Electronic cigarette battery detection method and system

Abstract

The application provides an electronic cigarette battery detection method and system, which comprises the following steps: collecting voltage detection signals and temperature change data of an electronic cigarette battery to be detected; constructing trend trajectories of the voltage detection signals in different scale spaces based on multiple voltage feature points in the voltage detection signals; determining change trend components of the battery voltage in each scale space in the electronic cigarette battery detection process according to the trend trajectories in all scale spaces; extracting multi-scale information entropy features of the electronic cigarette battery according to the information entropy proportion of the voltage detection signals and each change trend component; calculating a thermal relaxation equilibrium index in the charging process of the electronic cigarette battery according to the temperature change data; determining a dynamic risk coefficient of the electronic cigarette battery by combining the thermal relaxation equilibrium index with all the multi-scale information entropy features; and evaluating the charging health state of the electronic cigarette battery based on the dynamic risk coefficient. The above scheme can make a confident determination on the charging health state of the electronic cigarette battery based on the dynamic risk coefficient.

Description

Technical Field

[0001] This application relates to the field of battery testing technology, and more specifically, to a method and system for testing electronic cigarette batteries. Background Technology

[0002] With the widespread application of batteries, especially in new energy vehicles, consumer electronics, and energy storage systems, the importance of battery testing is becoming increasingly prominent. Batteries face various safety risks during use, such as performance degradation and leakage. Therefore, it is necessary to monitor the health status of batteries in real time through testing methods. With the advancement of technology, testing technologies are constantly innovating, which not only improves the accuracy and efficiency of testing but also reduces costs, thus promoting the development of battery testing technology towards intelligence and high efficiency.

[0003] In existing battery testing, battery testing is mainly based on a comprehensive evaluation of electrochemical characteristics and physical performance. This involves measuring the actual capacity of the battery through a charge-discharge cycle system and comparing it with the nominal value to verify performance consistency. Internal resistance changes are analyzed using AC impedance or DC discharge methods to reflect the degree of internal polarization and material aging. Furthermore, intelligent algorithms are used to analyze trends in battery testing data to assess health status. However, in e-cigarette battery testing, traditional testing methods rely solely on simple statistical characteristics of battery voltage changes (such as average and variance) to assess charging health status. This fails to effectively capture subtle fluctuations in the voltage detection signal, which are often closely related to internal anomalies in e-cigarette batteries. Consequently, early characteristics (i.e., slight abnormal signals exhibited by changes in the battery's physical or chemical characteristics before obvious abnormalities occur) are difficult to capture during e-cigarette battery charging health status assessment. This makes it impossible to make a confident assessment of the charging health status of e-cigarette batteries. Therefore, how to confidently determine the charging health status of e-cigarette batteries has become a challenge for the industry. Summary of the Invention

[0004] This application provides a method and system for testing electronic cigarette batteries, which can make a confident determination of the charging health status of electronic cigarette batteries.

[0005] In a first aspect, this application provides an electronic cigarette battery testing method for a pre-diagnostic detection of the charging health status of an electronic cigarette battery using an electronic cigarette battery testing system. The method includes the following steps:

[0006] Connect the electronic cigarette battery to be tested to a standard charging device and collect the voltage detection signal and temperature change data of the battery surface during charging.

[0007] Multiple voltage feature points in the voltage detection signal are identified, and a trend trajectory of the voltage detection signal at different scales is constructed based on all the voltage feature points during the charging process of the electronic cigarette battery.

[0008] Based on the trend trajectory under all scale spaces, extract the change trend components of the battery voltage under each scale space during the electronic cigarette battery detection process, and then determine the multi-scale information entropy characteristics of the electronic cigarette battery during the charging health status detection process based on the information entropy ratio of the voltage detection signal and each change trend component.

[0009] The thermal relaxation balance index of the electronic cigarette battery under long-term charging state is calculated based on the temperature change data. When the cumulative charging time of the electronic cigarette battery reaches a preset time threshold, the dynamic risk coefficient of the electronic cigarette battery is determined by combining the thermal relaxation balance index with the multi-scale information entropy feature. Then, the charging health status of the electronic cigarette battery is evaluated based on the dynamic risk coefficient.

[0010] In some embodiments, the method further includes: extracting voltage extreme points from the voltage detection signal as voltage feature points, wherein the voltage extreme points include voltage minimum points and voltage maximum points.

[0011] In some embodiments, constructing the trend trajectory of the voltage detection signal at different scales during the electronic cigarette battery charging process based on all voltage feature points specifically includes:

[0012] Distinguish between the first type of voltage feature point set and the second type of voltage feature point set from all voltage feature points;

[0013] The first feature trajectory and the second feature trajectory of the voltage detection signal are constructed based on the first type of voltage feature point set and the second type of voltage feature point set, respectively.

[0014] Based on the first feature trajectory and the second feature trajectory, the trend trajectory of the voltage detection signal during the charging process of the electronic cigarette battery is extracted at different scales.

[0015] In some embodiments, constructing the first feature trajectory and the second feature trajectory of the voltage detection signal based on the first type of voltage feature point set and the second type of voltage feature point set specifically includes:

[0016] Connect the voltage feature points in the first type of voltage feature point set in chronological order to obtain the first feature trajectory of the voltage detection signal;

[0017] The voltage feature points in the second type of voltage feature point set are connected in chronological order to obtain the second feature trajectory of the voltage detection signal.

[0018] In some embodiments, extracting the trend components of battery voltage variation in various scale spaces during the electronic cigarette battery detection process based on trend trajectories in all scale spaces specifically includes:

[0019] One scale space is selected from all scale spaces as the selected scale space, and the low-frequency component of the trend trajectory under the selected scale space is removed to obtain the change trend component of the battery voltage under the selected scale space during the electronic cigarette battery detection process.

[0020] Continue to determine the trend component of battery voltage variation in the remaining scale space during the electronic cigarette battery detection process.

[0021] In some embodiments, determining the multi-scale information entropy features of the electronic cigarette battery during the charging health status detection process based on the information entropy ratio of the voltage detection signal and each trend component specifically includes:

[0022] Determine the information entropy of the voltage detection signal and the information entropy corresponding to each trend component.

[0023] A trend component is selected as the selected trend component, and the ratio of the information entropy of the voltage detection signal to that of the selected trend component is determined by the information entropy of the selected trend component and the information entropy of the voltage detection signal.

[0024] Continue to determine the information entropy ratio between the voltage detection signal and the remaining trend components;

[0025] The multi-scale information entropy features of the electronic cigarette battery during the charging health status detection process are obtained by extracting the proportion of all information entropy.

[0026] In some embodiments, calculating the thermal relaxation equilibrium index of the electronic cigarette battery under long-term charging conditions based on the temperature change data specifically includes:

[0027] The battery temperature change curve, constructed from the temperature change data, is divided into multiple temperature change segments based on a preset sliding window.

[0028] The steady-state and transient temperature characteristics of the electronic cigarette battery during the charging process were extracted from all temperature change segments.

[0029] The steady-state temperature change trend during the charging of the electronic cigarette battery is determined based on the steady-state temperature characteristics, and the transient temperature fluctuation amplitude during the charging of the electronic cigarette battery is determined based on the transient temperature characteristics.

[0030] The thermal relaxation equilibrium index of the electronic cigarette battery under long-term charging state is determined based on the steady-state temperature change trend and the transient temperature fluctuation amplitude.

[0031] In some embodiments, determining the dynamic risk coefficient of the electronic cigarette battery by combining the thermal relaxation equilibrium index with the multi-scale information entropy feature specifically includes:

[0032] Based on the multi-scale information entropy features, an abnormal evolution index of the voltage detection signal during the charging process of the electronic cigarette battery is determined;

[0033] Determine the influencing factors of the abnormal evolution index and the thermal relaxation equilibrium index in the health status assessment of electronic cigarette batteries;

[0034] The dynamic risk coefficient of the electronic cigarette battery is determined based on the abnormal evolution index, the thermal relaxation equilibrium index, the influence factor corresponding to the abnormal evolution index, and the influence factor corresponding to the thermal relaxation equilibrium index.

[0035] In some embodiments, a voltage sensor is used to acquire the voltage detection signal of the electronic cigarette battery under test.

[0036] Secondly, this application provides an electronic cigarette battery testing system, comprising:

[0037] The acquisition module is used to connect the electronic cigarette battery to be tested to a standard charging device and acquire the voltage detection signal and temperature change data of the battery surface during charging.

[0038] The processing module is used to determine multiple voltage feature points in the voltage detection signal and construct the trend trajectory of the voltage detection signal in different scale spaces during the charging process of the electronic cigarette battery based on all the voltage feature points.

[0039] The processing module is also used to extract the trend components of battery voltage change in each scale space during the electronic cigarette battery detection process based on the trend trajectory in all scale spaces, and then determine the multi-scale information entropy features of the electronic cigarette battery during the charging health status detection process based on the information entropy ratio of the voltage detection signal and each trend component.

[0040] The execution module is used to calculate the thermal relaxation balance index of the electronic cigarette battery under long-term charging state based on the temperature change data. When the cumulative charging time of the electronic cigarette battery reaches a preset time threshold, the dynamic risk coefficient of the electronic cigarette battery is determined by combining the thermal relaxation balance index with the multi-scale information entropy feature, and then the charging health status of the electronic cigarette battery is evaluated based on the dynamic risk coefficient.

[0041] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0042] The electronic cigarette battery testing method and system provided in this application firstly connects the electronic cigarette battery to be tested to a standard charging device and collects the voltage detection signal and temperature change data of the battery surface during charging. Secondly, multiple voltage feature points in the voltage detection signal are determined, and a trend trajectory of the voltage detection signal under different scale spaces is constructed based on all voltage feature points during the charging process of the electronic cigarette battery. Further, the change trend components of the battery voltage under each scale space are extracted according to the trend trajectory under all scale spaces, and then the multi-scale information entropy characteristics of the electronic cigarette battery during the charging health status detection process are determined according to the information entropy ratio of the voltage detection signal and each change trend component. Finally, the thermal relaxation balance index of the electronic cigarette battery under long-term charging state is calculated based on the temperature change data. When the cumulative charging time of the electronic cigarette battery reaches a preset time threshold, the dynamic risk coefficient of the electronic cigarette battery is determined by combining the thermal relaxation balance index with the multi-scale information entropy characteristics, and then the charging health status of the electronic cigarette battery is evaluated based on the dynamic risk coefficient.

[0043] Therefore, this application can confidently determine the charging health status of e-cigarette batteries. First, it collects voltage detection signals and temperature change data on the battery surface during charging, providing basic data support for subsequent battery feature extraction and charging health status assessment. Second, it identifies multiple voltage feature points in the voltage detection signal to effectively reflect key turning points or abnormal fluctuations in the charging state of the e-cigarette battery, thus providing data support for diagnosing the health status of the e-cigarette battery. Based on all voltage feature points, it constructs trend trajectories of the voltage detection signal at different scales, and then determines the trend components of battery voltage change at each scale during the battery detection process based on these trend trajectories. This characterizes different levels of trend information in battery voltage evolution over time, thus more clearly revealing voltage change patterns at different scales. Furthermore, it determines the charging health status based on the information entropy ratio of the voltage detection signal and each trend component. The multi-scale information entropy features of the e-cigarette battery during the health status detection process effectively capture early characteristics of the e-cigarette battery during charging health status detection and distinguish between normal and fault states from multiple dimensions, thereby improving the accuracy of the charging health status assessment of the e-cigarette battery and avoiding the problem that simple statistical features cannot effectively capture subtle fluctuations in the voltage detection signal. Then, the thermal relaxation balance index during the charging process of the e-cigarette battery is calculated based on temperature change data to effectively assess whether the e-cigarette battery is in a good thermal balance state. Furthermore, the dynamic risk coefficient of the e-cigarette battery is determined by combining the thermal relaxation balance index with the multi-scale information entropy features to more reliably characterize the charging health status of the e-cigarette battery and improve the reliability of the health status assessment of the e-cigarette battery. Finally, the charging health status of the e-cigarette battery is assessed based on the dynamic risk coefficient. In summary, the technical solution provided in this application can make a confidence determination of the charging health status of e-cigarette batteries. Attached Figure Description

[0044] Figure 1 This is a schematic diagram illustrating an application scenario of an electronic cigarette battery testing method according to some embodiments of this application;

[0045] Figure 2 This is an exemplary flowchart of an electronic cigarette battery testing method according to some embodiments of this application;

[0046] Figure 3 This is an exemplary flowchart illustrating the determination of a trend trajectory according to some embodiments of this application;

[0047] Figure 4 This is an exemplary flowchart illustrating the determination of trend components according to some embodiments of this application;

[0048] Figure 5This is a schematic diagram of the structure of an electronic cigarette battery detection system according to some embodiments of this application;

[0049] Figure 6 This is a schematic diagram of the structure of a computer device for implementing an electronic cigarette battery detection method according to some embodiments of this application. Detailed Implementation

[0050] To better understand the technical solution of this application, the technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0051] Figure 1 This is a schematic diagram illustrating an application scenario of the electronic cigarette battery testing method provided in this application. The testing device communicates with a server via a communication network. A data storage system stores the data that the server needs to process. This data storage system can be integrated onto the server, or it can be located in the cloud or on another server. The data storage system can store voltage detection signals and battery surface temperature change data during charging of the electronic cigarette battery. The testing device reports the voltage detection signals and battery surface temperature change data of the electronic cigarette battery under test to the server. The server obtains the voltage detection signals and battery surface temperature change data of the electronic cigarette battery under test. The testing device can be a device for detecting the charging status of the electronic cigarette battery, including but not limited to standard charging equipment. The server can be a standalone server or a server cluster composed of multiple servers. It should be noted that the electronic cigarette battery testing method in this application can also be implemented using a testing device. The testing device acquires the voltage detection signal and temperature change data of the battery surface during charging of the electronic cigarette battery to be tested; determines multiple voltage feature points in the voltage detection signal, and constructs the trend trajectory of the voltage detection signal in different scale spaces during the charging process of the electronic cigarette battery based on all voltage feature points; extracts the change trend components of the battery voltage in each scale space during the testing process of the electronic cigarette battery based on the trend trajectory in all scale spaces, and then determines the multi-scale information entropy characteristics of the electronic cigarette battery during the charging health status detection process based on the information entropy ratio of the voltage detection signal and each change trend component; calculates the thermal relaxation balance index of the electronic cigarette battery under long-term charging state based on the temperature change data; when the cumulative charging time of the electronic cigarette battery reaches a preset time threshold, determines the dynamic risk coefficient of the electronic cigarette battery by combining the thermal relaxation balance index with the multi-scale information entropy characteristics, and then evaluates the charging health status of the electronic cigarette battery based on the dynamic risk coefficient, which will not be elaborated here.

[0052] refer to Figure 2The figure is an exemplary flowchart of an electronic cigarette battery detection method according to some embodiments of this application. The electronic cigarette battery detection method 100 of this embodiment can realize the pre-diagnosis detection of the charging health status of electronic cigarette batteries. Specifically, the method mainly includes the following steps:

[0053] In step 101, the electronic cigarette battery to be tested is connected to a standard charging device, and the voltage detection signal and temperature change data of the battery surface are collected during the charging of the electronic cigarette battery.

[0054] Among them, the voltage detection signal characterizes the voltage change information of the e-cigarette battery during the charging process. Through the voltage detection signal, the electrochemical reaction process inside the e-cigarette battery and the battery's performance under different operating conditions can be understood. In specific implementation, when the e-cigarette battery to be tested is connected to a standard charging device, the voltage detection signal can be collected by a voltage sensor installed on the e-cigarette battery, which will not be elaborated here.

[0055] In addition, temperature change data characterizes the temperature change information of e-cigarette batteries during the charging process. The temperature change data includes multiple temperature change values, which represent the amount of temperature change of the e-cigarette battery at different times during the charging process. In the charging health detection of e-cigarette batteries, the voltage detection signal and the battery surface temperature usually indicate the charging status of the e-cigarette. Therefore, by acquiring and analyzing the voltage detection signal and the temperature change data of the battery surface during the charging of the e-cigarette battery to be tested, the possible health status of the e-cigarette battery during the charging process can be effectively detected.

[0056] In step 102, multiple voltage feature points in the voltage detection signal are determined, and a trend trajectory of the voltage detection signal at different scales is constructed based on all the voltage feature points during the charging process of the electronic cigarette battery.

[0057] In this application, voltage feature points refer to data points in voltage detection signals that have significant change characteristics and can be used to reflect key turning points or abnormal fluctuations in the charging state of electronic cigarette batteries.

[0058] It should be noted that in the testing of e-cigarette batteries, especially during the charging process, if the e-cigarette battery is abnormal, the acquired voltage detection signal will change abnormally, such as a sudden increase or decrease, or abnormal fluctuation over a period of time. Therefore, the voltage extreme points in the voltage detection signal can be extracted as voltage feature points, including voltage minimum points and voltage maximum points.

[0059] In some embodiments, reference Figure 3As shown in the figure, this is an exemplary flowchart of determining a trend trajectory according to some embodiments of this application. In this embodiment, the trend trajectory of the voltage detection signal in different scale spaces during the charging process of an electronic cigarette battery based on all voltage feature points can be achieved by the following steps:

[0060] First, in step 1021, the first type of voltage feature point set and the second type of voltage feature point set are distinguished from all voltage feature points;

[0061] Then, in step 1022, the first feature trajectory and the second feature trajectory of the voltage detection signal are constructed according to the first type of voltage feature point set and the second type of voltage feature point set, respectively.

[0062] Finally, in step 1023, the trend trajectory of the voltage detection signal under different scale spaces during the charging process of the electronic cigarette battery is extracted based on the first feature trajectory and the second feature trajectory.

[0063] In specific implementation, firstly, all voltage feature points can be divided into two categories: one representing voltage maxima in the voltage detection signal and the other representing voltage minima. The first category represents voltage maxima, and the second category represents voltage minima. Secondly, the voltage feature points in the first category represent the maximum fluctuations of the voltage detection signal in a local time frame. Therefore, the curve obtained by connecting the voltage feature points in the first category in chronological order can be used as the first feature trajectory of the voltage detection signal. Similarly, the voltage feature points in the second category represent the minimum fluctuations of the voltage detection signal in a local time frame. Therefore, the curve obtained by connecting the voltage feature points in the second category in chronological order can be used as the second feature trajectory of the voltage detection signal.

[0064] It should be noted that in this embodiment, the first feature trajectory represents the maximum fluctuation trend curve of the voltage detection signal on the time scale, and the second feature trajectory represents the minimum fluctuation trend curve of the voltage detection signal on the time scale. By determining the first feature trajectory and the second feature trajectory, the detailed components of the voltage detection signal at different time scales can be effectively captured, thereby providing feature support for the health status assessment of current electronic cigarette batteries during charging.

[0065] In specific implementation, the trend trajectory of the voltage detection signal under different scale spaces during the charging process of the e-cigarette battery is extracted based on the first feature trajectory and the second feature trajectory. That is, the mean lines of the first feature trajectory and the second feature trajectory are removed from the voltage detection signal to obtain candidate voltage detection signals. It is then determined whether the candidate voltage detection signal meets the preset constraint conditions. If it does, the candidate voltage detection signal is used as the trend trajectory of the voltage detection signal under the corresponding scale space during the charging process of the e-cigarette battery. If it does not meet the constraint conditions, the candidate voltage detection signal is used as a new voltage detection signal to continue extracting corresponding new candidate voltage detection signals until the new candidate voltage detection signal meets the preset constraint conditions. When each step is completed... To extract the trend trajectory in the corresponding scale space, the voltage detection signal, after removing the trend trajectory obtained in the previous step, is processed according to the above implementation process to extract the trend trajectory in the corresponding scale space of the next voltage detection signal, until the residual signal of the voltage detection signal becomes a monotonic function, thus completing the extraction of the trend trajectory of the voltage detection signal in different scale spaces. The preset constraint condition is that the total number of maximum and minimum points in the candidate voltage detection signal differs from the number of zero-crossing points by at most one. The mean line represents the mean curve between the first feature trajectory and the second feature trajectory, which is the curve formed by reconnecting the average values ​​of the two feature trajectories at corresponding time points; this will not be elaborated further here.

[0066] It should be noted that in this application, the trend trajectory represents the trend information curve of the voltage detection signal at different scales. The trend trajectory is used to reflect the changing trend of the electronic cigarette battery voltage monitoring signal. By determining the trend trajectory, the evolution characteristics of the signal at different scales can be effectively analyzed, thereby effectively identifying the long-term trend and local fluctuations of the battery charging health status.

[0067] In step 103, the trend components of the battery voltage change in each scale space during the electronic cigarette battery detection process are extracted based on the trend trajectories in all scale spaces. Then, the multi-scale information entropy features of the electronic cigarette battery during the charging health status detection process are determined based on the information entropy ratio of the voltage detection signal and each trend component.

[0068] It should be noted that, in this application, the trend component represents the characteristic change portion of the electronic cigarette battery voltage detection signal at different scales, and the trend component is used to characterize the trend information of the battery voltage evolution over time at different levels.

[0069] In some embodiments, reference Figure 4As shown in the figure, this is an exemplary flowchart illustrating the determination of trend components according to some embodiments of this application. In this embodiment, the extraction of the trend components of battery voltage at various scales during the electronic cigarette battery detection process based on the trend trajectories at all scales can be achieved using the following steps:

[0070] In step 1031, one scale space is selected as the selected scale space, and the low-frequency component of the trend trajectory under the selected scale space is removed to obtain the change trend component of the battery voltage under the selected scale space during the electronic cigarette battery detection process.

[0071] In step 1032, the trend component of the battery voltage change in the remaining scale space during the electronic cigarette battery detection process is further determined.

[0072] In specific implementation, firstly, the low-frequency components of the trend trajectory in the selected scale space are removed by a high-pass filter, and the trend trajectory after removing the low-frequency components is used as the change trend component of the battery voltage in the selected scale space during the electronic cigarette battery detection process. Then, the change trend component of the battery voltage in the remaining scale space during the electronic cigarette battery detection process is further determined by the method of "removing the low-frequency components of the trend trajectory in the selected scale space to obtain the change trend component of the battery voltage in the selected scale space during the electronic cigarette battery detection process".

[0073] It should be noted that the trend component in this application includes multiple voltage change values. The voltage change values ​​are the voltage amplitudes at each sampling point after removing low-frequency components from the trend trajectory. These trend components, after removing low-frequency components or other irrelevant components, can more clearly reveal the voltage change patterns at different scales. The trend component is essentially the curve component of the trend trajectory after removing low-frequency components. The trend trajectory is a curve composed of signal amplitudes. Therefore, the trend component is also a curve composed of signal amplitudes. That is, the trend component is a frequency component extracted from the signal.

[0074] In some embodiments, determining the multi-scale information entropy characteristics of the electronic cigarette battery during the charging health status detection process based on the information entropy ratio of the voltage detection signal and each trend component can be achieved through the following steps:

[0075] Determine the information entropy of the voltage detection signal and the information entropy corresponding to each trend component.

[0076] A trend component is selected as the selected trend component, and the ratio of the information entropy of the voltage detection signal to that of the selected trend component is determined by the information entropy of the selected trend component and the information entropy of the voltage detection signal.

[0077] Continue to determine the information entropy ratio between the voltage detection signal and the remaining trend components;

[0078] The multi-scale information entropy features of the electronic cigarette battery during the charging health status detection process are obtained by extracting the proportion of all information entropy.

[0079] It should be noted that, in this embodiment, the information entropy of the voltage detection signal represents an index measuring the complexity of the change in voltage amplitude in the voltage detection signal, and the information entropy of the voltage detection signal reflects the degree of disorder in the voltage detection signal; in this embodiment, the information entropy corresponding to the trend component represents an index measuring the complexity of the change in voltage amplitude in the trend component, and the information entropy corresponding to the trend component reflects the degree of disorder or information richness of the voltage signal in the trend component. The information entropy corresponding to the trend component and the information entropy of the voltage detection signal have the same dimension, both of which are calculated from the frequency of occurrence of voltage amplitude.

[0080] In specific implementation, firstly, the information entropy of the voltage detection signal and the information entropy corresponding to each trend component can be calculated using the existing information entropy function. That is, the probability of different voltage amplitudes occurring in the voltage detection signal is input as an input variable to the information entropy function, which then outputs the information entropy of the voltage detection signal. For each trend component, the probability of different voltage amplitudes occurring in that trend component is input as an input variable to the information entropy function, which then outputs the information entropy of that trend component. This will not be elaborated further here. Secondly, by calculating the ratio of the information entropy of each trend component to the information entropy of the voltage detection signal, the information entropy can be evaluated. By estimating the contribution of each trend component to the complexity of the voltage detection signal, the overall structure and feature distribution of the voltage detection signal can be effectively understood. Therefore, for each trend component, the quotient of the information entropy corresponding to the trend component and the information entropy of the voltage detection signal can be used as the ratio of the information entropy of the voltage detection signal to the information entropy of the trend component. The information entropy ratio represents the proportion of the information entropy in the trend component to the information entropy of the voltage detection signal. Then, the above steps are repeated to continue to determine the information entropy ratio of the voltage detection signal to the remaining trend components. Finally, the set of all information entropy ratios is used as the multi-scale information entropy feature of the electronic cigarette battery during the charging health status detection process.

[0081] It should be noted that, in this application, the multi-scale information entropy feature represents the signal complexity of the voltage detection signal at multiple scales when assessing the charging health status of an e-cigarette battery. The multi-scale information entropy feature exhibits a clear trend of change as the e-cigarette battery evolves from a normal state to a fault state, which can improve the accuracy of assessing and diagnosing the charging health status of the e-cigarette battery under test. By determining the multi-scale information entropy feature, the early characteristics of the charging health status of the e-cigarette battery can be effectively captured, and the characteristics of the normal state and the fault state can be distinguished in multiple dimensions, thereby improving the accuracy of detecting the charging health status of the e-cigarette battery and avoiding the problem that simple statistical features cannot effectively capture the subtle fluctuations of the voltage detection signal.

[0082] In step 104, the thermal relaxation equilibrium index of the electronic cigarette battery under long-term charging state is calculated based on the temperature change data.

[0083] In some embodiments, calculating the thermal relaxation equilibrium index of the electronic cigarette battery under long-term charging conditions based on the temperature change data can be achieved by the following steps:

[0084] The battery temperature change curve, constructed from the temperature change data, is divided into multiple temperature change segments based on a preset sliding window.

[0085] The steady-state and transient temperature characteristics of the electronic cigarette battery during the charging process were extracted from all temperature change segments.

[0086] The steady-state temperature change trend during the charging of the electronic cigarette battery is determined based on the steady-state temperature characteristics, and the transient temperature fluctuation amplitude during the charging of the electronic cigarette battery is determined based on the transient temperature characteristics.

[0087] The thermal relaxation equilibrium index of the electronic cigarette battery under long-term charging state is determined based on the steady-state temperature change trend and the transient temperature fluctuation amplitude.

[0088] It should be noted that, in this embodiment, the steady-state temperature feature represents the feature used to distinguish steady-state temperature changes at different time periods; the transient temperature feature represents the feature used to distinguish significant temperature changes at different time periods. By determining the steady-state temperature feature and the transient temperature feature, the detailed temperature changes during the current electronic cigarette battery charging process can be identified more effectively; the steady-state temperature change trend in this embodiment represents the trend of steady-state temperature change of the battery surface over time during electronic cigarette battery charging; and the transient temperature fluctuation amplitude in this embodiment represents the trend of transient temperature fluctuation of the battery surface over time during electronic cigarette battery charging.

[0089] In specific implementation, firstly, existing curve fitting methods can be used to fit the temperature change data to obtain the battery temperature change curve, which will not be elaborated here. The battery temperature change curve can be divided into multiple temperature change segments based on a preset sliding window. Each temperature change segment represents a local curve obtained after dividing the battery temperature change curve. The sliding window can be set according to actual needs; for example, it can be set to a length of 6 time points. Secondly, within different temperature change segments, a lower temperature change rate indicates a more stable temperature change. Therefore, the average of all battery temperature values ​​below the temperature change rate threshold within each temperature change segment can be used as the steady-state temperature parameter. The peak battery temperature within different temperature change segments characterizes the significant temperature characteristics of that period. Therefore, the peak battery temperature within each temperature change segment can be used as the transient temperature parameter. The set of all steady-state temperature parameters and the set of all transient temperature parameters are respectively used as the steady-state temperature characteristics and transient temperature characteristics of the electronic cigarette battery during charging. The temperature change rate threshold can be taken as a fraction of the temperature change rate during normal charging. Using the 95th percentile as a threshold, the steady-state temperature parameter represents the temperature that changes steadily over a time period, while the transient temperature parameter represents the temperature that changes significantly over a time period. Then, the variances of all steady-state temperature parameters in the steady-state temperature characteristics and the variances of all transient temperature parameters in the transient temperature characteristics can be used as the steady-state temperature change trend and transient temperature fluctuation amplitude during the charging of the e-cigarette battery, respectively. Finally, weight coefficients are set for the steady-state temperature change trend and the transient temperature fluctuation amplitude, respectively, to initialize a multi-objective fusion model based on weighted fusion. The steady-state temperature change trend and the transient temperature fluctuation amplitude are used as input parameters of the objective function in this multi-objective fusion model, and each weight coefficient is used as the objective weight in the multi-objective fusion model. The output of this multi-objective fusion model is used as the thermal relaxation equilibrium index of the e-cigarette battery under long-term charging conditions. The weight coefficients corresponding to the steady-state temperature change trend and the transient temperature fluctuation amplitude can be set between 0 and 1 according to the degree of influence on the assessment of the e-cigarette battery's charging health status; no limitation is made here.

[0090] It should be noted that the thermal relaxation balance index in this application represents an indicator for measuring the thermal balance state of an e-cigarette battery during charging. By combining the steady-state temperature change trend and the transient temperature fluctuation amplitude to determine the thermal relaxation balance index, the thermal stability of the e-cigarette battery during charging can be comprehensively evaluated. The steady-state temperature change trend reflects the overall thermal management efficiency of the battery, while the transient temperature fluctuation amplitude characterizes the dynamic response of local heat distribution. By weighting and integrating the two, it is possible to capture both long-term thermal runaway risks and identify short-term abnormal fluctuations, thereby comprehensively quantifying the thermal balance state of the battery, assessing the thermal runaway risk of the e-cigarette battery, and further judging the charging health status of the e-cigarette battery.

[0091] In step 105, when the cumulative charging time of the e-cigarette battery reaches a preset time threshold, the dynamic risk coefficient of the e-cigarette battery is determined by combining the thermal relaxation balance index with the multi-scale information entropy feature, and then the charging health status of the e-cigarette battery is evaluated based on the dynamic risk coefficient.

[0092] In specific implementation, when the cumulative charging time of the e-cigarette battery reaches a preset time threshold, it indicates that the detection time of the e-cigarette battery has reached the required time. Then, the dynamic risk coefficient of the e-cigarette battery can be determined by combining the thermal relaxation balance index with the multi-scale information entropy feature. The preset time threshold represents a pre-set standard charging time. The preset time threshold can be set according to the historical experimental data of the e-cigarette battery. For example, the average charging time of the e-cigarette battery can be used as the preset time threshold. In addition, in other embodiments, it can be set according to actual needs, which is not limited here.

[0093] In some embodiments, determining the dynamic risk coefficient of the electronic cigarette battery by combining the thermal relaxation equilibrium index with the multi-scale information entropy feature can be achieved through the following steps:

[0094] Based on the multi-scale information entropy features, an abnormal evolution index of the voltage detection signal during the charging process of the electronic cigarette battery is determined;

[0095] Determine the influencing factors of the abnormal evolution index and the thermal relaxation equilibrium index in the health status assessment of electronic cigarette batteries;

[0096] The dynamic risk coefficient of the electronic cigarette battery is determined based on the abnormal evolution index, the thermal relaxation equilibrium index, the influence factor corresponding to the abnormal evolution index, and the influence factor corresponding to the thermal relaxation equilibrium index.

[0097] It should be noted that the multi-scale information entropy feature in this embodiment includes the proportion of information entropy in the voltage detection signal at different scales when the electronic cigarette battery is being assessed for health status. The proportion of information entropy measures the degree of disorder and complexity of the voltage detection signal at the corresponding scale. The greater the degree of disorder and complexity of the voltage detection signal at the corresponding scale, the more it indicates that the electronic cigarette battery is in an abnormal evolutionary state during the charging process.

[0098] In specific implementation, firstly, the average proportion of all information entropy percentages in the multi-scale information entropy features can be used as an abnormal evolution index of the voltage detection signal during the charging process of the e-cigarette battery. This abnormal evolution index represents the degree of abnormality in the voltage detection signal during the charging process. Then, the influence factors corresponding to the abnormal evolution index and the thermal relaxation balance index in the e-cigarette battery health status assessment can be set between 0 and 1 according to the importance of the assessment. For example, in this embodiment, the influence factors corresponding to the abnormal evolution index and the thermal relaxation balance index can be set to 0.57 and 0.43, respectively. Furthermore, they can be set according to actual needs; no limitation is made here. The sub-parameters represent the parameters affecting the battery health status assessment. Finally, the abnormal evolution index and the thermal relaxation balance index are normalized to initialize a multi-index fusion model based on weighted fusion. The normalized abnormal evolution index and thermal relaxation balance index are used as input parameters of the objective function in the multi-index fusion. The influence factors corresponding to the abnormal evolution index and the thermal relaxation balance index are used as the target weights in the multi-index fusion. The result of the multi-index fusion is used as the dynamic risk coefficient of the e-cigarette battery. The multi-scale information entropy feature and the thermal relaxation balance index can be normalized to between 0 and 1 through max-min normalization to unify their dimensions, which will not be elaborated here.

[0099] It should be noted that the dynamic risk coefficient in this application represents an indicator for measuring the charging health status of e-cigarette batteries. A single fault feature may have the risk of noise or misjudgment. However, the dynamic risk coefficient, by integrating multi-scale information entropy features and thermal relaxation balance index, can more comprehensively characterize the charging health status of e-cigarette batteries and improve the reliability of charging health status assessment. Traditional charging battery health status assessment technology usually relies on monitoring a single physical quantity (such as voltage). This application innovatively integrates multi-scale information entropy features and thermal relaxation balance index to construct a comprehensive charging health status assessment system, ensuring the balanced contribution of thermal and electrical features. This design overcomes the one-sidedness of a single parameter, and can capture early signs of thermal failure and identify electrochemical degradation, making the assessment results of the charging health status of e-cigarette batteries more robust and more practically valuable. The dynamic risk coefficient can accurately characterize the charging health status of e-cigarette batteries under different operating conditions, avoiding the problems of single features being susceptible to environmental interference and having a high misjudgment rate.

[0100] In some embodiments, assessing the charging health status of the electronic cigarette battery based on the dynamic risk coefficient can be achieved using the following steps:

[0101] The dynamic risk coefficient is compared with a preset dynamic risk coefficient threshold. If the dynamic risk coefficient is greater than the preset dynamic risk coefficient threshold, the charging health status of the electronic cigarette battery is determined to be a fault risk status.

[0102] If the dynamic risk coefficient is less than or equal to a preset dynamic risk coefficient threshold, then the charging health status of the electronic cigarette battery is determined to be a non-fault risk status.

[0103] It should be noted that the dynamic risk coefficient threshold in this embodiment is a pre-set standard dynamic risk coefficient used to determine whether the current e-cigarette battery is in a fault risk state. The dynamic risk coefficient threshold can be set according to actual needs. Traditional methods usually assess charging health status based on a single feature (such as voltage or temperature), which is easily affected by changes in the external environment and operating conditions, leading to misjudgment or omission. The dynamic risk coefficient, through feature fusion and data correlation analysis, can effectively distinguish different charging health states, reduce the risk of misjudging charging health status, and thus improve the accuracy of e-cigarette battery charging health status assessment.

[0104] In another aspect, in some embodiments, this application provides an electronic cigarette battery detection system, with reference to... Figure 5 The figure is a schematic diagram of the structure of an electronic cigarette battery detection system according to some embodiments of this application. The electronic cigarette battery detection system 200 includes: a data acquisition module 201, a processing module 202, and an execution module 203, which are described below:

[0105] The acquisition module 201 in this application is mainly used to connect the electronic cigarette battery to be tested to a standard charging device and to acquire the voltage detection signal and the temperature change data of the battery surface during charging.

[0106] Processing module 202, in this application, is mainly used to determine multiple voltage feature points in the voltage detection signal, and to construct the trend trajectory of the voltage detection signal in different scale spaces during the charging process of the electronic cigarette battery based on all the voltage feature points;

[0107] The processing module 202 is also used to extract the trend components of the battery voltage change in each scale space during the electronic cigarette battery detection process based on the trend trajectory in all scale spaces, and then determine the multi-scale information entropy features of the electronic cigarette battery during the charging health status detection process based on the information entropy ratio of the voltage detection signal and each trend component.

[0108] The execution module 203 in this application is mainly used to calculate the thermal relaxation balance index of the electronic cigarette battery under long-term charging state based on the temperature change data. When the cumulative charging time of the electronic cigarette battery reaches a preset time threshold, the dynamic risk coefficient of the electronic cigarette battery is determined by combining the thermal relaxation balance index with the multi-scale information entropy feature, and then the charging health status of the electronic cigarette battery is evaluated based on the dynamic risk coefficient.

[0109] In addition, this application also provides a computer device, the computer device including a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the above-described electronic cigarette battery detection method.

[0110] In some embodiments, reference Figure 6 The figure is a schematic diagram of the structure of a computer device implementing an electronic cigarette battery detection method according to some embodiments of this application. The electronic cigarette battery detection method in the above embodiments can be implemented through... Figure 6 The computer device shown is used to implement this, and the computer device 300 includes at least one processor 301, a communication bus 302, a memory 303, and at least one communication interface 304.

[0111] The processor 301 may be a general-purpose central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more devices used to control the execution of the electronic cigarette battery detection method in this application.

[0112] The communication bus 302 can be used to transmit information between the aforementioned components.

[0113] The memory 303 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or it may be an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital versatile optical discs, Blu-ray discs, etc.), a magnetic disk or other magnetic storage device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto. The memory 303 may exist independently and be connected to the processor 301 via a communication bus 302. The memory 303 may also be integrated with the processor 301.

[0114] The memory 303 stores program code for executing the scheme of this application, and its execution is controlled by the processor 301. The processor 301 executes the program code stored in the memory 303. The program code may include one or more software modules. In the above embodiments, the determination of the electronic cigarette battery detection method can be implemented by the processor 301 and one or more software modules in the program code in the memory 303.

[0115] Communication interface 304 uses any transceiver-like device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0116] In a specific implementation, as one example, a computer device may include multiple processors, each of which may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. Here, a processor may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0117] The aforementioned computer device can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device can be a desktop computer, a portable computer, a network server, a handheld digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer device.

[0118] In addition, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described electronic cigarette battery detection method.

[0119] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0120] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. An electronic cigarette battery detection method, used for pre-diagnosis detection of the state of charge of an electronic cigarette battery by an electronic cigarette battery detection system, characterized in that, The method comprises the following steps: connecting the electronic cigarette battery to be detected to a standard charging device, and collecting voltage detection signals and temperature change data of the surface of the electronic cigarette battery during charging of the electronic cigarette battery; determining a plurality of voltage feature points in the voltage detection signals, and constructing trend trajectories of the voltage detection signals in different scale spaces during charging of the electronic cigarette battery based on all the voltage feature points; extracting change trend components of the battery voltage in each scale space during detection of the electronic cigarette battery according to the trend trajectories in all scale spaces, and then determining multi-scale information entropy features of the electronic cigarette battery during charging health state detection according to information entropy proportions of the voltage detection signals and each change trend component; calculating a thermal relaxation equilibrium index of the electronic cigarette battery in a long-time charging state according to the temperature change data, determining a dynamic risk coefficient of the electronic cigarette battery by combining the thermal relaxation equilibrium index and the multi-scale information entropy features when a cumulative charging time length of the electronic cigarette battery reaches a preset time length threshold, and then evaluating the charging health state of the electronic cigarette battery based on the dynamic risk coefficient.

2. The method of claim 1, wherein, Further comprising: extracting voltage extreme points in the voltage detection signals as voltage feature points, wherein the voltage extreme points include voltage minimum points and voltage maximum points.

3. The method of claim 1, wherein, The step of constructing trend trajectories of the voltage detection signals in different scale spaces during charging of the electronic cigarette battery based on all the voltage feature points specifically comprises: distinguishing a first set of voltage feature points and a second set of voltage feature points from all the voltage feature points; constructing a first feature trajectory and a second feature trajectory of the voltage detection signals according to the first set of voltage feature points and the second set of voltage feature points, respectively; extracting trend trajectories of the voltage detection signals in different scale spaces during charging of the electronic cigarette battery according to the first feature trajectory and the second feature trajectory.

4. The method of claim 3, wherein, The step of constructing a first feature trajectory and a second feature trajectory of the voltage detection signals according to the first set of voltage feature points and the second set of voltage feature points specifically comprises: connecting voltage feature points in the first set of voltage feature points in time sequence to obtain the first feature trajectory of the voltage detection signals; connecting voltage feature points in the second set of voltage feature points in time sequence to obtain the second feature trajectory of the voltage detection signals.

5. The method of claim 1, wherein, The step of extracting change trend components of the battery voltage in each scale space during detection of the electronic cigarette battery according to the trend trajectories in all scale spaces specifically comprises: selecting one scale space from all the scale spaces as a selected scale space, removing low-frequency components from the trend trajectory in the selected scale space to obtain a change trend component of the battery voltage in the selected scale space during detection of the electronic cigarette battery; continuing to determine change trend components of the battery voltage in the remaining scale spaces during detection of the electronic cigarette battery.

6. The method of claim 1, wherein, The step of determining multi-scale information entropy features of the electronic cigarette battery during charging health state detection according to information entropy proportions of the voltage detection signals and each change trend component specifically comprises: determining information entropy of the voltage detection signal and information entropy corresponding to each variation trend component; selecting one variation trend component as a selected variation trend component, and determining information entropy proportion of the voltage detection signal and the selected variation trend component according to information entropy corresponding to the selected variation trend component and information entropy of the voltage detection signal; continuing to determine information entropy proportion of the voltage detection signal and remaining variation trend components; extracting a multi-scale information entropy feature of the electronic cigarette battery in the charging health state detection process through all information entropy proportions.

7. The method of claim 1, wherein, calculating the thermal relaxation balance index of the electronic cigarette battery in the long-time charging state according to the temperature change data specifically includes: dividing a battery temperature change curve constructed based on the temperature change data into multiple temperature change segments based on a preset sliding window; extracting steady-state temperature features and transient temperature features of the electronic cigarette battery in the charging process from all temperature change segments; determining a steady-state temperature variation trend of the electronic cigarette battery during charging according to the steady-state temperature features, and determining a transient temperature fluctuation amplitude of the electronic cigarette battery during charging through the transient temperature features; determining the thermal relaxation balance index of the electronic cigarette battery in the long-time charging state based on the steady-state temperature variation trend and the transient temperature fluctuation amplitude.

8. The method of claim 1, wherein, determining the dynamic risk coefficient of the electronic cigarette battery through the thermal relaxation balance index combined with the multi-scale information entropy feature specifically includes: determining an abnormal evolution index of the voltage detection signal of the electronic cigarette battery in the charging process based on the multi-scale information entropy feature; determining influence factors corresponding to the abnormal evolution index and the thermal relaxation balance index in the electronic cigarette battery health state evaluation; determining the dynamic risk coefficient of the electronic cigarette battery based on the abnormal evolution index, the thermal relaxation balance index, the influence factor corresponding to the abnormal evolution index, and the influence factor corresponding to the thermal relaxation balance index.

9. The method of claim 1, wherein, acquiring a voltage detection signal of an electronic cigarette battery to be detected through a voltage sensor.

10. An electronic cigarette battery detection system characterized by, includes: an acquisition module, configured to connect the electronic cigarette battery to be detected to a standard charging device, and acquire a voltage detection signal and temperature change data of a battery surface of the electronic cigarette battery during charging; a processing module, configured to determine a plurality of voltage feature points in the voltage detection signal, and construct trend trajectories of the voltage detection signal of the electronic cigarette battery in different scale spaces during charging based on all voltage feature points; the processing module is further configured to extract variation trend components of battery voltage in each scale space during detection of the electronic cigarette battery according to the trend trajectories in all scale spaces, and then determine a multi-scale information entropy feature of the electronic cigarette battery in the charging health state detection process according to information entropy proportions of the voltage detection signal and each variation trend component; The execution module is configured to calculate a thermal relaxation equilibrium index of the e-cig battery in a long-time charging state according to the temperature change data, and determine a dynamic risk coefficient of the e-cig battery by combining the thermal relaxation equilibrium index with the multi-scale information entropy feature when a cumulative charging time length of the e-cig battery reaches a preset time length threshold, and further evaluate a charging health state of the e-cig battery based on the dynamic risk coefficient.

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