A method and device for detecting battery aging

Abstract

The application provides a battery aging detection method and device, and belongs to the technical field of lithium battery aging detection. The application obtains first ultrasonic echo response data corresponding to standard charging processing, standard temperature field data and standard electrochemical data, and obtains second ultrasonic echo response data corresponding to micro overcharging processing, micro overcharging temperature field data and micro overcharging electrochemical data. Based on the first ultrasonic echo response data and the second ultrasonic echo response data, an ultrasonic differential parameter is obtained. Based on the standard temperature field data and the micro overcharging temperature field data, a micro overcharging temperature rise feature is determined. Based on the standard electrochemical data and the micro overcharging electrochemical data, a micro overcharging electrochemical change feature is obtained. The ultrasonic differential parameter, the micro overcharging temperature rise feature and the micro overcharging electrochemical change feature are input into an aging recognition model to obtain an aging recognition result. In this way, the accuracy of battery aging detection can be improved, and various application scenarios can be adapted.

Description

Technical Field

[0001] This application relates to the field of lithium battery aging detection technology, and in particular to a method and apparatus for detecting battery aging. Background Technology

[0002] Currently, micro-overcharging (charging voltage exceeding the rated cutoff voltage by 5%-10% for several hours to several days) is one of the key causes of lithium battery aging and failure. It may lead to problems such as electrolyte decomposition, solid electrolyte interface (SEI) film thickening and lithium plating, which can easily cause thermal runaway in the long term.

[0003] Existing detection technologies mainly fall into three categories, all of which lack the ability to identify the weak aging signals in the early stages of micro-overcharging: The first category is ultrasonic testing, which, while capable of identifying structural defects, produces weak ultrasonic signal changes due to SEI film thickening and electrolyte decomposition in the early stages of micro-overcharging. These changes are easily masked by noise and difficult to detect. The second category is infrared thermal imaging, which can capture temperature rises, but the local temperature rise in the early stages of micro-overcharging is only 0.1-0.5℃, making it difficult to distinguish from environmental noise and resulting in low detection accuracy. The third category is electrochemical impedance spectroscopy, which, while reflecting the degree of aging, cannot locate the aging area, and the detection process requires interruption of charging and discharging, making in-situ monitoring impossible and unsuitable for various application scenarios.

[0004] In summary, existing battery aging detection methods have low accuracy, are difficult to consider multiple factors, and are not adaptable to various application scenarios. Summary of the Invention

[0005] This application provides a method and apparatus for detecting battery aging, which to some extent solves the problems of low accuracy, difficulty in considering multiple factors, and difficulty in adapting to various application scenarios in existing battery aging detection methods.

[0006] According to one aspect of this application, a method for detecting battery aging is provided. The method includes: performing a standard charging treatment and a micro-overcharge treatment on the battery to be tested, respectively, and acquiring first ultrasonic echo response data, standard temperature field data, and standard electrochemical data corresponding to the standard charging treatment; and acquiring second ultrasonic echo response data, micro-overcharge temperature field data, and micro-overcharge chemical data corresponding to the micro-overcharge treatment; the standard charging treatment is normal charging based on the rated parameters of the battery to be tested; the micro-overcharge treatment is abnormal charging with a charging voltage higher than the rated parameters; and differential processing is performed on the first ultrasonic echo response data and the second ultrasonic echo response data to calculate ultrasonic differential parameters; the ultrasonic differential parameters include at least: sound velocity difference, amplitude difference, and phase. Differential analysis is performed on the standard temperature field data and the micro-overcharge temperature field data. Noise reduction and gradient enhancement are applied to these data to calculate and determine the micro-overcharge temperature rise characteristics. These characteristics include at least the maximum temperature rise, temperature rise gradient, and area of ​​the abnormal temperature rise region. The standard electrochemical data and the micro-overcharge chemical data are compared using change curves to obtain the micro-overcharge chemical change characteristics. These characteristics include at least the capacity decay rate and voltage offset. The ultrasonic differential parameters, micro-overcharge temperature rise characteristics, and micro-overcharge chemical change characteristics are input into a pre-trained aging identification model to obtain the aging identification results for the battery under test. The aging identification model is a random forest classification model. The aging identification results include at least one of the following: aging level, aging region, and aging type.

[0007] Furthermore, according to one aspect of the method of this application, acquiring first ultrasonic echo response data, standard temperature field data, and standard electrochemical data corresponding to a standard charging process includes: during the standard charging process of the battery under test, using an ultrasonic module to send a first ultrasonic signal to the battery under test within a preset first interval to obtain first ultrasonic echo response data; the first ultrasonic echo response data includes at least: standard sound velocity, standard amplitude, and standard phase; the standard charging process includes: first constant current charging to the rated voltage of the rated parameters, and then constant voltage charging to the rated current of the rated parameters; using an infrared thermal imager to acquire standard temperature field data of the surface of the battery under test within a preset second interval; the standard temperature field data includes at least: standard average temperature and temperature standard deviation; using a charge-discharge module to acquire standard electrochemical data; the standard electrochemical data includes at least: standard charging capacity and standard voltage.

[0008] Furthermore, according to one aspect of this application, acquiring second ultrasonic echo response data, micro-overcharge temperature field data, and micro-overcharge chemical data corresponding to micro-overcharge treatment includes: during the micro-overcharge treatment of the battery under test, sending a second ultrasonic signal to the battery under test using an ultrasonic module within a first interval to obtain second ultrasonic echo response data of the battery under test; the second ultrasonic echo response data includes at least: micro-overcharge sound velocity, micro-overcharge amplitude, and micro-overcharge phase; the micro-overcharge treatment includes: first constant current charging to a preset overcharge voltage value higher than the rated voltage of the rated parameters, and then constant voltage charging to the rated current of the rated parameters; acquiring micro-overcharge temperature field data of the surface of the battery under test using an infrared thermal imager within a second interval; the micro-overcharge temperature field data includes at least: micro-overcharge average temperature and micro-overcharge temperature standard deviation; acquiring micro-overcharge chemical data using a charge-discharge module; the micro-overcharge chemical data includes at least: micro-overcharge capacity and micro-overcharge voltage.

[0009] Furthermore, according to one aspect of the method of this application, differential processing is performed on the first ultrasonic echo response data and the second ultrasonic echo response data to calculate ultrasonic differential parameters, including: calculating the difference between the standard sound velocity and the micro-overcharge sound velocity to obtain the sound velocity difference; calculating the difference between the standard amplitude and the micro-overcharge amplitude to obtain the amplitude difference; calculating the difference between the standard phase and the micro-overcharge phase to obtain the phase difference; and determining the sound velocity difference, amplitude difference, and phase difference as ultrasonic differential parameters.

[0010] Furthermore, according to one aspect of the method of this application, noise reduction and gradient enhancement processing are performed on standard temperature field data and micro-overcharge temperature field data to calculate and determine the micro-overcharge temperature rise characteristics, including: sequentially performing Gaussian filtering noise reduction and gradient sharpening enhancement processing on the standard temperature field data and micro-overcharge temperature field data, and generating a temperature field comparison map of the standard temperature field data and micro-overcharge temperature field data; the Gaussian filtering noise reduction is performed using a Gaussian filter with a preset variance; the gradient sharpening enhancement processing is performed using the Sobel operator to calculate the gradient; based on the temperature field comparison map, temperature field comparison data of the standard temperature field data and micro-overcharge temperature field data is obtained; based on the temperature field comparison data, the average micro-overcharge temperature in the micro-overcharge temperature field data is higher than the standard temperature. The region where the sum of the standard average temperature and the standard deviation of temperature in the overcharge temperature field data meets a preset first threshold is defined as the abnormal temperature rise region. For the abnormal temperature rise region, the area is calculated using image pixel statistics. Based on the temperature field comparison data, the temperature difference between the overcharge temperature field data of all pixels and the corresponding standard temperature field data is determined. The maximum value among all pixel temperature differences is determined as the maximum temperature rise. Based on the temperature field comparison data, the temperature change rate of adjacent pixels in the overcharge temperature field data is calculated, and the maximum value among all temperature change rates is determined as the temperature rise gradient. The maximum temperature rise, the temperature rise gradient, and the area of ​​the abnormal temperature rise region are defined as the overcharge temperature rise characteristics.

[0011] Furthermore, according to one aspect of the method of this application, the change curves of standard electrochemical data and micro-overcharge chemical data are compared to obtain micro-overcharge chemical change characteristics, including: calculating the capacity decay rate based on the ratio of standard charging capacity to micro-overcharge charging capacity; obtaining the voltage offset based on the offset difference between standard voltage and micro-overcharge voltage in the same charging stage; and determining the capacity decay rate and voltage offset as micro-overcharge chemical change characteristics.

[0012] Furthermore, according to one aspect of the method of this application, the aging levels include: Level 1 slight aging, Level 2 moderate aging, and Level 3 severe aging; the aging types include at least one of the following: electrolyte decomposition, lithium plating, and solid electrolyte interphase (SEI) film thickening.

[0013] Furthermore, according to one aspect of the method of this application, ultrasonic differential parameters, micro-overcharge temperature rise characteristics, and micro-overcharge chemical change characteristics are input into a pre-trained aging identification model to obtain the aging identification result of the battery under test. This includes: normalizing the ultrasonic differential parameters, micro-overcharge temperature rise characteristics, and micro-overcharge chemical change characteristics, and constructing a multi-dimensional feature vector based on the normalized ultrasonic differential parameters, micro-overcharge temperature rise characteristics, and micro-overcharge chemical change characteristics; inputting the multi-dimensional feature vector into a random forest classification model to output the aging level, aging region, and aging type of the battery under test, thereby obtaining the aging identification result; the aging region is the abnormal temperature rise region.

[0014] Furthermore, according to one aspect of the method of this application, the method further includes: determining the aging type as electrolyte decomposition when the sound velocity difference is greater than or equal to 0.5 percent and the maximum temperature rise is greater than or equal to 0.3 percent; determining the aging type as lithium plating when the amplitude difference is greater than or equal to 2 percent and the area of ​​the abnormal temperature rise region meets a preset second threshold; and determining the aging type as SEI film thickening when the phase difference is greater than or equal to 2 percent and the capacitance decay rate is greater than or equal to 5 percent.

[0015] According to another aspect of this application, a battery aging detection device is provided. The device includes: an acquisition unit, configured to perform standard charging and micro-overcharging on the battery under test, acquiring first ultrasonic echo response data, standard temperature field data, and standard electrochemical data corresponding to the standard charging treatment, and acquiring second ultrasonic echo response data, micro-overcharging temperature field data, and micro-overcharging chemical data corresponding to the micro-overcharging treatment; the standard charging treatment is normal charging based on the rated parameters of the battery under test; the micro-overcharging treatment is abnormal charging with a charging voltage higher than the rated parameters; a first calculation unit, configured to perform differential processing on the first ultrasonic echo response data and the second ultrasonic echo response data to calculate ultrasonic differential parameters; the ultrasonic differential parameters include at least: sound velocity difference, amplitude difference, and phase difference; the second... The second calculation unit performs noise reduction and gradient enhancement processing on the standard temperature field data and the micro-overcharge temperature field data to calculate and determine the micro-overcharge temperature rise characteristics. The micro-overcharge temperature rise characteristics include at least: maximum temperature rise, temperature rise gradient, and area of ​​abnormal temperature rise region. The comparison unit performs change curve comparison processing on the standard electrochemical data and the micro-overcharge chemical data to obtain the micro-overcharge chemical change characteristics. The micro-overcharge chemical change characteristics include at least: capacity decay rate and voltage offset. The aging identification unit inputs the ultrasonic differential parameters, micro-overcharge temperature rise characteristics, and micro-overcharge chemical change characteristics into a pre-trained aging identification model to obtain the aging identification results of the battery under test. The aging identification model is a random forest classification model. The aging identification results include at least one of the following: aging level, aging region, and aging type.

[0016] This application provides a method and apparatus for detecting battery aging. The method involves performing standard charging and micro-overcharging on the battery under test to obtain first ultrasonic echo response data, standard temperature field data, and standard electrochemical data corresponding to the standard charging treatment, and second ultrasonic echo response data, micro-overcharging temperature field data, and micro-overcharging electrochemical data corresponding to the micro-overcharging treatment. The standard charging treatment is normal charging based on the rated parameters of the battery under test; the micro-overcharging treatment is abnormal charging with a charging voltage higher than the rated parameters. The first and second ultrasonic echo response data are differentially processed to calculate ultrasonic differential parameters. These ultrasonic differential parameters include at least: sound velocity difference, amplitude difference, and phase difference. The standard temperature field data and micro-overcharging temperature field data are then processed. The overcharge temperature field data undergoes noise reduction and gradient enhancement processing to calculate and determine the micro-overcharge temperature rise characteristics. These characteristics include at least: maximum temperature rise, temperature rise gradient, and area of ​​abnormal temperature rise region. The change curves of standard electrochemical data and micro-overcharge chemical data are compared to obtain micro-overcharge chemical change characteristics. These characteristics include at least: capacity decay rate and voltage offset. Ultrasonic differential parameters, micro-overcharge temperature rise characteristics, and micro-overcharge chemical change characteristics are input into a pre-trained aging identification model to obtain the aging identification results of the battery under test. The aging identification model is a random forest classification model. The aging identification results include at least one of the following: aging level, aging region, and aging type. This addresses the shortcomings of existing single ultrasonic, infrared, or electrochemical detection methods, which suffer from insufficient sensitivity, inability to monitor in situ, and difficulty in locating aging regions. This application can achieve accurate identification, regional location, and aging mechanism tracing of early micro-overcharge aging by synergistically enhancing weak aging signals using ultrasonic, infrared, and electrochemical multi-dimensional features. In summary, the technical solution provided in this application can improve the accuracy of battery aging detection, can comprehensively consider multiple factors, and can adapt to various application scenarios.

[0017] It should be understood that both the foregoing general description and the following detailed description are exemplary and intended to provide further illustration of the claimed technology. Attached Figure Description

[0018] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The accompanying drawings are used to provide a further understanding of the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0019] Figure 1 A schematic flowchart illustrating a battery aging detection method provided in an embodiment of this application; Figure 2This is a structural block diagram of a battery aging detection device provided in an embodiment of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application more apparent, exemplary embodiments according to this application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein.

[0021] Currently, existing detection technologies mainly fall into three categories, all of which lack the ability to identify the weak aging signals in the early stages of micro-overcharging: The first category, ultrasonic testing, while capable of identifying structural defects, produces weak ultrasonic signal changes due to SEI film thickening and electrolyte decomposition in the early stages of micro-overcharging, easily masked by noise and difficult to detect. The second category, infrared thermal imaging, while capable of capturing temperature rises, shows localized temperature rises of only 0.1-0.5℃ in the early stages of micro-overcharging, making it difficult to distinguish from environmental noise and resulting in low detection accuracy. The third category, electrochemical impedance spectroscopy, while reflecting the degree of aging, cannot pinpoint the aging area, and requires interruption of charging and discharging, making in-situ monitoring impossible and unsuitable for various application scenarios. In summary, existing battery aging detection methods have low accuracy, fail to consider multiple factors, and are ill-suited for diverse application scenarios.

[0022] Therefore, to address the aforementioned problems, this application provides a method for detecting battery aging. Compared to existing methods that rely solely on ultrasound, infrared, or electrochemical detection, which suffer from insufficient sensitivity, inability to perform in-situ monitoring, and difficulty in locating aging areas, this application leverages the synergistic enhancement of weak aging signals through multi-dimensional features of ultrasound, infrared, and electrochemical methods. This enables accurate identification, localization, and tracing of aging mechanisms in early stages of micro-overcharging. In summary, the technical solution provided in this application improves the accuracy of battery aging detection, comprehensively considers multiple factors, and is adaptable to various application scenarios.

[0023] First, this application provides a method for detecting battery aging. Please refer to... Figure 1 , Figure 1 This is a schematic flowchart illustrating a battery aging detection method provided in an embodiment of this application. Figure 1 As shown, the method includes: In step S101, the battery under test is subjected to standard charging and micro-overcharging treatments, respectively. The first ultrasonic echo response data, standard temperature field data, and standard electrochemical data corresponding to the standard charging treatment are obtained, and the second ultrasonic echo response data, micro-overcharging temperature field data, and micro-overcharging chemical data corresponding to the micro-overcharging treatment are obtained. The standard charging treatment is a normal charging based on the rated parameters of the battery under test; the micro-overcharging treatment is an abnormal charging with a charging voltage higher than the rated parameters. In step S102, the first ultrasonic echo response data and the second ultrasonic echo response data are differentially processed to calculate ultrasonic differential parameters; the ultrasonic differential parameters include at least: sound velocity difference, amplitude difference and phase difference; In step S103, noise reduction and gradient enhancement processing are performed on the standard temperature field data and the micro-overcharge temperature field data, and the micro-overcharge temperature rise characteristics are calculated and determined; the micro-overcharge temperature rise characteristics include at least: maximum temperature rise, temperature rise gradient, and area of ​​abnormal temperature rise region; In step S104, the standard electrochemical data and the micro-overcharge chemical data are compared and processed to obtain the micro-overcharge chemical change characteristics; the micro-overcharge chemical change characteristics include at least: capacity decay rate and voltage offset. In step S105, the ultrasonic differential parameters, micro-overcharge temperature rise characteristics, and micro-overcharge chemical change characteristics are input into the pre-trained aging identification model to obtain the aging identification results of the battery to be tested; the aging identification model is a random forest classification model; the aging identification results include at least one of the following: aging level, aging region, and aging type.

[0024] In this application, standard charging processing can be understood as a routine charging operation performed according to the rated parameters (such as rated voltage, rated current, etc.) of the battery under test. Its purpose is to establish a healthy baseline state for the battery under test, eliminate the interference of initial state differences on subsequent test results, and ensure the accuracy and comparability of the test data. The first ultrasonic echo response data, standard temperature field data, and standard electrochemical data obtained after standard charging processing can be understood as the basic test data collected by the ultrasonic module, infrared thermal imager, and charge / discharge module under a healthy and normal battery state. These serve as the benchmark for subsequent comparative analysis with data under a slightly overcharged state, as will be explained in detail later.

[0025] In this application, micro-overcharge treatment can be understood as an abnormal charging operation where the charging voltage exceeds the rated voltage in the rated parameters of the battery under test. Its core is to simulate micro-overcharge scenarios that may occur in actual use, inducing early aging phenomena related to micro-overcharge in the battery under test (such as SEI film thickening, electrolyte decomposition, lithium plating, etc.), thereby capturing aging-related detection data under micro-overcharge conditions. The second ultrasonic echo response data, micro-overcharge temperature field data, and micro-overcharge chemical data obtained after micro-overcharge treatment can be understood as detection data, possibly containing aging signals, collected by the corresponding detection modules of the battery under test under micro-overcharge conditions. These are used to compare with the benchmark data corresponding to standard charging treatment, thereby extracting aging-related features. Details will be elaborated later.

[0026] In this application, ultrasonic differential parameters can be understood as parameters that reflect the difference between the ultrasonic signals of a first ultrasonic echo response data under standard charging conditions and a second ultrasonic echo response data under micro-overcharge conditions, obtained by differential calculation. Their core function is to amplify the weak ultrasonic signal changes caused by micro-overcharge, facilitating the capture of early aging signals during micro-overcharge. The ultrasonic differential parameters in this application include at least: sound velocity difference, amplitude difference, and phase difference. Specifically, the sound velocity difference is the difference between the standard sound velocity in the first ultrasonic echo response data and the micro-overcharge sound velocity in the second ultrasonic echo response data; the amplitude difference is the difference between the standard amplitude and the micro-overcharge amplitude; and the phase difference is the difference between the standard phase and the micro-overcharge phase. These three parameters together reflect the changes in the ultrasonic signal before and after micro-overcharge, providing an ultrasonic dimension basis for aging identification.

[0027] In this application, the micro-overcharge temperature rise feature can be understood as a feature parameter extracted from standard temperature field data and micro-overcharge temperature field data after noise reduction and gradient enhancement processing, which reflects the temperature change of the battery under micro-overcharge conditions. This parameter is used to capture the weak temperature rise signal in the early stages of micro-overcharge and assist in locating aging areas. The micro-overcharge temperature rise feature of this application includes at least: maximum temperature rise, temperature rise gradient, and abnormal temperature rise area. Specifically, the maximum temperature rise is the maximum difference between the battery surface temperature under micro-overcharge conditions and the corresponding temperature under standard conditions; the temperature rise gradient is the rate of temperature change between adjacent locations in the micro-overcharge temperature field, reflecting the severity of temperature changes; and the abnormal temperature rise area is the area occupied by regions where temperature changes exceed the normal range under micro-overcharge conditions, which can be used to locate aging areas.

[0028] In this application, the micro-overcharge chemical change characteristics can be understood as characteristic parameters that reflect the changes in battery electrochemical performance under micro-overcharge conditions, obtained by comparing the change curves of standard electrochemical data and micro-overcharge chemical data. These parameters are used to demonstrate the impact of micro-overcharge on battery electrochemical performance. The micro-overcharge chemical change characteristics of this application include at least: capacity decay rate and voltage offset. The capacity decay rate is calculated based on the ratio of the standard charging capacity to the micro-overcharge charging capacity, reflecting the battery capacity loss caused by micro-overcharge. The voltage offset is the difference between the standard voltage and the micro-overcharge voltage at the same charging stage, reflecting the impact of micro-overcharge on battery voltage characteristics.

[0029] In this application, the pre-trained aging recognition model can be understood as a model trained in advance with a large amount of battery sample data of different aging degrees and types, possessing aging recognition capabilities. Its core function is to receive multi-dimensional detection feature parameters and output the corresponding battery aging recognition results. The aging recognition model in this application is a random forest classification model. This is because the random forest classification model has strong noise resistance, generalization ability, and feature fusion ability, and can effectively process multi-dimensional feature parameters of ultrasound, infrared, and electrochemical, accurately explore the correlation between each feature and battery aging, adapt to the comprehensive recognition needs of weak aging signals in the early, middle, and late stages of micro-overcharging, and has a simple training process and high recognition efficiency, which can meet the detection needs of various application scenarios.

[0030] In this application, the aging identification result output by the aging identification model can be understood as a comprehensive judgment result made by the model based on the input multi-dimensional feature parameters on the battery aging state, used to intuitively reflect the aging condition of the battery. The aging identification result of this application includes at least one of the following: aging level, aging region, and aging type. Among them, the aging level is used to reflect the severity of battery aging, which can be classified in combination with the aging degree caused by micro-overcharging; the aging region is the abnormal temperature rise region determined in the micro-overcharging temperature rise feature, corresponding to the specific location where battery aging may occur; the aging type corresponds to the form of battery aging that may be caused by micro-overcharging, which can be determined by a comprehensive judgment based on multi-dimensional feature parameters. Details will be elaborated below.

[0031] Specifically, the following steps can be included when conducting battery aging tests: Step 1: Perform standard charging on the battery to be tested. Charge the battery normally according to its rated parameters. During the charging process, use an ultrasonic module to continuously collect the first ultrasonic echo response data, use an infrared thermal imager to continuously collect standard temperature field data, and use a charge-discharge module to continuously collect standard electrochemical data. Store all test data under standard conditions. Step 2: After the standard charging process is completed and the battery returns to a stable state, perform a micro-overcharge process on the battery to be tested. Use a charging voltage higher than the rated voltage of the battery to perform abnormal charging. During the micro-overcharge process, keep the acquisition parameters of the ultrasonic module, infrared thermal imager, and charge and discharge module consistent with those during the standard charging process. Simultaneously acquire the second ultrasonic echo response data, micro-overcharge temperature field data, and micro-overcharge chemical data, and store all detection data under the micro-overcharge state. Step 3: Perform differential processing on the collected first and second ultrasonic echo response data to calculate the sound velocity difference, amplitude difference, and phase difference, respectively, and obtain the ultrasonic differential parameters. Step 4: Denoising and gradient enhancement are performed on the standard temperature field data and the micro-overcharge temperature field data to eliminate interference from environmental noise and acquisition noise, enhance the identification of weak temperature rise signals, and then calculate and determine the maximum temperature rise, temperature rise gradient and abnormal temperature rise area to obtain the micro-overcharge temperature rise characteristics. Step 5: Compare the change curves of the standard electrochemical data and the micro-overcharge chemical data, plot the change curves of the two and perform comparative analysis, calculate the capacity decay rate and voltage offset, and obtain the chemical change characteristics of micro-overcharge. Step 6: Input the obtained ultrasonic differential parameters, micro-overcharge temperature rise characteristics, and micro-overcharge chemical change characteristics into the pre-trained random forest classification model; analyze and process the input multi-dimensional features through the random forest classification model, and output the aging identification result of the battery to be tested. The result may include one or more of the following: aging level, aging region, and aging type, thus completing the detection of battery aging.

[0032] The following will describe, in sequence, how to obtain the first ultrasonic echo response data, standard temperature field data, and standard electrochemical data corresponding to the standard charging treatment, and how to obtain the second ultrasonic echo response data, micro-overcharge temperature field data, and micro-overcharge chemical data corresponding to the micro-overcharge treatment, including: First, we will explain how to obtain the first ultrasonic echo response data, standard temperature field data, and standard electrochemical data corresponding to the standard charging process, including: During the standard charging process of the battery under test, the ultrasonic module sends a first ultrasonic signal to the battery under test within a preset first interval to obtain first ultrasonic echo response data; the first ultrasonic echo response data includes at least: standard sound velocity, standard amplitude and standard phase; the standard charging process includes: first constant current charging to the rated voltage of the rated parameters, and then constant voltage charging to the rated current of the rated parameters. Using an infrared thermal imager, standard temperature field data of the surface of the battery to be tested is acquired within a preset second interval; the standard temperature field data includes at least: standard average temperature and temperature standard deviation; Standard electrochemical data are obtained using a charge / discharge module; the standard electrochemical data includes at least the standard charge capacity and the standard voltage.

[0033] In this application, the specific standard charging process includes: first, constant current charging to the rated voltage of the rated parameters, and then constant voltage charging to the rated current of the rated parameters. This is because this charging method conforms to the normal operating conditions of the battery, enabling the battery to enter a stable and uniform normal charging state, avoiding additional interference introduced by abnormal charging methods, and ensuring that the collected benchmark data truly reflects the initial health state of the battery.

[0034] In this application, the first ultrasonic echo response data includes at least: standard sound velocity, standard amplitude, and standard phase. The standard sound velocity reflects the uniformity and density of the internal medium of the battery; the standard amplitude characterizes the transmission loss of the ultrasonic signal within the battery; and the standard phase reflects the propagation delay characteristics of the ultrasonic signal at different interfaces within the battery. These three elements together constitute the ultrasonic reference characteristics under normal battery conditions.

[0035] In this application, the standard temperature field data includes at least: standard average temperature and temperature standard deviation. The standard average temperature characterizes the overall heat generation level of the battery under normal charging conditions, while the temperature standard deviation reflects the uniformity of the temperature distribution on the battery surface, eliminating the influence of ambient temperature fluctuations and localized accidental temperature rises, thus ensuring the stability of the temperature reference.

[0036] In this application, the standard electrochemical data includes at least: standard charge capacity and standard voltage. The standard charge capacity reflects the battery's actual energy storage capacity under normal operating conditions, while the standard voltage reflects the voltage plateau characteristics of the battery during normal charging. Both accurately characterize the electrochemical performance benchmark of the battery under normal conditions.

[0037] Specifically, when acquiring data during the standard charging process, this includes: The battery under test is placed in a stable environment and charged according to the rated parameters using constant current and constant voltage standard charging. During the entire charging process, ultrasonic signals are periodically emitted and echo data is collected at the first interval, and battery surface temperature field data is continuously collected at the second interval. At the same time, the charging capacity and voltage changes are recorded in real time through the charging and discharging module, and all collected data are saved as standard reference data.

[0038] Next, the method for obtaining the second ultrasonic echo response data, micro-overcharge temperature field data, and micro-overcharge chemical data corresponding to the micro-overcharge treatment is explained, including: During the micro-overcharge treatment of the battery under test, the ultrasonic module sends a second ultrasonic signal to the battery under test within the first interval to obtain the second ultrasonic echo response data of the battery under test; the second ultrasonic echo response data includes at least: micro-overcharge sound velocity, micro-overcharge amplitude and micro-overcharge phase; the micro-overcharge treatment includes: first constant current charging to a preset overcharge voltage value higher than the rated voltage of the rated parameters, and then constant voltage charging to the rated current of the rated parameters; Using an infrared thermal imager within the second interval, micro-overcharge temperature field data of the surface of the battery under test is acquired; the micro-overcharge temperature field data includes at least: the average micro-overcharge temperature and the standard deviation of the micro-overcharge temperature; The chemical data of micro-overcharge is obtained using a charge-discharge module; the chemical data of micro-overcharge includes at least: micro-overcharge capacity and micro-overcharge voltage.

[0039] In this application, the specific micro-overcharge processing includes: first, constant current charging to a preset overcharge voltage value higher than the rated voltage of the rated parameters, and then constant voltage charging to the rated current of the rated parameters. This is because this method can accurately simulate the slight overcharge scenarios commonly seen in actual use, causing the battery to gradually produce early aging phenomena such as SEI film thickening and electrolyte decomposition, while maintaining a constant voltage cutoff current consistent with standard charging, ensuring that the detection conditions under the two charging states are comparable, facilitating subsequent extraction of differential features.

[0040] In this application, the second ultrasonic echo response data includes at least: micro-overcharge sound velocity, micro-overcharge amplitude, and micro-overcharge phase. Among them, the micro-overcharge sound velocity is used to reflect the degree of degradation of the internal dielectric of the battery after micro-overcharging, the micro-overcharge amplitude is used to reflect the additional attenuation of the ultrasonic signal due to internal aging, and the micro-overcharge phase is used to characterize the time delay shift caused by changes in the internal interface structure. The three can intuitively reflect the changes in ultrasonic characteristics caused by micro-overcharging.

[0041] In this application, the micro-overcharge temperature field data includes at least: the average micro-overcharge temperature and the standard deviation of the micro-overcharge temperature. The average micro-overcharge temperature reflects the overall temperature rise level of the battery under micro-overcharge conditions, while the standard deviation of the micro-overcharge temperature characterizes the non-uniformity of the temperature distribution on the battery surface. This effectively identifies localized abnormal heating areas caused by micro-overcharge, providing a basis for locating aging areas.

[0042] In this application, the micro-overcharge chemical data includes at least: micro-overcharge capacity and micro-overcharge voltage. The micro-overcharge capacity reflects the degradation of the battery's energy storage capacity caused by micro-overcharging, while the micro-overcharge voltage reflects the degree of voltage plateau shift and distortion during charging, directly characterizing the impact of micro-overcharging on the battery's electrochemical performance.

[0043] Specifically, when acquiring data during the micro-overcharge processing, the following are included: Under the same environmental conditions as standard charging, the battery under test is subjected to constant current and constant voltage micro-overcharge with a preset overcharge voltage. The first and second interval acquisition strategies are consistent with standard charging, and ultrasonic echo data, temperature field data and charge and discharge data are collected simultaneously. All response data under micro-overcharge state are fully recorded for comparison with standard reference data.

[0044] The following will describe in sequence how to obtain ultrasonic differential parameters, micro-overcharge temperature rise characteristics, and micro-overcharge chemical change characteristics, including: First, we will explain how to use the first and second ultrasonic echo response data for differential processing to calculate the ultrasonic differential parameters, including: The difference between the standard speed of sound and the slightly overcharged speed of sound is calculated to obtain the speed of sound difference; The difference between the standard amplitude and the micro-overcharge amplitude is calculated to obtain the amplitude difference; The phase difference is obtained by calculating the difference between the standard phase and the micro-overcharge phase; The sound velocity difference, amplitude difference, and phase difference are defined as ultrasonic differential parameters.

[0045] For example, when calculating ultrasonic differential parameters, the following can be included: Using the standard sound velocity, standard amplitude, and standard phase collected under standard charging conditions as a reference, the difference calculation is performed with the sound velocity, amplitude, and phase at the corresponding positions under micro-overcharge conditions. Common mode noise caused by the environment and the acquisition system is eliminated, highlighting the weak signal changes caused by micro-overcharge aging, and finally obtaining ultrasonic differential parameters that can reflect the degree of aging inside the battery.

[0046] Next, the paper explains how to use standard temperature field data and micro-overcharge temperature field data for noise reduction and gradient enhancement, and calculate and determine the micro-overcharge temperature rise characteristics, including: Gaussian filtering and gradient sharpening enhancement are performed sequentially on the standard temperature field data and the micro-overcharge temperature field data, and a temperature field comparison map of the standard temperature field data and the micro-overcharge temperature field data is generated. The Gaussian filtering and noise reduction uses a Gaussian filter with a preset variance. The gradient sharpening enhancement is performed by calculating the gradient using the Sobel operator. Based on the temperature field comparison diagram, temperature field comparison data of standard temperature field data and micro-overcharge temperature field data are obtained; Based on temperature field comparison data, the region in the micro-overcharge temperature field data that has a higher average temperature than the sum of the standard average temperature and temperature standard deviation in the standard temperature field data, and the region in the micro-overcharge temperature field data that meets the preset first threshold, is identified as an abnormal temperature rise region. For areas of abnormal temperature rise, the area of ​​the abnormal temperature rise region is calculated using image pixel statistics; Based on temperature field comparison data, the temperature difference between the micro-overcharge temperature field data of all pixels and the standard temperature field data at the corresponding positions is determined. The maximum temperature rise is determined by taking the maximum value among the temperature differences of all pixels. Based on temperature field comparison data, the temperature change rate of adjacent pixels in the micro-overcharge temperature field data is calculated, and the maximum value among all temperature change rates is determined as the temperature rise gradient. The maximum temperature rise, temperature rise gradient, and area of ​​abnormal temperature rise region are defined as the characteristics of micro-overcharge temperature rise.

[0047] In this application, the noise reduction is Gaussian filtering noise reduction, which uses a Gaussian filter with a preset variance. This is because Gaussian filtering can smooth environmental noise and acquisition noise while better preserving the overall distribution characteristics of the temperature field, without excessively blurring the real micro-temperature rise area. It is suitable for the preprocessing of weak temperature signals in the early stage of micro-overcharging and can be used in this invention.

[0048] In this application, gradient enhancement is gradient sharpening enhancement, which is performed by using the Sobel operator (Sobel) to calculate the gradient. This is because the Sobel operator has a good detection effect on the edges of grayscale (temperature) changes, and can significantly enhance the contour and gradient changes of small temperature rise areas, making it easier to identify local hot spots caused by micro-overcharging and improve the positioning accuracy of aging areas. It can be used in this invention.

[0049] In this application, the temperature field comparison diagram can be understood as a visual comparison of the standard temperature field and the micro-overcharge temperature field in the form of an image, which intuitively shows the temperature distribution difference of the battery surface under the two charging states, making it easier to observe the location and range of abnormal temperature rise.

[0050] In this application, the temperature field comparison data can be understood as quantitative data obtained by subtracting standard temperature field data from micro-overcharge temperature field data pixel by pixel and normalizing it, which is used to objectively reflect the magnitude and distribution of temperature changes at each location.

[0051] In this application, the abnormal temperature rise area can be understood as a region on the battery surface that experiences localized heating due to aging reactions (such as electrolyte decomposition and lithium plating) caused by micro-overcharging, with a temperature significantly higher than that under normal charging conditions and unevenly distributed.

[0052] In this application, image pixel statistics can be understood as counting the number of pixels in the regions marked as abnormal temperature rises in a temperature field image, and then converting this number to the actual physical size of the image to obtain the actual area of ​​the abnormal region. Thus, the area of ​​the abnormal temperature rise region can be obtained through image pixel statistics.

[0053] In this application, the temperature difference between the micro-overcharge temperature field data of all pixels and the standard temperature field data of the corresponding position can be understood as the difference between the temperature of each position under the micro-overcharge state and the temperature of the same position under the normal charging state. It can reflect the temperature rise caused by micro-overcharge at that position, and then the maximum value can be taken from it to determine the maximum temperature rise.

[0054] In this application, the temperature change rate of adjacent pixels in the micro-overcharge temperature field data can be understood as the rate of temperature change per unit distance, reflecting the steepness of the temperature distribution and the severity of the local aging reaction. The maximum value can then be taken as the temperature rise gradient.

[0055] For example, when determining the characteristics of micro-overcharge temperature rise, the following may be included: First, Gaussian filtering is applied to the two sets of temperature field data to remove noise. Then, the Sobel operator is used to enhance the temperature edges, generate a temperature field comparison map, and extract the comparison data. The abnormal temperature rise area is delineated based on the temperature difference, and the abnormal area is obtained through pixel statistics. The maximum temperature rise is obtained by traversing all pixels, and the temperature rise gradient is obtained by calculating the temperature change rate of adjacent points. Finally, the data are combined to form a complete micro-overcharge temperature rise feature.

[0056] Then, we will explain in detail how to use standard electrochemical data and micro-overcharge chemical data to compare and process the change curves to obtain the characteristics of micro-overcharge chemical changes, including: The capacity decay rate is calculated based on the ratio of standard charging capacity to micro-overcharge charging capacity. The voltage offset is obtained based on the offset difference between the standard voltage and the micro-overcharge voltage during the same charging stage; The capacitance decay rate and voltage offset were identified as characteristics of micro-overcharge chemical changes.

[0057] In this application, the capacity decay rate is determined based on the ratio of the standard charging capacity to the micro-overcharge charging capacity. This is because capacity decay can directly reflect the degree of damage to the battery's energy storage capacity caused by micro-overcharging. Using a ratio can eliminate the influence of individual battery initial capacity differences, making the aging degree of different batteries comparable.

[0058] In this application, the voltage offset can be obtained based on the offset difference between the standard voltage and the micro-overcharge voltage at the same charging stage. This is because the voltage change at the same charging stage can better reflect the changes in the internal electrochemical characteristics of the battery, avoid the interference of voltage differences caused by different charging progress, and accurately characterize the voltage plateau distortion caused by micro-overcharging.

[0059] For example, determining the characteristics of micro-overcharge chemical changes may include: Using the charging capacity and voltage curves during the standard charging process as a benchmark, the corresponding curves during the micro-overcharge process are aligned and compared at the same charging time and the same charging capacity point. The relative capacity change is calculated to obtain the capacity decay rate, and the voltage difference at the same charging stage is calculated to obtain the voltage offset. Finally, the micro-overcharge chemical change characteristics used for aging judgment are formed.

[0060] The following details how to determine the aging identification result of the battery under test based on ultrasonic differential parameters, micro-overcharge temperature rise characteristics, and micro-overcharge chemical change characteristics, including: First, let me state the aging identification results of this application: The aging identification results include three levels: Level 1 Slight Aging, Level 2 Moderate Aging, and Level 3 Severe Aging. Level 1 Slight Aging corresponds to batteries that only show initial signs of aging, with no significant performance degradation, and can still be used normally. Level 2 Moderate Aging corresponds to batteries that show obvious aging characteristics and some performance degradation, requiring monitoring of their usage. Level 3 Severe Aging corresponds to batteries that have significantly aged, posing a safety risk, and it is recommended to stop using them.

[0061] The aging types identified in the aging identification results include at least one of the following: electrolyte decomposition, lithium plating, and SEI film thickening at the solid electrolyte interface. Electrolyte decomposition occurs when micro-overcharging causes side reactions in the electrolyte, producing gases and byproducts. Lithium plating occurs when lithium ions precipitate on the negative electrode surface, forming lithium dendrites. SEI film thickening is caused by the continuous growth of the solid electrolyte interface film, leading to increased battery impedance and reduced capacity.

[0062] The aging area in the aging identification results can be understood as the specific location on the battery surface where local temperature rise and internal structure deterioration occur due to micro-overcharge aging reaction. It is the area where battery aging occurs in a concentrated manner.

[0063] Next, it explains how to obtain aging identification results using ultrasonic differential parameters, micro-overcharge temperature rise characteristics, and micro-overcharge chemical change characteristics, including: The ultrasonic differential parameters, micro-overcharge temperature rise characteristics, and micro-overcharge chemical change characteristics were normalized, and a multi-dimensional feature vector was constructed based on the normalized ultrasonic differential parameters, micro-overcharge temperature rise characteristics, and micro-overcharge chemical change characteristics. The multi-dimensional feature vector is input into the random forest classification model, which outputs the aging level, aging region and aging type of the battery to be detected, and obtains the aging identification result; the aging region is the abnormal temperature rise region.

[0064] In this application, normalization can be understood as mapping ultrasonic, infrared, and electrochemical feature data of different dimensions and orders of magnitude to a unified numerical range, eliminating the impact of dimensional differences on the model's recognition accuracy, and improving the accuracy of feature fusion and classification judgment.

[0065] In this application, the multi-dimensional feature vector can be understood as a one-dimensional feature sequence formed by combining ultrasonic differential parameters, micro-overcharge temperature rise features, and micro-overcharge chemical change features in a preset order, which is used as a unified input for the random forest classification model to realize the comprehensive utilization of multi-dimensional information.

[0066] For example, when determining the aging identification result, it may include: First, the features of each dimension are normalized to eliminate the influence of differences in numerical range. Then, the normalized features are combined to construct a multi-dimensional feature vector. This vector is then input into a pre-trained random forest classification model, which comprehensively judges and outputs the corresponding aging level, aging region, and aging type to determine the aging identification result.

[0067] Finally, in addition to the above embodiments, the method for determining aging identification results in this application also includes: When the sound velocity difference is greater than or equal to 0.5 percent and the maximum temperature rise is greater than or equal to 0.3 percent, the aging type is determined to be electrolyte decomposition. When the amplitude difference is greater than or equal to 2 percent and the area of ​​the abnormal temperature rise region meets the preset second threshold, the aging type is determined to be lithium plating. When the phase difference is greater than or equal to 2 percent and the capacitance decay rate is greater than or equal to 5 percent, the aging type is determined to be SEI film thickening.

[0068] In one embodiment of this application, by combining the sound velocity difference and the maximum temperature rise as the judgment criteria, the aging type of electrolyte decomposition caused by micro-overcharging can be accurately identified, thereby achieving targeted aging mechanism judgment.

[0069] In another embodiment of this application, the combined determination criteria of amplitude difference and abnormal temperature rise area can effectively identify lithium plating aging, which is especially suitable for early lithium plating detection of lithium metal batteries and high-rate lithium batteries.

[0070] In another embodiment of this application, by combining the phase difference and the capacity decay rate as judgment conditions, it is possible to accurately identify SEI film thickening aging and reflect the intrinsic relationship between battery interface impedance change and capacity decay.

[0071] For example, this application also provides another complete method for detecting battery aging, including: Step 1: Select 3-5 new batteries from the same batch as a reference sample group, place them in a constant temperature environment of 25±0.5℃, and perform a standard charging regime (0.5C constant current charging to the rated voltage, then constant voltage charging to the current ≤0.01C, where C is the reference current rate corresponding to the rated capacity of the battery, and 1C means charging and discharging the battery with a current equal to the rated capacity of the battery). The broadband ultrasonic module uses a frequency scanning mode of 5-15MHz to synchronously collect ultrasonic echo signals from different depths of each reference sample, and extracts the sound velocity, amplitude, and phase parameters as unified reference parameters. The high-resolution infrared thermal imager continuously collects the surface temperature field of each reference sample, records the average temperature and temperature standard deviation, and uses them as reference temperature field data. The programmable charge and discharge module records the standard charging capacity and voltage curves of each reference sample as reference electrochemical data. The above unified reference parameters, reference temperature field data, and reference electrochemical data are stored in the database of the collaborative analysis unit to complete the reference calibration.

[0072] Step 2: Place the battery under test in a constant temperature environment consistent with the benchmark calibration stage. First, perform a 0.05C low-current discharge to the cutoff voltage to eliminate differences in residual battery charge. Then, let it stand for 30 minutes to ensure that the battery temperature is consistent with the ambient temperature, ensuring a uniform initial state for testing. Perform a micro-overcharge program on the battery under test, where the charging voltage is set to 1.05-1.1 times the rated cutoff voltage and the charging current is 0.1C. The charging and discharging module records the voltage, current, and charging capacity changes in real time during the charging process. Simultaneously, start ultrasonic and infrared collaborative detection. The ultrasonic module uses the 5-15MHz frequency scanning mode of the benchmark calibration stage, acquiring ultrasonic echo signals every 5 minutes to extract the sound velocity, amplitude, and phase parameters of the battery under test. The infrared thermal imager acquires surface temperature field data of the battery under test every 1 minute, recording the real-time average temperature and temperature standard deviation. All acquired data are time-stamped precisely aligned through a signal synchronization acquisition card to ensure the temporal consistency of ultrasonic, infrared, and electrochemical data.

[0073] Step 3: Targeted enhancement processing is performed on the acquired signals. For the ultrasonic signals, a differential comparison algorithm is used. First, environmental noise and acquisition system noise are filtered out. Then, the ultrasonic parameters of the battery under test are differentially calculated with the unified reference parameters in the database to obtain the sound velocity difference. , amplitude difference Phase difference The infrared signal employs a gradient enhancement algorithm. First, it removes ambient background temperature interference, then enhances the temperature gradient using the Sobel operator to extract the micro-temperature rise characteristics of the battery surface under test, including the maximum temperature rise. Temperature gradient G and anomalous region area S; electrochemical data: by comparing the charging capacity and voltage curves of the battery under test with those of the reference electrochemical data, the charging capacity decay rate C and voltage plateau shift are calculated. .

[0074] Step 4: Construct three-dimensional feature vectors for ultrasound, infrared, and electrochemical imaging, and then... , , As an ultrasonic feature G and S are infrared features, C and S are infrared features. As an electrochemical feature, the pre-trained random forest classification model is input (this model is trained with 1000 battery samples of different aging degrees and types, with a classification accuracy of ≥95%). The model outputs the aging level of the battery to be tested (Level I: slight aging, Level II: moderate aging, Level III: severe aging). At the same time, the aging area inside the battery is located by combining the ultrasonic phase difference distribution and verified by the abnormal temperature rise area in the infrared temperature field, so as to achieve accurate location of the aging area (location error ≤0.5mm).

[0075] Step 5: Matching can also be performed based on the three-dimensional feature vectors and the aging mechanism database: If Decrease ≥0.5% and If the temperature is ≥0.3℃, it is considered electrolyte decomposition, and a warning is issued to reduce the charging voltage to avoid exacerbating side reactions; if If the temperature drops by ≥1% and localized temperature rises occur, it is determined to be lithium plating, and a warning is issued to stop micro-overcharging and check the charging parameters; if... If the offset is ≥2° and the C attenuation is ≥5%, it is determined to be SEI film thickening.

[0076] For example, this application also provides two specific embodiments of experiments using the methods of this application, including: Example of the first experiment: The lithium metal battery (rated voltage 3.8V, capacity 2500mAh) was tested for battery aging.

[0077] The detection parameters are: micro-overcharge voltage 4.0V (1.05 times the rated voltage), charging current 0.1C, and infrared abnormal temperature rise threshold. ≥0.2℃, ambient temperature during testing: 25℃.

[0078] Test results: After 15 hours of charging, ultrasonic differential analysis showed... =0.4%, =1.1%, =1.8°; Infrared detection showed a local temperature rise of 0.25°C in the negative electrode area, with an abnormal area of ​​0.5mm²; Electrochemical data showed a capacity decay of 2.2% and a slight shift in the voltage plateau; The classification model determined it to be a level I moderate aging, traced back to early lithium plating, and it is recommended to optimize the charging cut-off strategy and reduce the risk of overcharging.

[0079] Example of the second experiment: Battery aging tests were conducted on high-nickel ternary lithium-ion batteries (rated voltage 3.6V, capacity 3000mAh).

[0080] The detection parameters are as follows: micro-overcharge voltage 3.96V (1.1 times the rated voltage), charging current 0.1C, ultrasonic scanning frequency 1–20MHz, and detection ambient temperature 25℃.

[0081] Test results: After 18 hours of charging, ultrasonic differential analysis showed... =0.6%, =0.9%, =2.1°; Infrared detection showed a maximum overall temperature rise of 0.35°C with no obvious local hot spots; Electrochemical data showed a capacity decay of 5.2% and a significant voltage plateau shift; The classification model determined it to be Level II aging, and the source was traced to a significant thickening of the SEI film. It is recommended to adjust the charging rate and optimize the battery formation process.

[0082] This application also provides a device for detecting battery aging. Figure 2 A structural block diagram of a battery aging detection device provided in this application embodiment is shown below. Figure 2 As shown, the battery aging detection device 200 includes: The acquisition unit 201 is used to perform standard charging and micro-overcharging on the battery under test, respectively, to acquire the first ultrasonic echo response data, standard temperature field data, and standard electrochemical data corresponding to the standard charging process, and to acquire the second ultrasonic echo response data, micro-overcharging temperature field data, and micro-overcharging chemical data corresponding to the micro-overcharging process; the standard charging process is normal charging based on the rated parameters of the battery under test; the micro-overcharging process is abnormal charging with a charging voltage higher than the rated parameters; The first calculation unit 202 is used to perform differential processing on the first ultrasonic echo response data and the second ultrasonic echo response data to calculate ultrasonic differential parameters; the ultrasonic differential parameters include at least: sound velocity difference, amplitude difference and phase difference. The second calculation unit 203 is used to perform noise reduction and gradient enhancement processing on the standard temperature field data and the micro-overcharge temperature field data, and to calculate and determine the micro-overcharge temperature rise characteristics; the micro-overcharge temperature rise characteristics include at least: the maximum temperature rise, the temperature rise gradient, and the area of ​​the abnormal temperature rise region. The comparison unit 204 is used to perform change curve comparison processing on standard electrochemical data and micro-overcharge chemical data to obtain micro-overcharge chemical change characteristics; the micro-overcharge chemical change characteristics include at least: capacity decay rate and voltage offset. The aging identification unit 205 is used to input ultrasonic differential parameters, micro-overcharge temperature rise characteristics and micro-overcharge chemical change characteristics into the pre-trained aging identification model to obtain the aging identification result of the battery to be tested; the aging identification model is a random forest classification model; the aging identification result includes at least one of the following: aging level, aging region and aging type.

[0083] In one exemplary embodiment, the acquisition unit 201 is specifically used to: during the standard charging process of the battery under test, send a first ultrasonic signal to the battery under test using an ultrasonic module within a preset first interval to obtain first ultrasonic echo response data; the first ultrasonic echo response data includes at least: standard sound velocity, standard amplitude, and standard phase; the standard charging process includes: first constant current charging to the rated voltage of the rated parameters, and then constant voltage charging to the rated current of the rated parameters; using an infrared thermal imager within a preset second interval to acquire standard temperature field data of the surface of the battery under test; the standard temperature field data includes at least: standard average temperature and temperature standard deviation; and using a charge-discharge module to acquire standard electrochemical data; the standard electrochemical data includes at least: standard charging capacity and standard voltage.

[0084] In one exemplary embodiment, the acquisition unit 201 is specifically used to: during the micro-overcharge treatment of the battery under test, send a second ultrasonic signal to the battery under test using an ultrasonic module within a first interval to obtain second ultrasonic echo response data of the battery under test; the second ultrasonic echo response data includes at least: micro-overcharge sound velocity, micro-overcharge amplitude, and micro-overcharge phase; the micro-overcharge treatment includes: first constant current charging to a preset overcharge voltage value higher than the rated voltage of the rated parameters, and then constant voltage charging to the rated current of the rated parameters; using an infrared thermal imager within a second interval, acquire micro-overcharge temperature field data of the surface of the battery under test; the micro-overcharge temperature field data includes at least: micro-overcharge average temperature and micro-overcharge temperature standard deviation; and using a charge-discharge module to acquire micro-overcharge chemical data; the micro-overcharge chemical data includes at least: micro-overcharge capacity and micro-overcharge voltage.

[0085] In one exemplary embodiment, the first calculation unit 202 is specifically used to: calculate the difference between the standard sound velocity and the micro-overcharge sound velocity to obtain the sound velocity difference; calculate the difference between the standard amplitude and the micro-overcharge amplitude to obtain the amplitude difference; calculate the difference between the standard phase and the micro-overcharge phase to obtain the phase difference; and determine the sound velocity difference, amplitude difference, and phase difference as ultrasonic differential parameters.

[0086] In one exemplary embodiment, the second calculation unit 203 is specifically used to: sequentially perform Gaussian filtering noise reduction and gradient sharpening enhancement processing on the standard temperature field data and the micro-overcharge temperature field data, and generate a temperature field comparison map of the standard temperature field data and the micro-overcharge temperature field data; the Gaussian filtering noise reduction is performed using a Gaussian filter with a preset variance; the gradient sharpening enhancement processing is performed using the Sobel operator to calculate the gradient; based on the temperature field comparison map, temperature field comparison data of the standard temperature field data and the micro-overcharge temperature field data is obtained; based on the temperature field comparison data, the average temperature of micro-overcharge in the micro-overcharge temperature field data is higher than the standard average temperature and temperature standard deviation in the standard temperature field data. The regions in the micro-overcharge temperature field data whose standard deviation of micro-overcharge temperature meets a preset first threshold are identified as abnormal temperature rise regions. For abnormal temperature rise regions, the area of ​​the abnormal temperature rise region is calculated using image pixel statistics. Based on temperature field comparison data, the temperature difference between the micro-overcharge temperature field data of all pixels and the standard temperature field data at the corresponding positions is determined. The maximum value among the temperature differences of all pixels is taken as the maximum temperature rise. Based on temperature field comparison data, the temperature change rate of adjacent pixels in the micro-overcharge temperature field data is calculated, and the maximum value among all temperature change rates is determined as the temperature rise gradient. The maximum temperature rise, the temperature rise gradient, and the area of ​​the abnormal temperature rise region are determined as micro-overcharge temperature rise features.

[0087] In one exemplary embodiment, the comparison unit 204 is specifically used to: calculate the capacity decay rate based on the ratio of the standard charging capacity to the micro-overcharge charging capacity; obtain the voltage offset based on the offset difference between the standard voltage and the micro-overcharge voltage at the same charging stage; and determine the capacity decay rate and voltage offset as micro-overcharge chemical change characteristics.

[0088] In one exemplary embodiment, the aging identification unit 205 is specifically used to: aging levels include: Level 1 slight aging, Level 2 moderate aging, and Level 3 severe aging; aging types include at least one of the following: electrolyte decomposition, lithium plating, and solid electrolyte interphase (SEI) film thickening.

[0089] In one exemplary embodiment, the aging identification unit 205 is specifically used to: normalize the ultrasonic differential parameters, micro-overcharge temperature rise characteristics, and micro-overcharge chemical change characteristics, and construct a multi-dimensional feature vector based on the normalized ultrasonic differential parameters, micro-overcharge temperature rise characteristics, and micro-overcharge chemical change characteristics; input the multi-dimensional feature vector into a random forest classification model, and output the aging level, aging region, and aging type of the battery to be detected, thereby obtaining the aging identification result; the aging region is the abnormal temperature rise region.

[0090] In one exemplary embodiment, the aging identification unit 205 is further configured to: determine the aging type as electrolyte decomposition when the sound velocity difference is greater than or equal to 0.5 percent and the maximum temperature rise is greater than or equal to 0.3 percent; determine the aging type as lithium plating when the amplitude difference is greater than or equal to 2 percent and the area of ​​the abnormal temperature rise region meets a preset second threshold; and determine the aging type as SEI film thickening when the phase difference is greater than or equal to 2 percent and the capacitance decay rate is greater than or equal to 5 percent.

[0091] In summary, this application provides a method and apparatus for detecting battery aging. This application acquires first ultrasonic echo response data, standard temperature field data, and standard electrochemical data corresponding to the standard charging treatment by performing standard charging treatment and micro-overcharging treatment on the battery under test, and acquires second ultrasonic echo response data, micro-overcharging temperature field data, and micro-overcharging chemical data corresponding to the micro-overcharging treatment. The standard charging treatment is normal charging based on the rated parameters of the battery under test; the micro-overcharging treatment is abnormal charging with a charging voltage higher than the rated parameters. The first and second ultrasonic echo response data are differentially processed to calculate ultrasonic differential parameters. The ultrasonic differential parameters include at least: sound velocity difference, amplitude difference, and phase difference; the standard temperature field data and micro-overcharging temperature field data are then processed. The overcharge temperature field data undergoes noise reduction and gradient enhancement processing to calculate and determine the micro-overcharge temperature rise characteristics. These characteristics include at least: maximum temperature rise, temperature rise gradient, and area of ​​abnormal temperature rise region. The change curves of standard electrochemical data and micro-overcharge chemical data are compared to obtain micro-overcharge chemical change characteristics. These characteristics include at least: capacity decay rate and voltage offset. Ultrasonic differential parameters, micro-overcharge temperature rise characteristics, and micro-overcharge chemical change characteristics are input into a pre-trained aging identification model to obtain the aging identification results of the battery under test. The aging identification model is a random forest classification model. The aging identification results include at least one of the following: aging level, aging region, and aging type. This addresses the shortcomings of existing single ultrasonic, infrared, or electrochemical detection methods, which suffer from insufficient sensitivity, inability to monitor in situ, and difficulty in locating aging regions. This application can achieve accurate identification, regional location, and aging mechanism tracing of early micro-overcharge aging by synergistically enhancing weak aging signals using ultrasonic, infrared, and electrochemical multi-dimensional features. In summary, the technical solution provided in this application can improve the accuracy of battery aging detection, can comprehensively consider multiple factors, and can adapt to various application scenarios.

[0092] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0093] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0094] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0095] Additionally, as used herein, the "or" used in a list of items beginning with "at least one" indicates a separate list, such that a list of, for example, "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not imply that the described example is preferred or better than other examples.

[0096] It should also be noted that in the system and method of this application, the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered as equivalent solutions of this application.

[0097] Various changes, substitutions, and modifications can be made to the technology described herein without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims is not limited to the specific aspects of the processes, machines, manufactures, events, means, methods, and actions described above. Currently existing or later-developed processes, machines, manufactures, events, means, methods, or actions that perform substantially the same function or achieve substantially the same result as the corresponding aspects described herein can be utilized. Therefore, the appended claims include such processes, machines, manufactures, events, means, methods, or actions within their scope.

[0098] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0099] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A method for detecting battery aging, characterized in that, The method includes: The battery under test is subjected to standard charging and micro-overcharging treatments. First ultrasonic echo response data, standard temperature field data, and standard electrochemical data corresponding to the standard charging treatment are obtained. Second ultrasonic echo response data, micro-overcharging temperature field data, and micro-overcharging chemical data corresponding to the micro-overcharging treatment are also obtained. The standard charging treatment is normal charging based on the rated parameters of the battery under test. The micro-overcharging treatment is abnormal charging where the charging voltage is higher than the rated parameters. The first ultrasonic echo response data and the second ultrasonic echo response data are subjected to differential processing to calculate ultrasonic differential parameters; the ultrasonic differential parameters include at least: sound velocity difference, amplitude difference and phase difference; The standard temperature field data and the micro-overcharge temperature field data are subjected to noise reduction and gradient enhancement processing to calculate and determine the micro-overcharge temperature rise characteristics; the micro-overcharge temperature rise characteristics include at least: maximum temperature rise, temperature rise gradient, and area of ​​abnormal temperature rise region. The standard electrochemical data and the micro-overcharge chemical data are compared by curve analysis to obtain the micro-overcharge chemical change characteristics; the micro-overcharge chemical change characteristics include at least: capacity decay rate and voltage offset. The ultrasonic differential parameters, the micro-overcharge temperature rise characteristics, and the micro-overcharge chemical change characteristics are input into a pre-trained aging identification model to obtain the aging identification result of the battery under test; the aging identification model is a random forest classification model; the aging identification result includes at least one of the following: aging level, aging region, and aging type; The step of performing noise reduction and gradient enhancement processing on the standard temperature field data and the micro-overcharge temperature field data, and calculating and determining the micro-overcharge temperature rise characteristics, includes: The standard temperature field data and the micro-overcharge temperature field data are sequentially subjected to Gaussian filtering for noise reduction and gradient sharpening for enhancement, and a temperature field comparison map of the standard temperature field data and the micro-overcharge temperature field data is generated; the Gaussian filtering for noise reduction is performed using a Gaussian filter with a preset variance; the gradient sharpening for enhancement is performed using the Sobel operator for gradient calculation; Based on the temperature field comparison diagram, temperature field comparison data of the standard temperature field data and the micro-overcharge temperature field data are obtained; Based on the temperature field comparison data, the region in the micro-overcharge temperature field data that has a micro-overcharge average temperature higher than the sum of the standard average temperature and temperature standard deviation in the standard temperature field data, and the region in the micro-overcharge temperature field data that meets a preset first threshold, is defined as an abnormal temperature rise region. The area of ​​the abnormal temperature rise region is calculated using image pixel statistics. Based on the temperature field comparison data, the temperature difference between the micro-overcharge temperature field data of all pixels and the standard temperature field data at the corresponding positions is determined. The maximum temperature rise is determined by taking the maximum value among the temperature differences of all the pixels. Based on the temperature field comparison data, the temperature change rate of adjacent pixels in the micro-overcharge temperature field data is calculated, and the maximum value among all temperature change rates is determined as the temperature rise gradient. The maximum temperature rise, the temperature rise gradient, and the area of ​​the abnormal temperature rise region are defined as the micro-overcharge temperature rise characteristics. The method further includes: When the sound velocity difference is greater than or equal to 0.5 percent and the maximum temperature rise is greater than or equal to 0.3 percent, the aging type is determined to be electrolyte decomposition. When the amplitude difference is greater than or equal to 2 percent and the area of ​​the abnormal temperature rise region meets the preset second threshold, the aging type is determined to be lithium plating. When the phase difference is greater than or equal to 2 percent and the capacitance decay rate is greater than or equal to 5 percent, the aging type is determined to be SEI film thickening.

2. The method according to claim 1, characterized in that, The acquisition of the first ultrasonic echo response data, standard temperature field data, and standard electrochemical data corresponding to the standard charging process includes: During the standard charging process of the battery under test, an ultrasonic module sends a first ultrasonic signal to the battery under test within a preset first interval to obtain the first ultrasonic echo response data; the first ultrasonic echo response data includes at least: standard sound velocity, standard amplitude and standard phase; the standard charging process includes: first constant current charging to the rated voltage of the rated parameters, and then constant voltage charging to the rated current of the rated parameters; The standard temperature field data of the surface of the battery to be tested is acquired using an infrared thermal imager within a preset second interval; the standard temperature field data includes at least: standard average temperature and temperature standard deviation; The standard electrochemical data is obtained using a charge / discharge module; the standard electrochemical data includes at least: standard charging capacity and standard voltage.

3. The method according to claim 1, characterized in that, The acquisition of the second ultrasonic echo response data, micro-overcharge temperature field data, and micro-overcharge chemical data corresponding to the micro-overcharge treatment includes: During the micro-overcharge treatment of the battery under test, a second ultrasonic signal is sent to the battery under test within a first interval using an ultrasonic module to obtain the second ultrasonic echo response data of the battery under test; the second ultrasonic echo response data includes at least: micro-overcharge sound velocity, micro-overcharge amplitude, and micro-overcharge phase; the micro-overcharge treatment includes: first, constant current charging to a preset overcharge voltage value higher than the rated voltage of the rated parameters, and then constant voltage charging to the rated current of the rated parameters; The micro-overcharge temperature field data of the surface of the battery under test is acquired using an infrared thermal imager within the second interval; the micro-overcharge temperature field data includes at least: the average micro-overcharge temperature and the standard deviation of the micro-overcharge temperature. The micro-overcharge chemical data is obtained using a charge-discharge module; the micro-overcharge chemical data includes at least: micro-overcharge capacity and micro-overcharge voltage.

4. The method according to claim 1, characterized in that, The step of performing differential processing on the first ultrasonic echo response data and the second ultrasonic echo response data to calculate ultrasonic differential parameters includes: The difference between the standard speed of sound and the slightly overcharged speed of sound is calculated to obtain the speed of sound difference; The amplitude difference is obtained by calculating the difference between the standard amplitude and the micro-overcharge amplitude; The phase difference is obtained by calculating the difference between the standard phase and the micro-overcharge phase; The sound velocity difference, the amplitude difference, and the phase difference are determined as the ultrasonic differential parameters.

5. The method according to claim 1, characterized in that, The comparison of the change curves between the standard electrochemical data and the micro-overcharge chemical data yields the micro-overcharge chemical change characteristics, including: The capacity decay rate is calculated based on the ratio of the standard charging capacity to the micro-overcharge charging capacity. The voltage offset is obtained based on the offset difference between the standard voltage and the micro-overcharge voltage during the same charging stage; The capacitance decay rate and the voltage offset are determined as the characteristics of the micro-overcharge chemical change.

6. The method according to claim 1, characterized in that, The aging levels include: Level 1 slight aging, Level 2 moderate aging, and Level 3 severe aging.

7. The method according to claim 1, characterized in that, The step of inputting the ultrasonic differential parameters, the micro-overcharge temperature rise characteristics, and the micro-overcharge chemical change characteristics into the pre-trained aging identification model to obtain the aging identification result of the battery under test includes: The ultrasonic differential parameters, the micro-overcharge temperature rise characteristics, and the micro-overcharge chemical change characteristics are normalized, and a multi-dimensional feature vector is constructed based on the normalized ultrasonic differential parameters, the micro-overcharge temperature rise characteristics, and the micro-overcharge chemical change characteristics. The multi-dimensional feature vector is input into the random forest classification model, which outputs the aging level, aging region, and aging type of the battery to be detected, thus obtaining the aging identification result; the aging region is the abnormal temperature rise region.

8. A battery aging detection device, characterized in that, The device includes: The acquisition unit is used to perform standard charging and micro-overcharging on the battery under test, respectively, and acquire first ultrasonic echo response data, standard temperature field data, and standard electrochemical data corresponding to the standard charging process, and acquire second ultrasonic echo response data, micro-overcharging temperature field data, and micro-overcharging chemical data corresponding to the micro-overcharging process; the standard charging process is normal charging based on the rated parameters of the battery under test; the micro-overcharging process is abnormal charging with a charging voltage higher than the rated parameters. The first calculation unit is used to perform differential processing on the first ultrasonic echo response data and the second ultrasonic echo response data to calculate ultrasonic differential parameters; the ultrasonic differential parameters include at least: sound velocity difference, amplitude difference and phase difference; The second calculation unit is used to perform noise reduction and gradient enhancement processing on the standard temperature field data and the micro-overcharge temperature field data, and to calculate and determine the micro-overcharge temperature rise characteristics; the micro-overcharge temperature rise characteristics include at least: maximum temperature rise, temperature rise gradient, and area of ​​abnormal temperature rise region. The comparison unit is used to perform change curve comparison processing on the standard electrochemical data and the micro-overcharge chemical data to obtain micro-overcharge chemical change characteristics; the micro-overcharge chemical change characteristics include at least: capacity decay rate and voltage offset. An aging identification unit is used to input the ultrasonic differential parameters, the micro-overcharge temperature rise characteristics, and the micro-overcharge chemical change characteristics into a pre-trained aging identification model to obtain the aging identification result of the battery under test; the aging identification model is a random forest classification model; the aging identification result includes at least one of the following: aging level, aging region, and aging type; The step of performing noise reduction and gradient enhancement processing on the standard temperature field data and the micro-overcharge temperature field data, and calculating and determining the micro-overcharge temperature rise characteristics, includes: The standard temperature field data and the micro-overcharge temperature field data are sequentially subjected to Gaussian filtering for noise reduction and gradient sharpening for enhancement, and a temperature field comparison map of the standard temperature field data and the micro-overcharge temperature field data is generated; the Gaussian filtering for noise reduction is performed using a Gaussian filter with a preset variance; the gradient sharpening for enhancement is performed using the Sobel operator for gradient calculation; Based on the temperature field comparison diagram, temperature field comparison data of the standard temperature field data and the micro-overcharge temperature field data are obtained; Based on the temperature field comparison data, the region in the micro-overcharge temperature field data that has a micro-overcharge average temperature higher than the sum of the standard average temperature and temperature standard deviation in the standard temperature field data, and the region in the micro-overcharge temperature field data that meets a preset first threshold, is defined as an abnormal temperature rise region. The area of ​​the abnormal temperature rise region is calculated using image pixel statistics. Based on the temperature field comparison data, the temperature difference between the micro-overcharge temperature field data of all pixels and the standard temperature field data at the corresponding positions is determined. The maximum temperature rise is determined by taking the maximum value among the temperature differences of all the pixels. Based on the temperature field comparison data, the temperature change rate of adjacent pixels in the micro-overcharge temperature field data is calculated, and the maximum value among all temperature change rates is determined as the temperature rise gradient. The maximum temperature rise, the temperature rise gradient, and the area of ​​the abnormal temperature rise region are defined as the micro-overcharge temperature rise characteristics. The device further includes: When the sound velocity difference is greater than or equal to 0.5 percent and the maximum temperature rise is greater than or equal to 0.3 percent, the aging type is determined to be electrolyte decomposition. When the amplitude difference is greater than or equal to 2 percent and the area of ​​the abnormal temperature rise region meets the preset second threshold, the aging type is determined to be lithium plating. When the phase difference is greater than or equal to 2 percent and the capacitance decay rate is greater than or equal to 5 percent, the aging type is determined to be SEI film thickening.

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