic multi-scale implicit coding vector in the dense sequence of the capacity-voltage semantic multi-scale implicit coding vector is subjected to feature static potential energy measurement to obtain a dense sequence of capacity-voltage semantic multi-scale feature static potential energy measurement coefficients. That is, this measurement helps to have a deeper understanding of the electrochemical behavior inside the battery, suggesting possible energy changes in the future, which is of great significance for predicting the occurrence of events such as lithium plating.

[0047] Accordingly, in order to further optimize and refine the feature representation and highlight the most representative and influential features under specific conditions, the dense sequence of static potential energy measurement coefficients of the capacity-voltage semantic multi-scale features is subjected to feature energy level gating screening to screen out those features with higher static potential energy measurement coefficients by setting specific thresholds, thereby obtaining a dense sequence of static potential energy weight factors of the capacity-voltage semantic multi-scale features.

[0048] More specifically, in an embodiment of the present application, the dense sequence of the capacity-voltage semantic multi-scale feature static potential energy measurement coefficients is subjected to feature energy level gating screening to obtain the dense sequence of the capacity-voltage semantic multi-scale feature static potential energy weight factors, including: The sigmoid function is used to standardize each capacity-voltage semantic multi-scale feature static potential energy measurement coefficient in the dense sequence of the capacity-voltage semantic multi-scale feature static potential energy measurement coefficient to obtain a dense sequence of standardized capacity-voltage semantic multi-scale feature static potential energy measurement coefficients. The process can be expressed by the formula: ;in, is the first in the dense sequence of static potential energy measurement coefficients of capacity-voltage semantic multi-scale features The capacity-voltage semantic multi-scale feature static potential energy measurement coefficient, yes function, is the first in the dense sequence of the static potential energy measurement coefficients of the standardized capacity-voltage semantic multi-scale feature A standardized capacity-voltage semantic multi-scale feature static potential energy measurement coefficient; The dense sequence of the standardized capacity-voltage semantic multi-scale feature static potential energy measurement coefficients is input into the gated screening module based on the mask function to obtain the dense sequence of the capacity-voltage semantic multi-scale feature static potential energy weight factors. The process can be expressed by the formula: ;in, is the first in the dense sequence of the static potential energy measurement coefficients of the standardized capacity-voltage semantic multi-scale feature The standardized capacity-voltage semantic multi-scale feature static potential energy measurement coefficient, is the preset threshold, is a masking operation, is the first in the dense sequence of static potential weight factors of capacity-voltage semantic multi-scale features The static potential energy weight factor of the capacity-voltage semantic multi-scale feature.

[0049] Specifically, in step S4312, the dense sequence of the capacity-voltage semantic multi-scale feature static potential weight factors is used as a weighted sequence, and the position-weighted sum of the dense sequence of the capacity-voltage semantic multi-scale implicit encoding vector is calculated to obtain the capacity-voltage semantic multi-scale pseudo-anchor aggregation center representation vector as the aggregation center representation. The process can be expressed by the formula: ;in, is the dense sequence of the capacity-voltage semantic multi-scale implicit encoding vector capacity-voltage semantic multi-scale implicit encoding vector, is the first in the dense sequence of static potential weight factors of capacity-voltage semantic multi-scale features The capacity-voltage semantic multi-scale feature static potential weight factor, is the number of vectors in the dense sequence of the capacity-voltage semantic multi-scale implicit encoding vector, It is the capacity-voltage semantic multi-scale pseudo-anchor aggregation center representation vector.

[0050] It should be understandable that the dense sequence of capacity-voltage semantic multi-scale feature static potential weight factors is used as weights to calculate the position-weighted sum of the dense sequence of capacity-voltage semantic multi-scale implicit encoding vectors to notice how each feature vector is affected by the central point, and based on this, the evolution trajectory of the feature vector in the feature space is inferred to obtain a more representative and comprehensive feature representation of the capacity-voltage semantic multi-scale pseudo-anchored aggregate central representation vector.

[0051] Specifically, in step S432, based on the aggregation center representation, the dense sequence of the capacity-voltage semantic multi-scale implicit coding features is dynamically aggregated to obtain the capacity-voltage semantic significant aggregation features, including: The aggregate movement direction of each capacity-voltage semantic multi-scale implicit coding vector in the dense sequence of the capacity-voltage semantic multi-scale implicit coding vector relative to the capacity-voltage semantic multi-scale pseudo-anchor aggregation center representation vector is calculated to obtain a dense sequence of capacity-voltage semantic multi-scale aggregation running directions. The process can be expressed by the formula: ;in, is the dense sequence of the capacity-voltage semantic multi-scale implicit encoding vector capacity-voltage semantic multi-scale implicit encoding vector, is the capacity-voltage semantic multi-scale pseudo-anchor aggregation center representation vector, is the length of the vector, is the inverse cosine function, is the first in the dense sequence of capacity-voltage semantic multi-scale aggregation running direction Capacity-voltage semantic multi-scale aggregation operation direction; Based on the dense sequence of the capacity-voltage semantic multi-scale aggregation running direction, the dense sequence of the capacity-voltage semantic multi-scale implicit coding vector is dynamically aggregated toward the capacity-voltage semantic multi-scale pseudo-anchor aggregation center representation vector to obtain the capacity-voltage semantic significant aggregation coding vector as the capacity-voltage semantic significant aggregation feature. The process can be expressed by the formula: ;in, is the first in the dense sequence of capacity-voltage semantic multi-scale aggregation running direction capacity-voltage semantic multi-scale aggregation operation direction, is the first in the dense sequence of static potential energy measurement coefficients of capacity-voltage semantic multi-scale features The capacity-voltage semantic multi-scale feature static potential energy measurement coefficient, is the dense sequence of the capacity-voltage semantic multi-scale implicit encoding vector capacity-voltage semantic multi-scale implicit encoding vector, is the capacity-voltage semantic multi-scale pseudo-anchor aggregation center representation vector, is the number of vectors in the dense sequence of the capacity-voltage semantic multi-scale implicit encoding vector, is a sine function, is the first in the sequence of weight matrices A weight matrix, is the first in the sequence of bias vectors A bias vector, is the capacity-voltage semantically significant aggregation encoding vector.

[0052] Accordingly, the aggregation movement direction of each capacity-voltage semantic multi-scale implicit encoding vector relative to the capacity-voltage semantic multi-scale pseudo-anchor aggregation center representation vector is calculated to obtain a dense sequence of capacity-voltage semantic multi-scale aggregation running directions. The goal of this process is to measure the dynamic change trend of each capacity-voltage semantic multi-scale implicit encoding vector relative to the aggregation center representation. These trends reveal whether the feature vector is moving toward or away from the center point, mapping the mutual influence and change pattern between vectors in the feature space, which is crucial for understanding the relative position and overall dynamic behavior between feature vectors. In other words, the aggregation trend toward the center helps to identify feature changes that have a significant impact on specific events. For example, the occurrence of lithium deposition is often accompanied by specific directional changes.

[0053] Finally, based on the running direction of each capacity-voltage semantic multi-scale aggregation, the dense sequence of capacity-voltage semantic multi-scale implicit coding vectors is dynamically aggregated toward the central representation vector to obtain the capacity-voltage semantic significant aggregation coding vector. In other words, based on the previously determined aggregation movement direction and the static potential energy measurement coefficient of the capacity-voltage semantic multi-scale implicit coding vector, the pseudo-kinetic model is applied to adjust and optimize these feature vectors to make them closer to the aggregation center vector. In this way, the dynamic characteristics of capacity-voltage can be captured more effectively, and then the spatial layout of the feature vectors can be dynamically adjusted and optimized to create a more compact, representative and recognizable feature representation, which helps to improve the model's subsequent judgment accuracy on the critical point of lithium precipitation.

[0054] In step S44, the critical point of lithium deposition at which lithium deposition of the tested lithium-ion battery begins to occur is obtained according to the capacity-voltage semantically significant aggregation feature. Specifically, in an embodiment of the present application, the critical point of lithium deposition at which lithium deposition of the tested lithium-ion battery begins to occur is obtained according to the capacity-voltage semantically significant aggregation feature, including: inputting the capacity-voltage semantically significant aggregation coding vector into a decoder-based lithium deposition critical point identifier to obtain the critical point of lithium deposition at which lithium deposition of the tested lithium-ion battery begins to occur. That is, the capacity-voltage semantically significant aggregation feature obtained by semantically dynamic aggregation of the dense sequence of the capacity-voltage semantic multi-scale implicit coding vector is decoded and processed to intelligently identify the critical point of lithium deposition at which lithium deposition of the tested lithium-ion battery begins to occur. In this way, full automation of data processing and critical point identification is achieved without manual intervention, ensuring the consistency and objectivity of the results. And it can capture complex electrochemical dynamic behavior changes, rather than just relying on preset thresholds or simple mathematical models, making the model more flexible and able to accurately reflect the true state of the battery.

[0055] In particular, in a specific embodiment of the present application, the specific processing process of inputting the capacity-voltage semantically significant aggregated coding vector into a decoder-based lithium precipitation critical point identifier to obtain the lithium precipitation critical point at which lithium precipitation of the tested lithium-ion battery begins to occur is as follows: First, choose a suitable decoder architecture, such as recurrent neural networks (RNNs) such as LSTM and GRU, which are suitable for processing time series data and can capture long-term dependencies; convolutional neural networks (CNNs), which are very effective for local feature extraction, especially when spatial structure needs to be considered; and transformers, which use the attention mechanism to capture global and local features at the same time, and are particularly suitable for processing long sequence data.

[0056] Then, a large amount of historical data is used for training, including data samples with known critical points for lithium precipitation. During the training process, the model learns how to identify characteristic patterns related to lithium precipitation from the capacity-voltage semantically significant aggregate encoding vector. An appropriate loss function, such as cross entropy loss or mean square error, is defined to measure the difference between the predicted value and the true label and guide the optimization of the model parameters.

[0057] Next, the decoder model identifies those characteristic patterns that are highly correlated with the lithium deposition phenomenon based on the input capacity-voltage semantically significant aggregated encoding vector. These patterns usually manifest as abnormal changes or trends at a specific time point. By analyzing the critical point of lithium deposition in the training data, a reasonable threshold or rule is set to determine when the critical state of lithium deposition is reached. For example, when a certain feature value exceeds the preset threshold, the time point is considered to be the critical point of lithium deposition. Combine feature information on different time scales to ensure the accuracy and reliability of the recognition results. For example, both short-term rapid changes and long-term trends are considered.

[0058] Subsequently, the decoder model outputs the probability of whether the lithium deposition critical point is reached at each time point or directly gives the specific critical point location.

[0059] Specific example: Assume that there is a trained LSTM-based decoder model for identifying the critical point of lithium deposition. The specific identification process is as follows: input the capacity-voltage semantically significant aggregated encoding vector into the decoder model; the model calculates the probability of lithium deposition at each time point based on the input encoding vector, or directly outputs the critical point position; set a threshold, and when the probability of lithium deposition at a certain time point exceeds this threshold, the time point is considered to be the critical point of lithium deposition. For example, if the threshold is set to 0.5, it means that when the probability of lithium deposition exceeds 50%, it is identified as a critical point; finally, the decoder model outputs the critical point of lithium deposition of the tested lithium-ion battery.

[0060] In one example, considering that the dense sequence of the first capacity-voltage semantic implicit coding vector and the dense sequence of the second capacity-voltage semantic implicit coding vector respectively represent the implicit coding features of the capacity-voltage point dense sequence, when performing corresponding sequence cascading and feature aggregation based on feature dynamics analysis, due to the difference in fairness of the dynamics analysis level caused by the implicit pattern differences of the feature population attributes and the sequence distribution differences, the capacity-voltage semantic significant aggregation coding vector will have a feature distribution aggregation inclusiveness loss, thereby affecting the accuracy of the lithium deposition critical point at which lithium deposition begins to occur in the tested lithium-ion battery obtained by inputting the decoder-based lithium deposition critical point identifier.

[0061] Therefore, in this example, when the capacity-voltage semantically significant aggregated coding vector is input into the decoder-based lithium precipitation critical point identifier, the capacity-voltage semantically significant aggregated coding vector is optimized, and the specific optimization process is: Determining an eigenvalue mean and an eigenvalue standard deviation corresponding to the capacity-voltage semantically significant aggregated encoding vector; Multiply the point-wise subtraction vector of the capacity-voltage semantically significant aggregate coding vector and the eigenvalue mean by the eigenvalue standard deviation to obtain a first capacity-voltage semantically significant aggregate coding intermediate vector, and multiply the point-wise subtraction vector of the capacity-voltage semantically significant aggregate coding vector and the eigenvalue standard deviation by the eigenvalue mean to obtain a second capacity-voltage semantically significant aggregate coding intermediate vector; After performing a dot multiplication of the bit-by-bit inverse of the second capacity-voltage semantically significant aggregate coding intermediate vector and the first capacity-voltage semantically significant aggregate coding intermediate vector, a bit-by-bit logarithm with base 2 is calculated to obtain a capacity-voltage semantically significant aggregate coding correction vector; The square root of the quotient of the eigenvalue mean divided by the eigenvalue standard deviation is multiplied by the weight hyperparameter, and then added to the capacity-voltage semantically significant aggregation coding correction vector to obtain an optimized capacity-voltage semantically significant aggregation coding vector.

[0062] That is, taking into account the attribute level fairness differences of the data population corresponding to the fusion features of the capacity-voltage semantically significant aggregation coding vector, in order to improve the aggregation inclusiveness under the feature distribution diversity of the capacity-voltage semantically significant aggregation coding vector, the crossover probability value constraint based on the capacity-voltage semantically significant aggregation coding vector is used as the interactive fairness target representation to correct the group feature information interactive propagation of the capacity-voltage semantically significant aggregation coding vector, and the unified statistical feature response interaction based on the capacity-voltage semantically significant aggregation coding vector is used as the feature distribution multi-level fairness target bias to achieve a robust distribution fairness unified representation of the capacity-voltage semantically significant aggregation coding vector, form a fair collaboration paradigm under the feature distribution framework of the capacity-voltage semantically significant aggregation coding vector, and improve the accuracy of the lithium deposition critical point at which lithium deposition begins to occur in the tested lithium-ion battery obtained by the decoder-based lithium deposition critical point identifier.

[0063] In summary, step S4 is explained, which uses data analysis and encoding technology based on deep learning to embed and encode the dense sequence of each capacity-voltage point, and then performs multi-scale implicit encoding on each capacity-voltage embedded feature, so as to intelligently identify the critical point of lithium deposition where lithium deposition of the tested lithium-ion battery begins to occur according to the dynamic pressure semantic significant aggregation representation between each capacity-voltage semantic multi-scale implicit coding feature. In this way, full automation of data processing and critical point identification is achieved without manual intervention, ensuring the consistency and objectivity of the results. And it can capture complex electrochemical dynamic behavior changes, rather than just relying on preset thresholds or simple mathematical models, making the model more flexible and able to accurately reflect the true state inside the battery.

[0064] In summary, the automatic testing method for lithium-ion batteries based on the embodiment of the present application is explained, which collects the voltage and capacity data of the negative electrode of the lithium-ion battery during the charging test, then performs differential processing on these data to generate a capacity-voltage differential curve, and then densely samples the differential curve to obtain a series of detailed capacity-voltage data points, and finally, uses these dense data points to determine whether there is lithium deposition inside the battery. In this way, the internal state of the lithium-ion battery during the charging process, especially the lithium deposition phenomenon, can be accurately monitored and evaluated, which is crucial for battery performance evaluation, safety testing and life prediction.

[0065] Figure 5 FIG. 1 is a block diagram of a lithium-ion battery automatic testing system according to an embodiment of the present application. Figure 5As shown, according to the lithium-ion battery automatic testing system 100 of the embodiment of the present application, it includes: a charging data acquisition module 110, which is used to collect the voltage and capacity data of the negative electrode of the tested lithium-ion battery during the charging test; a voltage-capacity differential processing module 120, which is used to perform differential processing on the collected voltage and capacity data of the negative electrode to obtain a capacity-voltage differential curve; a differential curve dense sampling module 130, which is used to densely sample the capacity-voltage differential curve to obtain a dense sequence of capacity-voltage points; a battery internal lithium deposition state judgment module 140, which is used to judge the battery internal lithium deposition state of the tested lithium-ion battery based on the dense sequence of capacity-voltage points; wherein the battery internal lithium deposition state judgment module includes: a capacity-voltage embedded coding unit, which is used to embed and code each capacity-voltage point in the dense sequence of capacity-voltage points to obtain a dense sequence of capacity-voltage embedded features; a capacity-voltage semantic multi-scale coding unit, which is used to The dense sequence of the capacity-voltage embedded features is subjected to multi-scale semantic implicit coding to obtain a dense sequence of capacity-voltage semantic multi-scale implicit coding features; a capacity-voltage semantic aggregation unit is used to perform capacity-voltage semantic dynamic feature significant aggregation on the dense sequence of the capacity-voltage semantic multi-scale implicit coding features to obtain capacity-voltage semantic significant aggregation features, and the capacity-voltage semantic aggregation unit includes: a capacity-voltage semantic feature aggregation center representation subunit, which is used to calculate the aggregation center representation of the dense sequence of the capacity-voltage semantic multi-scale implicit coding features; a capacity-voltage semantic dynamic aggregation subunit, which is used to perform dynamic aggregation on the dense sequence of the capacity-voltage semantic multi-scale implicit coding features based on the aggregation center representation to obtain the capacity-voltage semantic significant aggregation features; a lithium precipitation critical point judgment unit is used to obtain the lithium precipitation critical point of the tested lithium-ion battery at the beginning of lithium precipitation according to the capacity-voltage semantic significant aggregation.

[0066] Here, those skilled in the art will appreciate that the specific operations of each step in the above-mentioned lithium-ion battery automatic testing system have been described in detail above. Figures 1 to 4 The invention has been described in detail in the description of the automatic testing method for lithium-ion batteries, and therefore, its repeated description will be omitted.

[0067] As described above, the lithium-ion battery automatic test system 100 according to the embodiment of the present disclosure can be implemented in various wireless terminals, such as a server with a lithium-ion battery automatic test algorithm. In a possible implementation, the lithium-ion battery automatic test system 100 according to the embodiment of the present disclosure can be integrated into the wireless terminal as a software module and / or a hardware module. For example, the lithium-ion battery automatic test system 100 can be a software module in the operating system of the wireless terminal, or can be an application developed for the wireless terminal; of course, the lithium-ion battery automatic test system 100 can also be one of the many hardware modules of the wireless terminal.

[0068] Alternatively, in another example, the lithium-ion battery automatic testing system 100 and the wireless terminal may also be separate devices, and the lithium-ion battery automatic testing system 100 may be connected to the wireless terminal via a wired and / or wireless network and transmit interactive information in a predetermined data format.

[0069] The above is merely an example of the principles of the present disclosure, and various modifications may be made by those skilled in the art without departing from the scope of the present disclosure. The above embodiments are presented for the purpose of illustration rather than limitation. The present disclosure may also take many forms other than those explicitly described herein.

Claims

1. A lithium-ion battery automatic testing method, characterized in that: include: S1: Collect the voltage and capacity data of the negative electrode of the tested lithium-ion battery during the charging test; S2: performing differential processing on the collected voltage and capacity data of the negative electrode to obtain a capacity-voltage differential curve; S3: densely sampling the capacity-voltage differential curve to obtain a dense sequence of capacity-voltage points; S4: judging the internal lithium deposition state of the tested lithium-ion battery based on the dense sequence of capacity-voltage points; Wherein, said S4 includes: Embedding and encoding each capacity-voltage point in the dense sequence of capacity-voltage points to obtain a dense sequence of capacity-voltage embedded features; Performing multi-scale semantic implicit coding on the dense sequence of capacity-voltage embedded features to obtain a dense sequence of capacity-voltage semantic multi-scale implicit coding features; The dense sequence of the capacity-voltage semantic multi-scale implicit coding features is subjected to capacity-voltage semantic dynamic feature significant aggregation to obtain the capacity-voltage semantic significant aggregation feature, including: calculating the aggregation center representation of the dense sequence of the capacity-voltage semantic multi-scale implicit coding features; based on the aggregation center representation, the dense sequence of the capacity-voltage semantic multi-scale implicit coding features is subjected to dynamic aggregation to obtain the capacity-voltage semantic significant aggregation feature; According to the capacity-voltage semantically significant aggregation feature, a lithium deposition critical point at which lithium deposition of the tested lithium-ion battery begins to occur is obtained.

2. The automatic testing method for lithium-ion batteries according to claim 1, characterized in that: Embedding and encoding each capacity-voltage point in the dense sequence of capacity-voltage points to obtain a dense sequence of capacity-voltage embedded features, including: using a capacity-voltage embedded coding matrix to embed and encode each capacity-voltage point in the dense sequence of capacity-voltage points to obtain a dense sequence of capacity-voltage embedded coding vectors as the dense sequence of capacity-voltage embedded features.

3. The automatic testing method for lithium-ion batteries according to claim 2, characterized in that: Performing multi-scale semantic implicit coding on the dense sequence of the capacity-voltage embedded features to obtain a dense sequence of capacity-voltage semantic multi-scale implicit coding features, including: Inputting the dense sequence of the capacity-voltage embedded coding vectors into a sequence encoder based on an LSTM-RNN hybrid model to obtain a dense sequence of the first capacity-voltage semantic implicit coding vector and a dense sequence of the second capacity-voltage semantic implicit coding vector; Each group of corresponding first capacity-voltage semantic implicit coding vectors and second capacity-voltage semantic implicit coding vectors in the dense sequence of the first capacity-voltage semantic implicit coding vectors and the dense sequence of the second capacity-voltage semantic implicit coding vectors are cascaded to obtain a dense sequence of capacity-voltage semantic multi-scale implicit coding vectors as a dense sequence of the capacity-voltage semantic multi-scale implicit coding features.

4. The automatic testing method for lithium-ion batteries according to claim 3, characterized in that: Calculating the aggregate center representation of the dense sequence of the capacity-voltage semantic multi-scale implicit encoding features, including: Calculating the static potential energy weight factor of each capacity-voltage semantic multi-scale implicit coding vector in the dense sequence of the capacity-voltage semantic multi-scale implicit coding vector to obtain a dense sequence of capacity-voltage semantic multi-scale feature static potential energy weight factors; Taking the dense sequence of the capacity-voltage semantic multi-scale feature static potential weight factors as the weighted sequence, the position-weighted sum of the dense sequence of the capacity-voltage semantic multi-scale implicit encoding vector is calculated to obtain the capacity-voltage semantic multi-scale pseudo-anchor aggregation center representation vector as the aggregation center representation.

5. The automatic testing method for lithium-ion batteries according to claim 4, characterized in that: Calculating the static potential energy weight factor of each capacity-voltage semantic multi-scale implicit coding vector in the dense sequence of the capacity-voltage semantic multi-scale implicit coding vector to obtain a dense sequence of capacity-voltage semantic multi-scale feature static potential energy weight factors, including: Performing characteristic static potential energy measurement on each capacity-voltage semantic multi-scale implicit coding vector in the dense sequence of the capacity-voltage semantic multi-scale implicit coding vector to obtain a dense sequence of capacity-voltage semantic multi-scale characteristic static potential energy measurement coefficients; The dense sequence of the capacity-voltage semantic multi-scale feature static potential energy measurement coefficients is subjected to feature energy level gating screening to obtain a dense sequence of the capacity-voltage semantic multi-scale feature static potential energy weight factors.

6. The automatic testing method for lithium-ion batteries according to claim 5, characterized in that: Performing feature level gating screening on the dense sequence of capacity-voltage semantic multi-scale feature static potential energy measurement coefficients to obtain a dense sequence of capacity-voltage semantic multi-scale feature static potential energy weight factors, including: Using a sigmoid function, each capacity-voltage semantic multi-scale feature static potential energy measurement coefficient in the dense sequence of the capacity-voltage semantic multi-scale feature static potential energy measurement coefficient is standardized to obtain a dense sequence of standardized capacity-voltage semantic multi-scale feature static potential energy measurement coefficients; The dense sequence of the standardized capacity-voltage semantic multi-scale feature static potential energy measurement coefficients is input into a gated screening module based on a mask function to obtain a dense sequence of the capacity-voltage semantic multi-scale feature static potential energy weight factors.

7. The automatic testing method for lithium-ion batteries according to claim 6, characterized in that: Based on the aggregation center representation, the dense sequence of the capacity-voltage semantic multi-scale implicit coding features is dynamically aggregated to obtain the capacity-voltage semantic significant aggregation features, including: Calculating the aggregate movement direction of each capacity-voltage semantic multi-scale implicit coding vector in the dense sequence of the capacity-voltage semantic multi-scale implicit coding vector relative to the capacity-voltage semantic multi-scale pseudo-anchor aggregation center representation vector to obtain a dense sequence of capacity-voltage semantic multi-scale aggregation running directions; Based on the dense sequence of the capacity-voltage semantic multi-scale aggregation running direction, the dense sequence of the capacity-voltage semantic multi-scale implicit coding vectors is dynamically aggregated toward the capacity-voltage semantic multi-scale pseudo-anchor aggregation center representation vector to obtain the capacity-voltage semantic significant aggregation coding vector as the capacity-voltage semantic significant aggregation feature.

8. The lithium-ion battery automatic testing method according to claim 7, characterized in that: According to the capacity-voltage semantically significant aggregation feature, a lithium deposition critical point at which lithium deposition begins to occur in the tested lithium-ion battery is obtained, including: inputting the capacity-voltage semantically significant aggregation coding vector into a decoder-based lithium deposition critical point identifier to obtain the lithium deposition critical point at which lithium deposition begins to occur in the tested lithium-ion battery.

9. A lithium-ion battery automatic testing system, characterized in that: include: A charging data acquisition module, used to collect the voltage and capacity data of the negative electrode of the tested lithium-ion battery during the charging test; a voltage-capacity differential processing module, used to perform differential processing on the collected voltage and capacity data of the negative electrode to obtain a capacity-voltage differential curve; a differential curve dense sampling module, used to perform dense sampling on the capacity-voltage differential curve to obtain a dense sequence of capacity-voltage points; a battery internal lithium deposition state judgment module, used to judge the battery internal lithium deposition state of the tested lithium-ion battery based on the dense sequence of capacity-voltage points; wherein the battery internal lithium deposition state judgment module includes: A capacity-voltage embedding coding unit, used for embedding and coding each capacity-voltage point in the dense sequence of capacity-voltage points to obtain a dense sequence of capacity-voltage embedding features; A capacity-voltage semantic multi-scale encoding unit, used for performing multi-scale semantic implicit encoding on the dense sequence of the capacity-voltage embedded features to obtain a dense sequence of capacity-voltage semantic multi-scale implicit encoding features; A capacity-voltage semantic aggregation unit, used to perform capacity-voltage semantic dynamic feature significant aggregation on the dense sequence of the capacity-voltage semantic multi-scale implicit coding features to obtain capacity-voltage semantic significant aggregation features, the capacity-voltage semantic aggregation unit comprising: a capacity-voltage semantic feature aggregation center representation subunit, used to calculate the aggregation center representation of the dense sequence of the capacity-voltage semantic multi-scale implicit coding features; a capacity-voltage semantic dynamic aggregation subunit, used to perform dynamic aggregation on the dense sequence of the capacity-voltage semantic multi-scale implicit coding features based on the aggregation center representation to obtain the capacity-voltage semantic significant aggregation features; The lithium deposition critical point judgment unit is used to obtain the lithium deposition critical point at which lithium deposition begins to occur in the tested lithium-ion battery according to the capacity-voltage semantic significant aggregation.

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