Fault processing system adaptive to lithium iron carbonate battery

Abstract

The invention belongs to the technical field of battery supervision, and particularly relates to a fault processing system adaptive to a lithium iron carbonate battery, which comprises a fault monitoring processing end and a user monitoring end, the fault monitoring processing end comprises a high-precision monitoring transmission module, an intelligent fault diagnosis engine module, a self-adaptive fault processing strategy library, a fault response module and a fault processing tracking module; according to the invention, fault identification and classification of the lithium iron phosphate battery are realized through the high-precision monitoring transmission module and the intelligent fault diagnosis engine module, and the fault response module determines the most appropriate processing scheme according to the fault diagnosis result and adjusts the operation of the lithium iron phosphate battery, so that the operation safety of the lithium iron phosphate battery is ensured; and the use risk of the lithium iron phosphate battery in the detection period is analyzed through the battery use risk judgment module, and the lithium iron phosphate battery is checked and maintained when a use high-risk signal is generated, so that the use risk and the operation management difficulty of the lithium iron phosphate battery are remarkably reduced.

Description

Technical Field

[0001] The invention relates to the technical field of battery supervision, and in particular to a fault handling system adapted for lithium iron carbonate batteries. Background Art

[0002] Lithium iron phosphate battery refers to a lithium-ion battery with lithium iron phosphate as the positive electrode material. It is mainly composed of lithium iron phosphate positive electrode, graphite negative electrode, electrolyte and separator. Its positive electrode material lithium iron phosphate has a special crystal structure, which enables lithium ions to be stably embedded and de-embedded during the charging and discharging process, thereby realizing the charging and discharging function of the battery. Due to its high energy density, long cycle life and good safety performance, it is widely used in electric vehicles, energy storage systems and other fields; However, in actual operation, lithium iron carbonate batteries often face multiple failure risks such as unbalanced single-cell batteries, overheating, overcharging, and over-discharging. These failures not only affect battery performance, but also easily lead to safety accidents. At present, it is difficult to monitor and identify lithium iron carbonate battery failures in real time and respond to them in a timely manner. It is also impossible to reasonably assess the risks of using lithium iron phosphate batteries and issue a rapid warning, which is not conducive to ensuring the safe operation of lithium iron phosphate batteries and reducing the difficulty of their operation management. In view of the above technical defects, a solution is now proposed. Summary of the invention

[0003] The purpose of the present invention is to provide a fault handling system suitable for lithium iron carbonate batteries, which solves the problems that the prior art is difficult to monitor and identify lithium iron carbonate battery faults in real time and respond to them in a timely manner, and is unable to reasonably assess the use risks of lithium iron phosphate batteries and quickly issue warnings, making it difficult to ensure the safe operation of lithium iron phosphate batteries, and has great difficulty in operation management and low intelligence.

[0004] To achieve the above object, the present invention provides the following technical solutions: A fault handling system adapted for lithium iron phosphate batteries includes a fault monitoring and processing end and a user monitoring end, and the fault monitoring and processing end includes a high-precision monitoring and transmission module, an intelligent fault diagnosis engine module, an adaptive fault handling strategy library, a fault response module, and a fault handling tracking module; the high-precision monitoring and transmission module uses high-precision sensors and signal processing technology to monitor the operating status of the lithium iron phosphate battery in real time, obtains various operating information of the lithium iron phosphate battery and sends it to the intelligent fault diagnosis engine module; The intelligent fault diagnosis engine module combines machine learning algorithms with the lithium iron carbonate battery fault mode library to build an intelligent fault diagnosis model. The operation information of the lithium iron phosphate battery is input into the intelligent fault diagnosis model. The intelligent fault diagnosis model automatically identifies abnormal data and determines the fault type. When a fault is determined to exist, the fault diagnosis result is sent to the fault response module and the user monitoring terminal. The adaptive fault handling strategy library contains specific response measures for different fault types. The fault response module selects the most appropriate treatment plan from the adaptive fault handling strategy library according to the fault diagnosis results, and adjusts the operation of the lithium iron phosphate battery based on the selected treatment plan. The fault handling tracking module tracks the operation adjustment process. If the corresponding fault of the lithium iron phosphate battery is not eliminated within the corresponding specified time, a response alarm signal is generated and sent to the user monitoring end. The user monitoring end issues a corresponding warning when receiving the response alarm signal.

[0005] Furthermore, the fault monitoring and processing end also includes a battery usage risk judgment module, which is used to set a detection period, analyze the usage risk of the lithium iron phosphate battery during the detection period, and generate a high-risk usage signal or a low-risk usage signal for the corresponding lithium iron phosphate battery through analysis, and send the high-risk usage signal or the low-risk usage signal to the user monitoring end. When the user monitoring end receives the high-risk usage signal, it will issue a corresponding warning.

[0006] Furthermore, the specific analysis process of the battery use risk judgment module is as follows: The total operating time of the corresponding lithium iron phosphate battery during the detection period is obtained and marked as the operating condition value, and the number of responses in which the fault response module fails to eliminate the corresponding fault of the lithium iron phosphate battery within the corresponding specified time during the detection period is collected and marked as the response delay value, and the response delay value is calculated by ratioing the operating condition value to obtain the response risk value; the response risk value is numerically compared with the preset response risk threshold, and if the response risk value exceeds the preset response risk threshold, a high-risk signal is generated.

[0007] Furthermore, if the response risk value does not exceed the preset response risk threshold, the total number of failures of the corresponding lithium iron phosphate battery during the detection period is obtained and the total number of failures is calculated by comparing the total number of failures with the operating condition value to obtain a failure frequency value, and the use risk judgment value is obtained by numerically calculating the response risk value and the failure frequency value, and the use risk judgment value is numerically compared with the preset use risk judgment threshold. If the use risk judgment value exceeds the preset use risk judgment threshold, a high use risk signal is generated; If the usage risk judgment value does not exceed the preset usage risk judgment threshold, analysis is performed to determine whether an abnormal fault occurs. If an abnormal fault occurs, a high-risk usage signal is generated; if no abnormal fault occurs, a low-risk usage signal is generated.

[0008] Further, the specific analysis process of determining whether there is an abnormal fault through analysis is as follows: All faults occurring in the corresponding lithium iron phosphate battery during the detection period are obtained, the number of occurrences of the corresponding type of fault during the detection period is marked as a fault occurrence value, and the time difference between two adjacent fault occurrence moments of the same type of fault is calculated to obtain a same type interval value, and the same type interval value is numerically compared with the corresponding preset same type interval threshold. If the same type interval value does not exceed the preset same type interval threshold, the corresponding same type interval value is marked as a same type abnormal isolation value; the number of same type abnormal isolation values ​​corresponding to the corresponding type of fault during the detection period is obtained and marked as a same type of dangerous isolation value, and a fault judgment value is obtained by numerically calculating the fault occurrence value and the same type of dangerous isolation value, and the fault judgment value is numerically compared with the corresponding preset fault judgment threshold. If the fault judgment value exceeds the corresponding preset fault judgment threshold, the corresponding type of fault is marked as an abnormal fault.

[0009] Further, the fault monitoring processing end is communicatively connected to the battery manufacturer management end, and the battery manufacturer management end includes a battery scrap information analysis module and a battery production optimization analysis module. The battery scrap information analysis module obtains all scrapped lithium iron phosphate batteries of the same type shipped from the corresponding battery manufacturer, marks the corresponding scrapped lithium iron phosphate batteries of the same type as scrapped battery i, and i is a natural number greater than 1, performs scrap tracing analysis on the scrapped battery i to obtain a scrap tracing coefficient, and sends the scrap tracing coefficient of the scrapped battery i to the battery production optimization analysis module; The battery production optimization analysis module determines whether it is necessary to optimize the production of the corresponding type of lithium iron phosphate battery through battery production optimization analysis, and generates an optimization alarm signal or an optimization non-essential signal accordingly. When the optimization alarm signal is generated, it notifies the battery manufacturer to make reasonable production optimization measures for the corresponding type of lithium iron phosphate battery.

[0010] Furthermore, the specific analysis process of scrap traceability analysis is as follows: The production date of the scrapped battery i is obtained, the interval between the production date and the current date is marked as the production time, the total operation time of the scrapped battery i in the historical stage is marked as the usage time, and the total number of charging times of the scrapped battery i in the historical stage is marked as the charging frequency; and the life impact factor is obtained through life impact analysis, and the production time, usage time, charging frequency and life impact factor are numerically calculated to obtain the scrap traceability coefficient of the scrapped battery i.

[0011] Furthermore, the specific analysis process of life impact analysis is as follows: When the real-time standby power of the scrapped battery i does not exceed the preset real-time standby power threshold, it is judged to be in a low-power state, and the total duration of the scrapped battery i in the low-power state in the historical stage is marked as the low-power duration, and the number of occurrences of the single continuous duration of the scrapped battery i in the low-power state in the historical stage exceeding the corresponding duration threshold is marked as the low-power frequency; and collecting the temperature of the scrapped battery i and the humidity of the environment in which it is located, performing difference calculation between the temperature and the median of the set safe temperature range and taking the absolute value to obtain a temperature detection value, and performing difference calculation between the humidity and the median of the set safe humidity range and taking the absolute value to obtain a humidity detection value, and marking the vibration amplitude of the scrapped battery i as the vibration detection value; Assign preset weight coefficients to the temperature detection value, humidity detection value, and vibration detection value, respectively, multiply the temperature detection value, humidity detection value, and vibration detection value by the corresponding preset weight coefficients, and sum the three sets of product results to obtain a damage analysis value, compare the damage analysis value with the preset damage analysis threshold, and if the damage analysis value exceeds the preset damage analysis threshold, it is determined that the scrapped battery i is in a high damage state; obtain the total duration of the scrapped battery i in the high damage state in the historical stage and mark it as the high damage duration, and mark the number of occurrences of the single continuous duration of the scrapped battery i in the high damage state in the historical stage exceeding the corresponding preset duration threshold as the high damage frequency; The battery damage value is obtained by numerically calculating the power-out duration, risk-out duration, high-damage duration and high-damage frequency, and several groups of preset battery damage value ranges are set in advance, and each group of preset battery damage value ranges corresponds to a group of preset factors; the battery damage value is compared with all preset battery damage value ranges one by one, the preset battery damage value range containing the corresponding battery damage value is marked as a damage range, and the preset factor corresponding to the damage range is marked as the life influencing factor of the scrapped battery i.

[0012] Furthermore, the specific analysis process of battery production optimization analysis is as follows: Obtain the scrapping traceability coefficient of the scrapped battery i, compare the scrapping traceability coefficient with the preset scrapping traceability coefficient threshold, and if the scrapping traceability coefficient does not exceed the preset scrapping traceability coefficient threshold, mark the scrapped battery i as an unqualified battery; The percentage of unqualified batteries is obtained and marked as the usage anomaly value, and the usage anomaly value is compared with the preset usage anomaly threshold. If the usage anomaly value exceeds the preset usage anomaly threshold, an optimization alarm signal is generated; if the usage anomaly value does not exceed the preset usage anomaly threshold, an optimization unnecessary signal is generated.

[0013] Compared with the prior art, the present invention has the following beneficial effects: 1. In the present invention, the fault identification and classification of the lithium iron phosphate battery is realized through the high-precision monitoring transmission module and the intelligent fault diagnosis engine module. The fault response module adjusts the operation of the lithium iron phosphate battery according to the fault diagnosis result to ensure the safe operation of the lithium iron phosphate battery. The use risk of the lithium iron phosphate battery during the detection period is analyzed through the battery use risk judgment module. When a high-risk use signal is generated, the lithium iron phosphate battery is inspected and repaired, which significantly reduces the use risk and operation management difficulty of the lithium iron phosphate battery. 2. In the present invention, the corresponding scrapped lithium iron phosphate batteries are subjected to scrapping tracing analysis through the battery scrapping information analysis module to obtain the scrapping tracing coefficient, which is convenient for battery manufacturers to grasp the service life and performance of all scrapped batteries of the corresponding type in detail, and to determine whether the production optimization of the corresponding type of lithium iron phosphate batteries is needed through battery production optimization analysis. When the optimization alarm signal is generated, the battery manufacturer is notified to make reasonable production optimization measures for the corresponding type of lithium iron phosphate batteries, which is conducive to improving the sales volume and reputation of the corresponding type of lithium iron phosphate batteries and ensuring the product competitiveness of the battery manufacturer. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings; Figure 1 This is a system block diagram of Embodiment 1 of the present invention; Figure 2 This is a system block diagram of Embodiment 2 and Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0015] 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.

[0016] Embodiment 1: Figure 1 As shown, the fault handling system adapted to lithium iron carbonate batteries proposed in the present invention includes a fault monitoring and processing end and a user monitoring end, and the fault monitoring and processing end includes a high-precision monitoring transmission module, an intelligent fault diagnosis engine module, an adaptive fault handling strategy library, a fault response module and a fault handling tracking module; The high-precision monitoring and transmission module uses high-precision sensors and signal processing technology to monitor the operating status of the lithium iron phosphate battery in real time, obtain various operating information of the lithium iron phosphate battery (real-time monitoring of key parameters such as voltage, current, temperature, etc. of the battery pack, analysis of fluctuations in parameters such as voltage, current, temperature, and consistency evaluation and analysis of the battery pack) and send it to the intelligent fault diagnosis engine module.

[0017] The intelligent fault diagnosis engine module combines machine learning algorithms with the lithium iron carbonate battery fault mode library to build an intelligent fault diagnosis model. The operating information of the lithium iron phosphate battery is input into the intelligent fault diagnosis model. The intelligent fault diagnosis model automatically identifies abnormal data and determines the fault type (such as single cell battery imbalance, internal short circuit, thermal runaway, etc.), and sends the fault diagnosis results to the fault response module and the user monitoring end when a fault is determined to exist.

[0018] The adaptive fault handling strategy library contains specific response measures for different fault types. The fault response module selects the most appropriate treatment plan from the adaptive fault handling strategy library according to the fault diagnosis results, and adjusts the operation of the lithium iron phosphate battery based on the selected treatment plan, such as dynamically adjusting the charging and discharging strategy, starting the battery balancing program, triggering the cooling mechanism, etc., to ensure that the fault is controlled in a timely and effective manner, realize automatic and reasonable response to the lithium iron phosphate battery operation failure, and ensure the operation safety of the lithium iron phosphate battery.

[0019] The fault handling tracking module tracks the operation adjustment process. If the corresponding fault of the lithium iron phosphate battery is not eliminated within the corresponding specified time, a response alarm signal is generated and sent to the user monitoring end. When the user monitoring end receives the response alarm signal, it issues a corresponding warning. When the user receives the corresponding warning, he takes corresponding manual control measures, further reducing the safety hazards of the lithium iron phosphate battery and alleviating the difficulty of operation and management of the lithium iron phosphate battery.

[0020] The fault monitoring processing end also includes a battery use risk judgment module, which is used to set a detection period, preferably, the detection period is twenty days; analyze the use risk of the lithium iron phosphate battery during the detection period, and generate a high risk signal or a low risk signal for the corresponding lithium iron phosphate battery through analysis; And send high-risk use signals or low-risk use signals to the user monitoring end. When the user monitoring end receives the high-risk use signal, it will issue a corresponding warning. After receiving the corresponding warning, the user will promptly inspect and repair the lithium iron phosphate battery, which significantly reduces the use risk and operation management difficulty of the lithium iron phosphate battery. The specific analysis process of the battery use risk judgment module is as follows: The total operating time of the corresponding lithium iron phosphate battery during the detection period is obtained and marked as the operating condition value, and the number of responses in which the fault response module fails to eliminate the corresponding fault of the lithium iron phosphate battery within the corresponding prescribed time during the detection period is collected and marked as the response delay value, and the response delay value is calculated by ratio with the operating condition value to obtain the response risk value; Compare the response risk value with the preset response risk threshold. If the response risk value exceeds the preset response risk threshold, it indicates that the automatic response efficiency for lithium iron phosphate battery failure during the detection period is poor, which is not conducive to ensuring the safe operation of the lithium iron phosphate battery, and a high-risk signal is generated; If the response risk value does not exceed the preset response risk threshold, the total number of failures of the corresponding lithium iron phosphate battery during the detection period is obtained and the ratio is calculated with the operating condition value to obtain the fault frequency value, and the response risk value QS and the fault frequency value FM are numerically calculated by the formula XW=up×QS+se×FM to obtain the use risk judgment value XW; wherein up and se are preset weight coefficients, up>se>0; and the larger the value of the use risk judgment value XW, the higher the overall operating risk of the lithium iron phosphate battery during the detection period; The usage risk judgment value XW is numerically compared with the preset usage risk judgment threshold. If the usage risk judgment value XW exceeds the preset usage risk judgment threshold, it indicates that the operation hidden dangers of the lithium iron phosphate battery during the detection period are generally high, and a high-risk usage signal is generated; If the usage risk judgment value XW does not exceed the preset usage risk judgment threshold, all faults occurring in the corresponding lithium iron phosphate battery during the detection period are obtained, the number of occurrences of the corresponding type of faults during the detection period is marked as the fault occurrence value, and the time difference between two adjacent fault occurrences of the same type of fault is calculated to obtain a similar interval value, and the similar interval value is numerically compared with the corresponding preset similar interval threshold. If the similar interval value does not exceed the preset similar interval threshold, the corresponding similar interval value is marked as a similar different interval value; The number of the same type of different isolation values ​​corresponding to the corresponding type of faults in the detection period is obtained and marked as the same type of isolation value, and the fault occurrence value QK and the same type of isolation value LW are numerically calculated by the formula TX=rq×QK+em×LW to obtain the fault judgment value TX; wherein rq and em are preset weight coefficients with values ​​greater than zero, and the larger the value of the fault judgment value TX is, the more abnormal the occurrence of the corresponding type of fault in the lithium iron phosphate battery in the detection period is; The fault judgment value TX is numerically compared with the corresponding preset fault judgment threshold. If the fault judgment value TX exceeds the corresponding preset fault judgment threshold, it indicates that the occurrence of the corresponding type of fault in the lithium iron phosphate battery during the detection period is relatively abnormal, and the corresponding type of fault is marked as an abnormal fault. If an abnormal fault exists, it indicates that the operation risk of the lithium iron phosphate battery during the detection period is relatively large, and a high-risk signal is generated for use; if there is no abnormal fault, it indicates that the operation risk of the lithium iron phosphate battery during the detection period is relatively small, and a low-risk signal is generated for use.

[0021] Embodiment 2: Figure 2 As shown, the difference between this embodiment and the first embodiment is that the fault monitoring processing end is communicatively connected to the battery manufacturer management end, and the battery manufacturer management end includes a battery scrapping information analysis module, and the battery scrapping information analysis module obtains all the same type of lithium iron phosphate batteries that have been scrapped after use from the corresponding battery manufacturer, and marks the corresponding scrapped lithium iron phosphate batteries of the same type as scrapped batteries i, and i is a natural number greater than 1; The scrapped battery i is subjected to scrapping traceability analysis to obtain the scrapping traceability coefficient HWi, which is convenient for battery manufacturers to grasp the service life and performance of all scrapped batteries of the corresponding type in detail, and is conducive to battery manufacturers to determine whether to make reasonable optimization and improvement measures for the production of the corresponding type of lithium iron phosphate batteries according to needs, to ensure the quality of the lithium iron phosphate batteries produced and to improve their sales and reputation; the specific analysis process of scrapping traceability analysis is as follows: Obtain the production date of the scrapped battery i, mark the interval between the production date and the current date as the production time, mark the total operation time of the scrapped battery i in the historical stage as the usage time, and mark the total number of times the scrapped battery i is charged in the historical stage as the charging frequency; And through life impact analysis to obtain the life impact factor ti, specifically: when the real-time standby power of the scrapped battery i does not exceed the preset real-time standby power threshold, it is judged to be in a low-power state, the total time of the scrapped battery i in the low-power state in the historical stage is marked as the low-power duration, and the number of occurrences of the single continuous duration of the scrapped battery i in the low-power state in the historical stage exceeding the corresponding duration threshold is marked as the low-power frequency; and collecting the temperature of the scrapped battery i and the humidity of the environment in which it is located, performing difference calculation between the temperature and the median of the set safe temperature range and taking the absolute value to obtain a temperature detection value, and performing difference calculation between the humidity and the median of the set safe humidity range and taking the absolute value to obtain a humidity detection value, and marking the vibration amplitude of the scrapped battery i as the vibration detection value; Assigning preset weight coefficients greater than zero to the temperature detection value, the humidity detection value, and the vibration detection value, respectively, multiplying the temperature detection value, the humidity detection value, and the vibration detection value by the corresponding preset weight coefficients, and summing the three sets of product results to obtain a damage analysis value; it should be noted that the larger the value of the damage analysis value, the greater the damage suffered by the scrapped battery i at the corresponding moment; The damage analysis value is numerically compared with the preset damage analysis threshold. If the damage analysis value exceeds the preset damage analysis threshold, it is determined that the scrapped battery i is in a high damage state; the total duration of the scrapped battery i in the high damage state in the historical stage is obtained and marked as the high damage duration, and the number of occurrences of the single duration of the scrapped battery i in the high damage state in the historical stage exceeding the corresponding preset duration threshold is marked as the high damage frequency; The battery damage value LXi is obtained by numerically calculating the power-loss duration SYi, the risk-loss duration WFi, the high-loss duration NPi and the high-loss frequency QXi through the formula LXi=(rg×SYi+hy×WFi+tu×NPi+sq×QXi) / 2; wherein rg, hy, tu and sq are preset proportional coefficients with values ​​greater than zero, and the larger the value of the battery damage value LXi is, the greater the damage suffered by the scrapped battery i in the historical stage is, and the more unfavorable it is to ensure its service life and performance; A plurality of preset battery damage value ranges are set in advance, and each preset battery damage value range corresponds to a set of preset factors with values ​​greater than zero; it should be noted that the larger the value of the preset battery damage value range is, the larger the value of the preset factor matching it is; the battery damage value is compared with all preset battery damage value ranges one by one, the preset battery damage value range containing the corresponding battery damage value is marked as a damage range, and the preset factor corresponding to the damage range is marked as the life impact factor ti of the scrapped battery i; The scrapping traceability coefficient HWi of the scrapped battery i is obtained by numerically calculating the factory time FXi, usage time GYi, charging frequency WPi and life influencing factor ti through the formula HWi=(eq×FXi+uy×GYi+kp×WPi) / ti; wherein eq, uy, kp are preset proportional coefficients with values ​​greater than zero, and the smaller the value of the scrapping traceability coefficient HWi, the worse the service life performance of the scrapped battery i, and the greater the probability that the scrapped battery i has quality problems when leaving the factory.

[0022] Embodiment 3: Figure 2As shown, the difference between this embodiment and the first and second embodiments is that the battery manufacturer management end further includes a battery production optimization analysis module, and the battery scrap information analysis module sends the scrap traceability coefficient of the scrapped battery i to the battery production optimization analysis module. The battery production optimization analysis module determines whether it is necessary to optimize the production of the corresponding type of lithium iron phosphate battery through battery production optimization analysis, and generates an optimization alarm signal or an optimization non-essential signal accordingly; When the optimization alarm signal is generated, the battery manufacturer is notified to make reasonable production optimization measures for the corresponding type of lithium iron phosphate batteries to ensure the performance and service life of the corresponding type of lithium iron phosphate batteries produced subsequently, which is conducive to improving the sales volume and reputation of the corresponding type of lithium iron phosphate batteries; the specific analysis process of battery production optimization analysis is as follows: Obtain the scrapping traceability coefficient HWi of the scrapped battery i, compare the scrapping traceability coefficient HWi with the preset scrapping traceability coefficient threshold, if the scrapping traceability coefficient HWi does not exceed the preset scrapping traceability coefficient threshold, it indicates that the service life performance of the scrapped battery i is poor, and the scrapped battery i is likely to have quality problems when leaving the factory, then the scrapped battery i is marked as an unqualified battery; The percentage of unqualified batteries is obtained and marked as the usage anomaly value, and the usage anomaly value is compared with the preset usage anomaly threshold. If the usage anomaly value exceeds the preset usage anomaly threshold, it indicates that the quality of the corresponding type of lithium iron phosphate batteries produced by the battery manufacturer is poor, and targeted production improvement measures are needed for the corresponding type of lithium iron phosphate batteries, then an optimization alarm signal is generated; if the usage anomaly value does not exceed the preset usage anomaly threshold, it indicates that the quality of the corresponding type of lithium iron phosphate batteries produced by the battery manufacturer is good, then an optimization non-essential signal is generated.

[0023] The working principle of the present invention is as follows: when in use, the operating status of the lithium iron phosphate battery is monitored in real time through a high-precision monitoring and transmission module, and various operating information of the lithium iron phosphate battery is sent to the intelligent fault diagnosis engine module. The intelligent fault diagnosis engine module inputs the operating information of the lithium iron phosphate battery into the intelligent fault diagnosis model. The intelligent fault diagnosis model automatically identifies abnormal data and determines the fault type. The fault response module selects the most appropriate processing scheme from the adaptive fault processing strategy library according to the fault diagnosis result, and adjusts the operation of the lithium iron phosphate battery based on the selected processing scheme. If the corresponding fault of the lithium iron phosphate battery is not eliminated within the corresponding prescribed time, a response alarm signal is generated and sent to the user monitoring end to ensure the safe operation of the lithium iron phosphate battery. The battery use risk judgment module analyzes the use risk of the lithium iron phosphate battery during the detection period, and inspects and repairs the lithium iron phosphate battery when a high-risk use signal is generated, which significantly reduces the use risk and operation management difficulty of the lithium iron phosphate battery.

[0024] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions. The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the contents of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can understand and use the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A fault handling system adapted for lithium iron carbonate batteries, characterized in that: It includes a fault monitoring and processing end and a user monitoring end, and the fault monitoring and processing end includes a high-precision monitoring transmission module, an intelligent fault diagnosis engine module, an adaptive fault processing strategy library, a fault response module and a fault processing tracking module; The high-precision monitoring and transmission module monitors the operating status of the lithium iron phosphate battery in real time, obtains various operating information of the lithium iron phosphate battery and sends it to the intelligent fault diagnosis engine module; The intelligent fault diagnosis engine module combines machine learning algorithms with the lithium iron carbonate battery fault mode library to build an intelligent fault diagnosis model. The operation information of the lithium iron phosphate battery is input into the intelligent fault diagnosis model. The intelligent fault diagnosis model automatically identifies abnormal data and determines the fault type. When a fault is determined to exist, the fault diagnosis result is sent to the fault response module and the user monitoring terminal. The adaptive fault handling strategy library contains specific response measures for different fault types. The fault response module selects the most appropriate treatment plan from the adaptive fault handling strategy library according to the fault diagnosis results, and adjusts the operation of the lithium iron phosphate battery based on the selected treatment plan. The fault handling tracking module tracks the operation adjustment process. If the corresponding fault of the lithium iron phosphate battery is not eliminated within the corresponding specified time, a response alarm signal is generated and sent to the user monitoring end.

2. The fault handling system adapted for lithium iron carbonate batteries according to claim 1, characterized in that: The fault monitoring and processing end also includes a battery usage risk judgment module, which is used to set a detection period, analyze the usage risk of the lithium iron phosphate battery during the detection period, and generate a high-risk usage signal or a low-risk usage signal for the corresponding lithium iron phosphate battery through analysis, and send the high-risk usage signal or the low-risk usage signal to the user monitoring end.

3. The fault handling system adapted for lithium iron carbonate batteries according to claim 2, characterized in that: The specific analysis process of the battery use risk judgment module is as follows: The total operating time of the corresponding lithium iron phosphate battery during the detection period is obtained and marked as the operating condition value, and the number of responses in which the fault response module fails to eliminate the corresponding fault of the lithium iron phosphate battery within the corresponding prescribed time during the detection period is collected and marked as the response delay value, and the response delay value is calculated by ratio with the operating condition value to obtain the response risk value; If the response risk value exceeds the preset response risk threshold, a high risk signal is generated.

4. The fault handling system adapted for lithium iron carbonate batteries according to claim 3, characterized in that: If the response risk value does not exceed the preset response risk threshold, the total number of failures of the corresponding lithium iron phosphate battery during the detection period is obtained and the total number of failures is calculated by comparing it with the operating condition value to obtain the failure frequency value. The use risk judgment value is obtained by numerically calculating the response risk value and the failure frequency value. If the use risk judgment value exceeds the preset use risk judgment threshold, a high risk signal is generated. If the usage risk judgment value does not exceed the preset usage risk judgment threshold, an analysis is performed to determine whether there is an abnormal fault. If there is an abnormal fault, a high-risk usage signal is generated; If there is no abnormal fault, a low risk signal is generated.

5. The fault handling system adapted for lithium iron carbonate batteries according to claim 1, characterized in that: The specific analysis process to determine whether there is an abnormal fault is as follows: All faults occurring in the corresponding lithium iron phosphate battery during the detection period are obtained, the number of occurrences of the corresponding type of fault during the detection period is marked as a fault occurrence value, and the time difference between two adjacent fault occurrence moments of the same type of fault is calculated to obtain a similar interval value, and the similar interval value is numerically compared with the corresponding preset similar interval threshold. If the similar interval value does not exceed the preset similar interval threshold, the corresponding similar interval value is marked as a similar different isolation value; the number of similar different isolation values ​​corresponding to the corresponding type of fault during the detection period is obtained and marked as a similar dangerous isolation value, and the fault occurrence value of the corresponding type of fault during the detection period is numerically calculated with the similar dangerous isolation value to obtain a fault judgment value. If the fault judgment value exceeds the corresponding preset fault judgment threshold, the corresponding type of fault is marked as an abnormal fault.

6. The fault handling system adapted for lithium iron carbonate batteries according to claim 1, characterized in that: The fault monitoring processing end is communicated with the battery manufacturer management end, which includes a battery scrap information analysis module and a battery production optimization analysis module. The battery scrap information analysis module obtains all scrapped lithium iron phosphate batteries of the same type shipped from the corresponding battery manufacturer, marks the corresponding scrapped lithium iron phosphate batteries of the same type as scrapped battery i, where i is a natural number greater than 1, performs scrap traceability analysis on the scrapped battery i to obtain a scrap traceability coefficient, and sends the scrap traceability coefficient of the scrapped battery i to the battery production optimization analysis module; The battery production optimization analysis module determines whether it is necessary to optimize the production of the corresponding type of lithium iron phosphate battery through battery production optimization analysis, and notifies the battery manufacturer to make reasonable production optimization measures for the corresponding type of lithium iron phosphate battery when generating an optimization alarm signal.

7. The fault handling system adapted for lithium iron carbonate batteries according to claim 6, characterized in that: The specific analysis process of scrap traceability analysis is as follows: The life impact factor is obtained through life impact analysis, and the factory time, usage time, charging frequency and life impact factor are numerically calculated to obtain the scrap traceability coefficient of scrapped battery i.

8. The fault handling system adapted for lithium iron carbonate batteries according to claim 7, characterized in that: The specific analysis process of life impact analysis is as follows: The battery damage value is obtained by numerically calculating the battery loss time, risk loss time, high damage time and high damage frequency, and the preset battery damage value range containing the corresponding battery damage value is marked as the damage range, and the preset factor corresponding to the damage range is marked as the life influencing factor of the scrapped battery i.

9. The fault handling system adapted for lithium iron carbonate batteries according to claim 6, characterized in that: The specific analysis process of battery production optimization analysis is as follows: Obtain the scrap traceability coefficient of the scrapped battery i. If the scrap traceability coefficient does not exceed the preset scrap traceability coefficient threshold, the scrapped battery i is marked as an unqualified battery. The percentage of unqualified batteries is obtained and marked as an abnormal usage value. If the abnormal usage value exceeds a preset abnormal usage threshold, an optimization alarm signal is generated; otherwise, an optimization unnecessary signal is generated.

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