A lithium battery remote monitoring method and system based on the Internet of Things

Abstract

The application discloses a lithium battery remote monitoring method and system based on Internet of Things, which is used for reducing the time delay of fault repair and further reducing the safety risk of lithium batteries. The method comprises the following steps: acquiring real-time running state data of a lithium battery to be tested; identifying abnormal state data in the real-time running state data, and determining an abnormal state type and an abnormal state risk level of the abnormal state data; screening an initial adjustment instruction corresponding to the abnormal state type and the abnormal state risk level from a preset adjustment instruction mapping database; determining an environmental influence factor of the abnormal state data according to the correlation degree of historical running environment data and historical running state data of the lithium battery to be tested; correcting the initial adjustment instruction based on the environmental influence factor to obtain a target adjustment instruction; and sending the target adjustment instruction to a battery management system of the lithium battery to be tested through an Internet of Things communication link, so as to instruct the battery management system to execute the target adjustment instruction.

Description

Technical Field

[0001] This application relates to the field of lithium battery management technology, and in particular to a method and system for remote monitoring of lithium batteries based on the Internet of Things. Background Technology

[0002] With the rapid development of new energy technologies, lithium batteries, as core energy storage devices, play a crucial role in electric vehicles, energy storage power stations, portable electronic devices, and other fields. The safety and reliability of lithium batteries are directly related to the stable operation of the entire system and the safety of personnel and property, making effective monitoring and management a key link in ensuring the safety of energy systems.

[0003] During lithium battery operation, remote monitoring systems identify various abnormal states such as overcharging, over-discharging, and overheating, and alert relevant maintenance personnel via data transmission or alarm notifications to promptly address these issues and eliminate potential safety hazards. However, due to time delays in on-site response and the uncertainty of human factors, this passive approach often misses the optimal time for safety protection, allowing the lithium battery to continue operating in abnormal conditions and significantly increasing safety risks. Summary of the Invention

[0004] This application provides a method and system for remote monitoring of lithium batteries based on the Internet of Things, which can reduce the time delay in fault repair and thus reduce the safety risks of lithium batteries.

[0005] The first aspect of this application provides a method for remote monitoring of lithium batteries based on the Internet of Things, including:

[0006] Acquire real-time operating status data of the lithium battery under test;

[0007] Identify abnormal status data in the real-time operating status data, and determine the abnormal status type and abnormal status risk level of the abnormal status data;

[0008] Initial adjustment instructions corresponding to the abnormal state type and the abnormal state risk level are selected from the preset adjustment instruction mapping database.

[0009] The environmental impact factor of the abnormal state data is determined based on the correlation between the historical operating environment data and the historical operating status data of the lithium battery under test.

[0010] Based on the environmental impact factors, the initial adjustment instruction is revised to obtain the target adjustment instruction;

[0011] The target adjustment command is sent to the battery management system of the lithium battery under test via an IoT communication link, so as to instruct the battery management system to execute the target adjustment command.

[0012] Optionally, after sending the target adjustment instruction to the battery management system of the lithium battery under test via the Internet of Things communication link to instruct the battery management system to execute the target adjustment instruction, the method further includes:

[0013] After the battery management system executes the target adjustment command, it updates the real-time operating status data;

[0014] Determine whether the abnormal status data in the updated real-time running status data has been restored to a normal state;

[0015] If so, a fault repair report will be generated and saved to the operation and maintenance log;

[0016] If not, an alarm will be issued and the operating circuit of the lithium battery under test will be cut off.

[0017] Optionally, identifying abnormal status data in the real-time operating status data and determining the abnormal status type and risk level of the abnormal status data includes:

[0018] Each operating parameter in the real-time operating status data is compared with preset operating conditions, and different operating parameters correspond to different preset operating conditions;

[0019] Operational parameters that do not conform to the corresponding preset operating conditions are identified as abnormal status data;

[0020] The abnormal state type of the abnormal state data is determined based on the parameter type of the abnormal state data and the comparison result. The abnormal state type includes overcharge, over-discharge, overload, over-temperature and short circuit.

[0021] The abnormal state risk level of the abnormal state data is determined based on the degree of deviation between the abnormal state data and the corresponding preset operating conditions.

[0022] Optionally, the step of filtering the initial adjustment instructions corresponding to the abnormal state type and the abnormal state risk level according to the preset adjustment instruction mapping database includes:

[0023] Acquire historical operating data and maintenance data of several sample lithium batteries, wherein the sample lithium batteries have the same model and equipment parameters as the lithium battery under test;

[0024] Based on the historical operating data and maintenance data of each sample lithium battery, the first mapping relationship between the abnormal state type, the abnormal state risk level and the manual adjustment measures is determined.

[0025] Based on the aforementioned manual adjustment measures, a preset adjustment instruction is generated, and a second mapping relationship between the abnormal state type and the abnormal state risk level and the preset adjustment instruction is determined.

[0026] A preset adjustment instruction mapping database is established based on all second mapping relationships;

[0027] The preset adjustment instructions are selected from the preset adjustment instruction mapping database and associated with the abnormal state type and abnormal state risk level of the lithium battery under test. The preset adjustment instructions are then determined as the initial adjustment instructions.

[0028] Optionally, determining the environmental impact factor of the abnormal state data based on the correlation between the historical operating environment data and the historical operating state data of the lithium battery under test includes:

[0029] Acquire the historical operating environment data and historical operating status data of the lithium battery under test;

[0030] Calculate the correlation coefficient between a single environmental parameter in the historical operating environment data and a single operating parameter in the historical operating status data, wherein the timestamps of the single environmental parameter and the single operating parameter are the same;

[0031] When the correlation coefficient is greater than the preset coefficient threshold, the corresponding environmental parameters and operating parameters are determined to be a strongly correlated combination.

[0032] Environmental parameters that match the operating parameters in the abnormal state data are selected from all strongly correlated combinations, and the selected environmental parameters are identified as environmental impact factors.

[0033] Optionally, the step of revising the initial adjustment instruction based on the environmental impact factor to obtain the target adjustment instruction includes:

[0034] Acquire real-time operating environment data of the lithium battery under test within a preset time period;

[0035] The fluctuation trend of the environmental impact factors is determined based on the real-time operating environment data;

[0036] The initial adjustment instruction is modified based on the fluctuation trend to obtain the target adjustment instruction.

[0037] A second aspect of this application provides an Internet of Things-based remote monitoring system for lithium batteries, comprising:

[0038] The acquisition unit is used to acquire real-time operating status data of the lithium battery under test.

[0039] The identification unit is used to identify abnormal status data in the real-time operating status data, and to determine the abnormal status type and abnormal status risk level of the abnormal status data.

[0040] The filtering unit is used to filter out initial adjustment instructions corresponding to the abnormal state type and the abnormal state risk level according to the preset adjustment instruction mapping database;

[0041] The determining unit is used to determine the environmental impact factor of the abnormal state data based on the correlation between the historical operating environment data and the historical operating status data of the lithium battery under test.

[0042] The correction unit is used to correct the initial adjustment instruction based on the environmental impact factors to obtain the target adjustment instruction;

[0043] The sending unit is used to send the target adjustment instruction to the battery management system of the lithium battery under test via an Internet of Things communication link, so as to instruct the battery management system to execute the target adjustment instruction.

[0044] Optionally, the IoT-based remote monitoring system for lithium batteries further includes:

[0045] An update unit is used to update the real-time operating status data after the battery management system executes the target adjustment instruction;

[0046] The judgment unit is used to determine whether the abnormal state data in the updated real-time running status data has been restored to the normal state;

[0047] The generation unit is used to generate a fault repair report and save it to the operation and maintenance log when the abnormal status data in the updated real-time operating status data is restored to the normal status.

[0048] An alarm unit is used to issue an alarm and cut off the operating circuit of the lithium battery under test when the abnormal status data in the updated real-time operating status data has not been restored to the normal state.

[0049] Optionally, the identification unit is specifically used for:

[0050] Each operating parameter in the real-time operating status data is compared with preset operating conditions, and different operating parameters correspond to different preset operating conditions;

[0051] Operational parameters that do not conform to the corresponding preset operating conditions are identified as abnormal status data;

[0052] The abnormal state type of the abnormal state data is determined based on the parameter type of the abnormal state data and the comparison result. The abnormal state type includes overcharge, over-discharge, overload, over-temperature and short circuit.

[0053] The abnormal state risk level of the abnormal state data is determined based on the degree of deviation between the abnormal state data and the corresponding preset operating conditions.

[0054] Optionally, the filtering unit is specifically used for:

[0055] Acquire historical operating data and maintenance data of several sample lithium batteries, wherein the sample lithium batteries have the same model and equipment parameters as the lithium battery under test;

[0056] Based on the historical operating data and maintenance data of each sample lithium battery, the first mapping relationship between the abnormal state type, the abnormal state risk level and the manual adjustment measures is determined.

[0057] Based on the aforementioned manual adjustment measures, a preset adjustment instruction is generated, and a second mapping relationship between the abnormal state type and the abnormal state risk level and the preset adjustment instruction is determined.

[0058] A preset adjustment instruction mapping database is established based on all second mapping relationships;

[0059] The preset adjustment instructions are selected from the preset adjustment instruction mapping database and associated with the abnormal state type and abnormal state risk level of the lithium battery under test. The preset adjustment instructions are then determined as the initial adjustment instructions.

[0060] As can be seen from the above technical solutions, this application has the following effects:

[0061] First, real-time operating status data of the lithium battery under test is acquired. Then, abnormal status data within the real-time operating status data is identified, and the abnormal status type and risk level are determined. Next, initial adjustment instructions corresponding to the abnormal status type and risk level are filtered out according to a preset adjustment instruction mapping database. Then, the environmental impact factor of the abnormal status data is determined based on the correlation between the historical operating environment data and the historical operating status data of the lithium battery under test. Further, the initial adjustment instructions are revised based on the environmental impact factor to obtain the target adjustment instruction. Finally, the target adjustment instruction is sent to the battery management system of the lithium battery under test via an IoT communication link to instruct the battery management system to execute the target adjustment instruction. In this way, after identifying the abnormal status type and risk level of the lithium battery under test, matching initial adjustment instructions are filtered out according to a preset adjustment instruction mapping database. The impact of historical operating environment on the current abnormal status is considered, and the initial adjustment instructions are revised using environmental impact factors, making the obtained target adjustment instruction more accurate and reasonable. Finally, the target adjustment command is sent to the battery management system, which adjusts the operating status of the lithium battery under test according to the target adjustment command, eliminating the need to wait for maintenance personnel to come to the site for handling, thereby reducing the time delay of fault repair and thus reducing the safety risks of lithium batteries. Attached Figure Description

[0062] Figure 1This is a schematic diagram of an embodiment of a remote monitoring method for lithium batteries based on the Internet of Things in this application;

[0063] Figure 2-1 , Figure 2-2 and Figure 2-3 This is a schematic diagram of another embodiment of a lithium battery remote monitoring method based on the Internet of Things in this application;

[0064] Figure 3 This is a schematic diagram of one embodiment of an IoT-based remote monitoring system for lithium batteries in this application. Detailed Implementation

[0065] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0066] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0067] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0068] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0069] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0070] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0071] In existing technologies, remote monitoring systems for lithium batteries identify various abnormal states that may occur during operation, such as overcharging, over-discharging, and overheating. These systems then alert relevant maintenance personnel via data transmission or alarm notifications to promptly address the anomalies and eliminate potential safety hazards. However, due to time delays in on-site response and the uncertainty of human factors, this passive approach often misses the optimal time for safety protection, allowing the lithium battery to continue operating in abnormal conditions and significantly increasing safety risks.

[0072] Based on this, this application discloses a method and system for remote monitoring of lithium batteries based on the Internet of Things, which can reduce the time delay in fault repair and thus reduce the safety risks of lithium batteries.

[0073] The technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0074] The IoT-based remote monitoring of lithium batteries described in this application is implemented on a remote monitoring platform, which can be a system, a terminal, or a server, and is not specifically limited here.

[0075] Please see Figure 1 As shown, one embodiment of the IoT-based remote monitoring method for lithium batteries in this application includes:

[0076] 101. Obtain real-time operating status data of the lithium battery under test;

[0077] In this embodiment, the real-time operating status data of the lithium battery under test specifically includes: individual cell voltage, total battery voltage, charge / discharge current, battery temperature, state of charge, health status, cycle count, and other operating parameters. The real-time operating status data of the lithium battery under test is collected in real time by various high-precision sensors deployed on the battery. Specifically, a Hall voltage sensor can be used to monitor the battery voltage in real time; a closed-loop Hall current sensor can be used to monitor the charge / discharge current in real time; and multiple digital temperature sensors can be evenly arranged inside the battery to monitor the battery temperature at different locations. The data collected by the sensors is preprocessed (including data filtering and format conversion) by a data transmission module with edge computing capabilities, and then transmitted in real time at a fixed frequency to a remote monitoring platform via a communication module. The remote monitoring platform receives this data to obtain the real-time operating status data of the lithium battery under test.

[0078] 102. Identify abnormal status data in real-time operating status data, and determine the abnormal status type and abnormal status risk level of the abnormal status data;

[0079] In this embodiment, the real-time operating status data includes various operating parameters. After acquiring the real-time operating status data, each operating parameter is compared and judged against its corresponding operating standard. For example, the operating standard for a certain model of electric vehicle lithium battery is: single cell voltage: 3.0V-3.65V, total voltage: 320V-380V, charging and discharging current: -150A to +120A, battery temperature: -20℃-50℃, and nuclear power status: 5%-95%. When an operating parameter does not meet its operating standard, the real-time operating status data corresponding to that parameter is determined to be abnormal status data. Then, the abnormal status type and abnormal status risk level are determined based on the type and degree of abnormality of the abnormal status data. Specifically, the abnormal status type can include overvoltage, over-discharge, and over-temperature, and the abnormal status risk level can include high risk, medium risk, and low risk levels.

[0080] 103. Filter out the initial adjustment instructions corresponding to the abnormal state type and abnormal state risk level according to the preset adjustment instruction mapping database;

[0081] In this embodiment, the preset adjustment instruction mapping database is a relational database pre-built based on a large amount of historical data. It uses a two-dimensional mapping table structure to store data, with the horizontal axis representing the abnormal state type and the vertical axis representing the abnormal state risk level. The intersection points represent the corresponding adjustment instructions. The adjustment instructions stored in the preset adjustment instruction mapping database cover various operation types, including current adjustment, voltage adjustment, and start / stop control. For example, when the abnormal state type is over-temperature and the abnormal state risk level is low risk, the corresponding initial adjustment instruction is: "Start primary cooling and set the cooling fan speed to 30%"; when the abnormal state type is over-temperature and the abnormal state risk level is medium risk, the corresponding initial adjustment instruction is: "Start secondary cooling, set the cooling fan speed to 70%, and reduce the charging current by 20%"; when the abnormal state type is over-voltage and the abnormal state risk level is high risk, the corresponding initial adjustment instruction is: "Immediately stop charging and activate pressure relief protection." After determining the abnormal state type and abnormal state risk level, the remote monitoring platform performs a precise search from the preset adjustment instruction mapping database using a query statement to select the uniquely matching initial adjustment instruction. Understandably, if multiple matching results exist, the latest adjustment instruction can be selected as the initial adjustment instruction based on its update time.

[0082] 104. Determine the environmental impact factor of abnormal state data based on the correlation between the historical operating environment data and the historical operating status data of the lithium battery under test;

[0083] In this embodiment, historical operating environment data is a set of environmental parameters stored by the lithium battery under test within a preset storage period according to a certain time unit. This historical operating environment data includes environmental parameters such as ambient temperature, ambient humidity, atmospheric pressure, and light intensity. Historical operating status data is a set of operating parameters stored within the same preset storage period as the historical operating environment data according to a certain time unit. The parameter types of the historical operating status data and the real-time operating status data are consistent. The impact of the operating environment on the operating status of the lithium battery under test is determined by calculating the correlation between the historical operating environment data and the historical operating status data. Specifically, the correlation between each environmental parameter in the historical operating environment data and each operating parameter in the historical operating status data can be calculated first. Then, environmental parameters whose correlation with the operating parameters in the abnormal state data meets preset conditions are selected as environmental influence factors affecting the abnormal state data.

[0084] 105. Based on the environmental impact factors, the initial adjustment instructions are revised to obtain the target adjustment instructions;

[0085] In this embodiment, after determining the environmental impact factor of the abnormal state data, the initial adjustment instruction is modified based on the real-time environmental data of this environmental impact factor to obtain the target adjustment instruction. For example, if the abnormal state data is charging current, the abnormal state type is overcharging, and the abnormal state risk level is low, the initial adjustment instruction is to reduce the charging current by 30%. The environmental impact factor of the charging current is ambient humidity, and the correlation between charging current and ambient humidity is positive. If the real-time ambient humidity is continuously rising, the abnormal state risk level may rise to medium risk. In this case, the initial adjustment instruction can be modified to reduce the charging current by 50%, and the modified initial adjustment instruction is the target adjustment instruction.

[0086] 106. Send the target adjustment command to the battery management system of the lithium battery under test through the Internet of Things communication link to instruct the battery management system to execute the target adjustment command.

[0087] The target adjustment command is encapsulated using an encryption protocol, generating a data packet containing a command ID, a unique lithium battery identifier, command content, a timestamp, and a checksum. This packet is then sent to the battery management system (BMS) of the lithium battery under test via a selected communication link. Upon receiving the data packet, the BMS first decrypts and verifies it. If the verification passes, it parses the command content and executes the corresponding operation. For example, for current / voltage adjustment commands, the internal power regulation module changes the charging and discharging parameters; for heat dissipation control commands, the cooling fan or water cooling system is driven to adjust its operating status; for start / stop control commands, the charging and discharging circuit is disconnected or connected.

[0088] In this embodiment, firstly, real-time operating status data of the lithium battery under test is acquired; then, abnormal status data in the real-time operating status data is identified, and the abnormal status type and risk level of the abnormal status data are determined; next, initial adjustment instructions corresponding to the abnormal status type and risk level are filtered out according to a preset adjustment instruction mapping database; then, the environmental impact factor of the abnormal status data is determined based on the correlation between the historical operating environment data and the historical operating status data of the lithium battery under test; further, the initial adjustment instructions are corrected based on the environmental impact factor to obtain the target adjustment instruction; finally, the target adjustment instruction is sent to the battery management system of the lithium battery under test through an IoT communication link to instruct the battery management system to execute the target adjustment instruction. In this way, after identifying the abnormal status type and risk level of the lithium battery under test, initial adjustment instructions matching it are filtered out according to a preset adjustment instruction mapping database, and the impact of historical operating environment on the current abnormal status is considered. The initial adjustment instructions are corrected through environmental impact factors, making the obtained target adjustment instruction more accurate and reasonable. Finally, the target adjustment command is sent to the battery management system, which adjusts the operating status of the lithium battery under test according to the target adjustment command, eliminating the need to wait for maintenance personnel to come to the site for handling, thereby reducing the time delay of fault repair and thus reducing the safety risks of lithium batteries.

[0089] Please see Figure 2-1 , Figure 2-2 and Figure 2-3 As shown, another embodiment of the IoT-based remote monitoring method for lithium batteries in this application includes:

[0090] 201. Obtain the real-time operating status data of the lithium battery under test;

[0091] Step 201 in this embodiment is the same as described above. Figure 1 Step 101 in the illustrated embodiment is similar and will not be described again here.

[0092] 202. Compare each operating parameter in the real-time operating status data with the preset operating conditions. Different operating parameters correspond to different preset operating conditions.

[0093] 203. Determine that the operating parameters that do not conform to the corresponding preset operating conditions are abnormal status data;

[0094] 204. Determine the abnormal state type of the abnormal state data based on the parameter type and comparison results. The abnormal state type includes overcharge, over-discharge, overload, over-temperature, and short circuit.

[0095] 205. Determine the abnormal status risk level of the abnormal status data based on the degree of deviation between the abnormal status data and the corresponding preset operating conditions;

[0096] Optionally, in this embodiment, the preset operating conditions are parameter thresholds or ranges pre-set according to the model, specifications, usage scenario, and safety standards of the lithium battery under test. Different operating parameters correspond to different preset operating conditions. For example, the preset conditions for voltage parameters include the upper limit of charging termination voltage and the lower limit of discharging termination voltage; the preset conditions for temperature parameters are the normal operating temperature range (e.g., -20℃ to 60℃); and the preset conditions for current parameters include the maximum charging and discharging current threshold, etc. When the real-time value of a certain operating parameter does not match the corresponding preset operating conditions, the operating parameter is determined to be abnormal state data. For example, if the real-time charging voltage of a lithium battery in an electric vehicle exceeds the preset upper limit of charging termination voltage, then the real-time charging voltage will be identified as abnormal state data. After determining the abnormal state data, the abnormal state type needs to be further clarified according to its parameter type and the comparison result with the preset operating conditions. The classification of abnormal state types is based on common fault modes of lithium batteries, which may include overcharging, over-discharging, overload, over-temperature, and short circuit, etc., and is not specifically limited here. The abnormal state types can be determined according to the following rules: When the real-time value of the voltage parameter exceeds the preset charging termination voltage upper limit and the duration reaches the set threshold, combined with the parameter type being voltage, it is determined to be an overcharge state; if the real-time value of the voltage parameter is lower than the preset discharging termination voltage lower limit and the parameter type is voltage, it is determined to be an over-discharge state; when the real-time value of the current parameter exceeds the preset maximum charging and discharging current threshold and the parameter type is current, it is determined to be an overload state; when the real-time value of the temperature parameter exceeds the preset normal operating temperature range and the parameter type is temperature, it is determined to be an over-temperature state; when the current parameter suddenly increases and the voltage parameter suddenly decreases in a very short time, and the parameter change pattern conforms to the characteristics of a short circuit, combined with the parameter types of current and voltage and the comparison results, it is determined to be a short circuit state. The determination of the abnormal state risk level is mainly based on the degree of deviation between the abnormal state data and the corresponding preset operating conditions. The greater the deviation, the higher the risk level. The abnormal state risk level can be divided into low risk, medium risk, and high risk. Specifically, when the deviation between the abnormal state data and the preset operating conditions is small and does not exceed the safety tolerance range, it can be determined to be low risk. For example, if the temperature parameter is within 5°C above the preset upper limit, or the voltage parameter is slightly higher than the charging termination voltage but has not reached the danger threshold, the lithium battery will not experience serious failure in the short term. When the deviation of the abnormal state data exceeds the safe tolerance range but has not yet reached the critical danger value, it can be judged as medium risk. For example, if the temperature is 5°C to 10°C higher than the preset upper limit, or the current exceeds the maximum threshold by 10% to 20%, the lithium battery has a certain risk of performance degradation or failure. When the deviation of the abnormal state data is extremely large, approaching or reaching the critical danger value, it can be judged as high risk. For example, if the temperature exceeds the preset upper limit by more than 10°C, the voltage far exceeds the charging termination voltage leading to a sharp increase in the risk of battery bulging, or there is a sudden increase in current indicating a short circuit, the lithium battery faces serious safety hazards.

[0097] 206. Obtain historical operating data and maintenance data of several sample lithium batteries. The sample lithium batteries are the same as the lithium batteries under test in terms of model and equipment parameters.

[0098] 207. Based on the historical operating data and maintenance data of each sample lithium battery, determine the first mapping relationship between the abnormal state type, the abnormal state risk level and the manual adjustment measures;

[0099] 208. Generate preset adjustment instructions based on manual adjustment measures, and determine the second mapping relationship between the abnormal state type and the abnormal state risk level and the preset adjustment instructions;

[0100] 209. Establish a preset adjustment instruction mapping database based on all second mapping relationships;

[0101] 210. Filter out the preset adjustment instructions that are associated with the abnormal state type and abnormal state risk level of the lithium battery under test from the preset adjustment instruction mapping database, and determine the preset adjustment instructions as the initial adjustment instructions.

[0102] Optionally, in this embodiment, firstly, it is necessary to acquire historical operating data and maintenance data of several sample lithium batteries, and these sample lithium batteries have the same model and equipment parameters as the lithium battery under test. This is because lithium batteries of the same model and parameters have a high degree of consistency in operating characteristics and fault performance, making the sample lithium batteries more valuable for reference. Historical operating data covers key indicators of the sample lithium batteries under different operating conditions, such as voltage, current, temperature, number of charge / discharge cycles, and battery life. This data can intuitively reflect changes in the operating status of the lithium batteries. Maintenance data includes fault records, repair time, replaced parts, and emergency measures taken manually when the sample lithium batteries experience abnormalities. It records the entire process from the occurrence of an abnormality to the recovery of the sample lithium batteries. Then, based on the above-mentioned abnormal state identification method for the lithium batteries under test, the historical operating data is analyzed to identify the abnormal state type and abnormal state risk level of each sample lithium battery at different times. Afterwards, the manual adjustment measures taken for different abnormal state types and risk levels are extracted from the maintenance data, thereby establishing a one-to-one mapping relationship between abnormal state types, abnormal state risk levels, and manual adjustment measures.

[0103] Manual adjustment measures are typically actions taken by maintenance personnel based on experience. These actions need to be converted into instructions that can be recognized and executed by the Internet of Things (IoT). For example, a manual adjustment measure might be "reduce the charging current to 80% of the rated current," and the corresponding preset adjustment instruction could be set as "Charging current adjustment instruction: target value = rated current × 80%." A manual adjustment measure might be "emergency power cut and start the cooling system," and the corresponding preset adjustment instructions could be divided into "power cut instruction: execute" and "cooling system start instruction: execute." Through this conversion, the manual adjustment measures in the first mapping relationship are replaced with the corresponding preset adjustment instructions, thus obtaining a second mapping relationship with a one-to-one correspondence between abnormal state types, abnormal state risk levels, and preset adjustment instructions.

[0104] The aforementioned second mapping relationship is systematically organized and categorized according to abnormal state type and abnormal state risk level, forming a structured database. Each record in the database contains fields such as abnormal state type, abnormal state risk level, and corresponding preset adjustment instructions, and has fast query and retrieval functions. Understandably, as new sample data accumulates, the database can be dynamically updated and improved, adding new abnormal state types, abnormal state risk levels, and corresponding preset adjustment instructions to enhance its comprehensiveness and accuracy. When an abnormal state is detected in the lithium battery under test, its abnormal state type and corresponding abnormal state risk level are first determined. Subsequently, based on these two key parameters, a matching query is performed in the preset adjustment instruction mapping database to filter out the associated preset adjustment instructions. Since this database is built based on a large amount of sample lithium battery data of the same model and parameters, the filtered preset adjustment instructions have high applicability and reliability, and are therefore determined as the initial adjustment instructions. In this way, a precise conversion from historical data to initial adjustment instructions is achieved, fully utilizing the advantages of IoT technology in data collection and analysis, making remote monitoring of lithium batteries more scientific and efficient, and effectively improving the operational safety and lifespan of lithium batteries.

[0105] 211. Obtain historical operating environment data and historical operating status data of the lithium battery under test;

[0106] 212. Calculate the correlation coefficient between a single environmental parameter in the historical operating environment data and a single operating parameter in the historical operating status data, where the timestamps of the single environmental parameter and the single operating parameter are the same;

[0107] 213. When the correlation coefficient is greater than the preset coefficient threshold, the corresponding environmental parameters and operating parameters are determined to be a strongly correlated combination;

[0108] 214. Select environmental parameters that match the operating parameters in the abnormal state data from all strongly correlated combinations, and determine the selected environmental parameters as environmental impact factors.

[0109] Optionally, in this embodiment, since the impact of environmental factors on the state of lithium batteries is real-time, it is necessary to ensure that the acquired historical operating environment data and historical operating state data have temporal correspondence when acquiring them. That is, each set of operating environment data should have a matching operating state data at the same time. The correlation coefficient is a statistical indicator that measures the degree of linear correlation between two variables. Its value ranges from -1 to 1; the closer the absolute value is to 1, the stronger the correlation between the two parameters. In calculating the correlation coefficient between a single environmental parameter in historical operating environment data and a single operating parameter in historical operating state data, environmental parameters and operating parameters with the same timestamp should be selected as data pairs. The correlation coefficient can be calculated using the following formula:

[0110]

[0111] Where K represents the correlation coefficient between a single environmental parameter and a single operational parameter; n represents the number of data pairs, i.e., the total number of samples of environmental and operational parameters at the same time stamp involved in the calculation; x i This represents the value of a single environment parameter corresponding to i timestamps; This represents the average value of all individual environmental parameters; y i This represents the value of a single runtime parameter corresponding to i timestamps; This represents the average value of all individual operating parameters.

[0112] By pairing and calculating each individual environmental parameter with an individual operating parameter, multiple sets of correlation coefficients can be obtained, quantifying the degree of correlation between environmental factors and battery state parameters. When the correlation coefficient is greater than a preset threshold, the corresponding environmental parameter and operating parameter are determined to be a strongly correlated combination. When the calculated correlation coefficient is greater than this threshold, it indicates that there is a significant linear correlation between the corresponding environmental parameter and operating parameter. For example, the correlation coefficient between the ambient temperature parameter and the battery temperature operating parameter is 0.85, which is greater than the preset threshold of 0.7, so "ambient temperature - battery temperature" can be determined to be a strongly correlated combination; while the correlation coefficient between the air pressure parameter and the battery voltage operating parameter is 0.3, which is less than the threshold, so it does not belong to a strongly correlated combination.

[0113] Finally, environmental parameters matching the operating parameters in the abnormal state data are selected from all strongly correlated combinations, and these selected environmental parameters are identified as environmental impact factors. The operating parameters in the abnormal state data refer to the state parameters exhibited by the tested lithium battery when an abnormality occurs. For example, when the tested lithium battery exhibits an abnormal state of excessively high temperature, the corresponding operating parameter is battery temperature. In this case, combinations containing this operating parameter need to be found from the previously identified strongly correlated combinations, such as the "ambient temperature - battery temperature" combination. The ambient temperature in this combination is then the environmental impact factor causing the excessively high temperature abnormality. It is understandable that if the abnormal state data involves multiple operating parameters, such as simultaneous voltage and charge / discharge rate abnormalities, environmental parameters matching these two operating parameters need to be selected separately. These environmental parameters together constitute the environmental impact factor of the abnormal state data. In this way, environmental factors that significantly influence the abnormal state can be accurately identified, providing a clear environmental correlation basis for subsequent targeted adjustment instructions, making the analysis results of lithium battery anomalies more comprehensive and reliable.

[0114] 215. Obtain real-time operating environment data of the lithium battery under test within a preset time period;

[0115] 216. Determine the fluctuation trend of environmental impact factors based on real-time operating environment data;

[0116] 217. Based on the fluctuation trend, revise the initial adjustment instruction to obtain the target adjustment instruction;

[0117] Optionally, in this embodiment, the preset time period needs to be determined based on the application scenario and environmental change frequency of the lithium battery under test. For example, in outdoor equipment with large temperature fluctuations, the preset time period can be set to 5 minutes to capture rapid environmental changes at a high frequency; while in indoor equipment with a relatively stable environment, the preset time period can be set to 30 minutes to reduce data redundancy while ensuring data validity. After acquiring the real-time operating environment data, the processed data is analyzed using time series analysis methods to identify the changing patterns of environmental influencing factors in the real-time operating environment data, thereby determining the fluctuation trend of environmental influencing factors. For example, whether the ambient temperature shows a continuous upward trend, periodic fluctuation, or slow downward trend within the preset time period; whether the humidity remains stable, rises and falls sharply, or changes in a stepwise manner, etc. In the process of correcting for environmental impact factors by utilizing their fluctuation trends, a correlation rule between the fluctuation trend and the initial adjustment command can be established in advance. For example, when the ambient temperature shows a continuous upward trend and is approaching the tolerance threshold of the lithium battery, the initial adjustment command of "reducing the charging current to 80% of the rated current" needs to be further reduced to 60% of the rated current to reduce battery heat generation. If the ambient humidity shows a sudden upward trend, the initial adjustment command of "starting the cooling system" needs to be supplemented with a sub-command of "activating the moisture protection mode" to prevent condensation from affecting the battery circuit. For environmental impact factors with periodic fluctuations, the timing of the adjustment command can be adjusted in advance according to the fluctuation cycle. For example, before the temperature enters the rising cycle, the charging power can be reduced in advance to prevent the battery temperature from rising excessively. Through the above corrections, the initial adjustment command is adapted to the changing characteristics of the real-time environment, ultimately resulting in a target adjustment command that better meets actual needs, ensuring that the lithium battery can maintain stable and safe operation in complex and ever-changing environments.

[0118] 218. Send the target adjustment command to the battery management system of the lithium battery under test through the Internet of Things communication link to instruct the battery management system to execute the target adjustment command.

[0119] Step 218 in this embodiment is the same as described above. Figure 1 Step 106 in the illustrated embodiment is similar and will not be described again here.

[0120] 219. After the battery management system executes the target adjustment command, update the real-time operating status data;

[0121] 220. Determine whether the abnormal status data in the updated real-time running status data has been restored to the normal state. If yes, proceed to step 221; otherwise, proceed to step 222.

[0122] 221. Generate a fault repair report and save it to the operation and maintenance log;

[0123] 222. Issue an alarm and disconnect the operating circuit of the lithium battery under test.

[0124] Optionally, in this embodiment, the execution of the target adjustment command directly changes the operating parameters of the lithium battery under test, thereby affecting its core status indicators. Therefore, it is necessary to update the real-time operating status data in a timely manner to reflect the actual situation after adjustment. After obtaining the updated real-time operating status data, it is determined whether the abnormal status data in the real-time operating status data has returned to the normal state. The determination process can adopt a combination of threshold comparison and trend analysis. Specifically, the updated abnormal status data is first compared with the preset normal range one by one to filter out the operating parameters that are still in the abnormal range; then, a short-term trend judgment is made on these operating parameters. For example, although the voltage of a certain cell has not fully returned to the normal range after adjustment, it shows a continuous downward trend and the rate is stable, which may be in the recovery process; if the indicator continues to deviate from the normal range and the fluctuation amplitude increases, it indicates that the abnormal state has not been improved. If the abnormal status data has returned to the normal state, it means that the target adjustment command has effectively solved the operating abnormality of the lithium battery under test. At this time, a fault repair report is generated and saved to the operation and maintenance log. The fault repair report can include information such as the initial manifestations of the abnormal state, the specific content of the target adjustment instruction, the state recovery process, and the coordinated changes of environmental impact factors, providing a reference for the diagnosis and handling of similar faults in the future. The operation and maintenance log uses a structured storage format and supports retrieval by time, battery number, fault type, and other dimensions, facilitating the tracing of historical operation and maintenance records. If the abnormal state data has not recovered to the normal state, it indicates that the target adjustment instruction has not achieved the expected effect, and an alarm should be issued immediately and the operating circuit of the lithium battery under test should be disconnected. Alarm notifications can be implemented through an audible and visual alarm module or a remote notification mechanism, such as sending SMS messages or software push notifications to operation and maintenance personnel. Alarm information can include abnormal state data, updated data, and analysis of the reasons for failure to recover, enabling operation and maintenance personnel to respond quickly. Disconnecting the operating circuit is a protective measure to prevent the fault from escalating. By sending a power-off command to the battery management system, the electronic relay in the circuit is triggered to disconnect, stopping the charging and discharging operation of the lithium battery under test. The circuit can only be restored manually after the fault has been manually investigated and eliminated. Understandably, for lithium batteries that power critical equipment, the backup power supply can be activated simultaneously with the main circuit cut off to avoid secondary impacts caused by power outages. This achieves real-time verification and closed-loop control of the execution effect of target adjustment commands, enabling timely confirmation of fault repair results to optimize subsequent maintenance strategies, and rapid implementation of protective measures when adjustments are ineffective, minimizing lithium battery operational risks and ensuring the safe and stable operation of equipment.

[0125] Please see Figure 3 As shown, one embodiment of the IoT-based remote monitoring system for lithium batteries in this application includes:

[0126] Acquisition unit 301 is used to acquire real-time operating status data of the lithium battery under test;

[0127] The identification unit 302 is used to identify abnormal status data in real-time operating status data and determine the abnormal status type and abnormal status risk level of the abnormal status data.

[0128] The filtering unit 303 is used to filter out the initial adjustment instructions corresponding to the abnormal state type and the abnormal state risk level according to the preset adjustment instruction mapping database.

[0129] The determination unit 304 is used to determine the environmental impact factor of abnormal state data based on the correlation between the historical operating environment data and the historical operating status data of the lithium battery under test.

[0130] Correction unit 305 is used to correct the initial adjustment instruction based on environmental impact factors to obtain the target adjustment instruction;

[0131] The sending unit 306 is used to send the target adjustment command to the battery management system of the lithium battery under test through the Internet of Things communication link, so as to instruct the battery management system to execute the target adjustment command.

[0132] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0133] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0134] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0135] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0136] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for remote monitoring of lithium batteries based on the Internet of Things, characterized in that, include: Acquire real-time operating status data of the lithium battery under test; Identify abnormal status data in the real-time operating status data, and determine the abnormal status type and abnormal status risk level of the abnormal status data; Initial adjustment instructions corresponding to the abnormal state type and the abnormal state risk level are selected from the preset adjustment instruction mapping database. The environmental impact factor of the abnormal state data is determined based on the correlation between the historical operating environment data and the historical operating status data of the lithium battery under test. Specifically, this includes: acquiring the historical operating environment data and the historical operating status data of the lithium battery under test; calculating the correlation coefficient between a single environmental parameter in the historical operating environment data and a single operating parameter in the historical operating status data, wherein the timestamps of the single environmental parameter and the single operating parameter are the same; when the correlation coefficient is greater than a preset coefficient threshold, determining that the corresponding environmental parameter and operating parameter are strongly correlated combinations; selecting environmental parameters that match the operating parameters in the abnormal state data from all strongly correlated combinations, and determining the selected environmental parameters as environmental impact factors. The initial adjustment instruction is corrected based on the environmental impact factors to obtain the target adjustment instruction. Specifically, this includes: acquiring real-time operating environment data of the lithium battery under test within a preset time period; determining the fluctuation trend of the environmental impact factors based on the real-time operating environment data; and correcting the initial adjustment instruction based on the fluctuation trend to obtain the target adjustment instruction. The target adjustment command is sent to the battery management system of the lithium battery under test via an IoT communication link, so as to instruct the battery management system to execute the target adjustment command.

2. The method for remote monitoring of lithium batteries based on the Internet of Things as described in claim 1, characterized in that, After sending the target adjustment command to the battery management system of the lithium battery under test via the Internet of Things communication link to instruct the battery management system to execute the target adjustment command, the method further includes: After the battery management system executes the target adjustment command, it updates the real-time operating status data; Determine whether the abnormal status data in the updated real-time running status data has been restored to a normal state; If so, a fault repair report will be generated and saved to the operation and maintenance log; If not, an alarm will be issued and the operating circuit of the lithium battery under test will be cut off.

3. The method for remote monitoring of lithium batteries based on the Internet of Things as described in claim 1, characterized in that, The process of identifying abnormal status data in the real-time operating status data and determining the abnormal status type and risk level of the abnormal status data includes: Each operating parameter in the real-time operating status data is compared with preset operating conditions, and different operating parameters correspond to different preset operating conditions; Operational parameters that do not conform to the corresponding preset operating conditions are identified as abnormal status data; The abnormal state type of the abnormal state data is determined based on the parameter type of the abnormal state data and the comparison result. The abnormal state type includes overcharge, over-discharge, overload, over-temperature and short circuit. The abnormal state risk level of the abnormal state data is determined based on the degree of deviation between the abnormal state data and the corresponding preset operating conditions.

4. The method for remote monitoring of lithium batteries based on the Internet of Things as described in claim 1, characterized in that, The step of filtering initial adjustment instructions corresponding to the abnormal state type and the abnormal state risk level according to the preset adjustment instruction mapping database includes: Acquire historical operating data and maintenance data of several sample lithium batteries, wherein the sample lithium batteries have the same model and equipment parameters as the lithium battery under test; Based on the historical operating data and maintenance data of each sample lithium battery, the first mapping relationship between the abnormal state type, the abnormal state risk level and the manual adjustment measures is determined. Based on the aforementioned manual adjustment measures, a preset adjustment instruction is generated, and a second mapping relationship between the abnormal state type and the abnormal state risk level and the preset adjustment instruction is determined. A preset adjustment instruction mapping database is established based on all second mapping relationships; The preset adjustment instructions are selected from the preset adjustment instruction mapping database and associated with the abnormal state type and abnormal state risk level of the lithium battery under test. The preset adjustment instructions are then determined as the initial adjustment instructions.

5. A remote monitoring system for lithium batteries based on the Internet of Things, characterized in that, include: The acquisition unit is used to acquire real-time operating status data of the lithium battery under test. The identification unit is used to identify abnormal status data in the real-time operating status data, and to determine the abnormal status type and abnormal status risk level of the abnormal status data. The filtering unit is used to filter out initial adjustment instructions corresponding to the abnormal state type and the abnormal state risk level according to the preset adjustment instruction mapping database; The determining unit is used to determine the environmental impact factor of the abnormal state data based on the correlation between the historical operating environment data and the historical operating state data of the lithium battery under test. Specifically, it includes: acquiring the historical operating environment data and the historical operating state data of the lithium battery under test; calculating the correlation coefficient between a single environmental parameter in the historical operating environment data and a single operating parameter in the historical operating state data, wherein the timestamps of the single environmental parameter and the single operating parameter are the same; when the correlation coefficient is greater than a preset coefficient threshold, determining that the corresponding environmental parameter and operating parameter are strongly correlated combinations; selecting environmental parameters that match the operating parameters in the abnormal state data from all strongly correlated combinations, and determining the selected environmental parameters as environmental impact factors. The correction unit is used to correct the initial adjustment instruction based on the environmental impact factor to obtain the target adjustment instruction. Specifically, it includes: acquiring real-time operating environment data of the lithium battery under test within a preset time period; determining the fluctuation trend of the environmental impact factor based on the real-time operating environment data; and correcting the initial adjustment instruction based on the fluctuation trend to obtain the target adjustment instruction. The sending unit is used to send the target adjustment instruction to the battery management system of the lithium battery under test via an Internet of Things communication link, so as to instruct the battery management system to execute the target adjustment instruction.

6. The IoT-based remote monitoring system for lithium batteries according to claim 5, characterized in that, The IoT-based remote monitoring system for lithium batteries also includes: An update unit is used to update the real-time operating status data after the battery management system executes the target adjustment instruction; The judgment unit is used to determine whether the abnormal state data in the updated real-time running status data has been restored to the normal state; The generation unit is used to generate a fault repair report and save it to the operation and maintenance log when the abnormal status data in the updated real-time operating status data is restored to the normal status. An alarm unit is used to issue an alarm and cut off the operating circuit of the lithium battery under test when the abnormal status data in the updated real-time operating status data has not been restored to the normal state.

7. The IoT-based remote monitoring system for lithium batteries as described in claim 5, characterized in that, The identification unit is specifically used for: Each operating parameter in the real-time operating status data is compared with preset operating conditions, and different operating parameters correspond to different preset operating conditions; Operational parameters that do not conform to the corresponding preset operating conditions are identified as abnormal status data; The abnormal state type of the abnormal state data is determined based on the parameter type of the abnormal state data and the comparison result. The abnormal state type includes overcharge, over-discharge, overload, over-temperature and short circuit. The abnormal state risk level of the abnormal state data is determined based on the degree of deviation between the abnormal state data and the corresponding preset operating conditions.

8. The IoT-based remote monitoring system for lithium batteries as described in claim 5, characterized in that, The filtering unit is specifically used for: Acquire historical operating data and maintenance data of several sample lithium batteries, wherein the sample lithium batteries have the same model and equipment parameters as the lithium battery under test; Based on the historical operating data and maintenance data of each sample lithium battery, the first mapping relationship between the abnormal state type, the abnormal state risk level and the manual adjustment measures is determined. Based on the aforementioned manual adjustment measures, a preset adjustment instruction is generated, and a second mapping relationship between the abnormal state type and the abnormal state risk level and the preset adjustment instruction is determined. A preset adjustment instruction mapping database is established based on all second mapping relationships; preset adjustment instructions that are associated with the abnormal state type and abnormal state risk level of the lithium battery under test are selected from the preset adjustment instruction mapping database, and the preset adjustment instructions are determined as the initial adjustment instructions.

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