Battery performance detection method and system for battery discharge process

Abstract

The present invention discloses a battery performance detection method and system for the battery discharge process, which relates to the field of battery performance detection technology. The method includes: under a standard test environment, a discharge test is performed on a target battery, and battery parameter information during the discharge process is recorded; a discharge fault identification module is constructed; a battery element structure of the target battery is obtained; a battery charging circuit connected to a charging power source in the battery element structure is provided with a first fault node distribution diagram; a battery discharge circuit connected to a load unit in the battery element structure is provided with a second fault node distribution diagram; a fault node full connection structure is formed, and a fault fixed-point warning is performed. The present invention solves the technical problems of incomplete fault detection and untimely warning in the prior art, and the existence of certain safety risks, and achieves the technical effect of realizing accurate fault location and timely warning of the battery, and significantly improving the safety and reliability of the battery discharge process.

Description

Technical Field

[0001] The present invention relates to the technical field of battery performance detection, and in particular to a battery performance detection method and system used in a battery discharge process. Background Art

[0002] With the continuous growth of electricity demand in modern society, batteries, as an important energy storage device, have been widely used in various fields. From the backup power supply of base stations in the communication field, to the power source of new energy vehicles, to the key components of various emergency equipment, batteries play a vital role. However, various faults may occur in the battery during the discharge process, such as abnormal voltage, current fluctuation, excessive energy loss, etc. These faults will not only affect the performance and service life of the battery, but also may pose a serious threat to the safe and stable operation of the entire system. At present, traditional battery performance detection methods often have problems such as low detection accuracy, inaccurate fault location, and untimely early warning. For example, relying solely on a single battery parameter for fault judgment is difficult to fully and accurately reflect the actual state of the battery; lack of comprehensive consideration of the progress of fault deterioration and battery life, it is impossible to reasonably determine the maintenance time window, resulting in untimely fault handling or excessive maintenance.

[0003] The existing technology has technical problems such as incomplete fault detection and untimely early warning, which poses certain safety risks. Summary of the invention

[0004] The present application provides a battery performance detection method and system for use in a battery discharge process, which is used to solve the technical problems in the prior art of incomplete fault detection and untimely early warning, which pose certain safety risks.

[0005] In view of the above problems, the present application provides a method and system for detecting battery performance during battery discharge.

[0006] In a first aspect of the present application, a method for detecting battery performance during battery discharge is provided, the method comprising:

[0007] Under a standard test environment, a discharge test is performed on a target battery, and battery parameter information during the discharge process is recorded, wherein the battery parameter information includes voltage, current, and temperature; a discharge fault identification module based on the target battery is constructed according to the battery parameter information, wherein the discharge fault identification module has a built-in safe discharge mode; a battery element structure of the target battery is acquired, wherein the battery element structure includes a battery charging circuit connected to a charging power source and a battery discharging circuit connected to a load unit; based on the discharge fault identification module, a first fault node distribution diagram is set in the battery charging circuit connected to the charging power source in the battery element structure; based on the discharge fault identification module, a second fault node distribution diagram is set in the battery discharging circuit connected to the load unit in the battery element structure; in the battery element structure, the first fault node distribution diagram and the second fault node distribution diagram are transmitted back to form a fully connected structure of fault nodes, and a fault fixed-point warning is performed in combination with the battery parameter information.

[0008] In a second aspect of the present application, a battery performance detection system for a battery during discharge is provided, the system comprising:

[0009] A discharge test module, which is used to perform a discharge test on a target battery under a standard test environment and record battery parameter information during the discharge process, wherein the battery parameter information includes voltage, current, and temperature; a discharge fault identification building module, which is used to build a discharge fault identification module based on the target battery according to the battery parameter information, wherein the discharge fault identification module has a built-in safe discharge mode; a battery component structure acquisition module, which is used to acquire the battery component structure of the target battery, wherein the battery component structure includes a battery charging circuit connected to a charging power source and a battery discharging circuit connected to a load unit; a first fault node distribution diagram A setting module, wherein the first fault node distribution diagram setting module sets a first fault node distribution diagram in a battery charging circuit connected to a charging power source in the battery element structure based on the discharge fault identification module; a second fault node distribution diagram setting module, wherein the second fault node distribution diagram setting module sets a second fault node distribution diagram in a battery discharge circuit connected to a load unit in the battery element structure based on the discharge fault identification module; a fault fixed-point warning module, wherein the fault fixed-point warning module is used to transmit the first fault node distribution diagram and the second fault node distribution diagram back in the battery element structure to form a fault node fully connected structure, and perform fault fixed-point warning in combination with the battery parameter information.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] Under the standard test environment, the target battery is subjected to a discharge test, and the battery parameter information during the discharge process is recorded; according to the battery parameter information, a discharge fault identification module based on the target battery is constructed; the battery element structure of the target battery is obtained; based on the discharge fault identification module, a first fault node distribution diagram is set for the battery charging circuit connected to the charging power source in the battery element structure; based on the discharge fault identification module, a second fault node distribution diagram is set for the battery discharge circuit connected to the load unit in the battery element structure; a fully connected structure of fault nodes is formed, and a fault fixed-point warning is performed in combination with the battery parameter information. The technical effect of realizing accurate fault location and timely warning of the battery and significantly improving the safety and reliability of the battery during discharge is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0013] Figure 1 A schematic flow chart of a method for detecting battery performance during battery discharge provided in an embodiment of the present application;

[0014] Figure 2 A schematic diagram of the structure of a battery performance detection system for a battery during discharge provided in an embodiment of the present application.

[0015] Explanation of the accompanying drawings: discharge test module 10, discharge fault identification building module 20, battery element structure acquisition module 30, first fault node distribution map setting module 40, second fault node distribution map setting module 50, fault fixed-point warning module 60. DETAILED DESCRIPTION

[0016] The present application provides a battery performance detection method and system for a battery during discharge, aiming to solve the technical problems in the prior art of incomplete fault detection and untimely early warning, which pose certain safety risks.

[0017] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0018] Embodiment 1, as Figure 1 As shown, the present application provides a method for detecting battery performance during battery discharge, the method comprising:

[0019] Step S100: Under a standard test environment, a discharge test is performed on a target battery, and battery parameter information during the discharge process is recorded, wherein the battery parameter information includes voltage, current, and temperature.

[0020] Specifically, it is crucial to perform a discharge test on the target battery under a standard test environment, which can ensure the accuracy and repeatability of the test results. During the discharge process, closely record the battery parameter information, including voltage, current, and temperature. Voltage reflects the potential difference of the battery and is an important indicator for measuring the battery's output capacity. Current represents the flow of electrons in the circuit, which directly affects the battery's discharge rate and power output. Temperature has a significant impact on the battery's performance and life. Too high or too low a temperature may cause the battery to degrade or even damage its performance. By recording these parameter information, basic data can be provided for subsequent fault analysis and early warning.

[0021] Step S200: constructing a discharge fault identification module based on the target battery according to the battery parameter information, wherein the discharge fault identification module has a built-in safe discharge mode.

[0022] Specifically, a discharge fault identification module for the target battery is built based on the collected battery parameter information. The battery parameter information mainly includes voltage, current, temperature, etc. These parameters play a key role in accurately judging the battery status and potential faults. In order to build this fault identification module, the support vector machine (SVM) algorithm is used. By training a large amount of known battery parameter data in normal and faulty states, SVM establishes a classification model that can determine whether the battery is in a faulty state based on the input new battery parameter data. During the training process, the battery parameter characteristics corresponding to different fault types are used as the basis for classification, so that the model can accurately identify various fault conditions. At the same time, the deep neural network (DNN) algorithm is combined. DNN automatically learns the complex relationship between battery parameters and conducts a deeper mining of the fault characteristics. Through the connection and training of multiple layers of neurons, DNN efficiently processes and analyzes the input battery parameters to improve the accuracy and reliability of fault identification. In this discharge fault identification module, a safe discharge mode is embedded. The safe discharge mode ensures the safety of the battery during the discharge process by setting a series of safety parameters and thresholds. For example, when the voltage is below a certain threshold, it means that the battery is too low or there is an internal short circuit. The safe discharge mode will automatically adjust the discharge current or cut off the discharge circuit to prevent the battery from being damaged by excessive discharge. If the temperature exceeds the safe range, the safe discharge mode can start the heat dissipation device or reduce the discharge power to reduce the battery temperature and avoid safety accidents caused by overheating.

[0023] In summary, by adopting SVM and DNN algorithms and embedding a safe discharge mode, the discharge fault identification module constructed can effectively detect and identify the fault conditions of the target battery during the discharge process, providing a strong guarantee for the safe and stable operation of the battery.

[0024] Step S300: Acquire the battery element structure of the target battery, wherein the battery element structure includes a battery charging circuit connected to a charging power source and a battery discharging circuit connected to a load unit.

[0025] Specifically, the battery component structure of the target battery is obtained. The battery component structure is crucial for understanding the working principle of the battery and performing fault detection and early warning. The battery component structure mentioned here includes a battery charging circuit connected to a charging power source and a battery discharging circuit connected to a load unit.

[0026] For the battery charging circuit connected to the charging power source, it is responsible for transmitting the electrical energy of the external charging power source to the battery. This circuit usually includes the charging interface, charging controller, charging cable, and the charging circuit inside the battery. The charging controller plays a key role in regulating the charging current and voltage to ensure that the battery can be charged in a safe and stable manner. The charging cable is responsible for transmitting the electrical energy of the charging power source to the battery. Its quality and connection reliability directly affect the charging effect and safety. The charging circuit inside the battery is responsible for converting the input electrical energy into chemical energy for storage.

[0027] The battery discharge circuit connected to the load unit is a channel that converts the chemical energy stored in the battery into electrical energy and outputs it to the load unit. This circuit includes the discharge interface, discharge controller, discharge cable, and discharge circuit inside the battery. The discharge controller adjusts the discharge current and voltage according to the load requirements to ensure that the load can work normally. The discharge cable transmits the battery's electrical energy to the load unit, and its quality and connection reliability must also be guaranteed. The discharge circuit inside the battery plays a key role in the discharge process, converting chemical energy into electrical energy and outputting it to the load unit. Understanding these battery component structures of the target battery can help better analyze the battery's behavior during the charging and discharging process, determine the location where failures may occur, and provide an important basis for subsequent fault detection and early warning.

[0028] Step S400: Based on the discharge fault identification module, a first fault node distribution diagram is set in the battery charging circuit connected to the charging power source in the battery element structure.

[0029] Specifically, based on the previously constructed discharge fault identification module, the battery charging circuit connected to the charging power supply in the target battery element structure is analyzed. This discharge fault identification module already has the ability to detect and identify possible faults that may occur in the battery during the discharge process. It is now applied to troubleshooting the battery charging circuit.

[0030] First, the battery charging circuit connected to the charging power source is comprehensively monitored and evaluated. This circuit includes all links from the charging power source to the battery, such as the charging interface, charging cable, charging controller, and the charging circuit inside the battery. By real-time monitoring of the electrical parameters of these links, such as voltage, current, power, etc., combined with the analysis algorithm of the discharge fault identification module, the nodes that may fail are found. Then, according to the results of monitoring and analysis, the first fault node distribution map is set. On this distribution map, each faulty node is marked and the type of fault is indicated. For example, if the voltage at a charging interface fluctuates abnormally, the interface is marked as a faulty node, and it is noted that it is caused by poor contact or internal circuit failure. For the charging cable, if the current is too large or too small, it is also marked as a faulty node, and the cause of the fault is analyzed, such as cable aging, short circuit or open circuit. By setting the first fault node distribution map, the potential fault points in the battery charging circuit can be intuitively understood, providing a clear direction for subsequent troubleshooting and maintenance. Based on this distribution map, the problem can be quickly located and appropriate measures can be taken to repair it, ensuring that the battery charging process is safe, stable and efficient.

[0031] Step S500: Based on the discharge fault identification module, a second fault node distribution diagram is set in the battery discharge circuit connected to the load unit in the battery element structure.

[0032] Specifically, based on the established discharge fault identification module, the battery discharge circuit connected to the load unit in the target battery element structure is deeply analyzed. The discharge fault identification module plays an important role in the battery discharge process. Now it is applied to the fault detection of the battery discharge circuit. The battery discharge circuit connected to the load unit covers all parts from the battery to the load unit, including the discharge interface, discharge cable, discharge controller, and the discharge circuit inside the battery. By continuously monitoring the electrical parameters of these components, such as discharge voltage, discharge current, power output, etc., and combining the intelligent analysis algorithm of the discharge fault identification module, potential fault nodes are found. For example, when the discharge voltage fluctuates or decreases abnormally, it means that there is a problem in a certain link in the discharge circuit; if the discharge current is unstable or exceeds the normal range, it also means that there is a fault in the circuit. According to the results of monitoring and analysis, a second fault node distribution diagram is set. On this distribution diagram, each fault node is clearly marked and the corresponding fault type is indicated. For example, if overheating occurs at a discharge interface, the interface can be marked as a faulty node, and it can be inferred that poor contact causes increased resistance and thus heat; if the discharge cable is damaged or short-circuited, it is also marked as a faulty node, and the impact on the discharge process is analyzed.

[0033] The setting of the second fault node distribution map provides an intuitive basis for troubleshooting. By viewing the distribution map, potential problem points in the battery discharge circuit can be quickly identified so that targeted maintenance measures can be taken to ensure that the battery supplies power to the load unit stably and efficiently.

[0034] Step S600: In the battery element structure, the first fault node distribution map and the second fault node distribution map are transmitted back to form a fault node full connection structure, and a fault fixed-point warning is performed in combination with the battery parameter information.

[0035] Specifically, the operation is performed in the battery element structure of the target battery, and the first fault node distribution map and the second fault node distribution map obtained previously are transmitted back and integrated. The first fault node distribution map is set for the battery charging circuit connected to the charging power source, marking the nodes and fault types that may fail in the circuit; the second fault node distribution map is set for the battery discharge circuit connected to the load unit, and also marks the potential fault points in the discharge circuit. By transmitting these two distribution maps back, they are integrated together to form a fully connected structure of fault nodes. This fully connected structure can fully display all nodes that fail in the battery during the charging and discharging process, as well as the association relationship between these nodes. For example, if a node is marked as a fault node in both the charging circuit and the discharging circuit, it can be inferred that this node may have a greater impact on the performance of the entire battery. At the same time, combined with the battery parameter information recorded in step S100, such as voltage, current and temperature, a fault fixed-point warning is performed. When these parameters change abnormally during the operation of the battery, the specific location where the fault may occur can be quickly determined according to the fully connected structure of the fault node. For example, if the voltage drops suddenly, by comparing the full connection structure and the current battery parameter information, it can be determined whether there is a problem at a node in the charging circuit or a fault in the discharge circuit that causes the voltage abnormality. Once the fault location is determined, an early warning signal can be issued to remind technicians to take timely measures for maintenance and treatment to ensure the safe and stable operation of the battery and avoid further damage caused by the further expansion of the fault.

[0036] In a possible implementation, step S500 further includes:

[0037] Step S510: In the battery discharge circuit connected to the load unit in the battery element structure, the battery performance of the target battery is monitored to obtain discharge test data.

[0038] Step S520: perform fuzzy decision making and data feedback on the load unit's energy conversion loss according to the discharge test data, perform fault identification in combination with the discharge fault identification module, and obtain a set of fault node markers.

[0039] Step S530: Based on the fault node mark set, the battery discharge circuit connected to the load unit in the battery element structure is marked to obtain the second fault node distribution map.

[0040] Specifically, in the battery element structure of the target battery, the battery performance monitoring is focused on the battery discharge circuit connected to the load unit. Through various monitoring devices and sensors, various data of the target battery in the process of discharging to the load unit are collected in real time. These data together constitute the discharge test data. The discharge test data includes the size and change of the discharge current, the fluctuation of the discharge voltage, the change of the battery temperature, etc. These data reflect the performance of the target battery under actual working conditions, and provide a basic basis for subsequent analysis and fault identification.

[0041] According to the obtained discharge test data, the load unit's energy conversion loss fuzzy decision and data feedback are performed. The energy conversion loss fuzzy decision is a method that comprehensively considers multiple factors and evaluates the loss of the load unit during the energy conversion process. By analyzing the relationship between the current, voltage and other parameters in the discharge test data and the actual needs of the load unit, it is determined whether the load unit has abnormal losses during the energy conversion process. At the same time, these analysis results are fed back to the system. Combined with the discharge fault identification module, the returned data is fault identified using its built-in algorithms and models. By comparing the parameter range under normal conditions with the current data, the nodes with faults are found. Finally, these faulty nodes are marked to form a fault node mark set, which contains the various locations where faults occur in the battery discharge circuit and their corresponding fault characteristics.

[0042] Based on the obtained fault node marking set, the battery discharge circuit connected to the load unit in the battery element structure is marked, and each fault node in the fault node marking set is clearly marked on the battery discharge circuit to intuitively display the location of the fault. Through this marking, the second fault node distribution map is obtained. This distribution map presents the potential fault points in the battery discharge circuit in detail, providing powerful guidance for troubleshooting and maintenance. According to the distribution map, the problem is quickly located, and corresponding measures are taken to solve the fault, ensuring the stability and reliability of the target battery during the discharge process to the load unit.

[0043] In a possible implementation, step S520 further includes:

[0044] Step S521: According to the discharge test data, fuzzy decision making and data feedback of the load unit's energy conversion loss are performed, and the discharge fault identification module is used to perform fault identification to obtain a preliminary fault node set.

[0045] Step S522: Calculate the energy loss rate and abnormal current fluctuation rate of each preliminary fault node in the preliminary fault node set.

[0046] Step S523: Based on the energy loss rate and abnormal current fluctuation rate of each preliminary fault node, and with characteristic threshold and occurrence probability as constraints, perform F-index binarization processing on the fault distribution corresponding to the load unit to obtain the fault node mark set, wherein each fault node mark represents a fault node cluster.

[0047] Specifically, first, based on the obtained discharge test data, a fuzzy decision is made on the energy conversion loss of the load unit. This process comprehensively considers multiple factors, such as the relationship between the discharge current, voltage, temperature and other data and the actual working requirements of the load unit, to evaluate the loss of the load unit during the energy conversion process. At the same time, the decision result data is transmitted back to the system, and then the discharge fault identification module is used to identify the faults of the returned data. The module conducts in-depth analysis of the data to find out the nodes with faults, thereby obtaining a preliminary set of fault nodes. This set contains locations that are initially judged to be possible faults, but further analysis and processing are required to determine their accuracy.

[0048] In the preliminary fault node set, a detailed calculation is performed for each preliminary fault node to calculate the energy loss rate of each preliminary fault node, that is, by analyzing the energy input and output at the node, the proportion of energy loss at this location is determined. At the same time, the abnormal current fluctuation rate is calculated, which is an important indicator to measure the stability of the current at the node. By analyzing the current fluctuation, it is determined whether there is an abnormal current change at the node, such as a sudden increase or decrease in current. These calculation results provide specific quantitative indicators for subsequent fault analysis and processing.

[0049] First, the energy loss rate and abnormal current fluctuation rate of each preliminary fault node are analyzed. The energy loss rate reflects the degree of energy loss at a specific preliminary fault node, while the abnormal current fluctuation rate reflects the current stability at the node. The pre-set characteristic threshold and the probability of occurrence obtained based on historical data and statistical analysis are used as constraints. The characteristic threshold is used to judge the severity of the fault. When the energy loss rate or abnormal current fluctuation rate of a preliminary fault node exceeds this threshold, it indicates that there is a more serious fault risk. The probability of occurrence helps to assess the possibility of a certain fault. Then, the fault distribution corresponding to the load unit is binarized by the F index. The F index usually considers multiple factors to evaluate the fault situation. Through binarization, the continuous F index value is converted into two discrete values, such as 0 and 1. Among them, 0 can represent the normal state and 1 represents the fault state. After such processing, a set of fault node markers is obtained. In this set, each tag represents a faulty node cluster, which means that multiple preliminary faulty nodes with similar fault characteristics are grouped into one cluster so that fault diagnosis and repair can be performed more targeted. In this way, the scope and severity of the fault can be more clearly determined, providing clear goals and directions for subsequent fault handling.

[0050] In a possible implementation, step S523 further includes:

[0051] Step S5231: Through the safety discharge mode, the minimum linear error and the allowable error range are set.

[0052] Step S5232: According to the minimized linear error and the allowable error range, the fault distribution corresponding to the load unit is mapped to obtain a relative error value.

[0053] Step S5233: Based on the relative error value, a binary threshold is selected for the logarithmic relationship between the fault severity and the fault risk probability to determine a plurality of fault node markers in the fault node marker set.

[0054] Specifically, the minimum linear error and the allowable error range are set through the safe discharge mode. The safe discharge mode is designed to ensure the safety and stability of the battery during the discharge process. In this mode, the setting of the minimum linear error is to minimize the deviation between the actual measured value and the theoretical value. For example, when monitoring the current, voltage and other parameters during the battery discharge process, it is hoped that the linear relationship between the actual value and the expected value is as close as possible to ensure the accuracy of the system. At the same time, the setting of the allowable error range takes into account the inevitable measurement errors and system fluctuations in practical applications. By setting this range reasonably, small deviations can be tolerated to a certain extent to avoid false alarms and excessive intervention.

[0055] According to the minimized linear error and allowable error range set in step S5231, the fault distribution corresponding to the load unit is mapped. The process of this mapping process is to compare and convert the fault data actually measured with the set error standard. In this way, a relative error value can be obtained, that is, the degree of deviation between the actual fault condition and the ideal state. For example, if the actual current value of a node exceeds the expected range, the relative error value of the current of the node can be calculated through mapping processing. This relative error value can more intuitively reflect the severity of the fault.

[0056] Based on the relative error value obtained, the binary threshold is selected according to the logarithmic relationship between the fault severity and the fault risk probability, which means that the relationship between the fault severity and the fault risk probability is transformed through a logarithmic function to better determine the binary threshold. When the fault severity is low, the corresponding fault risk probability is also relatively low; when the fault severity is high, the fault risk probability increases rapidly. Through the processing of this logarithmic relationship, it is possible to more accurately determine which faulty nodes need to be marked as faulty. Finally, multiple faulty node tags in the faulty node tag set are determined, and each tag represents a faulty node cluster. This processing method can classify nodes with similar fault characteristics and risk levels into one category, which is convenient for subsequent fault diagnosis and repair work.

[0057] In a possible implementation, step S523 further includes:

[0058] Step S5234: Identify the fault distribution corresponding to the load unit through the multiple fault node tags in the fault node tag set, and associate the mapping feature threshold and the occurrence probability according to the energy loss rate and the abnormal current fluctuation rate of each preliminary fault node to obtain multiple fault node clusters.

[0059] Step S5235: Calculate the average energy loss rate and average current abnormal fluctuation rate corresponding to the first fault node cluster as the battery performance index of the first fault node cluster, wherein the first fault node cluster is any one of the multiple fault node clusters.

[0060] Step S5236: traverse the multiple fault node clusters, compare the battery performance indicators of the multiple fault node clusters with the battery performance requirements, and determine a binary matrix, wherein the rows of the binary matrix correspond one-to-one to the multiple fault node clusters.

[0061] Specifically, multiple fault node tags in the fault node tag set are used to identify the fault distribution corresponding to the load unit, which accurately points out the possible location of the fault. Then, based on the two key parameters of the energy loss rate and the abnormal current fluctuation rate of each preliminary fault node, these parameters are associated with the characteristic threshold and the probability of occurrence. The characteristic threshold is a standard value used to measure the severity of the fault. When the parameter of the fault node exceeds this threshold, it means that there is a more serious fault. The probability of occurrence is the possibility of a certain fault occurring based on historical data and statistical analysis. Through this association mapping method, fault nodes with similar characteristics can be classified into one category, thereby forming multiple fault node clusters. These clusters represent different types of fault distribution patterns, which provide an important basis for more targeted fault diagnosis and processing in the future.

[0062] The first fault node cluster is selected from multiple fault node clusters for analysis. In order to evaluate the battery performance status represented by the cluster, the corresponding average energy loss rate and average current abnormal fluctuation rate are calculated. The average energy loss rate is obtained by summing up the energy loss rates of all nodes in the first fault node cluster and then taking the average. It reflects the average loss degree of the battery during energy transmission under this specific fault node cluster. The average current abnormal fluctuation rate is the average current abnormal fluctuation rate of each node in the cluster. The current abnormal fluctuation rate reflects the stability of the current. The average current abnormal fluctuation rate can measure the overall stability of the current in the first fault node cluster. Using these two average values ​​as the battery performance indicators of the first fault node cluster can comprehensively evaluate the battery performance corresponding to the cluster from two important aspects: energy loss and current stability, and provide a specific quantitative basis for the subsequent fault analysis and processing of the cluster.

[0063] Multiple fault node clusters are traversed one by one, and the battery performance indicators of each fault node cluster, namely the average energy loss rate and the average current abnormal fluctuation rate, are compared with the predetermined battery performance requirements. The battery performance requirements are the standard performance indicator ranges set according to the actual application scenarios and system requirements. Through this comparison, it can be determined whether the battery performance status represented by each fault node cluster meets the requirements. A binary matrix is ​​determined based on the comparison results. Each row of this binary matrix corresponds to a fault node cluster. If the battery performance indicator of a fault node cluster meets the battery performance requirements, then the corresponding position in the binary matrix can be marked as "1", indicating that the battery performance of the cluster is good; if it does not meet the performance requirements, it is marked as "0", indicating that there are problems with the battery performance of the cluster. Such a binary matrix can clearly and intuitively present the performance status of each fault node cluster, providing a clear basis and direction for subsequent fault diagnosis, processing and system optimization.

[0064] In a possible implementation, step S600 further includes:

[0065] Step S610: Determine a maintenance time window through the binarization matrix, where the maintenance time window is determined based on the fault deterioration progress and the battery service life.

[0066] Step S620: Based on the maintenance time window, priority sorting of fault fixed-point warning management is performed to obtain a fault fixed-point warning management sequence, wherein the fault fixed-point warning management sequence includes a maintenance timestamp.

[0067] Step S630: Performing a fault fixed-point warning on the target battery according to the fault fixed-point warning management sequence.

[0068] Specifically, the maintenance time window is determined using the information provided by the binary matrix. The binary matrix clearly shows the performance status of each fault node cluster. By analyzing the data in the matrix, the severity and impact range of different fault nodes can be understood. The determination of the maintenance time window is based on two important factors: the progress of fault deterioration and the battery life. First, the progress of fault deterioration is obtained by evaluating the current state of the fault node and its development trend. For example, if the performance index of a fault node drops sharply in a short period of time, it can be judged that the fault deteriorates rapidly and needs to be maintained as soon as possible. By comprehensively analyzing the deterioration progress of multiple fault nodes, the overall development speed of faults in the entire system can be roughly determined. At the same time, battery life is also a key consideration. Batteries at different stages have different performance characteristics and remaining life. If a battery is approaching the end of its service life, even if a minor fault occurs, it needs to be maintained as soon as possible to avoid complete failure of the battery. On the contrary, if the battery is still in a relatively new state, then for some minor faults, a certain amount of time can be given for observation and monitoring. By comprehensively considering the two factors of fault deterioration progress and battery life, a reasonable maintenance time window can be determined. This time window provides a clear time range. Performing maintenance work within this range can minimize the impact of the fault on the system, while also making full use of the remaining life of the battery to improve the reliability and stability of the entire system.

[0069] The determination of the maintenance time window provides an important basis for the priority sorting of fault point warning management. First, the impact of each fault point on battery performance is analyzed. If a fault point causes the battery output voltage to be unstable, the current to fluctuate abnormally, or the energy loss rate to rise sharply, then this fault point has a greater impact on the battery performance and should be given a higher priority. For those fault points that affect the battery life, they also need to be handled with priority. For example, some faults will accelerate the aging process of the battery or cause the chemical reaction inside the battery to be unbalanced, thereby shortening the battery life. These fault points should occupy a higher position in the priority sorting. At the same time, considering the impact of the fault point on battery safety, if the fault causes safety problems such as battery overheating, short circuit or even explosion, it must be handled immediately and given the highest priority. In the process of priority sorting, the fault point warning management sequence obtained contains the maintenance timestamp, which is determined based on factors such as the severity of the fault, the impact on battery performance, and the urgency of maintenance. For those fault points that have a greater impact on battery performance, may endanger battery safety, and deteriorate faster, the maintenance timestamp will be set relatively early to ensure that maintenance can be carried out in time to ensure the performance, safety and service life of the battery. Through this priority sorting based on battery performance, the fault point warning management work can be arranged more scientifically and reasonably, minimizing the damage to battery performance caused by faults and improving battery reliability and stability.

[0070] According to the fault point warning management sequence, the target battery is given a fault point warning. This sequence is obtained through a series of complex analysis and processing steps. It clarifies the priority of each fault point and the corresponding maintenance timestamp. When the warning is given according to this sequence, the fault point with the highest priority will be paid attention to first. If the maintenance timestamp of the fault point shows that the warning time is about to arrive or has exceeded the warning time, a clear alarm signal will be issued immediately to notify the relevant personnel that the target battery has a serious fault at a specific location and needs to be processed as soon as possible. The alarm information will include the specific location description of the fault, the type of fault, and the impact on the battery performance and the entire system. As time goes by, other fault points are monitored and warned in turn according to priority. For those fault points whose maintenance timestamps have not yet arrived but have a deteriorating trend, an early warning is issued in advance to remind the relevant personnel to pay close attention to their development dynamics so as to take preventive measures at the right time. Through this fault point warning method, it can be ensured that when the target battery fails, the relevant personnel can understand the fault situation in a timely and accurate manner, take effective measures to repair and maintain it, thereby ensuring the normal operation and service life of the battery and improving the reliability and stability of the entire system.

[0071] In a possible implementation, step S5236 further includes:

[0072] When the comparison result is greater than the preset performance threshold corresponding to the battery performance requirement, the next standard action of the safe discharge mode is no longer obtained, so that the battery performance index of the fault fixed point coordinates corresponding to the fault fixed point warning of the target battery continues to be compared with the battery performance requirement during the fault fixed point warning management, until the comparison result is less than the preset performance threshold corresponding to the battery performance requirement.

[0073] Specifically, when the comparison result is greater than the preset performance threshold corresponding to the battery performance requirement, it means that the performance of the current target battery fails to meet the preset standard requirements. In this case, the system no longer obtains the next standard action of the safe discharge mode. This is to avoid continuing to perform potentially inappropriate actions under the current poor performance state, thereby preventing further deterioration of the battery condition or causing other potential problems.

[0074] At this time, during the fault point warning management process, the battery performance index of the fault point coordinates corresponding to the fault point warning of the target battery will be continuously compared with the battery performance requirements. Through continuous monitoring and comparison, the performance changes of the battery can be grasped in real time. The purpose of doing so is to wait until the comparison result is less than the preset performance threshold corresponding to the battery performance requirement, that is, when the battery performance index meets the preset requirements, it indicates that the battery status tends to be good or normal. In this process, the system always keeps a close eye on the battery so that appropriate measures can be taken in time to adjust and repair it to ensure that the battery can operate stably and reliably.

[0075] Embodiment 2 is based on the same inventive concept as the battery performance detection method used in the battery discharge process in the above embodiment. Figure 2 As shown, the present application provides a battery performance detection system for a battery during discharge, and the embodiments of the present application and the method embodiment are based on the same inventive concept. The system includes:

[0076] The discharge test module 10 is used to perform a discharge test on a target battery under a standard test environment and record battery parameter information during the discharge process, wherein the battery parameter information includes voltage, current, and temperature.

[0077] A discharge fault identification building module 20 is used to build a discharge fault identification module based on the target battery according to the battery parameter information, and the discharge fault identification module has a built-in safe discharge mode.

[0078] The battery component structure acquisition module 30 is used to acquire the battery component structure of the target battery, wherein the battery component structure includes a battery charging circuit connected to a charging power source and a battery discharging circuit connected to a load unit.

[0079] The first fault node distribution diagram setting module 40 sets a first fault node distribution diagram in a battery charging circuit connected to a charging power source in the battery element structure based on the discharge fault identification module.

[0080] The second fault node distribution diagram setting module 50 sets a second fault node distribution diagram based on the discharge fault identification module in the battery discharge circuit connected to the load unit in the battery element structure.

[0081] The fault fixed-point warning module 60 is used to transmit the first fault node distribution map and the second fault node distribution map back in the battery element structure to form a fault node full connection structure, and perform fault fixed-point warning in combination with the battery parameter information.

[0082] Furthermore, the second fault node distribution diagram setting module 50 further includes:

[0083] A discharge test data acquisition unit is used to monitor the battery performance of the target battery in the battery discharge circuit connected to the load unit in the battery element structure to obtain discharge test data.

[0084] A fault node mark set acquisition unit is used to perform fuzzy decision and data feedback of the load unit's energy conversion loss according to the discharge test data, perform fault identification in combination with the discharge fault identification module, and acquire a fault node mark set.

[0085] A battery discharge circuit identification unit is used to identify the battery discharge circuit connected to the load unit in the battery element structure based on the fault node mark set to obtain the second fault node distribution map.

[0086] Furthermore, the fault node mark set acquisition unit further includes:

[0087] A preliminary fault node set acquisition unit is used to perform fuzzy decision and data feedback on the load unit's energy conversion loss according to the discharge test data, use the discharge fault identification module to perform fault identification, and acquire a preliminary fault node set.

[0088] A preliminary fault node calculation unit is used to calculate the energy loss rate and current abnormal fluctuation rate of each preliminary fault node in the preliminary fault node set.

[0089] A binarization processing unit is used to perform F-index binarization processing on the fault distribution corresponding to the load unit according to the energy loss rate and abnormal current fluctuation rate of each preliminary fault node, with characteristic threshold and occurrence probability as constraints, to obtain the fault node mark set, wherein each fault node mark represents a fault node cluster.

[0090] Furthermore, the binarization processing unit further includes:

[0091] An error setting unit is used to set a minimized linear error and an allowable error range through the safe discharge mode.

[0092] A relative error value acquisition unit is used to map the fault distribution corresponding to the load unit according to the minimized linear error and the allowable error range to obtain a relative error value.

[0093] A plurality of fault node marking determination units are provided, wherein the plurality of fault node marking determination units select a binary threshold value based on the relative error value and the logarithmic relationship between the fault severity and the fault risk probability, and determine a plurality of fault node markings in the fault node marking set.

[0094] Furthermore, the binarization processing unit further includes:

[0095] Multiple fault node cluster acquisition units, the multiple fault node cluster acquisition units are used to identify the fault distribution corresponding to the load unit through multiple fault node tags in the fault node tag set, and associate mapping feature thresholds and occurrence probabilities based on the energy loss rate and abnormal current fluctuation rate of each preliminary fault node to obtain multiple fault node clusters.

[0096] A battery performance indicator acquisition unit for the first fault node cluster, the battery performance indicator acquisition unit for the first fault node cluster is used to calculate the average energy loss rate and the average current abnormal fluctuation rate corresponding to the first fault node cluster as the battery performance indicators of the first fault node cluster, wherein the first fault node cluster is any one of the multiple fault node clusters.

[0097] A binary matrix determination unit is used to traverse the multiple fault node clusters, compare the battery performance indicators of the multiple fault node clusters with the battery performance requirements, and determine a binary matrix, wherein the rows of the binary matrix correspond one-to-one to the multiple fault node clusters.

[0098] Furthermore, the fault fixed-point warning module 60 further includes:

[0099] A maintenance time window determination unit is used to determine a maintenance time window through the binarization matrix, wherein the maintenance time window is determined based on the fault deterioration progress and the battery service life.

[0100] A fault fixed-point warning management sequence acquisition unit is configured to perform priority sorting of fault fixed-point warning management based on the maintenance time window to obtain a fault fixed-point warning management sequence, wherein the fault fixed-point warning management sequence includes a maintenance timestamp.

[0101] A battery fault fixed-point warning unit is used to perform a fault fixed-point warning on the target battery according to the fault fixed-point warning management sequence.

[0102] Furthermore, the binarization matrix determining unit further includes:

[0103] A comparison result judgment unit, wherein the comparison result judgment unit is used to no longer obtain the next standard action of the safe discharge mode when the comparison result is greater than a preset performance threshold corresponding to the battery performance requirement, so as to continue to compare the battery performance index of the fault fixed point coordinates corresponding to the fault fixed point warning of the target battery with the battery performance requirement during fault fixed point warning management, until the comparison result is less than the preset performance threshold corresponding to the battery performance requirement.

[0104] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0105] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0106] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A method for detecting battery performance during battery discharge, characterized in that: The method comprises: Under a standard test environment, a discharge test is performed on the target battery, and battery parameter information during the discharge process is recorded, wherein the battery parameter information includes voltage, current, and temperature; According to the battery parameter information, a discharge fault identification module based on the target battery is constructed, wherein the discharge fault identification module has a built-in safe discharge mode; Acquire a battery element structure of the target battery, wherein the battery element structure includes a battery charging circuit connected to a charging power source and a battery discharging circuit connected to a load unit; Based on the discharge fault identification module, a first fault node distribution diagram is set in a battery charging circuit connected to a charging power source in the battery element structure; Based on the discharge fault identification module, a second fault node distribution diagram is set in the battery discharge circuit connected to the load unit in the battery element structure; In the battery element structure, the first fault node distribution map and the second fault node distribution map are transmitted back to form a fault node full connection structure, and a fault fixed-point warning is performed in combination with the battery parameter information; Based on the discharge fault identification module, a second fault node distribution diagram is set in a battery discharge circuit connected to a load unit in the battery element structure, and the method includes: In the battery discharge circuit connected to the load unit in the battery element structure, the battery performance of the target battery is monitored to obtain discharge test data; According to the discharge test data, a fuzzy decision on the load unit's energy conversion loss and data feedback are performed, and the discharge fault identification module is combined to perform fault identification and obtain a set of fault node markers; Based on the fault node mark set, a battery discharge circuit connected to the load unit in the battery element structure is marked to obtain the second fault node distribution map; According to the discharge test data, a fuzzy decision on the load unit's energy loss is made and data is transmitted back, and the discharge fault identification module is combined to identify the fault and obtain a set of fault node marks. The method includes: According to the discharge test data, a fuzzy decision on the load unit's energy conversion loss and data feedback are performed, and the discharge fault identification module is used to perform fault identification to obtain a preliminary fault node set; In the preliminary fault node set, calculating the energy loss rate and the abnormal current fluctuation rate of each preliminary fault node; According to the energy loss rate and abnormal current fluctuation rate of each preliminary fault node, and with characteristic threshold and occurrence probability as constraints, the fault distribution corresponding to the load unit is binarized by F index to obtain the fault node mark set, wherein each fault node mark represents a fault node cluster.

2. The battery performance detection method for a battery during discharge as claimed in claim 1, characterized in that: Performing F index binarization processing on the fault distribution corresponding to the load unit, the method comprising: By means of the safe discharge mode, the minimized linear error and the allowable error range are set; According to the minimized linear error and the allowable error range, mapping the fault distribution corresponding to the load unit to obtain a relative error value; Based on the relative error value, a binary threshold is selected according to a logarithmic relationship between the fault severity and the fault risk probability to determine a plurality of fault node markers in the fault node marker set.

3. The battery performance detection method for a battery during discharge as claimed in claim 2, characterized in that: According to the energy loss rate and abnormal current fluctuation rate of each preliminary fault node, the fault distribution corresponding to the load unit is subjected to F index binarization processing with characteristic threshold and occurrence probability as constraint conditions to obtain the fault node tag set, and the method includes: Identify the fault distribution corresponding to the load unit through multiple fault node tags in the fault node tag set, and obtain multiple fault node clusters by associating mapping feature thresholds and occurrence probabilities according to the energy loss rate and abnormal current fluctuation rate of each preliminary fault node; Calculate an average energy loss rate and an average current abnormal fluctuation rate corresponding to a first fault node cluster as a battery performance index of the first fault node cluster, wherein the first fault node cluster is any one of the multiple fault node clusters; The multiple fault node clusters are traversed, the battery performance indicators of the multiple fault node clusters are compared with the battery performance requirements, and a binary matrix is ​​determined, wherein rows of the binary matrix correspond one-to-one to the multiple fault node clusters.

4. The battery performance detection method for a battery during discharge as claimed in claim 3, characterized in that: In combination with the battery parameter information, a fault point warning is performed, and the method includes: Determine a maintenance time window through the binary matrix, wherein the maintenance time window is determined based on the fault deterioration progress and the battery service life; Based on the maintenance time window, priority sorting of fault fixed-point warning management is performed to obtain a fault fixed-point warning management sequence, wherein the fault fixed-point warning management sequence includes a maintenance timestamp; According to the fault fixed-point warning management sequence, a fault fixed-point warning is performed on the target battery.

5. The battery performance detection method for a battery during discharge as claimed in claim 4, characterized in that: Traversing the multiple fault node clusters, comparing the battery performance indicators of the multiple fault node clusters with the battery performance requirements, and determining a binary matrix, the method comprising: When the comparison result is greater than the preset performance threshold corresponding to the battery performance requirement, the next standard action of the safe discharge mode is no longer obtained, so that the battery performance index of the fault fixed point coordinates corresponding to the fault fixed point warning of the target battery continues to be compared with the battery performance requirement during the fault fixed point warning management, until the comparison result is less than the preset performance threshold corresponding to the battery performance requirement.

6. A battery performance detection system for a battery during discharge, characterized in that: The system comprises: A discharge test module, which is used to perform a discharge test on a target battery under a standard test environment and record battery parameter information during the discharge process, wherein the battery parameter information includes voltage, current, and temperature; A discharge fault identification building module, wherein the discharge fault identification building module is used to build a discharge fault identification module based on the target battery according to the battery parameter information, and the discharge fault identification module has a built-in safe discharge mode; A battery element structure acquisition module, the battery element structure acquisition module is used to acquire the battery element structure of the target battery, the battery element structure includes a battery charging circuit connected to a charging power source, and a battery discharging circuit connected to a load unit; a first fault node distribution map setting module, which sets a first fault node distribution map for a battery charging circuit connected to a charging power source in the battery element structure based on the discharge fault identification module; A second fault node distribution diagram setting module, which sets a second fault node distribution diagram for a battery discharge circuit connected to a load unit in the battery element structure based on the discharge fault identification module; A fault fixed-point warning module, wherein the fault fixed-point warning module is used to transmit the first fault node distribution map and the second fault node distribution map back in the battery element structure to form a fault node full connection structure, and perform fault fixed-point warning in combination with the battery parameter information; The second fault node distribution diagram setting module also includes: A discharge test data acquisition unit, the discharge test data acquisition unit is used to monitor the battery performance of the target battery in the battery discharge circuit connected to the load unit in the battery element structure to obtain discharge test data; A fault node mark set acquisition unit, the fault node mark set acquisition unit is used to perform fuzzy decision and data feedback of the load unit's energy loss according to the discharge test data, perform fault identification in combination with the discharge fault identification module, and acquire a fault node mark set; a battery discharge circuit identification unit, wherein the battery discharge circuit identification unit identifies the battery discharge circuit connected to the load unit in the battery element structure based on the fault node mark set to obtain the second fault node distribution map; The fault node mark set acquisition unit further includes: A preliminary fault node set acquisition unit, the preliminary fault node set acquisition unit is used to perform fuzzy decision and data feedback of the load unit's energy conversion loss according to the discharge test data, use the discharge fault identification module to perform fault identification, and acquire a preliminary fault node set; A preliminary fault node calculation unit, the preliminary fault node calculation unit is used to calculate the energy loss rate and the current abnormal fluctuation rate of each preliminary fault node in the preliminary fault node set; A binarization processing unit is used to perform F-index binarization processing on the fault distribution corresponding to the load unit according to the energy loss rate and abnormal current fluctuation rate of each preliminary fault node, with characteristic threshold and occurrence probability as constraints, to obtain the fault node mark set, wherein each fault node mark represents a fault node cluster.

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