Battery fault intelligent monitoring system based on Internet of Things

Abstract

The invention discloses a battery fault intelligent monitoring system based on the Internet of Things, and particularly relates to the field of battery fault intelligent monitoring, which comprises a data batch division module, a battery data acquisition module, a battery data analysis module, a comprehensive data evaluation module and a real-time feedback module, the data batch division module is used for determining the to-be-collected data as target data; the battery data acquisition module comprises a system data acquisition unit and a safety data acquisition unit and is used for acquiring target data in real time; the battery data analysis module comprises a system data analysis unit and a safety data analysis unit and is used for analyzing the data transmitted by the battery data acquisition module; the comprehensive data evaluation module comprises a battery fault intelligent monitoring analysis unit and is used for comprehensively analyzing the data transmitted by the battery data analysis module; and the real-time feedback module is used for establishing a battery fault intelligent monitoring rationality index preset value.

Description

Technical Field

[0001] The present invention relates to the technical field of battery failure intelligent monitoring, and more specifically, to a battery failure intelligent monitoring system based on the Internet of Things. Background Art

[0002] With the rapid development of science and technology, the Internet of Things technology is gradually becoming a key force in promoting changes in many industries. In the field of batteries, the intelligent monitoring system for battery failure plays a vital role in ensuring the stable operation and safe use of batteries. The integration of Internet of Things technology has brought powerful real-time perception and intelligent analysis capabilities to the intelligent monitoring of battery failures, greatly improving the monitoring accuracy and response speed of the entire system.

[0003] The existing intelligent monitoring system for battery failure mainly includes: data acquisition module, data analysis module, risk assessment module, and information interaction module. The data acquisition module collects key data of the battery, such as the voltage fluctuation, current change trend, internal resistance dynamic characteristics, ambient temperature value, humidity level, and magnetic field interference degree of the battery, and stores these data efficiently to prepare for subsequent in-depth analysis. Through the data analysis module, the collected data is denoised, normalized, and feature extracted to mine key information that can reflect the battery status. Then, the risk assessment module uses these key data to build a risk assessment model to accurately assess the current status and potential failure risks of the battery, so as to intuitively understand the operating health of the battery. Finally, the information interaction module is responsible for real-time feedback of monitoring results. When an abnormal situation is detected, it can be warned in time through multiple channels, such as pushing messages to mobile devices and displaying warning signs on the monitoring platform. At the same time, users can remotely monitor through terminal devices such as mobile phones and computers, keep abreast of the battery's operating dynamics at any time, and perform remote operations to achieve more convenient and efficient battery management.

[0004] However, in practical applications, the system still has some shortcomings. For example, in the data collection process, there is a problem of limited data coverage, which makes it difficult to fully capture the various status information of the battery in different working scenarios, which may lead to incompleteness in subsequent analysis. In the data analysis process, facing the complex and changeable battery operation data, it is difficult to accurately locate the root cause of the fault, resulting in insufficient accuracy and pertinence of data analysis, affecting the accurate judgment and timely handling of battery faults, and having a negative impact on the operational reliability and effectiveness of the entire system.

[0005] Therefore, there is an urgent need to provide a battery fault intelligent monitoring system based on the Internet of Things to solve the problems of incomplete data collection, incomplete data analysis and inaccurate fault diagnosis results in the existing battery fault monitoring system. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a battery fault intelligent monitoring system based on the Internet of Things, which solves the problems raised in the above-mentioned background technology through the following scheme.

[0007] To achieve the above object, the present invention provides the following technical solution: a battery failure intelligent monitoring system based on the Internet of Things, comprising: Data batch division module: used to determine the data to be collected as target data, divide the target data into different batches according to equal time division, and mark them as 1, 2, ..., n in sequence; Battery data acquisition module: including a system data acquisition unit and a safety data acquisition unit, which are used to acquire target data in real time and transmit the acquired data to the battery data analysis module; the system data acquisition unit is used to acquire battery performance data, battery efficiency data and battery electrical data; the safety data acquisition unit is used to acquire battery internal safety data, battery environmental adaptability data and battery appearance safety data; Battery data analysis module: including a system data analysis unit and a safety data analysis unit, used to analyze the data transmitted by the battery data acquisition module and transmit the analysis results to the comprehensive data evaluation module; the system data analysis unit includes a battery performance data analysis node, a battery efficiency data analysis node and a battery electrical data analysis node; the safety data analysis unit includes a battery internal safety data analysis node, a battery environmental adaptability data analysis node and a battery appearance safety data analysis node; Comprehensive data evaluation module: including a battery fault intelligent monitoring and analysis unit, which is used to comprehensively analyze the data transmitted by the battery data analysis module and transmit the analysis results to the real-time feedback module; Real-time feedback module: used to establish a preset value of the battery fault intelligent monitoring rationality index, judge the battery fault intelligent monitoring rationality index value according to the preset value of the battery fault intelligent monitoring rationality index, and send a corresponding signal according to the judgment result.

[0008] Preferably, the battery performance data include electrolyte conductivity Ec, active material utilization Su and gas evolution rate Gr; the battery efficiency data include charging energy conversion efficiency Ce, discharge energy output efficiency De and charge and discharge cycle efficiency attenuation rate Ed; the battery electrical data include internal resistance growth rate Rg, voltage peak pulse amplitude Vs and low voltage duration Lv.

[0009] Preferably, the battery internal safety data include the maximum temperature rise rate Tr of the battery pole, the maximum air pressure fluctuation value Bf of the battery internal cavity, and the micro-current leakage rate Ml; the battery environmental adaptability data include the humidity adsorption rate Ha, the battery thermal runaway trigger temperature Cs, and the battery external environment pressure difference limit value Al; the battery appearance safety data include the shell deformation rate Sd and the tab welding fracture volume Fv.

[0010] Preferably, the battery performance data analysis node is used to establish a battery performance data calculation model, and import the battery performance data transmitted by the battery data acquisition module into the battery performance data calculation model to obtain the battery performance coefficient value; the battery performance data calculation model is specifically expressed as: , in, Indicates the battery performance coefficient value calculated for the i-th time, Ec i represents the electrolyte conductivity collected for the i-th time, Su i represents the utilization rate of active substances collected for the i-th time, Gr i represents the gas evolution rate collected for the i-th time.

[0011] Preferably, the battery efficiency data analysis node is used to establish a battery efficiency data calculation model, and the battery efficiency data transmitted by the battery data acquisition module is imported into the battery efficiency data calculation model to obtain a battery efficiency coefficient value; the battery efficiency data calculation model is specifically expressed as: , in, Indicates the battery efficiency coefficient value calculated for the i-th time, Ce i Denotes the charging energy conversion efficiency of the ith collection, De i represents the discharge energy output efficiency of the ith collection, Ed i The efficiency decay rate of the charge-discharge cycle collected for the i-th time.

[0012] Preferably, the battery electrical data analysis node is used to establish a battery electrical data calculation model, and the battery electrical data transmitted by the battery data acquisition module is imported into the battery electrical data calculation model to obtain the battery electrical coefficient value; the battery electrical data calculation model is specifically expressed as: , in, Represents the battery electrical coefficient value calculated for the i-th time, Rg i Represents the internal resistance growth rate of the ith collection, Vs i Indicates the voltage spike pulse amplitude collected for the i-th time, Lv i Indicates the duration of low voltage collected for the i-th time.

[0013] Preferably, the battery internal safety data analysis node is used to establish a battery internal safety data calculation model, import the battery internal safety data transmitted by the battery data acquisition module into the battery internal safety data calculation model, and obtain the battery internal safety factor value; the battery internal safety data calculation model is specifically expressed as: , in, Represents the internal safety factor of the battery calculated for the i-th time, Tr i Bf represents the maximum temperature rise rate of the battery pole collected for the i-th time. i Ml represents the maximum pressure fluctuation value of the battery cavity collected for the i-th time. i Represents the microcurrent leakage rate collected for the i-th time.

[0014] Preferably, the battery environment adaptability data analysis node is used to establish a battery environment adaptability data calculation model, and import the battery environment adaptability data transmitted by the battery data acquisition module into the battery environment adaptability data calculation model to obtain a battery environment adaptability coefficient value; the battery environment adaptability data calculation model is specifically expressed as: , in, Represents the battery environmental adaptability coefficient value calculated for the i-th time, Ha i represents the humidity adsorption rate collected for the i-th time, Cs i represents the battery thermal runaway trigger temperature collected for the i-th time, Al i Indicates the battery external environment pressure difference limit value collected for the i-th time.

[0015] Preferably, the battery appearance safety data analysis node is used to establish a battery appearance safety data calculation model, and the battery appearance safety data transmitted by the battery data acquisition module is imported into the battery appearance safety data calculation model to obtain the battery appearance safety factor value; the battery appearance safety data calculation model is specifically expressed as: , in, Indicates the battery appearance safety factor value calculated for the i-th time, Sd i represents the shell deformation rate collected for the i-th time, Fv i represents the welding fracture volume of the tab collected for the i-th time.

[0016] Preferably, the battery fault intelligent monitoring and analysis unit is used to establish a battery fault intelligent monitoring calculation model, and import the battery performance coefficient value, battery efficiency coefficient value, battery electrical coefficient value, battery internal safety coefficient value, battery environmental adaptability coefficient value and battery appearance safety coefficient value transmitted by the battery data analysis model into the battery fault intelligent monitoring calculation model to obtain the battery fault intelligent monitoring rationality index value. The battery fault intelligent monitoring calculation model is specifically expressed as: , Among them, R is the rationality index value of battery fault intelligent monitoring, k1= ,k2= , k3= ,k4= ,k5= ,k6= , represents the battery performance coefficient value calculated for the i-th time, represents the battery efficiency coefficient value calculated for the i-th time, represents the battery electrical coefficient value calculated for the i-th time, represents the internal safety factor of the battery calculated for the i-th time, represents the battery environmental adaptability coefficient value calculated for the i-th time, It indicates the battery appearance safety factor value calculated for the i-th time, i indicates starting from the i-th number, and n indicates ending at the n-th number.

[0017] Technical effects and advantages of the present invention: 1. The present invention divides the battery data into different monitoring batches through the data batch division module, which significantly improves the accuracy and efficiency of data processing; the data acquisition module collects battery performance data, battery efficiency data, battery electrical data, battery internal safety data, battery environmental adaptability data and battery appearance safety data in all directions, and reduces the limitations of traditional monitoring system data acquisition through multi-dimensional data acquisition methods, laying a solid and rich information foundation for subsequent accurate analysis; 2. Through the battery data analysis module, the collected data is deeply analyzed to obtain the battery performance coefficient value, battery efficiency coefficient value, battery electrical coefficient value, battery internal safety coefficient value, battery environmental adaptability coefficient value and battery appearance safety coefficient value, which clearly shows the performance of the battery and possible fault hazards; through the comprehensive evaluation module, the output results of the battery data analysis module are comprehensively considered and deeply integrated, which greatly enhances the credibility and accuracy of the analysis results; 3. The real-time monitoring module continuously tracks the working status of the battery. Once an abnormal situation is found, an alarm signal will be issued immediately. Managers and relevant decision-makers can grasp the operating status and potential faults of the battery at the first time, and take timely measures to repair and optimize the battery, providing strong support for efficient and intelligent battery monitoring management and scientific and reasonable decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the overall structure of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] As attached Figure 1 The battery fault intelligent monitoring system based on the Internet of Things shown includes a data batch division module, a battery data acquisition module, a battery data analysis module, a comprehensive data evaluation module, and a real-time feedback module.

[0021] The data batch division module is used to determine the data to be collected as target data, divide the target data into different batches in an equal time division manner, and mark them as 1, 2, ..., n in sequence; What needs to be specifically explained in this embodiment is that the method of dividing by equal time is to divide according to the characteristics of each target data through the background information management system, and according to the real-time requirements of each target data, collect it once at an equal time interval and divide it into different collection times.

[0022] The battery data acquisition module includes a system data acquisition unit and a safety data acquisition unit, which are used to acquire target data in real time and transmit the acquired data to the battery data analysis module; the system data acquisition unit is used to acquire battery performance data, battery efficiency data and battery electrical data; the safety data acquisition unit is used to acquire battery internal safety data, battery environmental adaptability data and battery appearance safety data.

[0023] In this embodiment, it is specifically necessary to explain that: the battery performance data includes the electrolyte conductivity Ec, the active material utilization rate Su and the gas evolution rate Gr; the battery efficiency data includes the charging energy conversion efficiency Ce, the discharging energy output efficiency De and the charge and discharge cycle efficiency attenuation rate Ed; the battery electrical data includes the internal resistance growth rate Rg, the voltage peak pulse amplitude Vs and the low voltage duration Lv; the battery internal safety data includes the battery pole maximum temperature rise rate Tr, the battery internal cavity maximum air pressure fluctuation value Bf and the micro-current leakage rate Ml; the battery environmental adaptability data includes the humidity adsorption rate Ha, the battery thermal runaway trigger temperature Cs and the battery external environment air pressure difference limit value Al; the battery appearance safety data includes the shell deformation rate Sd and the pole ear welding fracture volume Fv.

[0024] The electrolyte conductivity is an important indicator to measure the ion conductivity of the electrolyte, and has a direct impact on the performance of the battery. Higher conductivity means better current conduction capacity. The collection method is as follows: first, a portion of the electrolyte in the battery is taken out through a professional conductivity meter, and the battery to be tested is placed in the test container of the meter, ensuring the cleanliness of the test container and the electrode to avoid the influence of impurities on the measurement results. The temperature parameters of the meter are set to be close to the actual operating temperature of the battery, and the electrodes are inserted into the electrolyte. The meter will measure the current and voltage in the electrolyte by applying a certain electric field, thereby calculating the conductivity value of the electrolyte at this temperature.

[0025] The active material utilization rate reflects the degree to which the positive and negative electrode materials of the battery effectively participate in the electrochemical reaction during the charging and discharging process. A higher active material utilization rate means a higher energy density and a longer service life. The collection method is: first understand the design capacity of the battery and the theoretical capacity of the active material, then perform charge and discharge tests on the battery, record the voltage, current and time data of the battery at different charge and discharge stages, and during the charging process, calculate the amount of electricity charged into the battery based on the integral of the charging current and time. During the discharging process, calculate the amount of electricity discharged based on the integral of the discharge current and time. Compare the discharged amount with the theoretical capacity of the active material to get the active material utilization rate.

[0026] The gas evolution rate indicates the amount of gas generated inside the battery, which is crucial for evaluating the safety and service life of the battery. Too high a gas evolution rate may cause the internal pressure of the battery to increase and even cause safety problems. The gas evolution rate is collected by installing a gas collection device on the top of the battery to measure it. A vent is reserved on the top of the battery and a gas collection container is connected. The volume of gas collected per unit time is measured by a flow meter to calculate the gas evolution rate.

[0027] The charging energy conversion efficiency refers to the efficiency of converting the input electrical energy of the battery into stored chemical energy during the charging process. It is very critical for evaluating the overall performance of the battery. A higher conversion efficiency means that more electrical energy is effectively converted into chemical energy for storage. The collection method is as follows: during the charging process, a high-precision electric energy meter is used to measure the electrical energy input to the charger; at the same time, professional battery testing equipment is used to monitor the voltage, current and temperature parameters of the battery, and the amount of electricity charged into the battery, that is, the chemical energy stored in the battery, is calculated based on the integral of the charging time and current. The input electrical energy is compared with the amount of electricity charged into the battery to obtain the charging energy conversion efficiency.

[0028] The discharge energy output efficiency reflects the efficiency of the battery in converting chemical energy into electrical energy during the discharge process. High efficiency means that the battery can release the stored energy more effectively and improve the endurance of the device. At the same time, high discharge efficiency can also reduce energy loss, reduce battery heat generation, and improve battery safety and life. If the discharge energy output efficiency is low, it will shorten the device's range and increase energy waste. The collection method is: during the discharge process, use a high-precision electric energy meter to measure the electric energy output by the battery; at the same time, use battery testing equipment to monitor the battery's voltage, current and temperature parameters, and calculate the discharged electricity based on the integral of the discharge time and current, that is, the electric energy output by the battery. Compare the output electric energy with the chemical energy stored in the battery to get the discharge energy output efficiency.

[0029] The charge and discharge cycle efficiency decay rate describes the decay rate of the energy conversion efficiency after the battery has undergone multiple charge and discharge cycles. This is crucial for evaluating the long-term performance and life of the battery. A lower decay rate means that the battery can still maintain good performance after multiple cycles. The collection method is: perform multiple charge and discharge cycle tests on the battery, measure the charging energy conversion efficiency and the discharging energy output efficiency in each cycle, record the efficiency value of each cycle, and calculate the difference between the efficiencies of two adjacent cycles. The difference is compared with the initial efficiency value to obtain the charge and discharge cycle efficiency decay rate.

[0030] The internal resistance growth rate describes the rate at which the internal resistance of the battery increases over time, which is very important for evaluating the degree of battery aging. A higher growth rate means that the battery ages faster and may lead to performance degradation. The collection method is: use a professional battery internal resistance tester to measure the internal resistance of the battery at different time points. First, connect the battery to the internal resistance tester to ensure a good connection. After setting the parameters of the tester, start the tester and measure the internal resistance value of the battery under specific conditions. Record the measurement time and internal resistance data. As time goes by, perform internal resistance measurements regularly, calculate the ratio of the internal resistance difference at different time points to the initial internal resistance, and obtain the internal resistance growth rate.

[0031] The voltage spike pulse amplitude indicates the voltage mutation amplitude that may occur during the use of the battery, which is crucial for evaluating the stability of the battery. A larger spike amplitude may cause circuit damage or affect the performance of the battery. The acquisition method is: use a high sampling rate voltage monitoring device connected to the positive and negative poles of the battery, set the sampling frequency and voltage range of the monitoring device to ensure that the voltage spike pulse can be accurately captured. During the operation of the battery, the voltage changes at both ends of the battery are monitored in real time. When a voltage spike pulse occurs, the amplitude of the pulse, that is, the voltage spike pulse amplitude, is recorded.

[0032] The low voltage duration describes the length of time that the battery voltage is below a certain threshold, which is very important for evaluating the reliability and safety of the battery. A longer duration may indicate that the battery has performance problems. The collection method is: use a voltage monitoring device to continuously monitor the voltage changes of the battery. When the battery voltage is lower than the set low voltage threshold, start timing; at the same time, record the battery status and related parameters at this time until the battery voltage returns to the normal range or meets other specific conditions, then stop timing. Record the duration of the low voltage, that is, the low voltage duration.

[0033] The maximum temperature rise rate of the battery pole describes the fastest speed at which the temperature of the battery pole rises, which is very important for evaluating the thermal stability of the battery. A higher temperature rise rate may cause the internal temperature of the battery to be too high, thereby affecting the battery performance or causing safety problems. The collection method is: use a temperature sensor infrared thermometer installed on the battery pole, ensure that the sensor is in good contact with the pole, so as to accurately measure the temperature change of the pole. During the operation of the battery, the temperature of the pole is monitored in real time, the temperature change over time is recorded, and the temperature increase value per unit time, that is, the maximum temperature rise rate of the pole, is calculated.

[0034] The maximum air pressure fluctuation value of the battery's internal cavity represents the maximum amplitude of the air pressure fluctuation of the battery's internal cavity, which is very important for evaluating the safety of the battery. A larger air pressure fluctuation value may cause unstable pressure inside the battery, thereby affecting battery performance or causing safety problems. The collection method is: installing an air pressure sensor inside the battery to ensure that the sensor can accurately measure the air pressure changes inside the battery. During the operation of the battery, the air pressure inside the battery is monitored in real time, and the changes in air pressure over time, including the maximum air pressure value and the minimum air pressure value, are recorded. The difference between the maximum air pressure value and the minimum air pressure value, that is, the maximum air pressure fluctuation value of the battery's internal cavity, is calculated.

[0035] The micro-current leakage rate describes the degree of leakage of tiny current when the battery is at rest, which is very important for evaluating the sealing of the battery. A larger leakage rate may lead to battery performance degradation or safety problems. The collection method is: place the battery to be tested in a dry, insulated environment, ensure that the battery is completely disconnected from the external circuit, use a high-precision current measuring device, a microammeter, to connect to the positive and negative poles of the battery, measure the tiny current leakage of the battery in a static state, let the battery be placed in this environment for a period of time to obtain a more stable measurement result, and record the measured leakage current value.

[0036] The humidity adsorption rate describes the speed at which water is adsorbed on the battery surface, which is very important for evaluating the moisture resistance of the battery. A higher adsorption rate may lead to battery performance degradation or safety problems. The collection method is as follows: first, prepare a humidity-controlled environmental chamber, place the battery to be tested in it, set the humidity parameters of the environmental chamber to gradually increase, and use a humidity sensor to monitor the humidity changes in the environmental chamber; at the same time, regularly use high-precision weighing equipment to measure the weight change of the battery, and calculate the humidity adsorption rate of the battery at different humidities based on the relationship between weight change and time.

[0037] The battery thermal runaway trigger temperature describes the critical temperature that causes the battery to experience thermal runaway, which is extremely important for evaluating the safety of the battery. A lower battery thermal runaway trigger temperature means that the battery is more susceptible to thermal runaway, thereby increasing safety hazards. The temperature is collected by gradually heating the battery in a controlled environment. During the heating process, high-precision temperature and voltage monitoring equipment is used to ensure that the heating process is gradual and controllable. The temperature and voltage changes of the battery are closely monitored. When the battery begins to show a significant temperature rise, the temperature at this moment is recorded to obtain the battery thermal runaway trigger temperature.

[0038] The battery external environment air pressure difference limit value describes the maximum allowable range of the air pressure difference between the battery's internal air pressure and the battery's external environment air pressure. This is very important for evaluating the battery's adaptability to external air pressure changes. The collection method is: use an air pressure sensor to measure the air pressure inside and outside the battery respectively, install the sensor at appropriate positions inside and outside the battery to ensure that the air pressure changes can be accurately measured, record the air pressure values ​​inside and outside the battery, calculate the difference between the two, and obtain the battery external environment air pressure difference limit value.

[0039] The shell deformation rate reflects the degree of deformation of the shell of the battery during the charge and discharge process or when it is subjected to external pressure. Excessive deformation may cause damage to the internal structure of the battery, thereby affecting the performance and safety of the battery. The collection method is as follows: first, use a high-precision measuring tool, a three-dimensional scanner, to measure the external dimensions of the battery when it is not in use as the initial dimensions; then, the battery is subjected to charge and discharge tests and a certain external pressure is applied to simulate actual use. During the test, the external dimensions of the battery are regularly measured using a measuring tool, and the changes are recorded. The ratio of the dimensional change before and after deformation to the initial dimension is calculated to obtain the shell deformation rate.

[0040] The tab welding fracture volume reflects the mechanical strength of the battery tab welding point, which is crucial to ensuring the reliability and safety of the battery. A larger fracture volume means poor welding quality, which may lead to a decrease in battery performance. The data is collected by using a three-dimensional scanner to observe the morphology of the fracture and measure the volume of the fractured part, i.e., the tab welding fracture volume.

[0041] The battery data analysis module includes a system data analysis unit and a safety data analysis unit, which are used to analyze the data transmitted by the battery data acquisition module and transmit the analysis results to the comprehensive data evaluation module; the system data analysis unit includes a battery performance data analysis node, a battery efficiency data analysis node and a battery electrical data analysis node; the safety data analysis unit includes a battery internal safety data analysis node, a battery environmental adaptability data analysis node and a battery appearance safety data analysis node.

[0042] In this embodiment, it is specifically required to be explained that: the battery performance data analysis node is used to establish a battery performance data calculation model, and the battery performance data transmitted by the battery data acquisition module is imported into the battery performance data calculation model to obtain the battery performance coefficient value; the battery performance data calculation model is specifically expressed as: , in, Indicates the battery performance coefficient value calculated for the i-th time, Ec i represents the electrolyte conductivity collected for the i-th time, Su i represents the utilization rate of active substances collected for the i-th time, Gr i represents the gas evolution rate collected for the i-th time.

[0043] In this embodiment, it is specifically required to be explained that: the battery efficiency data analysis node is used to establish a battery efficiency data calculation model, and the battery efficiency data transmitted by the battery data acquisition module is imported into the battery efficiency data calculation model to obtain a battery efficiency coefficient value; the battery efficiency data calculation model is specifically expressed as: , in, Indicates the battery efficiency coefficient value calculated for the i-th time, Ce i Denotes the charging energy conversion efficiency of the ith collection, De i represents the discharge energy output efficiency of the ith collection, Ed i The efficiency decay rate of the charge-discharge cycle collected for the i-th time.

[0044] In this embodiment, it is specifically required to be explained that: the battery electrical data analysis node is used to establish a battery electrical data calculation model, and the battery electrical data transmitted by the battery data acquisition module is imported into the battery electrical data calculation model to obtain the battery electrical coefficient value; the battery electrical data calculation model is specifically expressed as: , in, Represents the battery electrical coefficient value calculated for the i-th time, Rg i Represents the internal resistance growth rate of the ith collection, Vs i Indicates the voltage spike pulse amplitude collected for the i-th time, Lv i Indicates the duration of low voltage collected for the i-th time.

[0045] In this embodiment, it is specifically required to be explained that: the battery internal safety data analysis node is used to establish a battery internal safety data calculation model, and the battery internal safety data transmitted by the battery data acquisition module is imported into the battery internal safety data calculation model to obtain the battery internal safety factor value; the battery internal safety data calculation model is specifically expressed as: , in, Represents the internal safety factor of the battery calculated for the i-th time, Tr i Bf represents the maximum temperature rise rate of the battery pole collected for the i-th time. i Ml represents the maximum pressure fluctuation value of the battery cavity collected for the i-th time. i Represents the microcurrent leakage rate collected for the i-th time.

[0046] In this embodiment, it is specifically required to be explained that: the battery environment adaptability data analysis node is used to establish a battery environment adaptability data calculation model, and the battery environment adaptability data transmitted by the battery data acquisition module is imported into the battery environment adaptability data calculation model to obtain the battery environment adaptability coefficient value; the battery environment adaptability data calculation model is specifically expressed as: , in, Represents the battery environmental adaptability coefficient value calculated for the i-th time, Ha i represents the humidity adsorption rate collected for the i-th time, Csi represents the battery thermal runaway trigger temperature collected for the i-th time, Al i Indicates the battery external environment pressure difference limit value collected for the i-th time.

[0047] In this embodiment, it is specifically required to be explained that: the battery appearance safety data analysis node is used to establish a battery appearance safety data calculation model, and the battery appearance safety data transmitted by the battery data acquisition module is imported into the battery appearance safety data calculation model to obtain the battery appearance safety factor value; the battery appearance safety data calculation model is specifically expressed as: , in, Indicates the battery appearance safety factor value calculated for the i-th time, Sd i represents the shell deformation rate collected for the i-th time, Fv i represents the welding fracture volume of the tab collected for the i-th time.

[0048] The comprehensive data evaluation module includes a battery fault intelligent monitoring and analysis unit, which is used to perform comprehensive analysis on the data transmitted by the battery data analysis module and transmit the analysis results to the real-time feedback module.

[0049] In this embodiment, it is specifically required to be explained that: the battery fault intelligent monitoring and analysis unit is used to establish a battery fault intelligent monitoring calculation model, and the battery performance coefficient value, battery efficiency coefficient value, battery electrical coefficient value, battery internal safety coefficient value, battery environmental adaptability coefficient value and battery appearance safety coefficient value transmitted by the battery data analysis model are imported into the battery fault intelligent monitoring calculation model to obtain the battery fault intelligent monitoring rationality index value. The battery fault intelligent monitoring calculation model is specifically expressed as: , Among them, R is the rationality index value of battery fault intelligent monitoring, k1= ,k2= , k3= ,k4= ,k5= ,k6= , represents the battery performance coefficient value calculated for the i-th time, represents the battery efficiency coefficient value calculated for the i-th time, represents the battery electrical coefficient value calculated for the i-th time, represents the internal safety factor of the battery calculated for the i-th time, represents the battery environmental adaptability coefficient value calculated for the i-th time, It indicates the battery appearance safety factor value calculated for the i-th time, i indicates starting from the i-th number, and n indicates ending at the n-th number.

[0050] The real-time feedback module is used to establish a preset value of the battery fault intelligent monitoring rationality index, judge the battery fault intelligent monitoring rationality index value according to the preset value of the battery fault intelligent monitoring rationality index, and send a corresponding signal according to the judgment result.

[0051] In this embodiment, it should be specifically noted that: the preset value of the battery fault intelligent monitoring rationality index is marked as R def , when R def <=R, a normal signal is issued, which indicates that the preset value of the battery fault intelligent monitoring rationality index is less than or equal to the battery fault intelligent monitoring rationality index value, indicating that the battery fault intelligent monitoring status is relatively reasonable; when R def >R, an early warning signal is issued to relevant technical management personnel for processing. This signal indicates that the preset value of the battery fault intelligent monitoring rationality index is greater than the battery fault intelligent monitoring rationality index value, indicating that the battery fault intelligent monitoring status is unreasonable.

[0052] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other; The present invention divides the battery data into different monitoring batches through the data batch division module, which significantly improves the accuracy and efficiency of data processing; the data acquisition module collects battery performance data, battery efficiency data, battery electrical data, battery internal safety data, battery environmental adaptability data and battery appearance safety data in all directions, and reduces the limitations of traditional monitoring system data acquisition through multi-dimensional data acquisition methods, laying a solid and rich information foundation for subsequent accurate analysis; the battery data analysis module conducts in-depth analysis of the collected data, and then obtains the battery performance coefficient value, battery efficiency coefficient value, battery electrical coefficient value, battery internal safety data, battery environmental adaptability data and battery appearance safety data. The full coefficient value, battery environmental adaptability coefficient value and battery appearance safety coefficient value clearly show the performance of the battery and possible hidden dangers of failure; the output results of the battery data analysis module are comprehensively considered and deeply integrated through the comprehensive evaluation module, which greatly enhances the credibility and accuracy of the analysis results; the real-time monitoring module continuously tracks the working status of the battery, and immediately issues an alarm signal once an abnormal situation is found. Managers and relevant decision makers can grasp the operating status and potential failures of the battery at the first time, and take timely measures to repair and optimize the battery, providing strong support for efficient and intelligent battery monitoring management and scientific and reasonable decision-making.

[0053] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A battery failure intelligent monitoring system based on the Internet of Things, characterized in that: include: Data batch division module: used to determine the data to be collected as target data, divide the target data into different batches according to equal time division, and mark them as 1, 2, ..., n in sequence; Battery data acquisition module: including system data acquisition unit and safety data acquisition unit, used to collect target data in real time and transmit the collected data to the battery data analysis module; The system data acquisition unit is used to collect battery performance data, battery efficiency data and battery electrical data; the safety data acquisition unit is used to collect battery internal safety data, battery environmental adaptability data and battery appearance safety data; Battery data analysis module: including a system data analysis unit and a safety data analysis unit, used to analyze the data transmitted by the battery data acquisition module and transmit the analysis results to the comprehensive data evaluation module; the system data analysis unit includes a battery performance data analysis node, a battery efficiency data analysis node and a battery electrical data analysis node; the safety data analysis unit includes a battery internal safety data analysis node, a battery environmental adaptability data analysis node and a battery appearance safety data analysis node; Comprehensive data evaluation module: including a battery fault intelligent monitoring and analysis unit, which is used to comprehensively analyze the data transmitted by the battery data analysis module and transmit the analysis results to the real-time feedback module; Real-time feedback module: used to establish a preset value of the battery fault intelligent monitoring rationality index, judge the battery fault intelligent monitoring rationality index value according to the preset value of the battery fault intelligent monitoring rationality index, and send a corresponding signal according to the judgment result.

2. According to claim 1, a battery failure intelligent monitoring system based on the Internet of Things is characterized by: The battery performance data includes electrolyte conductivity Ec, active material utilization rate Su and gas evolution rate Gr; the battery efficiency data includes charging energy conversion efficiency Ce, discharge energy output efficiency De and charge and discharge cycle efficiency attenuation rate Ed; the battery electrical data includes internal resistance growth rate Rg, voltage peak pulse amplitude Vs and low voltage duration Lv.

3. According to the battery failure intelligent monitoring system based on the Internet of Things according to claim 1, it is characterized in that: The battery internal safety data includes the maximum temperature rise rate Tr of the battery pole, the maximum air pressure fluctuation value Bf of the battery internal cavity and the micro-current leakage rate Ml; the battery environmental adaptability data includes the humidity adsorption rate Ha, the battery thermal runaway trigger temperature Cs and the battery external environment pressure difference limit value Al; the battery appearance safety data includes the shell deformation rate Sd and the tab welding fracture volume Fv.

4. According to claim 1, a battery failure intelligent monitoring system based on the Internet of Things is characterized by: The battery performance data analysis node is used to establish a battery performance data calculation model, import the battery performance data transmitted by the battery data acquisition module into the battery performance data calculation model, and obtain the battery performance coefficient value; the battery performance data calculation model is specifically expressed as: ， in, Indicates the battery performance coefficient value calculated for the i-th time, Ec i represents the electrolyte conductivity collected for the i-th time, Su i represents the utilization rate of active substances collected for the i-th time, Gr i represents the gas evolution rate collected for the i-th time.

5. According to the battery failure intelligent monitoring system based on the Internet of Things according to claim 1, it is characterized in that: The battery efficiency data analysis node is used to establish a battery efficiency data calculation model, import the battery efficiency data transmitted by the battery data acquisition module into the battery efficiency data calculation model, and obtain the battery efficiency coefficient value; the battery efficiency data calculation model is specifically expressed as: ， in, Indicates the battery efficiency coefficient value calculated for the i-th time, Ce i Denotes the charging energy conversion efficiency of the ith collection, De i represents the discharge energy output efficiency of the ith collection, Ed i The efficiency decay rate of the charge-discharge cycle collected for the i-th time.

6. The battery failure intelligent monitoring system based on the Internet of Things according to claim 1 is characterized in that: The battery electrical data analysis node is used to establish a battery electrical data calculation model, import the battery electrical data transmitted by the battery data acquisition module into the battery electrical data calculation model, and obtain the battery electrical coefficient value; the battery electrical data calculation model is specifically expressed as: ， in, Represents the battery electrical coefficient value calculated for the i-th time, Rg i Represents the internal resistance growth rate of the ith collection, Vs i Indicates the voltage spike pulse amplitude collected for the i-th time, Lv i Indicates the duration of low voltage collected for the i-th time.

7. The battery failure intelligent monitoring system based on the Internet of Things according to claim 1 is characterized in that: The battery internal safety data analysis node is used to establish a battery internal safety data calculation model, import the battery internal safety data transmitted by the battery data acquisition module into the battery internal safety data calculation model, and obtain the battery internal safety factor value; the battery internal safety data calculation model is specifically expressed as: ， in, Represents the internal safety factor of the battery calculated for the i-th time, Tr i Bf represents the maximum temperature rise rate of the battery pole collected for the i-th time. i Ml represents the maximum pressure fluctuation value of the battery cavity collected for the i-th time. i Represents the microcurrent leakage rate collected for the i-th time.

8. The battery failure intelligent monitoring system based on the Internet of Things according to claim 1 is characterized in that: The battery environment adaptability data analysis node is used to establish a battery environment adaptability data calculation model, import the battery environment adaptability data transmitted by the battery data acquisition module into the battery environment adaptability data calculation model, and obtain the battery environment adaptability coefficient value; the battery environment adaptability data calculation model is specifically expressed as: ， in, Represents the battery environmental adaptability coefficient value calculated for the i-th time, Ha i represents the humidity adsorption rate collected for the i-th time, Cs i represents the battery thermal runaway trigger temperature collected for the i-th time, Al i Indicates the battery external environment pressure difference limit value collected for the i-th time.

9. The battery failure intelligent monitoring system based on the Internet of Things according to claim 1 is characterized in that: The battery appearance safety data analysis node is used to establish a battery appearance safety data calculation model, import the battery appearance safety data transmitted by the battery data acquisition module into the battery appearance safety data calculation model, and obtain the battery appearance safety factor value; the battery appearance safety data calculation model is specifically expressed as: ， in, Indicates the battery appearance safety factor value calculated for the i-th time, Sd i represents the shell deformation rate collected for the i-th time, Fv i represents the welding fracture volume of the tab collected for the i-th time.

10. The battery failure intelligent monitoring system based on the Internet of Things according to claim 1 is characterized in that: The battery fault intelligent monitoring and analysis unit is used to establish a battery fault intelligent monitoring calculation model, and import the battery performance coefficient value, battery efficiency coefficient value, battery electrical coefficient value, battery internal safety coefficient value, battery environmental adaptability coefficient value and battery appearance safety coefficient value transmitted by the battery data analysis model into the battery fault intelligent monitoring calculation model to obtain the battery fault intelligent monitoring rationality index value. The battery fault intelligent monitoring calculation model is specifically expressed as: ， Among them, R is the rationality index value of battery fault intelligent monitoring, k1= ,k2= , k3= ,k4= ,k5= ,k6= , represents the battery performance coefficient value calculated for the i-th time, represents the battery efficiency coefficient value calculated for the i-th time, represents the battery electrical coefficient value calculated for the i-th time, represents the internal safety factor of the battery calculated for the i-th time, represents the battery environmental adaptability coefficient value calculated for the i-th time, Indicates the battery appearance safety factor value calculated for the i-th time, i means starting from the i-th number, and n means ending at the n-th number.

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