Fault self-diagnosis method, device and equipment for lithium battery pack of mobile power supply, medium and program product

Abstract

The invention relates to the technical field of battery management, in particular to a mobile power supply lithium battery pack fault self-diagnosis method, device and equipment, a medium and a program product. The method comprises the steps of obtaining event recording data of a target power supply in response to a condition that the target power supply meets a preset abnormal triggering condition; performing waveform comparison on the node recording data and pre-acquired baseline data of each node of the target power supply to acquire the dispersion of the node recording data and the baseline data; determining a fault positioning point based on the dispersion, and extracting fault recording data associated with the fault positioning point; and processing the fault recording data based on a preset feature extraction algorithm to obtain node waveform features, traversing the node waveform features and performing matching analysis with a predefined fault feature library to obtain a fault diagnosis result, the fault diagnosis result comprising a plurality of fault types matched with the fault recording data and corresponding confidence coefficients. By adopting the method, the diagnosis and analysis capability of the battery health can be improved.

Description

Technical Field

[0001] This application relates to the field of battery management technology, and in particular to a method, apparatus, device, medium, and program product for self-diagnosing faults in a mobile power lithium battery pack. Background Technology

[0002] With the widespread use of portable electronic devices such as smartphones, tablets, laptops, and wearable devices, people's demands for device battery life are increasing. Power banks, as portable energy storage and power supply devices, have become an indispensable accessory in modern life. They can replenish the power of electronic devices anytime, anywhere, greatly solving the "battery anxiety" problem for users in mobile scenarios, and their applications cover multiple fields such as daily commuting, outdoor travel, and emergency rescue.

[0003] The core energy storage unit of a power bank typically uses lithium-ion batteries or lithium polymer batteries (collectively referred to as lithium battery packs). Compared to other types of batteries, lithium battery packs have significant advantages such as high energy density, low self-discharge rate, and no memory effect, making them ideal for portable products like power banks that are sensitive to size and weight. A complete power bank system usually consists of battery cells (i.e., lithium battery packs), charge and discharge management circuitry, voltage conversion modules, and input / output interfaces. Among these, the performance, safety, and lifespan of the lithium battery pack directly determine the overall quality of the power bank and the user experience.

[0004] However, while lithium battery packs offer high energy density, they also inherently present safety risks and performance degradation issues. Under abnormal conditions such as overcharging, over-discharging, short circuits, high temperatures, or physical impacts, lithium battery packs may experience a sharp decline in performance, bulging, leakage, or even serious safety accidents such as combustion or explosion. Furthermore, with increased usage, battery packs inevitably age, manifesting as increased internal resistance and capacity decay, leading to shorter battery life and reduced output capacity of the power bank. These faults and performance degradation are often gradual or insidious, making it difficult for ordinary users to detect them promptly and accurately in daily use.

[0005] In related technologies, to improve the safety and reliability of power banks, fault self-diagnosis technology has been incorporated into product design. Existing power management technologies typically employ dedicated battery management chips. These chips can monitor battery voltage, current, and temperature, comparing them with preset fixed thresholds. For example, when the battery voltage is detected to be higher than the overcharge protection threshold, the chip will disconnect the charging circuit; when the temperature is detected to be outside the safe range, it will stop the charging and discharging operation.

[0006] However, current power supply fault self-diagnosis methods have the following technical problems: Existing fault self-diagnosis relies on simple diagnostic logic with limited dimensions, making it unable to monitor and analyze in-depth health indicators, and thus requires optimization. Summary of the Invention

[0007] Therefore, it is necessary to provide a self-diagnostic method, device, computer equipment, computer-readable storage medium, and computer program product for mobile power lithium battery packs that can improve the diagnostic analysis capability of battery health, in response to the above-mentioned technical problems.

[0008] Firstly, this application provides a self-diagnosis method for faults in a mobile power bank lithium battery pack. The method includes: In response to the target power supply meeting the preset abnormal triggering conditions, the event waveform data of the target power supply is acquired. The event waveform data includes the node waveform data of the beginning, end and cell connection points of the battery pack of the target power supply. The waveform of the node waveform recording is compared with the baseline data of each node of the target power supply to obtain the dispersion of the node waveform recording data and the baseline data. Based on the dispersion, the fault location point in the target power supply is determined, and the fault waveform data associated with the fault location point is extracted; The fault waveform data is processed based on a preset feature extraction algorithm to obtain node waveform features. The node waveform features are then traversed and matched with a predefined fault feature library to obtain fault diagnosis results. The fault diagnosis results include several fault types that match the fault waveform data and their corresponding confidence levels.

[0009] In one embodiment, the step of acquiring the event waveform data of the target power supply in response to the target power supply meeting a preset abnormal triggering condition includes: The fault indication signal of the target power supply is detected based on a preset first sampling rate. The fault indication signal is a basic electrical signal whose correlation with the occurrence of the fault meets a preset threshold. When the fault indication signal meets the preset trigger feature sequence, the event waveform data of the target power supply is acquired; The trigger feature sequence is a combination sequence of several trigger conditions, including threshold trigger conditions, rate of change trigger conditions, and event trigger conditions.

[0010] In one embodiment, the method includes: Based on a preset second sampling rate, the waveform recording data of each node of the target power supply is acquired and written into the first memory area; When the waveform data fills the tail of the first memory area, the waveform data is written back to the head of the first memory area through a preset loop pointer, and the old waveform data in the first memory area is overwritten in time sequence.

[0011] In one embodiment, the step of acquiring the event waveform data of the target power supply in response to the target power supply meeting a preset abnormal triggering condition includes: In response to the target power supply meeting the abnormal triggering condition, the waveform data in the first memory area at the time of the abnormal triggering is written into the second memory area; The waveform data within a preset time window after the abnormal triggering time is written into the second memory area, and the two waveform data segments are joined together to obtain the event waveform data. The second memory area is a non-volatile memory.

[0012] In one embodiment, before comparing the node waveform data with the baseline data of each node of the target power source in advance to obtain the dispersion between the node waveform data and the baseline data, the method further includes: Acquire a preset number of historical waveform data within a set number of battery cycle periods, clean the historical waveform data based on a preset data cleaning logic, and retain valid sample waveform data; Based on the sample waveform data, the baseline data associated with each node of the target power source are obtained by fitting the data respectively.

[0013] In one embodiment, before processing the fault waveform data based on a preset feature extraction algorithm to obtain node waveform features, and before traversing the node waveform features and performing matching analysis with a predefined fault feature library to obtain the fault diagnosis result, the method further includes: The impedance parameters of each cell in the battery pack of the target power source are detected and updated based on a preset period, and the fault feature waveforms in the fault feature library are updated based on the impedance parameters. The node waveform features are matched and analyzed based on the updated fault feature library.

[0014] Secondly, this application also provides a self-diagnostic device for mobile power bank lithium battery pack faults. The device includes: The data acquisition module is used to acquire event waveform data of the target power supply in response to the target power supply meeting the preset abnormal triggering conditions. The event waveform data includes node waveform data of the beginning, end and cell connection points of the battery pack of the target power supply. The feature comparison module is used to compare the waveform of the node waveform recording data with the baseline data of each node of the target power supply in advance, and to obtain the dispersion of the node waveform recording data and the baseline data. The fault location module is used to determine the fault location point in the target power supply based on the dispersion, and to extract the fault waveform data associated with the fault location point. The fault diagnosis module is used to process the fault waveform data based on a preset feature extraction algorithm to obtain node waveform features, traverse the node waveform features and perform matching analysis with a predefined fault feature library to obtain fault diagnosis results. The fault diagnosis results include several fault types that match the fault waveform data and their corresponding confidence levels.

[0015] In one embodiment, the data acquisition module includes: The basic signal monitoring module is used to detect the fault indication signal of the target power supply based on a preset first sampling rate. The fault indication signal is a basic electrical signal whose correlation with the occurrence of the fault meets a preset threshold. A trigger sequence module is used to acquire the event waveform data of the target power supply when the fault indication signal meets a preset trigger feature sequence. The trigger feature sequence is a combination sequence of several trigger conditions, including threshold trigger conditions, rate of change trigger conditions, and event trigger conditions.

[0016] In one embodiment, the device includes: The waveform acquisition module is used to acquire waveform data of each node of the target power supply based on a preset second sampling rate and write it into the first memory area; The loop write module is used to write the recorded waveform data back to the beginning of the first memory area through a preset loop pointer when the recorded waveform data fills the end of the first memory area, and to overwrite the old recorded waveform data in the first memory area in a time sequence.

[0017] In one embodiment, the data acquisition module includes: The pre-fault data module is used to write the waveform data in the first memory area into the second memory area in response to the target power supply meeting the abnormal triggering condition. The data integration module is used to write the waveform data within a preset time window after the abnormal triggering time into the second memory area, and the two waveform data segments are connected to obtain the event waveform data. The second memory area is a non-volatile memory.

[0018] In one embodiment, prior to the feature comparison module, the following is also included: The historical data module is used to acquire historical waveform data within a preset number of battery cycle cycles, and clean the historical waveform data based on a preset data cleaning logic to retain valid sample waveform data. The baseline data module is used to fit the sample waveform data to obtain the baseline data associated with each node of the target power source.

[0019] In one embodiment, prior to the fault diagnosis module, the system further includes: The impedance update module is used to detect and update the impedance parameters of each cell in the battery pack of the target power source based on a preset period, and update the fault feature waveforms in the fault feature library based on the impedance parameters. The matching analysis module is used to perform matching analysis on the node waveform features based on the updated fault feature library.

[0020] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of a self-diagnosis method for a mobile power bank lithium battery pack fault as described in any embodiment of the first aspect.

[0021] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps of a self-diagnosis method for a mobile power bank lithium battery pack fault as described in any embodiment of the first aspect.

[0022] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps of a self-diagnostic method for a mobile power bank lithium battery pack fault as described in any embodiment of the first aspect.

[0023] The aforementioned self-diagnosis method, apparatus, computer equipment, storage medium, and computer program product for mobile power bank lithium battery packs, derived from the technical features in the claims, can achieve the following beneficial effects corresponding to the technical problems in the background art: This application provides a self-diagnosis method for faults in a mobile power bank lithium battery pack. The method includes: in response to a target power source meeting preset abnormal triggering conditions, acquiring event waveform data of the target power source, the event waveform data including node waveform data at the beginning, end, and cell connection points of the battery pack; comparing the node waveform data with pre-acquired baseline data for each node of the target power source to obtain the dispersion between the node waveform data and the baseline data; determining fault location points in the target power source based on the dispersion, and extracting fault waveform data associated with the fault location points; processing the fault waveform data based on a preset feature extraction algorithm to obtain node waveform features, traversing the node waveform features and performing matching analysis with a predefined fault feature library to obtain fault diagnosis results, the fault diagnosis results including several fault types matching the fault waveform data and corresponding confidence levels. In implementation, monitoring node waveform data at different nodes of the target power bank battery pack helps to achieve a transition from overall battery pack assessment to precise individual diagnosis. First, by responding to abnormal triggering conditions and acquiring node waveform data containing spatial location information, the system can capture the dynamic response differences at different locations within the battery pack, thus providing a data foundation for fault diagnosis. Subsequently, by calculating the dispersion of each node's data from the baseline data, the system can quantify the degree to which each node deviates from its normal state. Based on this, the fault location point can be accurately determined, narrowing the fault range from the entire battery pack to a specific cell or connection point. Finally, by extracting node waveform features and matching them with a fault feature library, the diagnosis is upgraded from simple threshold comparison to multi-dimensional intelligent analysis based on pattern recognition, providing a quantitative judgment in the form of confidence levels, thereby improving the diagnostic analysis capabilities for the target power source. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram of the first process of a self-diagnosis method for a mobile power supply lithium battery pack fault in one embodiment. Figure 2 This is a schematic diagram of the second process of a self-diagnosis method for a mobile power lithium battery pack fault in another embodiment; Figure 3 This is a schematic diagram of the third process of a self-diagnosis method for a mobile power bank lithium battery pack fault in another embodiment; Figure 4 This is a schematic diagram of the fourth process of a self-diagnosis method for a mobile power bank lithium battery pack fault in another embodiment; Figure 5 This is a schematic diagram of the fifth step of a self-diagnosis method for mobile power lithium battery pack faults in another embodiment; Figure 6 This is a schematic diagram of the sixth process of a self-diagnosis method for mobile power lithium battery pack faults in another embodiment; Figure 7 This is a structural block diagram of a mobile power supply lithium battery pack fault self-diagnosis device in one embodiment. Figure 8 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0027] In one embodiment, such as Figure 1 As shown, a self-diagnostic method for lithium battery pack faults in a mobile power bank is provided. This embodiment illustrates the method by applying it to a terminal. It is understood that this method can also be applied to a server, and further to a system including both a terminal and a server, and is implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps: Step 102: In response to the target power supply meeting the preset abnormal triggering conditions, acquire the event waveform data of the target power supply.

[0028] The event waveform data includes the node waveform data of the beginning, end and cell connection points of the battery pack of the target power source.

[0029] Among them, waveform data can refer to the data obtained by continuously and at high speed “recording” one or more electrical signals that change over time. Waveform data can be used to record the complete waveforms of voltage, current and other signals in the battery pack at the moment of failure.

[0030] Step 104: Compare the waveforms of the node waveform recordings with the baseline data of each node of the target power source to obtain the dispersion of the node waveform recordings and the baseline data.

[0031] Among them, dispersion can characterize the degree of dispersion between the node's recorded waveform data and the corresponding baseline data. The higher the dispersion, the higher the possibility that the node deviates from the normal operating state.

[0032] Step 106: Determine the fault location point in the target power supply based on the dispersion, and extract the fault waveform data associated with the fault location point.

[0033] Step 108: Process the fault waveform data based on the preset feature extraction algorithm to obtain node waveform features, traverse the node waveform features and perform matching analysis with the predefined fault feature library to obtain fault diagnosis results.

[0034] The fault diagnosis results include several fault types that match the fault waveform data and their corresponding confidence levels.

[0035] In the above-mentioned self-diagnosis method for mobile power bank lithium battery pack faults, reasonable derivation is made based on the technical features in the embodiments to achieve the beneficial effect of solving the technical problems mentioned in the background art: This application provides a self-diagnosis method for faults in a mobile power bank lithium battery pack. The method includes: in response to a target power source meeting preset abnormal triggering conditions, acquiring event waveform data of the target power source, the event waveform data including node waveform data at the beginning, end, and cell connection points of the battery pack; comparing the node waveform data with pre-acquired baseline data for each node of the target power source to obtain the dispersion between the node waveform data and the baseline data; determining fault location points in the target power source based on the dispersion, and extracting fault waveform data associated with the fault location points; processing the fault waveform data based on a preset feature extraction algorithm to obtain node waveform features, traversing the node waveform features and performing matching analysis with a predefined fault feature library to obtain fault diagnosis results, the fault diagnosis results including several fault types matching the fault waveform data and corresponding confidence levels. In implementation, monitoring node waveform data at different nodes of the target power bank battery pack helps to achieve a transition from overall battery pack assessment to precise individual diagnosis. First, by responding to abnormal triggering conditions and acquiring node waveform data containing spatial location information, the system can capture the dynamic response differences at different locations within the battery pack, thus providing a data foundation for fault diagnosis. Subsequently, by calculating the dispersion of each node's data from the baseline data, the system can quantify the degree to which each node deviates from its normal state. Based on this, the fault location point can be accurately determined, narrowing the fault range from the entire battery pack to a specific cell or connection point. Finally, by extracting node waveform features and matching them with a fault feature library, the diagnosis is upgraded from simple threshold comparison to multi-dimensional intelligent analysis based on pattern recognition, providing a quantitative judgment in the form of confidence levels, thereby improving the diagnostic analysis capabilities for the target power source.

[0036] In one embodiment, such as Figure 2As shown, step 102 includes: Step 202: Detect the fault indication signal of the target power supply based on a preset first sampling rate. The fault indication signal is a basic electrical signal whose correlation with the occurrence of a fault meets a preset threshold.

[0037] Step 204: When the fault indication signal meets the preset trigger feature sequence, acquire the event waveform data of the target power supply.

[0038] The trigger feature sequence is a combination sequence of several trigger conditions, including threshold trigger conditions, rate of change trigger conditions, and event trigger conditions.

[0039] In this embodiment, by introducing a multi-condition, hierarchical judgment mechanism based on trigger feature sequences, the possibility of false alarms is reduced, and transient interference caused by usage conditions is effectively filtered out. On the other hand, detection based on fault indication signals helps to achieve anomaly monitoring in low-power scenarios, improving the stability of fault diagnosis applications.

[0040] In one embodiment, such as Figure 3 As shown, the method includes: Step 302: Obtain the waveform recording data of each node of the target power supply based on the preset second sampling rate, and write it into the first memory area.

[0041] Step 304: When the waveform data is written to the end of the first memory area, the waveform data is written back to the beginning of the first memory area through a preset loop pointer, and the old waveform data in the first memory area is overwritten in time sequence.

[0042] In this embodiment, by setting up a first memory area, it is helpful to form a circular buffer architecture, thereby making use of limited memory space to continuously record waveform data and achieving the effect of "dynamic preservation" of waveform data.

[0043] In one embodiment, such as Figure 4 As shown, step 102 includes: Step 402: In response to the target power supply meeting the abnormal triggering condition, write the waveform data in the first memory area at the time of the abnormal triggering into the second memory area.

[0044] Step 404: Write the waveform data within a preset time window after the abnormal triggering time into the second memory area, and connect the two waveform data segments to obtain the event waveform data. The second memory area is a non-volatile memory.

[0045] In this embodiment, segmented capture helps to solidify the waveform data before the fault point at the fault triggering time, and connects and integrates the waveform data before the fault point with the waveform data in the preset window after the fault point into node waveform data, thereby realizing the pre-triggered recording architecture. This allows the node waveform data to cover the complete timeline before the fault point, the fault time, and after the fault point, which helps to provide sufficient and complete evidence for fault diagnosis.

[0046] In one embodiment, such as Figure 5 As shown, before 104, it also includes: Step 502: Obtain a preset number of historical waveform data within battery cycle periods, clean the historical waveform data based on preset data cleaning logic, and retain valid sample waveform data.

[0047] Data cleaning logic can refer to the filtering logic for removing invalid historical data. It can be based on time or on the data quality itself. The specific cleaning logic can be determined by technical personnel according to actual application needs.

[0048] Step 504: Fit the sample waveform data to obtain the baseline data associated with each node of the target power source.

[0049] In this embodiment, based on the cleaned sample data, data fitting is performed on each node of the target power source to generate associated baseline data. This generates a personalized "health baseline" representing the typical waveform characteristics of the starting point, ending point, and each cell connection point under its own health condition. On the other hand, dividing the baseline data into individual nodes for fitting helps to express the node differences reflected by differences in the path impedance of the cells in the battery pack, which helps to achieve more accurate fault diagnosis.

[0050] In one embodiment, such as Figure 6 As shown, before step 108, the procedure further includes: Step 602: Based on a preset period, detect and update the impedance parameters of each cell in the battery pack of the target power source, and update the fault feature waveforms in the fault feature library based on the impedance parameters.

[0051] Step 604: Perform matching analysis on the node waveform features based on the updated fault feature library.

[0052] In this embodiment, by introducing a periodic parameter update and feature library evolution mechanism, the fault diagnosis system overcomes the limitations of static and rigid approaches. Specifically, the system detects and updates the impedance parameters of each cell in the battery pack based on a preset cycle. Impedance is one of the most critical internal parameters characterizing the state of health (SOH) of a battery, and its gradual increase with the number of cycles and usage time is an inevitable aging process. By actively and synchronously updating the fault feature waveforms in the fault feature library based on the updated impedance parameters, the system can learn and simulate the changes in voltage response, current characteristics, and other waveforms that occur after battery aging, thereby adjusting the "scale" used for pattern matching. Ultimately, this helps improve the reliability of fault diagnosis.

[0053] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0054] Based on the same inventive concept, this application also provides a mobile power bank lithium battery pack fault self-diagnosis device for implementing the aforementioned mobile power bank lithium battery pack fault self-diagnosis method. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the mobile power bank lithium battery pack fault self-diagnosis device provided below can be found in the above-described limitations of the mobile power bank lithium battery pack fault self-diagnosis method, and will not be repeated here.

[0055] In one embodiment, such as Figure 7 As shown, a self-diagnostic device for mobile power bank lithium battery pack faults is provided, including: a data acquisition module, a feature comparison module, a fault location module, and a fault diagnosis module, wherein: The data acquisition module is used to acquire event waveform data of the target power supply in response to the target power supply meeting the preset abnormal triggering conditions. The event waveform data includes node waveform data of the beginning, end and cell connection points of the battery pack of the target power supply. The feature comparison module is used to compare the waveform of the node waveform recording data with the baseline data of each node of the target power supply in advance, and to obtain the dispersion of the node waveform recording data and the baseline data. The fault location module is used to determine the fault location point in the target power supply based on the dispersion, and to extract the fault waveform data associated with the fault location point. The fault diagnosis module is used to process the fault waveform data based on a preset feature extraction algorithm to obtain node waveform features, traverse the node waveform features and perform matching analysis with a predefined fault feature library to obtain fault diagnosis results. The fault diagnosis results include several fault types that match the fault waveform data and their corresponding confidence levels.

[0056] In one embodiment, the data acquisition module includes: The basic signal monitoring module is used to detect the fault indication signal of the target power supply based on a preset first sampling rate. The fault indication signal is a basic electrical signal whose correlation with the occurrence of the fault meets a preset threshold. A trigger sequence module is used to acquire the event waveform data of the target power supply when the fault indication signal meets a preset trigger feature sequence. The trigger feature sequence is a combination sequence of several trigger conditions, including threshold trigger conditions, rate of change trigger conditions, and event trigger conditions.

[0057] In one embodiment, the device includes: The waveform acquisition module is used to acquire waveform data of each node of the target power supply based on a preset second sampling rate and write it into the first memory area; The loop write module is used to write the recorded waveform data back to the beginning of the first memory area through a preset loop pointer when the recorded waveform data fills the end of the first memory area, and to overwrite the old recorded waveform data in the first memory area in a time sequence.

[0058] In one embodiment, the data acquisition module includes: The pre-fault data module is used to write the waveform data in the first memory area into the second memory area in response to the target power supply meeting the abnormal triggering condition. The data integration module is used to write the waveform data within a preset time window after the abnormal triggering time into the second memory area, and the two waveform data segments are connected to obtain the event waveform data. The second memory area is a non-volatile memory.

[0059] In one embodiment, prior to the feature comparison module, the following is also included: The historical data module is used to acquire historical waveform data within a preset number of battery cycle cycles, and clean the historical waveform data based on a preset data cleaning logic to retain valid sample waveform data. The baseline data module is used to fit the sample waveform data to obtain the baseline data associated with each node of the target power source.

[0060] In one embodiment, prior to the fault diagnosis module, the system further includes: The impedance update module is used to detect and update the impedance parameters of each cell in the battery pack of the target power source based on a preset period, and update the fault feature waveforms in the fault feature library based on the impedance parameters. The matching analysis module is used to perform matching analysis on the node waveform features based on the updated fault feature library.

[0061] The various modules in the aforementioned self-diagnostic device for mobile power lithium battery pack faults can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0062] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 8 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements a self-diagnostic method for mobile power lithium battery pack faults. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0063] Those skilled in the art will understand that Figure 8The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0064] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0065] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0066] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0067] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0068] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0069] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0070] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A self-diagnosis method for faults in a mobile power bank lithium battery pack, characterized in that, The method includes: In response to the target power supply meeting the preset abnormal triggering conditions, the event waveform data of the target power supply is acquired. The event waveform data includes the node waveform data of the beginning, end and cell connection points of the battery pack of the target power supply. The waveform of the node waveform recording is compared with the baseline data of each node of the target power supply to obtain the dispersion of the node waveform recording data and the baseline data. Based on the dispersion, the fault location point in the target power supply is determined, and the fault waveform data associated with the fault location point is extracted; The fault waveform data is processed based on a preset feature extraction algorithm to obtain node waveform features. The node waveform features are then traversed and matched with a predefined fault feature library to obtain fault diagnosis results. The fault diagnosis results include several fault types that match the fault waveform data and their corresponding confidence levels.

2. The method according to claim 1, characterized in that, The process of acquiring event waveform data of the target power supply in response to the target power supply meeting preset abnormal triggering conditions includes: The fault indication signal of the target power supply is detected based on a preset first sampling rate. The fault indication signal is a basic electrical signal whose correlation with the occurrence of the fault meets a preset threshold. When the fault indication signal meets the preset trigger feature sequence, the event waveform data of the target power supply is acquired; The trigger feature sequence is a combination sequence of several trigger conditions, including threshold trigger conditions, rate of change trigger conditions, and event trigger conditions.

3. The method according to claim 2, characterized in that, The method includes: Based on a preset second sampling rate, the waveform recording data of each node of the target power supply is acquired and written into the first memory area; When the waveform data fills the tail of the first memory area, the waveform data is written back to the head of the first memory area through a preset loop pointer, and the old waveform data in the first memory area is overwritten in time sequence.

4. The method according to claim 3, characterized in that, The process of acquiring event waveform data of the target power supply in response to the target power supply meeting preset abnormal triggering conditions includes: In response to the target power supply meeting the abnormal triggering condition, the waveform data in the first memory area at the time of the abnormal triggering is written into the second memory area; The waveform data within a preset time window after the abnormal triggering time is written into the second memory area, and the two waveform data segments are joined together to obtain the event waveform data. The second memory area is a non-volatile memory.

5. The method according to any one of claims 1 to 4, characterized in that, Before comparing the node waveform data with the baseline data of each node of the target power source obtained in advance, and obtaining the dispersion between the node waveform data and the baseline data, the method further includes: Acquire a preset number of historical waveform data within a set number of battery cycle periods, clean the historical waveform data based on a preset data cleaning logic, and retain valid sample waveform data; Based on the sample waveform data, the baseline data associated with each node of the target power source are obtained by fitting the data respectively.

6. The method according to claim 1, characterized in that, Before processing the fault waveform data based on a preset feature extraction algorithm to obtain node waveform features, and then matching and analyzing these node waveform features against a predefined fault feature library to obtain the fault diagnosis result, the process further includes: The impedance parameters of each cell in the battery pack of the target power source are detected and updated based on a preset period, and the fault feature waveforms in the fault feature library are updated based on the impedance parameters. The node waveform features are matched and analyzed based on the updated fault feature library.

7. A self-diagnostic device for faults in a mobile power bank lithium battery pack, characterized in that, The device includes: The data acquisition module is used to acquire event waveform data of the target power supply in response to the target power supply meeting the preset abnormal triggering conditions. The event waveform data includes node waveform data of the beginning, end and cell connection points of the battery pack of the target power supply. The feature comparison module is used to compare the waveform of the node waveform recording data with the baseline data of each node of the target power supply in advance, and to obtain the dispersion of the node waveform recording data and the baseline data. The fault location module is used to determine the fault location point in the target power supply based on the dispersion, and to extract the fault waveform data associated with the fault location point. The fault diagnosis module is used to process the fault waveform data based on a preset feature extraction algorithm to obtain node waveform features, traverse the node waveform features and perform matching analysis with a predefined fault feature library to obtain fault diagnosis results. The fault diagnosis results include several fault types that match the fault waveform data and their corresponding confidence levels.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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