Retired battery rapid screening method based on ultrasonic array

Abstract

The invention discloses a method for rapidly screening out-of-service batteries based on an ultrasonic array, and relates to the technical field of battery quality detection.According to the method, a multi-array-element excitation / receiving plate is adopted, full-surface scanning imaging of a single battery is completed within 15 seconds under the condition that no liquid coupling agent is needed, and the detection accuracy is improved. And generating an ultrasonic array atlas representing internal physical state space distribution. By calculating the normalized Euclidean distance between the atlases of all batteries in the same batch and combining the average distance threshold and the statistical characteristics of the atlases, the abnormal batteries with internal defects can be efficiently and accurately identified. The method is directly operated in the time domain, the frequency domain conversion and complex fitting process is omitted, the test time is shortened by 98% or above compared with EIS, and a practical, efficient, economical and feasible technical approach is provided for high-flux and automatic echelon utilization and sorting of the retired lithium ion batteries.

Description

Technical Field

[0001] This invention relates to the field of battery quality testing technology, and more specifically to a rapid screening method for retired batteries based on an ultrasonic array. Background Technology

[0002] With the rapid development of electric vehicles and renewable energy storage, the large-scale, tiered utilization of retired lithium-ion batteries has become a key link in realizing a circular economy. However, retired batteries exhibit significant differences in state of health (SOH) between individual cells, potentially harboring safety hazards such as lithium plating, delamination, and electrolyte drying. Therefore, rapid, reliable, and low-cost high-throughput anomaly screening of retired batteries is a prerequisite for overcoming the "bottleneck" of secondary utilization.

[0003] Currently, the industry lacks effective diagnostic tools to meet this need. Traditional full-capacity testing is too time-consuming (several hours per unit) and cannot be used for production line sorting. While electrochemical impedance spectroscopy (EIS), the gold standard in laboratories, can reflect internal states, its measurement speed is slow, the equipment is expensive, and the data analysis is complex, making it difficult to apply on a large scale. On the other hand, existing ultrasonic non-destructive testing methods mostly use a single transducer with a liquid coupling agent for point scanning, which is not only inefficient, but the liquid coupling method is also unsuitable for automated production lines.

[0004] Therefore, there is an urgent need for a battery defect diagnosis technology that does not require liquid coupling and has a fast detection speed, so as to achieve efficient, economical and reliable anomaly screening of retired lithium-ion batteries. Summary of the Invention

[0005] In view of the above problems, this invention is proposed to provide a rapid screening method for retired batteries based on an ultrasonic array to overcome or at least partially solve the above problems. This invention operates directly in the time domain, eliminating the frequency domain conversion and complex fitting process, and shortening the test time by more than 98% compared with EIS. It provides a practical, efficient and economical technical approach for high-throughput, automated cascade utilization and sorting of retired lithium-ion batteries.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, embodiments of the present invention provide a rapid screening method for decommissioned batteries based on an ultrasonic array, characterized in that it includes: The retired lithium-ion battery to be tested is placed on an ultrasonic excitation array with a dry coupling interface, and the ultrasonic receiving array is pressed against the upper surface of the battery with constant pressure by a single-axis motion mechanism. The ultrasonic excitation array is used to emit ultrasonic waves into the battery, and the ultrasonic signal after penetrating the battery is received by the ultrasonic receiving array. Based on the received ultrasonic signals, an ultrasonic array map characterizing the spatial distribution of the internal physical state is synthesized for each battery under test. For the ultrasonic array spectra of all batteries under test in the same batch, calculate the Euclidean distance between any two ultrasonic array spectra and construct a normalized Euclidean distance matrix. Based on the normalized Euclidean distance matrix, the average normalized Euclidean distance of each battery under test relative to other batteries in the same batch is calculated, and abnormal batteries are identified based on the average normalized Euclidean distance.

[0007] Preferably, for the ultrasonic array spectra of all batteries under test in the same batch, the Euclidean distance between any two ultrasonic array spectra is calculated, and a normalized Euclidean distance matrix is ​​constructed, including: For any two batteries to be tested in the same batch, obtain the average value of the peak values ​​of the ultrasonic array spectrum. The absolute difference between the average peak values ​​of the two ultrasonic peaks is calculated as the Euclidean distance between them. Repeat the above steps to traverse all battery pairs in the batch, forming an m×m original Euclidean distance matrix, where m is the total number of batteries in the batch; Determine the minimum and maximum distances in the original Euclidean distance matrix; according to The normalized Euclidean distance matrix is ​​obtained, where, Indicates the minimum distance. Indicates the maximum distance. This represents the normalized Euclidean distance matrix. This represents the peak value of the ultrasound collected at the position of row i and column j.

[0008] Preferably, based on the normalized Euclidean distance matrix, the average normalized Euclidean distance of each battery under test relative to other batteries in the same batch is calculated, and abnormal batteries are identified based on the average normalized Euclidean distance, including: For each battery to be tested in the same batch, calculate the arithmetic mean of its distance values ​​with the other batteries in the normalized Euclidean distance matrix to obtain the average normalized Euclidean distance of the battery. The average normalized Euclidean distance of each battery under test is compared with a preset threshold of 0.5, and it is determined whether the average value of the ultrasonic peak-to-peak value of the battery under test is in the bottom 50% of all batteries in the same batch. A battery is marked as abnormal if and only if both of the above conditions are met; otherwise, it is marked as normal.

[0009] Preferably, both the ultrasonic excitation array and the ultrasonic receiving array are composed of piezoelectric ceramic transducer elements arranged in a 4×5 pattern.

[0010] Preferably, the emitted ultrasonic wave is a sinusoidal pulse modulated by a Hanning window, with a center frequency of 150 kHz and a peak-to-peak voltage of 20 Vpp.

[0011] Preferably, the dry coupling interface is achieved by attaching a special acoustic rubber to the head of the piezoelectric ceramic transducer element.

[0012] Preferably, the constant pressure applied by the single-axis motion mechanism is 100N.

[0013] Preferably, the single-axis motion mechanism is controlled to drive the ultrasonic excitation array to move at a constant speed with a step accuracy of 0.1 mm along the length direction of the battery.

[0014] As can be seen from the above technical solution, compared with the prior art, this invention discloses a rapid screening method for retired batteries based on an ultrasonic array, serving as a fast, practical, and non-destructive solution for screening retired lithium-ion batteries. By utilizing multi-region acoustic imaging and time-domain signal analysis, this method can complete a full-cell scan within 15 seconds, reducing measurement time by 98.33% compared to traditional EIS. The system requires no liquid coupling agent, integrates real-time force feedback, and employs a compact FPGA-based architecture, making it suitable for industrial deployment. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0016] Figure 1 This is a flowchart of a rapid screening method for decommissioned batteries based on an ultrasonic array, provided in an embodiment of the present invention. Figure 2 This is a framework diagram of the ultrasonic array scanning device for decommissioned batteries provided in an embodiment of the present invention; Figure 3 The above are EIS and ultrasonic scan results of the first-level battery provided in this embodiment of the invention. Figure 4 The image shows the ultrasonic scanning results and EIS results of abnormal cells in the first-level battery provided in this embodiment of the invention. Figure 5 The image shows the EIS and ultrasonic scan results of the second-level battery provided in this embodiment of the invention. Figure 6 The image shows the EIS and ultrasonic scan results of the third-level battery provided in this embodiment of the invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] This invention discloses a rapid screening method for decommissioned batteries based on an ultrasonic array, such as... Figure 1 As shown, it includes: The retired lithium-ion battery to be tested is placed on an ultrasonic excitation array with a dry coupling interface, and the ultrasonic receiving array is pressed against the upper surface of the battery with constant pressure through a single-axis motion mechanism. An ultrasonic excitation array is used to emit ultrasonic waves into the battery, and an ultrasonic receiving array is used to receive the ultrasonic signals that have penetrated the battery. Based on the received ultrasonic signals, an ultrasonic array map characterizing the spatial distribution of the internal physical state is synthesized for each battery under test. For the ultrasonic array spectra of all batteries under test in the same batch, calculate the Euclidean distance between any two ultrasonic array spectra and construct a normalized Euclidean distance matrix. Based on the normalized Euclidean distance matrix, the average normalized Euclidean distance of each battery under test relative to other batteries in the same batch is calculated, and abnormal batteries are identified based on the average normalized Euclidean distance.

[0019] This invention utilizes ultrasonic array detection for rapid initial screening of retired lithium-ion batteries, providing a highly efficient technical solution to the "reuse bottleneck." The process primarily achieves high-throughput, non-destructive testing of abnormal batteries by capturing spatial distribution differences in the internal physical states of the battery.

[0020] The overall structure of this invention is shown in Figure 2. The system mainly consists of the following components: a single-axis motion mechanism with pressure feedback, used to maintain consistent coupling between the ultrasonic array sensor and the battery surface during each test; a multi-channel ultrasonic excitation and acquisition board based on an FPGA main control chip, responsible for rapidly scanning multiple areas of a single battery; and an adaptive dry-coupled ultrasonic sensor, including an ultrasonic excitation array and an ultrasonic receiving array, maintaining stable acoustic coupling under dynamic testing conditions while avoiding the use of liquid coupling agents, ensuring the safety of retired batteries during the testing process. Regarding the testing method, the device adopts a "one-transmit, one-receive" transmission mode. The peak-to-peak value of the received signal is extracted to characterize the physical properties of materials in different regions inside the battery. Given that the multi-layered composite structure inside lithium-ion batteries causes significant ultrasonic attenuation, the excitation unit needs high energy output capability to obtain an effective transmission signal. Our system uses a sinusoidal pulse modulated by a Hanning window as the excitation signal. This signal was chosen because its energy is concentrated and its spectrum is controllable, which helps improve the signal-to-noise ratio and detection reliability. Considering the large thickness of the power battery cell, using excessively high ultrasonic frequencies would lead to severe acoustic attenuation. Since the goal here is rapid preliminary screening rather than detailed imaging, the final excitation parameters were set to a frequency of 150 kHz and a voltage of 20 Vpp. These parameters strike a good balance between signal penetration and detection sensitivity, making them suitable for rapid acoustic screening of decommissioned batteries.

[0021] The following is a detailed initial screening process based on this study: 1. Preparation of testing equipment and environmental setup Hardware configuration: The system integrates a 4×5 piezoelectric ceramic transducer array (a total of 20 sensors), uses an FPGA as the main control chip, and is equipped with a single-axis motion mechanism with pressure feedback.

[0022] Dry coupling interface: The transducer head is covered with special acoustic rubber, eliminating the need for liquid coupling agent, thus ensuring battery safety and ease of automated operation.

[0023] Battery status: The batteries under test should be in the same state of charge (e.g., SOC=0%) and left to stand for a sufficient time to ensure that the internal state is stable.

[0024] 2. Standardized scanning process Battery Placement: The battery under test is placed on an ultrasonic excitation array with a dry coupling interface, ensuring consistent orientation (e.g., tabs facing right). This ultrasonic excitation array consists of 20 sensors arranged in a 4×5 piezoelectric ceramic transducer array. Mechanically, the sensors integrate a dynamic force adjustment mechanism composed of a preloaded spring and an optical axis. Their heads are covered with specialized acoustic rubber to ensure good coupling under non-immersion conditions. The specialized acoustic rubber and dry coupling material meet the following requirements: In terms of hardness: The dry coupling material in this invention is soft, with a Shore hardness of approximately 30. Under slight contact pressure, the rubber surface can produce significant micro-elastic deformation, fully filling the micron-level rough texture and unevenness of the battery surface (typically an aluminum or steel casing). This micro-filling effect effectively eliminates the air layer between the contact interfaces, thereby establishing a continuous acoustic channel. In terms of acoustic impedance: The acoustic impedance of the dry coupling material selected in this invention is approximately 2 MRayl, close to the acoustic impedance of water (1.48 MRayl). Such acoustic impedance parameters significantly reduce the interface reflection coefficient, ensuring that ultrasonic energy can be coupled into the battery interior with high efficiency, rather than being lost at the interface. Each sensor has a diameter of 10 mm and an element spacing of 5 mm, therefore the effective test area of ​​the entire sensor array is 55 mm × 70 mm.

[0025] Mechanical coupling: The host computer controls a servo motor via a serial port to press the ultrasonic receiving array against the upper surface of the battery. The pressure is typically set to 100 N to balance the acoustic coupling effect and prevent damage to the battery structure.

[0026] Excitation signal transmission: The host computer sends the excitation-related parameters to the FPGA main control chip via serial port. Subsequently, the FPGA controls the analog switch and digital-to-analog converter (DAC) to enable the ultrasonic excitation array unit to generate 150 kHz, 20Vpp Hanning window modulated sinusoidal pulses in sequence. This frequency can achieve a good balance between signal penetration and sensitivity.

[0027] Signal Acquisition: The system adopts a "one-to-one" transmission mode. The host computer sends the relevant acquisition parameters to the FPGA main control chip via serial port. The FPGA then controls the analog switches, programmable amplifier gain, and analog-to-digital converter (ADC) to realize a multiplexed architecture, activating the ultrasonic receiving array channel by channel to capture the ultrasonic signals that have penetrated the battery, i.e., the time-domain acoustic signals. Subsequently, the acoustic signals acquired by each array element are temporarily stored in the FPGA, and then sent from the FPGA to the host computer software via USB 3.0 protocol.

[0028] Image synthesis: The peak-to-peak value of the received signal from each channel is extracted as the pixel value, and an ultrasonic array map representing the internal structure of the battery is synthesized. The entire scanning process can be completed within 15 seconds.

[0029] 3. Data Analysis and Anomaly Detection This process identifies anomalies by comparing the consistency of batteries from the same batch, and mainly includes the following mathematical evaluation metrics: For any two batteries to be tested in the same batch, obtain the average value of the peak values ​​of the ultrasonic array spectrum. The absolute difference between the average peak values ​​of the two ultrasonic peaks is calculated as the Euclidean distance between them. Repeat the above steps to traverse all battery pairs in the batch, forming an m×m original Euclidean distance matrix, where m is the total number of batteries in the batch; Determine the minimum and maximum distances in the original Euclidean distance matrix; according to The normalized Euclidean distance matrix is ​​obtained, where, Indicates the minimum distance. Indicates the maximum distance. This represents the normalized Euclidean distance matrix.

[0030] For each battery to be tested in the same batch, calculate the arithmetic mean of its distance values ​​with the other batteries in the normalized Euclidean distance matrix to obtain the average normalized Euclidean distance of the battery. The average normalized Euclidean distance of each battery under test is compared with a preset threshold of 0.5, and it is determined whether the average value of the ultrasonic peak-to-peak value of the battery under test is in the bottom 50% of all batteries in the same batch. A battery is marked as abnormal if and only if both of the above conditions are met simultaneously: that is, the average normalized Euclidean distance of each battery under test is greater than a preset threshold of 0.5 and the average value of the ultrasonic peak-to-peak value of the battery under test is in the bottom 50% of all batteries in the same batch; otherwise, it is marked as a normal battery.

[0031] Statistical characteristics for auxiliary analysis: Formula for calculating the mean peak value of ultrasound:

[0032] Formula for calculating the standard deviation of ultrasound peak value:

[0033] Low mean and high standard deviation: usually correspond to local defects, such as bubbles or localized electrolyte drying.

[0034] Low mean and low standard deviation: usually correspond to global uniform degradation, such as the general attenuation of acoustic energy caused by the depletion of the overall electrolyte.

[0035] To verify the applicability of ultrasonic array testing in the initial screening of abnormal batteries in retired power batteries, we simultaneously applied both EIS and the ultrasonic technology of this invention to 26 retired batteries of the same model from the same batch. Detailed specifications and parameters of these two batches of retired batteries are shown in Table 1.

[0036] Table 1. Different groupings of the two batches of retired batteries

[0037] As shown in Table 1, the batteries are classified into different grades based on their State of Harmony (SOH). All batteries have a factory-rated nominal capacity of 28 Ah. To ensure that observed differences in impedance and acoustic signal are attributable to variations within the battery itself rather than fluctuations in SOC, the SOC of each battery was maintained at 0% during individual EIS and ultrasonic mapping measurements. Batteries with similar SOH should exhibit minimal differences in impedance and acoustic signal when external factors such as temperature and measurement conditions are precisely controlled.

[0038] To assess the similarity between EIS and the ultrasonic spectra of this invention, the Euclidean distance between the two curves or two images can be calculated; a larger distance indicates a greater difference. When comparing individual batteries from different batches, the absolute range of the Euclidean distance may vary from batch to batch. Furthermore, without normalization, it is difficult to define a standardized threshold for assessing similarity and identifying outliers. Therefore, we recommend normalizing the Euclidean distance matrix within each level and using a coefficient greater than 0.5 as the standard threshold for screening outlier batteries.

[0039] Figure 3 Table A shows the EIS measurement results of 10 batteries from the first stage of this batch of batteries. The Euclidean distance was calculated directly using these 10 impedance curves, yielding... Figure 3 The distance matrix is ​​shown in B. It's important to note that no further feature extraction (e.g., determining internal resistance, charge transfer resistance, or diffusion resistance) was performed on the EIS data. This was done to avoid potential fitting errors associated with equivalent circuit modeling, ensuring all calculations are based on the original data. From the EIS Euclidean distance matrix, it's clear that batteries #A-3 and #A-4 exhibit slightly larger distances than the other batteries. Figure 3 Section C shows the average normalized Euclidean distance for each cell (calculated based on its distance to every other cell in the set). It can also be seen that the average normalized Euclidean distances for #A-3 and #A-4 both exceed the threshold of 0.5. Therefore, based on EIS analysis, we initially identify these two cells as anomalous cells in this category.

[0040] Ultrasonic array detection provides a physical perspective that complements electrochemical methods for identifying anomalous batteries. Part D in Figure 3 illustrates this. Figure 3The ultrasonic array spectra of the same group of cells in Part A of Figure 3 are shown. Based on these images, we calculated the normalized Euclidean distance matrix (Part E in Figure 3) and the average normalized Euclidean distance for each cell (Part F in Figure 3).

[0041] The results showed that the ultrasonic-based method also identified the same anomalous batteries #A-3 and #A-4, whose average normalized Euclidean distance exceeded the threshold of 0.5. Notably, the anomalies were more pronounced in the ultrasonic data: the average normalized Euclidean distance of the anomalous batteries was significantly higher than that obtained from EIS. Specifically, the average EIS distances for #A-3 and #A-4 were 0.513 and 0.598, respectively, while the corresponding ultrasonic values ​​were 0.558 and 0.781. This difference indicates that ultrasonic detection is more sensitive to changes in the internal material properties of aged batteries, giving it a potential advantage in anomaly detection. In terms of detection efficiency, the ultrasonic array technique also demonstrated significant superiority. A single EIS measurement (frequency range: 0.01 Hz–5 kHz) required 25 minutes per battery, while an ultrasonic array scan took only 15 seconds. In conclusion, the ultrasonic method not only showed high agreement with EIS results but also demonstrated higher sensitivity and efficiency, proving its reliability and practicality as a rapid, non-destructive battery screening method.

[0042] We further analyzed the physical characteristics of the previously identified anomalous batteries #A-3 and #A-4 from the first level using ultrasonic array imaging. The results are shown in Figure 4. The ultrasonic spectra in parts A and B of Figure 4 visually reveal significant physical anomalies in their internal structure: the obvious pale yellow areas represent regions with low ultrasonic wave transmission amplitude, which typically correspond to areas of increased acoustic impedance or severe attenuation of ultrasonic energy. This indicates that changes in the physical state of the battery may have occurred, such as gas accumulation, loss of electrolyte wettability of the electrode plates, or degradation of the electrode-separator interface contact. In contrast, the purple areas represent areas where ultrasonic waves propagate effectively, indicating areas of structural integrity. This physical inhomogeneity is further confirmed by statistical data. Ultrasonic amplitude analysis of 10 batteries in this level ( Figure 4Part C shows that the average amplitude values ​​of #A-3 and #A-4 deviate significantly from the group average, and their standard deviations are abnormal, statistically confirming that they are significant outliers in the population. Comparing the ultrasonic performance of the two anomalous cells, #A-4 exhibits the lowest overall transmission amplitude in the entire set, indicating extremely strong internal ultrasonic attenuation. According to acoustic principles, this strongly suggests the presence of gas inside the cell, as a gaseous medium would cause strong ultrasonic reflection and energy dissipation. In contrast, #A-3 exhibits milder ultrasonic attenuation, and its imaging characteristics are more consistent with localized changes in acoustic impedance caused by electrolyte drying. Therefore, we initially conclude that the main problem with this cell is insufficient internal wettability. Furthermore, the ultrasonic spectra of these two cells clearly depict the distribution of the anomalous region, providing visual evidence for locating the internal fault.

[0043] To further investigate the potential failure modes of anomalous batteries #A-3 and #A-4, we conducted an in-depth analysis of their electrochemical impedance spectroscopy (EIS) (parts D and E in Figure 4). First, the ohmic resistances of all batteries in this class (#A-1 to #A-10) were extracted, yielding values ​​of 1.696, 1.691, 1.761, 1.880, 1.683, 1.627, 1.632, 1.635, 1.687, and 1.667 mΩ, respectively. The data show that the ohmic resistances of #A-3 and #A-4 are significantly higher than the average for this class, which generally indicates a decrease in electrolyte ionic conductivity or a general thickening of the interfacial film. However, by combining the EIS characteristics in the mid-to-low frequency region, their primary failure mechanisms can be more accurately identified. Battery #A-3 exhibits a particularly significant increase in the semi-circular diameter in the mid-frequency region, indicating a sharp increase in charge transfer resistance. This strongly suggests a significant loss of active reaction area at the electrode / electrolyte interface, which aligns well with the suspected electrolyte drying region identified by ultrasonic detection. In contrast, battery #A-4 exhibits the most significant increase in ohmic resistance, but its mid-frequency semicircle expansion is relatively small, indicating that while interfacial reaction kinetics have degraded, the degree is not as severe as in #A-3. Furthermore, the pronounced distortion of its Warburg diffusion line further supports the inference that gas blockage has led to a more tortuous ion transport path, which is more consistent with the internal gas generation indicated by ultrasonic detection. In summary, EIS analysis reveals two distinct electrochemical degradation pathways: #A-3 primarily exhibits interfacial reaction failure dominated by electrolyte drying, while #A-4 is more consistent with ion transport blockage caused by gas accumulation.

[0044] We further applied the above comparison method to the remaining two levels. Detailed test results are as follows: Figures 5 to 6 As shown. Among the remaining two levels, the most core and consistent finding is that the normalized Euclidean distance matrices derived from ultrasonic spectra and EIS spectra are highly consistent in identifying anomalous batteries.

[0045] In the second and third levels, the identified anomalous batteries include #A-11, #A-13, #A-14, #A-21, and #A-25. Figure 5 Parts A and D in the middle, Figure 6 Sections A and D show the measured EIS impedance spectrum and ultrasonic spectrum results for each battery, respectively. (Source: EIS spectrum) Figure 5 Parts B and C Figure 6 Parts B and C) and ultrasound atlas ( Figure 5 Parts E and F Figure 6 The Euclidean distance analysis results (for parts E and F) are consistent and clearly demonstrate the anomalous properties of these batteries. Furthermore, statistical analysis of the mean and standard deviation of the ultrasonic spectra (…) Figure 5 Part G of China Figure 6 The results (part G) show that all five anomalous cells exhibited significant ultrasonic amplitude attenuation and marked inhomogeneity in their internal material properties. Incorporating the mean and standard deviation as auxiliary criteria effectively improved the robustness of the screening process. In situations where anomalous cells predominate and normal cells are scarce, relying solely on Euclidean distance may lead to some healthy cells being misclassified as anomalous. The combination of these statistical indicators helps prevent such misidentification, thereby enhancing the reliability of the screening results.

[0046] Based on a systematic analysis of ultrasonic and EIS results from the same batch of batteries, we conclude that the ultrasonic array screening method exhibits high consistency with the EIS method in identifying anomalous batteries from different sources and with varying degrees of degradation, confirming its reliability as a rapid screening tool. The advantages of this method extend beyond speed. It provides a unique perspective for observing the internal physical state of batteries through image-based spectral and statistical characteristics (mean and standard deviation), complementing the limitations of EIS and other electrochemical techniques that cannot directly obtain such physical information. This capability allows for the effective differentiation between localized sharp defects (e.g., swelling, structural damage) and globally uniform degradation (e.g., uniform drying, overall aging). Although determining the precise chemical mechanisms based solely on ultrasonic imaging remains challenging, its 15-second detection speed and ability to intuitively reveal internal physical defects fully meet the urgent industrial need for rapid, non-destructive, and low-cost preliminary screening of retired batteries. In summary, we have established a rapid preliminary screening process based on ultrasonic array detection, applicable to batches of batteries with similar state of equilibrium (SOH). Compared to EIS-based screening, this process improves detection efficiency by 98.33%.

[0047] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0048] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A rapid screening method for decommissioned batteries based on an ultrasonic array, characterized in that, include: The retired lithium-ion battery to be tested is placed on an ultrasonic excitation array with a dry coupling interface, and the ultrasonic receiving array is pressed against the upper surface of the battery with constant pressure by a single-axis motion mechanism. The ultrasonic excitation array is used to emit ultrasonic waves into the battery, and the ultrasonic signal after penetrating the battery is received by the ultrasonic receiving array. Based on the received ultrasonic signals, an ultrasonic array map characterizing the spatial distribution of the internal physical state is synthesized for each battery under test. For the ultrasonic array spectra of all batteries under test in the same batch, calculate the Euclidean distance between any two ultrasonic array spectra and construct a normalized Euclidean distance matrix. Based on the normalized Euclidean distance matrix, the average normalized Euclidean distance of each battery under test relative to other batteries in the same batch is calculated, and abnormal batteries are identified based on the average normalized Euclidean distance.

2. The method as described in claim 1, characterized in that, For the ultrasonic array spectra of all batteries under test in the same batch, calculate the Euclidean distance between any two ultrasonic array spectra and construct a normalized Euclidean distance matrix, including: For any two batteries to be tested in the same batch, obtain the average value of the peak values ​​of the ultrasonic array spectrum. The absolute difference between the average peak values ​​of the two ultrasound peaks is calculated as the Euclidean distance between them. Repeat the above steps to traverse all battery pairs in the batch, forming an m×m original Euclidean distance matrix, where m is the total number of batteries in the batch; Determine the minimum and maximum distances in the original Euclidean distance matrix; according to The normalized Euclidean distance matrix is ​​obtained, where, Indicates the minimum distance. Indicates the maximum distance. This represents the normalized Euclidean distance matrix. This represents the peak value of the ultrasound collected at the position of row i and column j.

3. The method as described in claim 2, characterized in that, Based on the normalized Euclidean distance matrix, the average normalized Euclidean distance of each battery under test relative to other batteries in the same batch is calculated, and abnormal batteries are identified based on the average normalized Euclidean distance, including: For each battery to be tested in the same batch, calculate the arithmetic mean of its distance values ​​with the other batteries in the normalized Euclidean distance matrix to obtain the average normalized Euclidean distance of the battery. The average normalized Euclidean distance of each battery under test is compared with a preset threshold of 0.5, and it is determined whether the average value of the ultrasonic peak-to-peak value of the battery under test is in the bottom 50% of all batteries in the same batch. A battery is marked as abnormal if and only if both of the above conditions are met; otherwise, it is marked as normal.

4. The method as described in claim 1, characterized in that, Both the ultrasonic excitation array and the ultrasonic receiving array are composed of piezoelectric ceramic transducer elements arranged in a 4×5 pattern.

5. The method as described in claim 1, characterized in that, The emitted ultrasonic waves are sinusoidal pulses modulated by a Hanning window, with a center frequency of 150 kHz and a peak-to-peak voltage of 20 Vpp.

6. The method as described in claim 4, characterized in that, The dry coupling interface is achieved by attaching a special acoustic rubber to the head of the piezoelectric ceramic transducer element.

7. The method as described in claim 1, characterized in that, The constant pressure applied by the single-axis motion mechanism is 100N.

8. The method as described in claim 7, characterized in that, The single-axis motion mechanism is controlled to drive the ultrasonic excitation array to move at a constant speed with a step accuracy of 0.1 mm along the length of the battery.

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