Battery system monitoring method and device

Abstract

The invention discloses a monitoring method and device of a battery system, and relates to the technical field of battery management and state monitoring. The method comprises the following steps: acquiring impedance data of a battery unit in a battery system, and determining that the battery unit is a suspicious battery unit when the impedance data meets an internal resistance abnormal condition; and acquiring voltage data of the suspicious battery unit, and determining that the suspicious battery unit is an abnormal target battery unit when the voltage data meets a voltage abnormal condition. According to the invention, accurate identification and early warning of the abnormal battery unit are realized by combining a multi-dimensional characteristic judgment mechanism of impedance and voltage.

Description

Technical Field

[0001] This application relates to the field of battery management and condition monitoring technology, and in particular to a monitoring method and apparatus for a battery system. Background Technology

[0002] In practical applications of battery systems, continuous monitoring and early warning of anomalies are typically required to ensure system safety and performance stability. The state of a battery system is influenced by multiple factors, including voltage, temperature, current, cell consistency, and aging. In real-world applications, the external measurement characteristics of a battery change under different operating conditions (such as charge / discharge plateaus). A single quantitative indicator cannot fully reflect the performance evolution and abnormal signs at the internal mechanism level. Therefore, early identification and precise location of anomalies present a significant challenge and a practical need.

[0003] Current early warning systems primarily rely on BMS protection strategies based on voltage, temperature, and current thresholds. This often results in situations where protection is not triggered but anomalies have already occurred. Furthermore, troubleshooting is time-consuming and often requires system shutdown, leading to delayed warnings and low location efficiency. Reduced voltage sensitivity during the charge / discharge plateau also weakens the judgment effect; relying solely on voltage changes is insufficient to characterize changes in the mechanistic performance of cells or battery modules, making it difficult to identify early performance anomalies in a timely manner.

[0004] The information disclosed in this background section is included only to enhance the understanding of the context of this disclosure, and therefore may contain information that does not constitute relevant technology currently known to those skilled in the art. Summary of the Invention

[0005] This application provides a battery system monitoring method and apparatus to solve the problem in the prior art that relies on threshold protection, making it difficult to identify early performance abnormalities that have not yet been triggered.

[0006] The technical solution adopted in this application is as follows: In a first aspect, this application provides a method for monitoring a battery system, including: Obtain the impedance data of the battery cells in the battery system, and determine the battery cell as a suspicious battery cell when the impedance data meets the internal resistance abnormality condition. Obtain voltage data of suspicious battery cells, and when the voltage data meets the voltage anomaly conditions, identify the suspicious battery cell as the target battery cell that has an anomaly.

[0007] This application achieves rapid and accurate identification and location of early performance abnormalities through a two-stage judgment process: first screening for internal resistance anomalies based on impedance data, and then verifying voltage anomalies in suspicious battery cells. This overcomes the problems of low sensitivity and delayed early warning of single voltage thresholds, and improves monitoring accuracy and location efficiency.

[0008] In conjunction with the first aspect, in one optional implementation, the battery system includes multiple battery cells. When the impedance data meets the internal resistance anomaly condition, the battery cell is determined to be a suspicious battery cell, including: The internal resistance of each battery cell in multiple battery cells is estimated based on impedance data. Based on the internal resistance values ​​of all battery cells in the battery system, the dispersion value of the internal resistance value of each battery cell is calculated. Battery cells whose corresponding dispersion value exceeds the first preset threshold are identified as suspicious battery cells.

[0009] This application uses relative dispersion within a group rather than a single absolute threshold: first, the internal resistance of each unit is estimated from impedance data, and then the dispersion of distribution within the group is calculated. Units that exceed the preset threshold are marked as suspicious, so as to more sensitively capture early internal resistance anomalies, reduce the influence of temperature and aging differences, and improve the robustness and positioning accuracy of screening.

[0010] In conjunction with the first aspect, in one optional implementation, battery cells whose corresponding dispersion values ​​exceed a first preset threshold are identified as suspicious battery cells, including: Battery cells whose corresponding dispersion values ​​exceed the first preset threshold but do not exceed the second preset threshold are identified as suspicious battery cells.

[0011] This application uses a dual-threshold grading system based on relative dispersion within a group to distinguish between minor and significant abnormalities, thereby improving early identification capabilities and reducing false alarms / false negatives.

[0012] In conjunction with the first aspect, in one alternative implementation, the method further includes: When the dispersion value of a battery cell exceeds a second preset threshold, the battery cell is determined to be the target battery cell.

[0013] This application effectively identifies battery cells with potential faults or performance degradation by monitoring the dispersion value of the battery cells. When the dispersion value of a battery cell exceeds a second preset threshold, the system automatically determines that the battery cell is the target battery cell. This process enables the battery management system to take timely maintenance or replacement measures, thereby improving the overall safety and reliability of the battery pack.

[0014] In conjunction with the first aspect, in one optional implementation, the first preset threshold is 10%, and the second preset threshold is 20%.

[0015] This application, by setting preset thresholds of 10% and 20%, can identify and warn when the performance of a battery cell deviates slightly from the normal range, thereby achieving early warning and accurate fault location of the battery cell, thus optimizing maintenance resources and improving the reliability of the overall system.

[0016] In conjunction with the first aspect, in one optional implementation, when the impedance data meets the internal resistance anomaly condition, the battery cell is determined to be a suspicious battery cell, including: The internal resistance of the battery cell is estimated based on impedance data; When the internal resistance value exceeds the preset resistance range, the battery cell is determined to be a suspicious battery cell.

[0017] This application estimates the internal resistance of a battery cell based on impedance data and compares it with a preset resistance range. When the internal resistance exceeds the preset range, it is promptly identified as a suspicious battery cell, thereby achieving early fault identification and prevention and improving the safety and reliability of the battery system.

[0018] In conjunction with the first aspect, in one optional implementation, when the voltage data meets the voltage anomaly conditions, the suspicious battery cell is identified as the target battery cell exhibiting the anomaly, including: Based on voltage data, the first moment when the voltage of a suspicious battery cell reaches the charging threshold and the second moment when the voltage of a suspicious battery cell reaches the discharging threshold are obtained. When the first moment of a suspicious battery cell is earlier than the first moment of other suspicious battery cells in the battery system, and the second moment of a suspicious battery cell is earlier than the second moment of other suspicious battery cells in the battery system, the suspicious battery cell is identified as the target battery cell.

[0019] This application analyzes voltage data to obtain the specific times when suspicious battery cells reach charging and discharging thresholds. By comparing these times with the corresponding times of other suspicious battery cells, it accurately identifies the battery cell that reaches the threshold earliest as the target battery cell, thereby achieving early detection and accurate location of battery anomalies and improving the monitoring efficiency and maintenance effect of the battery system.

[0020] In conjunction with the first aspect, in one alternative implementation, the method further includes: Obtain the location information and anomaly type of the target battery cell, and generate corresponding suggested measures; Alarm information is generated based on location information, anomaly type, and suggested measures, and then sent to the management platform.

[0021] This application improves the maintenance efficiency and safety of the battery system by obtaining the location information and anomaly type of the target battery cell after detecting an anomaly, generating corresponding suggested measures, and then generating detailed alarm information based on this information and sending it to the management platform. This ensures that relevant maintenance personnel can understand the battery anomaly in a timely and accurate manner and take appropriate countermeasures.

[0022] Secondly, this application provides a monitoring device for a battery system. The monitoring device includes a memory and a processor. The memory stores computer programs or instructions, which, when executed by the processor, implement the methods described in the first aspect or any possible implementation thereof.

[0023] Thirdly, this application provides a computer-readable storage medium. The storage medium stores a computer program or instructions that, when executed by a processor, implement the method described in the first aspect or any possible implementation thereof.

[0024] The beneficial effects of the second and third aspects mentioned above can be referred to the first aspect or any of the optional implementations of the first aspect, and will not be elaborated here. Based on the implementations provided above, this application can also be further combined to provide more implementations.

[0025] Other advantages, objectives and features of this application will be partly apparent from the description below, and partly understood by those skilled in the art through study and practice of this application. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0027] Figure 1 This is one of the flowcharts of the battery system monitoring method provided in the embodiments of this application; Figure 2 This is one of the sub-step diagrams of step S101 provided in the embodiments of this application; Figure 3 This is the second schematic diagram of a sub-step of step S101 provided in the embodiments of this application; Figure 4 This is the second flowchart of the battery system monitoring method provided in the embodiments of this application; Figure 5 This is the third sub-step diagram of step S101 provided in the embodiments of this application; Figure 6 This is a schematic diagram of a sub-step of step S103 provided in the embodiments of this application; Figure 7 This is the third flowchart of the battery system monitoring method provided in the embodiments of this application; Figure 8This is a schematic diagram of the structure of the monitoring device for the battery system provided in the embodiments of this application. Detailed Implementation

[0028] It should be noted that, in this application, the terms "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0029] The term "and / or" as used in this application includes any and all combinations of one or more of the associated listed items. In this application, "at least one" means one or more, and "more than one" means two or more. The terms "first," "second," and other ordinal terms used in this application may be used to describe various constituent elements, but these constituent elements are not limited by these terms. The purpose of using these terms is solely to distinguish one constituent element from others and should not be construed as indicating or implying relative importance. For example, without departing from the scope of this application, a first constituent element may be named a second constituent element, and similarly, a second constituent element may be named a first constituent element.

[0030] Before introducing the embodiments of this application, the background technology involved in this application will be introduced first.

[0031] In practical applications of battery systems, continuous monitoring of operating conditions and abnormal early warning are necessary to ensure safety and stable performance. Battery status is affected by multiple factors, including voltage, temperature, current, cell consistency, and aging. Different operating conditions, such as the charge / discharge plateau, can cause changes in external measurement characteristics. A single indicator cannot fully characterize the performance evolution and early signs of abnormalities at the internal mechanism level. Therefore, achieving early detection and accurate location of abnormalities is both challenging and of significant practical importance. Existing early warning systems mostly rely on voltage, temperature, and current threshold protection set by the BMS. Often, abnormalities occur without triggering the threshold, making troubleshooting time-consuming and potentially requiring system shutdown. This results in delayed early warnings and low location efficiency. During the charge / discharge plateau, the sensitivity of voltage to abnormalities further decreases. Relying solely on voltage changes is insufficient to reflect the performance degradation at the mechanism level of the cell or module, making it difficult to identify early abnormalities in a timely manner.

[0032] In summary, threshold-based early warning schemes in related technologies suffer from problems such as delayed early warning and low positioning efficiency.

[0033] To address the aforementioned problems, embodiments of this application provide a method for monitoring a battery system. (Reference) Figure 1 , Figure 1 This is one of the flowcharts for a battery system monitoring method provided in an embodiment of this application.

[0034] like Figure 1 As shown, the monitoring method for this battery system includes at least the following steps: S101: Obtain the impedance data of the battery cells in the battery system, and determine the battery cell as a suspicious battery cell when the impedance data meets the internal resistance abnormality condition. S103: Obtain the voltage data of the suspected battery cell, and when the voltage data meets the voltage anomaly conditions, determine the suspected battery cell as the target battery cell that has an anomaly.

[0035] Specifically, the battery system monitoring method identifies abnormal battery cells through a two-step screening process. First, in step S101, the system acquires the impedance data of each battery cell, which is an important indicator for assessing the battery's health status. When the impedance data of a battery cell exceeds a preset abnormal internal resistance condition, the battery cell is marked as a "suspected battery cell." This initial screening helps to quickly identify potentially problematic battery cells, thereby improving the efficiency of subsequent monitoring. For example, in a battery pack, if the internal resistance of a battery cell is significantly higher than that of other battery cells, this may indicate that the battery cell is at risk of aging or damage.

[0036] Next, in step S103, the system further acquires the voltage data of these suspicious battery cells. Voltage is another key indicator of battery performance; it not only shows the battery's charge / discharge status but also indicates its capacity health. When the voltage data of a suspicious battery cell meets preset voltage anomaly conditions, that battery cell is identified as the "target battery cell" with the anomaly. This process ensures that only those battery cells with genuine problems are identified, thereby reducing the false alarm rate and helping maintenance personnel focus their attention on batteries that require immediate attention. For example, if a suspicious battery cell has a significantly lower voltage than other cells under normal operating conditions, this may indicate that the cell has a short circuit or other serious problems, requiring immediate replacement or repair.

[0037] It should be noted that the battery cells in this solution may be, but are not limited to, battery cells or battery modules, and the implementer of this solution may be, but is not limited to, personal computers, servers, embedded systems, dedicated testing equipment, battery management systems (BMS), IoT platforms, mobile devices (such as smartphones or tablets), etc.

[0038] In some embodiments, an abnormal internal resistance condition may be that the dispersion of the internal resistance value exceeds a standard threshold, as referenced. Figure 2 , Figure 2 This is one of the schematic diagrams of a sub-step of step S101 provided in an embodiment of this application. For example... Figure 2As shown, a battery system may include multiple battery cells; therefore, the process of screening out suspicious battery cells based on abnormal internal resistance conditions may include at least the following steps: S201: Estimate the internal resistance of each battery cell in multiple battery cells based on impedance data; S203: Based on the internal resistance values ​​of all battery cells in the battery system, calculate the dispersion value of the internal resistance value of each battery cell. S205: Battery cells whose corresponding dispersion values ​​exceed the first preset threshold are identified as suspicious battery cells.

[0039] Specifically, battery systems identify potentially anomalous battery cells (i.e., suspicious cells) by analyzing the impedance data of battery cells, particularly using electrochemical impedance spectroscopy (EIS). EIS technology measures the impedance characteristics of a battery at different frequencies, providing detailed information about the battery's internal processes. First, the internal resistance of each battery cell can be estimated from the impedance data measured by EIS. Estimation methods can include, but are not limited to, equivalent circuit model fitting, Nyquist plot analysis, and Bode plot analysis. Internal resistance is a crucial criterion for assessing battery health because it is highly sensitive to battery aging and performance degradation.

[0040] Next, the internal resistance of each battery cell can be calculated, and the distribution and dispersion of these internal resistance values ​​throughout the battery system can be analyzed. Dispersion measures the degree to which a battery cell deviates from other battery cells in terms of impedance, typically expressed using statistical methods such as standard deviation or coefficient of variation. A significant deviation in the internal resistance of a battery cell from other cells may indicate an abnormality in that cell. Increased internal resistance is usually associated with battery aging, internal short circuits, plate corrosion, or electrolyte problems. These abnormalities can lead to decreased battery capacity, reduced efficiency, and even safety risks. Therefore, by calculating the dispersion of the internal resistance of each battery cell, cells that are significantly different in performance from other battery cells can be identified.

[0041] Finally, battery cells with dispersion values ​​exceeding a preset threshold should be marked as suspicious cells for priority inspection and maintenance. This approach helps identify potential problems early, avoids overall performance degradation of the entire battery system, and ensures system safety and reliability.

[0042] For example, suppose a battery system consists of 100 battery cells. Using EIS measurements, the internal resistance of each cell is estimated and then compared to the average internal resistance of the entire system. Assuming a dispersion threshold of 10%, if a cell's dispersion value is 12%, exceeding this preset threshold, then that cell is considered suspicious. This means that the cell's impedance characteristics differ significantly from most other cells, potentially indicating a underlying health problem. This method, by leveraging the sensitivity of EIS technology, can identify abnormal battery cells before serious battery failures occur, helping maintenance personnel prioritize inspections and maintenance, preventing larger system failures. This EIS-based analysis not only improves the accuracy of anomaly detection but also enhances the effectiveness of the battery management system in health monitoring.

[0043] In some embodiments, reference Figure 3 , Figure 3 This is a second schematic diagram of a sub-step of step S101 provided in an embodiment of this application. For example... Figure 3 As shown, the process of screening out suspicious battery cells based on the dispersion value (i.e., step S205) includes at least the following steps: S301: Battery cells whose corresponding dispersion values ​​exceed the first preset threshold but do not exceed the second preset threshold are identified as suspicious battery cells.

[0044] Specifically, identifying suspicious battery cells is a crucial step in ensuring system performance and safety during battery system management and maintenance. Analyzing the internal resistance and dispersion of battery cells effectively assesses their health status. To further differentiate the severity of problems, two thresholds can be set: a first preset threshold and a second preset threshold. When the dispersion value of a battery cell exceeds the first preset threshold but not the second preset threshold, these cells are identified as suspicious and trigger subsequent voltage verification. This dual-threshold strategy not only helps to accurately classify the severity of problems but also helps to prioritize inspection and maintenance work, making resource allocation more efficient and ensuring the stability of the battery system.

[0045] For example, suppose a battery system has a first preset threshold set at 2% and a second preset threshold set at 5%. If the internal resistance dispersion of a battery cell is 3%, this indicates that the cell's internal resistance deviates from the normal range beyond the first preset threshold, thus classifying it as a suspicious cell and triggering subsequent voltage verification. In this case, although the dispersion value does not exceed the second preset threshold, indicating that the cell's risk level has not yet reached a higher level, its deviation is already sufficient to attract attention. Through this dual-threshold strategy, managers can more accurately identify and classify the severity of battery cell problems, thereby effectively prioritizing inspection and maintenance work. This ensures that resources can be allocated rationally with limited resources, maintaining system stability and safety, and preventing potential problems from escalating further.

[0046] In some embodiments, reference Figure 4 , Figure 4 This is a second flowchart of a battery system monitoring method provided in an embodiment of this application. Figure 4 As shown, the monitoring method of the battery system further includes step S401: when the dispersion value of the battery cell exceeds the second preset threshold, the battery cell is determined to be the target battery cell.

[0047] Specifically, to more effectively manage the health status of battery systems, in addition to identifying suspicious battery cells, it is also necessary to identify those battery cells that require immediate attention and action. When the dispersion value of a battery cell exceeds a second preset threshold, the battery cell is identified as a target battery cell. This means that the state deviation of the battery cell has reached a critical level and may pose a direct threat to the performance and safety of the entire battery system. Therefore, these types of battery cells (i.e., target battery cells) usually need to be prioritized for inspection, maintenance, or replacement to prevent further impact on the system. In this way, managers can take timely measures to prevent potential system failures and safety hazards.

[0048] For example, suppose a battery system has a first preset threshold set at 2% and a second preset threshold set at 5%. If the internal resistance dispersion of a battery cell is 6%, it means that the cell's internal resistance deviates from the normal range beyond the second preset threshold, and it is identified as a target battery cell. In this case, the battery cell may have a serious health problem that could affect the overall function of the system. Therefore, the administrator needs to immediately conduct a detailed inspection and treatment of the battery cell to ensure system safety. This strategy ensures that appropriate response measures are taken according to the severity of the problem, maintaining the long-term stability and reliability of the system.

[0049] In some embodiments, a dual-threshold strategy is employed to effectively monitor and manage the health status of the battery system. A first preset threshold of 10% is used to identify suspicious battery cells requiring attention. This means that if the dispersion value of a battery cell exceeds 10%, it indicates that the cell's performance deviates from the normal range, requiring further observation and possible subsequent verification (such as voltage verification). However, this does not immediately mean that the battery cell has a serious problem, but rather that its state changes need to be closely monitored. A second preset threshold of 20% is used to identify target battery cells requiring immediate action. If the dispersion value of a battery cell exceeds 20%, it indicates that the cell's state deviation has reached a level requiring urgent handling, potentially posing a direct threat to the performance and safety of the entire battery system, thus requiring immediate inspection and maintenance. Through this dual-threshold mechanism, managers can take different measures based on the severity of the battery cell state deviation. This approach not only improves the management efficiency of the battery system but also enhances the system's reliability and safety.

[0050] In some embodiments, an abnormal internal resistance condition can be an internal resistance value exceeding the standard range, as referenced. Figure 5 , Figure 5 This is the third schematic diagram of a sub-step of step S101 provided in an embodiment of this application. For example... Figure 5 As shown, the process of screening out suspicious battery cells based on abnormal internal resistance conditions can include at least the following steps: S501: Estimate the internal resistance of the battery cell based on impedance data; S503: When the internal resistance value exceeds the preset resistance value range, the battery cell is determined to be a suspicious battery cell.

[0051] Specifically, in a battery management system, internal resistance is a crucial parameter used to assess the health and performance of battery cells. To identify potentially problematic cells, anomalies in internal resistance can be detected to screen for suspicious cells. First, the system estimates the internal resistance value based on impedance data collected from the battery cell. Next, the system compares the estimated internal resistance value to a preset range. This preset range is set according to battery performance standards under normal operating conditions. If the internal resistance value exceeds this range, it indicates potential performance degradation or other problems, and the cell is therefore identified as a suspicious cell. This process helps to identify and manage potentially problematic cells in the battery system in a timely manner, improving system reliability and safety.

[0052] For example, suppose in a battery management system, the normal internal resistance range is set to 0.1 to 0.5 ohms. During a periodic inspection, the impedance data of a battery cell is collected and used to estimate its internal resistance. In step S501, the system calculates the internal resistance of the battery cell to be 0.6 ohms, which is significantly higher than the upper limit of the normal range. In step S503, because the internal resistance value exceeds the preset range, the system marks the battery cell as a suspicious battery cell. This marking triggers further inspection and diagnostic procedures to determine whether measures, such as repair or replacement, are needed to prevent it from adversely affecting the performance of the entire battery system. Through this process, managers can promptly identify and address problematic cells in the battery system.

[0053] In some embodiments, an abnormal voltage condition may be that the time when a specific voltage threshold is reached during both charging and discharging is earlier than that of other suspected battery cells, reference... Figure 6 , Figure 6 This is a schematic diagram of the sub-steps of step S103 provided in an embodiment of this application. For example... Figure 6 As shown, the process of determining the target battery cell exhibiting an anomaly based on voltage anomaly conditions may include at least the following steps: S601: Based on voltage data, obtain the first moment when the voltage of the suspected battery cell reaches the charging threshold, and the second moment when the voltage of the suspected battery cell reaches the discharging threshold. S603: When the first moment of the suspicious battery cell is earlier than the first moment of other suspicious battery cells in the battery system, and the second moment of the suspicious battery cell is earlier than the second moment of other suspicious battery cells in the battery system, the suspicious battery cell is determined to be the target battery cell.

[0054] Specifically, in a battery management system, in addition to monitoring internal resistance, voltage data can be used to identify whether a battery cell is abnormal. When the voltage data of a battery cell meets certain abnormal conditions, this data can be further analyzed to determine the specific abnormal battery cell. In this case, the system first obtains the times when the suspected battery cell reaches specific voltage thresholds during charging and discharging based on the voltage data. This includes obtaining the first moment when the voltage of the suspected battery cell reaches the charging threshold and the second moment when it reaches the discharging threshold. If a suspected battery cell reaches these threshold moments earlier than other suspected battery cells in the battery system (i.e., both the first and second moments are earlier than other cells), it means that the battery cell requires a higher voltage during charging and its voltage drops faster or lower during discharging. This usually indicates that the battery performance is degraded or aging, and the battery health is poor. Therefore, this battery cell is identified as the abnormal target battery cell. This method helps to more accurately identify abnormal batteries that may affect system performance by comparing the performance of each battery cell during charging and discharging.

[0055] For example, suppose a battery system has multiple suspicious battery cells whose voltage data needs to be analyzed. By monitoring the data, the system discovers that a particular suspicious battery cell reaches its set charging voltage threshold as early as 10 minutes into the charging process, while other battery cells reach this threshold at 12 and 15 minutes, respectively. Simultaneously, this same battery cell also reaches its discharge voltage threshold as early as 20 minutes into the discharge process, while other battery cells reach this threshold at 22 and 25 minutes. Therefore, based on these time differences, this battery cell is identified as an abnormal target battery cell. This indicates that the battery cell is behaving abnormally during charging and discharging, potentially indicating reduced capacity or internal faults, thus requiring further inspection and maintenance. Through this method, the battery management system can more effectively identify and address potential battery problems, ensuring system stability and lifespan.

[0056] In some embodiments, reference Figure 7 , Figure 7 This is the third flowchart of a battery system monitoring method provided in an embodiment of this application. Figure 7 As shown, the monitoring method for this battery system also includes the following steps: S701: Obtain the location information and anomaly type of the target battery cell, and generate corresponding suggested measures; S703: Generate alarm information based on location information, anomaly type, and suggested measures, and send the alarm information to the management platform.

[0057] Specifically, this method also involves monitoring and managing battery cells to improve system safety and efficiency. First, the location information and anomaly type of the target battery cell can be acquired through sensors or a monitoring system. This information helps accurately identify the location of the battery cell and its problems, such as overheating, overcharging, or abnormal voltage. Next, corresponding recommended actions are generated based on this information, which may include adjusting charging parameters, replacing the battery cell, or conducting further inspections. Then, based on the collected location information, anomaly type, and recommended actions, detailed alarm information is generated. This alarm information is sent to the management platform so that relevant personnel can receive timely notifications and take necessary actions.

[0058] For example, suppose in a large battery energy storage system, the monitoring system detects an abnormally high temperature in a battery cell. The system first obtains the specific location of the battery cell and the type of anomaly (e.g., overheating). Then, the system may generate recommended actions, such as suggesting reducing the charging current or checking the battery cell's heat dissipation. Next, the system generates an alarm message containing the battery cell's location information, a detailed description of the temperature anomaly, and recommended countermeasures. This alarm message is sent to the management platform. Upon receiving the notification, the on-duty engineer can quickly locate the problematic battery and take appropriate action according to the recommended measures, such as reducing the charging rate or arranging an on-site inspection, thereby effectively avoiding potential safety risks.

[0059] Based on the same technical concept, embodiments of this application also provide a monitoring device for a battery system, see reference. Figure 8 , Figure 8 This is a schematic diagram of the structure of the monitoring device for the battery system provided in an embodiment of this application. Figure 8 As shown, the monitoring device of the battery system includes a memory 801 and a processor 802. The memory 801 is used to store computer instructions; when the processor 802 executes the computer instructions, it implements the method steps in any method embodiment.

[0060] The memory 801 includes at least one type of computer-readable storage medium, including flash memory, hard disk, multimedia card, random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), magnetic disk, optical disk, etc. In some embodiments, the computer-readable storage medium may be an internal storage unit of an electronic device, such as the hard disk or memory of the electronic device. In other embodiments, the computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, secure digital card (SD card), flash memory card, etc., equipped on the electronic device. Of course, the computer-readable storage medium may include both internal storage units and external storage devices of the electronic device. In this embodiment, the computer-readable storage medium is typically used to store the operating system and various application software installed on the electronic device, such as the program code of the battery system monitoring method in the embodiment. In addition, the computer-readable storage medium may also be used to temporarily store various types of data that have been output or will be output.

[0061] In some embodiments, processor 802 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other chip. Processor 802 is typically used to control the overall operation of the processing device, such as performing control and processing related to data interaction or communication with other entities. In this embodiment, processor 802 is used to run program code stored in memory 801 or process data.

[0062] Based on the same technical concept, this application also provides a computer-readable storage medium, which includes a computer program or instructions stored in the storage medium. When the computer program or instructions are executed by a processing device, they implement the method steps in any method embodiment. Further details can be found in the method embodiments, which will not be repeated here. In this embodiment, the computer-readable storage medium includes flash memory, hard disk, multimedia card, random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), magnetic disk, optical disk, etc. In some embodiments, the computer-readable storage medium can be an internal storage unit of an electronic device, such as the hard disk or memory of the electronic device. In other embodiments, the computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, secure digital card (SD card), flash memory card, etc., equipped on the electronic device. Of course, the computer-readable storage medium can also include both internal storage units and external storage devices of the electronic device. In this embodiment, the computer-readable storage medium is typically used to store the operating system and various application software installed on the electronic device, such as the program code of the battery system monitoring method in the embodiment. Furthermore, the computer-readable storage medium can also be used to temporarily store various types of data that have been output or will be output.

[0063] The above description involves various modules and units. It should be noted that the division of these modules and units in the description is for clarity. However, in actual implementation, the boundaries between various modules and units may be blurred. For example, any or all functional modules and units in this application may share various hardware and / or software elements. As another example, any and / or all functional modules in this application may be wholly or partially implemented by a shared processor executing software instructions. Furthermore, various software sub-modules executed by one or more processors may be shared among various software modules. Accordingly, unless expressly required, the scope of this application is not limited by mandatory boundaries between various hardware and / or software elements.

[0064] It should be noted that the order of description of the embodiments in this application is not intended to limit the priority of the embodiments.

[0065] Unless otherwise defined, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in this application and in its specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0066] It should be noted that, unless otherwise specified, the term "connected" or "linked" in this application includes not only directly connecting two entities, but also indirectly connecting them through other entities that have beneficial improvement effects.

[0067] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many forms under the guidance of this application without departing from the spirit and scope of protection of the claims. All equivalent transformations made based on the technical concept of this application and the content of the description and drawings of this application, or direct / indirect applications in other related technical fields, are included within the scope of patent protection of this application.

Claims

1. A method of monitoring a battery system, the method comprising: The method comprises: obtaining impedance data of a battery cell in a battery system, and determining the battery cell as a suspicious battery cell when the impedance data meets an internal resistance abnormal condition; obtaining voltage data of the suspicious battery cell, and determining the suspicious battery cell as a target battery cell with an abnormality when the voltage data meets a voltage abnormal condition.

2. The method of claim 1, wherein, The battery system comprises a plurality of battery cells, and the determining the battery cell as a suspicious battery cell when the impedance data meets an internal resistance abnormal condition comprises: estimating an internal resistance value of each battery cell in the plurality of battery cells based on the impedance data; calculating a dispersion degree value of the internal resistance value of each battery cell based on the internal resistance values of all the battery cells in the battery system; determining the battery cell corresponding to the dispersion degree value exceeding a first preset threshold as a suspicious battery cell.

3. The method of claim 2, wherein, The determining the battery cell corresponding to the dispersion degree value exceeding a first preset threshold as a suspicious battery cell comprises: determining the battery cell corresponding to the dispersion degree value exceeding the first preset threshold and not exceeding a second preset threshold as the suspicious battery cell.

4. The method of claim 3, wherein, The method further comprises: determining the battery cell as the target battery cell when the dispersion degree value of the battery cell exceeds the second preset threshold.

5. The method of claim 3, wherein, The first preset threshold is 10%, and the second preset threshold is 20%.

6. The method according to any one of claims 1-5, characterized in that, The determining the battery cell as a suspicious battery cell when the impedance data meets an internal resistance abnormal condition comprises: estimating an internal resistance value of the battery cell based on the impedance data; determining the battery cell as a suspicious battery cell when the internal resistance value exceeds a preset resistance value range.

7. The method according to any one of claims 1-5, characterized in that, The determining the suspicious battery cell as a target battery cell with an abnormality when the voltage data meets a voltage abnormal condition comprises: obtaining a first time when the voltage of the suspicious battery cell reaches a charging threshold and a second time when the voltage of the suspicious battery cell reaches a discharging threshold based on the voltage data; determining the suspicious battery cell as the target battery cell when the first time of the suspicious battery cell is earlier than the first time of other suspicious battery cells in the battery system, and the second time of the suspicious battery cell is earlier than the second time of other suspicious battery cells in the battery system.

8. The method according to any one of claims 1-5, characterized in that, The method further comprises: obtaining position information and an abnormal type of the target battery cell, and generating a corresponding suggestion measure; generating alarm information based on the position information, the abnormal type, and the suggestion measure, and sending the alarm information to a management platform.

9. A monitoring device of a battery system characterized by comprising: The device comprises a memory and a processor, the memory is used to store a computer program or instructions; when the computer program or instructions are executed by the processor, the method in any one of claims 1-8 is implemented.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program or instructions, and when the computer program or instructions are executed by the processor, the method in any one of claims 1-8 is implemented.

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