Real-time state on-line monitoring and early warning system and method for storage battery

Abstract

The invention discloses a storage battery real-time state on-line monitoring and early warning system and method, and relates to the technical field of storage battery state monitoring, and the system comprises a data processing module, a data analysis module, a comprehensive early warning analysis module, and an abnormity detection module. The data processing module collects various parameter data of the storage battery in real time through a sensor, preprocesses the data and transmits the data to the data analysis module; the data analysis module performs feature extraction on the processed data and integrates the processed data into a feature data set; the comprehensive early warning analysis module dynamically adjusts the threshold range of each parameter based on the characteristic data set of the data analysis module, synthesizes the change conditions of a plurality of parameters, calculates the comprehensive failure parameters of the storage battery, and carries out graded early warning reminding; and the anomaly detection module is used for receiving the early warning signal, analyzing and positioning the specific location of the anomaly according to the early warning signal, and finally generating a comprehensive failure report.

Description

Technical Field

[0001] The invention relates to the technical field of battery status monitoring, and in particular to a real-time online status monitoring and early warning system and method for a battery. Background Art

[0002] In key areas such as electricity, communications, transportation, data centers and new energy vehicles, batteries are important energy storage devices, and their performance and stability are directly related to the normal operation and safety of the entire system; however, with the increase of usage time, the performance of the battery will gradually decline, and various abnormal conditions will occur, such as voltage fluctuations, abnormal current, temperature rise, increased internal resistance, etc. If these abnormal phenomena are not discovered and accurately located in time, they may lead to system performance degradation, frequent failures, and even catastrophic consequences.

[0003] Traditional battery monitoring methods, such as manual inspections and periodic tests, have many limitations. Manual inspections rely on the experience and subjective judgment of inspectors, making it difficult to capture subtle abnormal changes. In addition, the inspection cycle is long and it is easy to miss the best time to deal with a fault. Although periodic testing can provide a more comprehensive performance evaluation, the testing process is cumbersome and requires interrupting system operation. In addition, the test data often lags behind the actual operating status and cannot reflect the health of the battery in real time. Summary of the invention

[0004] The purpose of the present invention is to provide a real-time online monitoring and early warning system and method for a battery to solve the problems raised in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A battery real-time status online monitoring and early warning system, the system comprising a data processing module, a data analysis module, a comprehensive early warning analysis module, and an abnormality detection module;

[0007] The data processing module collects various parameter data of the battery in real time through sensors and performs preprocessing and transmits them to the data analysis module, including a data collection unit and a data preprocessing unit;

[0008] The data acquisition unit collects various parameter data of the battery in real time through various sensors installed in the battery pack, and transmits the data to the data preprocessing unit through a communication protocol; the data preprocessing unit processes the received data, including cleaning, filtering, and denoising.

[0009] In the above technical solution, the data processing module captures the various parameter data of the battery pack in real time, and performs cleaning, filtering and denoising processing, effectively eliminating noise and interference, ensuring the accuracy and reliability of the data, and laying the foundation for subsequent data analysis.

[0010] Furthermore, the data analysis module extracts characteristic data related to battery performance from the preprocessed data and integrates them into a characteristic data set, including voltage characteristics, current characteristics, temperature characteristics, internal resistance characteristics, and capacity characteristics, and finally forms a characteristic data set.

[0011] In the above technical solution, the data analysis module extracts characteristic data related to battery performance from the preprocessed data and integrates it into a characteristic data set, highlighting the key factors affecting the health status of the battery and providing data support for subsequent comprehensive early warning analysis.

[0012] Furthermore, the comprehensive early warning analysis module dynamically adjusts the threshold range of each parameter based on the characteristic data set of the data analysis module and integrates the changes of multiple parameters to calculate the comprehensive failure parameters of the battery and perform graded early warning reminders, including a threshold setting unit and a parameter fusion analysis unit; the threshold setting unit dynamically adjusts the threshold range by calculating the mean and standard deviation, takes the characteristic data set of the data analysis module as input, and calculates the mean and standard deviation of each parameter observation value in the characteristic data set respectively;

[0013] First, calculate the mean μ of all observations i , according to the formula:

[0014]

[0015] Where N is the number of observations, x i,j represents the i-th observation value of the j-th observation;

[0016] Then calculate the standard deviation σ of each parameter i , according to the formula:

[0017]

[0018] Finally, based on the calculated mean and standard deviation, set the dynamic threshold, combined with the preset weight coefficient, dynamically adjust the threshold of each parameter according to the formula:

[0019] Y i =μ i +kσ i ;

[0020] L i =μ i -kσ i ;

[0021] Where k is the preset weight coefficient, Y i is the previous threshold, L iThe lower threshold is used to obtain the observed values ​​of each parameter in real time. When the real-time observed value of any parameter exceeds the set upper threshold or is lower than the lower threshold, it indicates that the parameter is abnormal. An early warning signal is output to remind and output the abnormal parameter.

[0022] The parameter fusion analysis unit comprehensively considers the changes of multiple parameters and calculates the comprehensive failure risk index H of the battery by weighted summation;

[0023] First, calculate the risk value of each parameter according to the formula:

[0024]

[0025] Where R i represents the risk value of the i-th parameter, P i Represents the current observation value;

[0026] Combined with the risk values ​​of all parameters, the comprehensive failure risk index H is calculated by weighted summation. According to the output result of the threshold setting module, the weight of the abnormal parameter is adjusted during weight allocation. A weight adjustment factor α is set for the abnormal parameter to increase the weight of the abnormal parameter, where α>1. At the same time, on the basis of ensuring that the sum of the weights of all parameters is 1, the weight of the non-abnormal parameter is adjusted accordingly. Set w i is the weight coefficient of the ith parameter, I is the indicator function, when the ith parameter is abnormal, I = 1, otherwise it is 0:

[0027]

[0028] Where y represents the total number of parameters, and m represents the number of abnormal parameters;

[0029] Combined with the weight distribution results of each parameter, the comprehensive failure risk index H is calculated according to the formula:

[0030]

[0031] Set risk thresholds and issue different levels of early warning reminders based on the size of the risk index.

[0032] In the above technical solution, the comprehensive early warning analysis module calculates the mean and standard deviation of each parameter and dynamically adjusts the threshold range in combination with the preset weight coefficient, so that the early warning system can more flexibly adapt to changes in battery performance; at the same time, the comprehensive failure risk index is calculated by the weighted summation method, and a graded early warning is performed in combination with the risk threshold, thereby ensuring the timeliness and accuracy of the early warning;

[0033] Furthermore, the model design unit is used to construct a three-dimensional space model by physically measuring the battery pack, including the overall length, width and height, the size of the single cells, the spacing between the single cells, and the installation position and angle of the sensor on the battery pack, placing the single cells in the model according to the measured size and position and assigning each single cell a unique number; placing a virtual sensor node in the model according to the actual installation position of the sensor on each single cell and setting the three-dimensional rectangular coordinate system and sensor parameters;

[0034] The abnormal positioning unit receives the warning signal and abnormal parameter information from the comprehensive warning analysis module, identifies the abnormal sensor node by comparing the real-time sensor data with the data of the virtual sensor node in the model, and locates the corresponding single battery and its corresponding number in the three-dimensional space model;

[0035] The comprehensive output unit is used to generate a comprehensive failure report, which includes abnormal parameters, abnormal locations, comprehensive failure risk index H and its corresponding warning level.

[0036] In the above technical solution, after receiving the warning signal, the anomaly detection module accurately locates the anomaly through the constructed three-dimensional space model, and uses the physical measurement data and the precise information of the sensor installation position to quickly identify the abnormal sensor node in the three-dimensional space and calculate the specific position of the abnormal single cell in the battery pack, thereby improving the accuracy and efficiency of anomaly detection. Finally, the comprehensive failure report generated by the comprehensive output unit records in detail the abnormal parameters, abnormal location, comprehensive failure risk index and its warning level, and provides comprehensive abnormal information and analysis results.

[0037] A method for online monitoring and early warning of real-time status of a battery, the method comprising the following steps:

[0038] Step S100 collects various parameter data of the battery in real time through various sensors installed in the battery pack, and transmits the data to the data preprocessing unit through the communication protocol. The data preprocessing unit processes the received data, including cleaning, filtering, and denoising;

[0039] Step S200 extracts characteristic data related to battery performance from the preprocessed data and integrates them into a characteristic data set, including voltage characteristics, current characteristics, temperature characteristics, internal resistance characteristics, and capacity characteristics, and finally forms a characteristic data set.

[0040] Step S300 dynamically adjusts the threshold range by calculating the mean and standard deviation, taking the feature data set of the data analysis module as input, and calculating the mean and standard deviation of each parameter observation value in the feature data set;

[0041] First, calculate the mean μ of all observations i , according to the formula:

[0042]

[0043] Where N is the number of observations, x i,j represents the i-th observation value of the j-th observation;

[0044] Then calculate the standard deviation σ of each parameter i , according to the formula:

[0045]

[0046] Finally, based on the calculated mean and standard deviation, set the dynamic threshold, combined with the preset weight coefficient, dynamically adjust the threshold of each parameter according to the formula:

[0047] Y i =μ i +kσ i ;

[0048] L i =μ i -kσ i ;

[0049] Where k is the preset weight coefficient, Y i is the previous threshold, L i The lower threshold is used to obtain the observed values ​​of each parameter in real time. When the real-time observed value of any parameter exceeds the set upper threshold or is lower than the lower threshold, it indicates that the parameter is abnormal. An early warning signal is output to remind and output the abnormal parameter.

[0050] Comprehensively consider the changes of multiple parameters and use weighted summation to calculate the comprehensive failure risk index H of the battery;

[0051] First, calculate the risk value of each parameter according to the formula:

[0052]

[0053] Where R i represents the risk value of the i-th parameter, P i Represents the current observation value;

[0054] Combined with the risk values ​​of all parameters, the comprehensive failure risk index H is calculated by weighted summation. According to the output result of the threshold setting module, the weight of the abnormal parameter is adjusted during weight allocation. A weight adjustment factor α is set for the abnormal parameter to increase the weight of the abnormal parameter, where α>1. At the same time, on the basis of ensuring that the sum of the weights of all parameters is 1, the weight of the non-abnormal parameter is adjusted accordingly. Set w iis the weight coefficient of the ith parameter, I is the indicator function, when the ith parameter is abnormal, I = 1, otherwise it is 0:

[0055]

[0056] Where y represents the total number of parameters, and m represents the number of abnormal parameters;

[0057] Combined with the weight distribution results of each parameter, the comprehensive failure risk index H is calculated according to the formula:

[0058]

[0059] Set risk thresholds and issue different levels of early warning reminders based on the size of the risk index.

[0060] Step S400 is used to construct a three-dimensional space model. By physically measuring the battery pack, including the overall length, width and height, the size of the single cell, the spacing between the single cells, and the installation position and angle of the sensor on the battery pack, the single cells are placed in the model according to the measured size and position and each single cell is assigned a unique number; on each single cell, according to the actual installation position of the sensor, a virtual sensor node is placed in the model and a three-dimensional rectangular coordinate system and sensor parameters are set; the comprehensive early warning analysis module receives early warning signals and abnormal parameter information, identifies abnormal sensor nodes by comparing real-time sensor data with data of virtual sensor nodes in the model, and locates the corresponding single cell and its corresponding number in the three-dimensional space model; finally, a comprehensive failure report is generated, which includes abnormal parameters, abnormal positions, comprehensive failure risk index H and its corresponding early warning level.

[0061] Compared with the prior art, the present invention has the following beneficial effects:

[0062] The present invention adopts dynamic adjustment of threshold range and parameter fusion analysis technology to enable the system to flexibly adjust the early warning strategy according to the actual performance changes of the battery. Compared with the fixed threshold method, this intelligent early warning mechanism can better adapt to the changes in battery performance under different circumstances, and improve the accuracy and reliability of early warning.

[0063] The present invention not only focuses on the abnormality of a single parameter, but also comprehensively evaluates the overall performance of the battery by calculating the comprehensive failure risk index. This comprehensive evaluation method helps to more accurately judge the health status of the battery and provide more comprehensive information support for operation and maintenance decisions.

[0064] By constructing a three-dimensional spatial model, the present invention can locate abnormal positions, which greatly shortens the troubleshooting time and improves operation and maintenance efficiency; at the same time, the rapid response early warning mechanism also provides strong support for timely repair measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 A system module diagram of a real-time online monitoring and early warning system for a battery of the present invention;

[0066] Figure 2 The present invention is a method flow chart of a method for online monitoring and early warning of the real-time status of a battery. DETAILED DESCRIPTION

[0067] 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.

[0068] Example: Figure 1-Figure 2 As shown, the present invention provides a technical solution.

[0069] A battery real-time status online monitoring and early warning system, the system comprising a data processing module, a data analysis module, a comprehensive early warning analysis module, and an abnormality detection module;

[0070] The data processing module collects various parameter data of the battery in real time through sensors and performs preprocessing and transmits them to the data analysis module, including a data collection unit and a data preprocessing unit;

[0071] The data acquisition unit collects various parameter data of the battery in real time through various sensors installed in the battery pack, and transmits the data to the data preprocessing unit through a communication protocol; the data preprocessing unit processes the received data, including cleaning, filtering, and denoising.

[0072] In the above technical solution, the data processing module captures the various parameter data of the battery pack in real time, and performs cleaning, filtering and denoising processing, effectively eliminating noise and interference, ensuring the accuracy and reliability of the data, and laying the foundation for subsequent data analysis.

[0073] The data analysis module extracts characteristic data related to battery performance from the preprocessed data and integrates them into a characteristic data set, including voltage characteristics, current characteristics, temperature characteristics, internal resistance characteristics, and capacity characteristics, and finally forms a characteristic data set.

[0074] In the above technical solution, the data analysis module extracts characteristic data related to battery performance from the preprocessed data and integrates it into a characteristic data set, highlighting the key factors affecting the health status of the battery and providing data support for subsequent comprehensive early warning analysis.

[0075] The comprehensive early warning analysis module dynamically adjusts the threshold range of each parameter based on the characteristic data set of the data analysis module and integrates the changes of multiple parameters to calculate the comprehensive failure parameters of the battery and perform graded early warning reminders, including a threshold setting unit and a parameter fusion analysis unit; the threshold setting unit dynamically adjusts the threshold range by calculating the mean and standard deviation, takes the characteristic data set of the data analysis module as input, and calculates the mean and standard deviation of each parameter observation value in the characteristic data set respectively;

[0076] First, calculate the mean μ of all observations i , according to the formula:

[0077]

[0078] Where N is the number of observations, x i,j represents the i-th observation value of the j-th observation;

[0079] Then calculate the standard deviation σ of each parameter i , according to the formula:

[0080]

[0081] Finally, based on the calculated mean and standard deviation, set the dynamic threshold, combined with the preset weight coefficient, dynamically adjust the threshold of each parameter according to the formula:

[0082] Y i =μ i +kσ i ;

[0083] L i =μ i -kσ i ;

[0084] Where k is the preset weight coefficient, Y i is the previous threshold, L i The lower threshold is used to obtain the observed values ​​of each parameter in real time. When the real-time observed value of any parameter exceeds the set upper threshold or is lower than the lower threshold, it indicates that the parameter is abnormal. An early warning signal is output to remind and output the abnormal parameter.

[0085] Comprehensively consider the changes of multiple parameters and use weighted summation to calculate the comprehensive failure risk index H of the battery;

[0086] First, calculate the risk value of each parameter according to the formula:

[0087]

[0088] Where R irepresents the risk value of the i-th parameter, P i Represents the current observation value;

[0089] Combined with the risk values ​​of all parameters, the comprehensive failure risk index H is calculated by weighted summation. According to the output result of the threshold setting module, the weight of the abnormal parameter is adjusted during weight allocation. A weight adjustment factor α is set for the abnormal parameter to increase the weight of the abnormal parameter, where α>1. At the same time, on the basis of ensuring that the sum of the weights of all parameters is 1, the weight of the non-abnormal parameter is adjusted accordingly. Set w i is the weight coefficient of the ith parameter, I is the indicator function, when the ith parameter is abnormal, I = 1, otherwise it is 0:

[0090]

[0091] Where y represents the total number of parameters, and m represents the number of abnormal parameters;

[0092] Combined with the weight distribution results of each parameter, the comprehensive failure risk index H is calculated according to the formula:

[0093]

[0094] Set the risk threshold H t , the comprehensive failure risk index H and the risk threshold H t Compare, if H>H t , the early warning mechanism is triggered, and different levels of early warning reminders are issued according to the size of the risk index.

[0095] In the above technical solution, the comprehensive early warning analysis module calculates the mean and standard deviation of each parameter and dynamically adjusts the threshold range in combination with the preset weight coefficient, so that the early warning system can more flexibly adapt to changes in battery performance; at the same time, the comprehensive failure risk index is calculated by the weighted summation method, and a graded early warning is performed in combination with the risk threshold, thereby ensuring the timeliness and accuracy of the early warning;

[0096] The model design unit is used to construct a three-dimensional space model by physically measuring the battery pack, including the overall length, width and height, the size of the single cells, the spacing between the single cells, and the installation position and angle of the sensor on the battery pack, placing the single cells in the model according to the measured size and position and assigning each single cell a unique number; placing a virtual sensor node in the model according to the actual installation position of the sensor on each single cell and setting the three-dimensional rectangular coordinate system and sensor parameters;

[0097] The abnormal positioning unit receives the warning signal and abnormal parameter information from the comprehensive warning analysis module, identifies the abnormal sensor node by comparing the real-time sensor data with the data of the virtual sensor node in the model, and locates the corresponding single battery and its corresponding number in the three-dimensional space model;

[0098] The comprehensive output unit is used to generate a comprehensive failure report, which includes abnormal parameters, abnormal locations, comprehensive failure risk index H and its corresponding warning level.

[0099] In the above technical solution, after receiving the warning signal, the anomaly detection module accurately locates the anomaly through the constructed three-dimensional space model, and uses the physical measurement data and the precise information of the sensor installation position to quickly identify the abnormal sensor node in the three-dimensional space and calculate the specific position of the abnormal single cell in the battery pack, thereby improving the accuracy and efficiency of anomaly detection. Finally, the comprehensive failure report generated by the comprehensive output unit records in detail the abnormal parameters, abnormal location, comprehensive failure risk index and its warning level, and provides comprehensive abnormal information and analysis results.

[0100] A method for online monitoring and early warning of real-time status of a battery, the method comprising the following steps:

[0101] Step S100 collects various parameter data of the battery in real time through various sensors installed in the battery pack, and transmits the data to the data preprocessing unit through the communication protocol. The data preprocessing unit processes the received data, including cleaning, filtering, and denoising;

[0102] Step S200 extracts characteristic data related to battery performance from the preprocessed data and integrates them into a characteristic data set, including voltage characteristics, current characteristics, temperature characteristics, internal resistance characteristics, and capacity characteristics, and finally forms a characteristic data set.

[0103] Step S300 dynamically adjusts the threshold range by calculating the mean and standard deviation, taking the feature data set of the data analysis module as input, and calculating the mean and standard deviation of each parameter observation value in the feature data set;

[0104] First, calculate the mean μ of all observations i , according to the formula:

[0105]

[0106] Where N is the number of observations, x i,j represents the i-th observation value of the j-th observation;

[0107] Then calculate the standard deviation σ of each parameter i , according to the formula:

[0108]

[0109] Finally, based on the calculated mean and standard deviation, set the dynamic threshold, combined with the preset weight coefficient, dynamically adjust the threshold of each parameter according to the formula:

[0110] Y i =μ i +kσ i ;

[0111] L i =μ i -kσ i ;

[0112] Where k is the preset weight coefficient, Y i is the previous threshold, L i The lower threshold is used to obtain the observed values ​​of each parameter in real time. When the real-time observed value of any parameter exceeds the set upper threshold or is lower than the lower threshold, it indicates that the parameter is abnormal. An early warning signal is output to remind and output the abnormal parameter.

[0113] Comprehensively consider the changes of multiple parameters and use weighted summation to calculate the comprehensive failure risk index H of the battery;

[0114] First, calculate the risk value of each parameter according to the formula:

[0115]

[0116] Where R i represents the risk value of the i-th parameter, P i Represents the current observation value;

[0117] Combined with the risk values ​​of all parameters, the comprehensive failure risk index H is calculated by weighted summation. According to the output result of the threshold setting module, the weight of the abnormal parameter is adjusted during weight allocation. A weight adjustment factor α is set for the abnormal parameter to increase the weight of the abnormal parameter, where α>1. At the same time, on the basis of ensuring that the sum of the weights of all parameters is 1, the weight of the non-abnormal parameter is adjusted accordingly. Set w i is the weight coefficient of the ith parameter, I is the indicator function, when the ith parameter is abnormal, I = 1, otherwise it is 0:

[0118]

[0119] Where y represents the total number of parameters, and m represents the number of abnormal parameters;

[0120] Combined with the weight distribution results of each parameter, the comprehensive failure risk index H is calculated according to the formula:

[0121]

[0122] Set risk thresholds and issue different levels of early warning alerts based on the size of the risk index.

[0123] Step S400 is used to construct a three-dimensional space model. By physically measuring the battery pack, including the overall length, width and height, the size of the single cell, the spacing between the single cells, and the installation position and angle of the sensor on the battery pack, the single cells are placed in the model according to the measured size and position and each single cell is assigned a unique number; on each single cell, according to the actual installation position of the sensor, a virtual sensor node is placed in the model and a three-dimensional rectangular coordinate system and sensor parameters are set; the comprehensive early warning analysis module receives early warning signals and abnormal parameter information, identifies abnormal sensor nodes by comparing real-time sensor data with data of virtual sensor nodes in the model, and locates the corresponding single cell and its corresponding number in the three-dimensional space model; finally, a comprehensive failure report is generated, which includes abnormal parameters, abnormal positions, comprehensive failure risk index H and its corresponding early warning level.

[0124] In this embodiment, it is assumed that the battery pack is composed of 10 single cells, and each single cell is equipped with a sensor to monitor its voltage, current, temperature, internal resistance and capacity; at a certain time point, a set of data is collected and preprocessed. The following is part of the preprocessed data:

[0125] Number of observations Voltage (V) Temperature(℃) 1 12.5 25.0 2 12.4 25.1 ... ... ... 10 12.3 25.2

[0126] 1. Calculate the mean and standard deviation:

[0127] Voltage: μ = 1 / 10 (12.5 + 12.4 + ... + 12.3) = 12.42σ = 0.05;

[0128] Temperature: μ = 25.08σ = 0.06;

[0129] 2. Set the dynamic threshold and weight coefficient K to 0.5, then:

[0130] Voltage upper threshold Y V =μ+kσ=12.42+0.5×0.05=12.445;

[0131] Voltage lower threshold L V = = 12.42-0.5×0.05 = 12.405;

[0132] Temperature upper threshold Y T = = 25.08 + 0.5 × 0.06 = 25.11;

[0133] The lower limit of temperature L T==25.08 - 0.5 × 0.06 = 25.05;

[0134] 3. Calculate the risk value:

[0135] The current temperature of a single battery is observed to be 25.15°C, which is within the normal range; the voltage is 12.38V, lower than the lower bound threshold, indicating that the voltage parameter is abnormal. An early warning signal is output for reminder and the voltage parameter is output;

[0136] Calculate the risk value: R v = |12.38 - 12.42| / 0.05 = 0.8;

[0137] R t = |25.1 - 25.08| / 0.06 = 0.33;

[0138] 4. Adjust the weights and calculate the comprehensive failure index H:

[0139] When allocating weights, adjust the weights of abnormal parameters; set a weight adjustment factor α of 1.5 for abnormal parameters, w v = 1.5 / 2 = 0.75, w T = 0.15;

[0140] The comprehensive failure risk index H = 0.75 * 0.8 + 0.15 * 0.33 = 0.65;

[0141] 5. Set the risk threshold for hierarchical early warning:

[0142] Low risk level: H ≤ 0.3, indicating that the battery state is good and no special attention is required;

[0143] Medium - low risk level: 0.3 < H ≤ 0.5, indicating that the battery performance has slightly declined, but is still within the acceptable range. It is recommended to conduct regular inspections;

[0144] Medium - high risk level: 0.5 < H ≤ 0.7, indicating that the battery has obvious performance degradation or potential failure risks, and professional personnel need to be arranged immediately for detailed inspection and maintenance;

[0145] High risk level: H > 0.7, indicating that the battery is already in a serious failure state, and emergency measures need to be taken immediately, such as replacing the battery or starting the backup power supply;

[0146] The calculated comprehensive risk index H = 0.65 above belongs to the medium - high level early warning. The system outputs a prompt "The battery has obvious performance degradation or potential failure risks, and professional personnel need to be arranged immediately for detailed inspection and maintenance";

[0147] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A battery real-time status online monitoring and early warning system, characterized in that: The system includes a data processing module, a data analysis module, a comprehensive early warning analysis module, and an anomaly detection module; The data processing module collects various parameter data of the battery in real time through sensors and performs pre-processing and transmits it to the data analysis module; The data analysis module extracts features from the processed data and integrates them into a feature data set; The comprehensive early warning analysis module dynamically adjusts the threshold range of each parameter based on the characteristic data set of the data analysis module and integrates the changes of multiple parameters to calculate the comprehensive failure parameters of the battery and provide graded early warning reminders; The anomaly detection module is used to receive the warning signal and locate the specific location of the anomaly based on the warning signal analysis, and finally generate a comprehensive failure report.

2. The battery real-time status online monitoring and early warning system according to claim 1 is characterized by: The data processing module includes a data acquisition unit and a data preprocessing unit; The data acquisition unit collects various parameter data of the battery in real time through various sensors installed in the battery pack, and transmits the data to the data preprocessing unit through the communication protocol; The data preprocessing unit processes the received data, including cleaning, filtering, and denoising.

3. The battery real-time status online monitoring and early warning system according to claim 1 is characterized by: The data analysis module extracts characteristic data related to battery performance from the preprocessed data and integrates them into a characteristic data set, including voltage characteristics, current characteristics, temperature characteristics, internal resistance characteristics, and capacity characteristics, and finally forms a characteristic data set.

4. The battery real-time status online monitoring and early warning system according to claim 1 is characterized by: The comprehensive early warning analysis module includes a threshold setting unit and a parameter fusion analysis unit; the threshold setting unit dynamically adjusts the threshold range by calculating the mean and standard deviation, takes the feature data set of the data analysis module as input, and calculates the mean and standard deviation of each parameter observation value in the feature data set respectively; First, calculate the mean μ of all observations i , according to the formula: Where N is the number of observations, x i,j represents the i-th observation value of the j-th observation; Then calculate the standard deviation σ of each parameter i , according to the formula: Finally, based on the calculated mean and standard deviation, set the dynamic threshold, combined with the preset weight coefficient, dynamically adjust the threshold of each parameter according to the formula: Y i =μ i +kσ i ; L i =μ i -kσ i ; Where k is the preset weight coefficient, Y i is the previous threshold, L i The lower threshold is used to obtain the observed values ​​of each parameter in real time. When the real-time observed value of any parameter exceeds the set upper threshold or is lower than the lower threshold, it indicates that the parameter is abnormal. An early warning signal is output to remind and output the abnormal parameter. The parameter fusion analysis unit comprehensively considers the changes of multiple parameters and calculates the comprehensive failure risk index H of the battery by weighted summation. First, the risk value of each parameter is calculated according to the formula: Where R i represents the risk value of the i-th parameter, P i Represents the current observation value; Combine the risk values ​​of all parameters, use weighted summation to calculate the comprehensive failure risk index H, and adjust the weights of abnormal parameters when allocating weights according to the output results of the threshold setting module; For abnormal parameters, a weight adjustment factor α is set to increase the weight of abnormal parameters, where α>1. At the same time, on the basis of ensuring that the sum of the weights of all parameters is 1, the weights of non-abnormal parameters are adjusted accordingly; set w i is the weight coefficient of the ith parameter, I is the indicator function, when the ith parameter is abnormal, I = 1, otherwise it is 0: Where y represents the total number of parameters, and m represents the number of abnormal parameters; Combined with the weight distribution results of each parameter, the comprehensive failure risk index H is calculated according to the formula: Set risk thresholds and issue different levels of early warning alerts based on the size of the risk index.

5. The battery real-time status online monitoring and early warning system according to claim 1 is characterized by: The anomaly detection module includes a model design unit and an anomaly location unit; The model design unit is used to construct a three-dimensional space model by physically measuring the battery pack, including the overall length, width and height, the size of the single cells, the spacing between the single cells, and the installation position and angle of the sensor on the battery pack, placing the single cells in the model according to the measured size and position and assigning each single cell a unique number; placing a virtual sensor node in the model according to the actual installation position of the sensor on each single cell and setting the three-dimensional rectangular coordinate system and sensor parameters; The abnormal positioning unit receives the warning signal and abnormal parameter information from the comprehensive warning analysis module, identifies the abnormal sensor node by comparing the real-time sensor data with the data of the virtual sensor node in the model, and locates the corresponding single battery and its corresponding number in the three-dimensional space model; The comprehensive output unit is used to generate a comprehensive failure report, which includes abnormal parameters, abnormal locations, comprehensive failure risk index H and its corresponding warning level.

6. A method for online monitoring and early warning of real-time battery status, characterized in that: The method comprises the following steps: Step S100: collecting various parameter data of the battery in real time through sensors and pre-processing and transmitting to the data analysis module; Step S200: extracting features from the processed data and integrating them into a feature data set; Step S300: Based on the characteristic data set of the data analysis module, dynamically adjust the threshold range of each parameter and integrate the changes of multiple parameters to calculate the comprehensive failure parameters of the battery and perform graded early warning reminders; Step S400: receiving the warning signal and locating the specific location of the abnormality according to the warning signal analysis, and finally generating a comprehensive failure report.

7. A method for online monitoring and early warning of real-time battery status according to claim 6, characterized in that: The step S100 collects various parameter data of the battery in real time through various sensors installed in the battery pack, and transmits them to the data preprocessing unit through the communication protocol. The data preprocessing unit processes the received data, including cleaning, filtering, and denoising; the step S200 extracts characteristic data related to the battery performance from the preprocessed data and integrates them into a characteristic data set, including voltage characteristics, current characteristics, temperature characteristics, internal resistance characteristics, and capacity characteristics, and finally forms a characteristic data set.

8. A method for online monitoring and early warning of real-time battery status according to claim 6, characterized in that: Step S300 dynamically adjusts the threshold range by calculating the mean and standard deviation, taking the feature data set of the data analysis module as input, and calculating the mean and standard deviation of each parameter observation value in the feature data set; First, calculate the mean μ of all observations i , according to the formula: Where N is the number of observations, x i,j represents the i-th observation value of the j-th observation; Then calculate the standard deviation σ of each parameter i , according to the formula: Finally, based on the calculated mean and standard deviation, set the dynamic threshold, combined with the preset weight coefficient, dynamically adjust the threshold of each parameter according to the formula: Y i =μ i +kσ i ; L i =μ i -kσ i ; Where k is the preset weight coefficient, Y i is the previous threshold, L i The lower threshold is used to obtain the observed values ​​of each parameter in real time. When the real-time observed value of any parameter exceeds the set upper threshold or is lower than the lower threshold, it indicates that the parameter is abnormal. An early warning signal is output to remind and output the abnormal parameter. Based on the changes of multiple parameters, the comprehensive failure risk index H of the battery is calculated by weighted summation; first, the risk value of each parameter is calculated according to the formula: Where R i represents the risk value of the i-th parameter, P i Represents the current observation value; Combine the risk values ​​of all parameters, use weighted summation to calculate the comprehensive failure risk index H, and adjust the weights of abnormal parameters when allocating weights according to the output results of the threshold setting module; For abnormal parameters, a weight adjustment factor α is set to increase the weight of abnormal parameters, where α>1. At the same time, on the basis of ensuring that the sum of the weights of all parameters is 1, the weights of non-abnormal parameters are adjusted accordingly; set w i is the weight coefficient of the ith parameter, I is the indicator function, when the ith parameter is abnormal, I = 1, otherwise it is 0: Where y represents the total number of parameters, and m represents the number of abnormal parameters; Combined with the weight distribution results of each parameter, the comprehensive failure risk index H is calculated according to the formula: Set risk thresholds and issue different levels of early warning reminders based on the size of the risk index.

9. A method for online monitoring and early warning of real-time battery status according to claim 6, characterized in that: Step S400 is used to construct a three-dimensional space model by physically measuring the battery pack, including the overall length, width and height, the size of the single cells, the spacing between the single cells, and the installation position and angle of the sensor on the battery pack. The single cells are placed in the model according to the measured size and position and each single cell is given a unique number; on each single cell, according to the actual installation position of the sensor, a virtual sensor node is placed in the model and a three-dimensional rectangular coordinate system and sensor parameters are set; Receive warning signals and abnormal parameter information from the comprehensive warning analysis module, identify abnormal sensor nodes by comparing real-time sensor data with the data of virtual sensor nodes in the model, and locate the corresponding single battery and its corresponding number in the three-dimensional space model; finally, generate a comprehensive failure report, which includes abnormal parameters, abnormal locations, comprehensive failure risk index H and its corresponding warning level.

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