CN116930764A - Power-electron-fused fault diagnosis and danger prediction method for lithium battery energy storage system - Google Patents
Power-electron-fused fault diagnosis and danger prediction method for lithium battery energy storage system Download PDFInfo
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- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 30
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
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- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/374—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] with means for correcting the measurement for temperature or ageing
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/378—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] specially adapted for the type of battery or accumulator
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- G—PHYSICS
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/389—Measuring internal impedance, internal conductance or related variables
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/392—Determining battery ageing or deterioration, e.g. state of health
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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Abstract
The application provides a fault diagnosis and danger prediction method of a lithium battery energy storage system integrating power electronics, which comprises the following steps of S1, constructing a high-perceptibility battery; s2, performing micro-short circuit fault simulation of an actual battery unit, extracting fault expression characteristics, and determining a nondestructive diagnosis threshold; s3, performing an overcharge and overdischarge experiment on the high-perceptibility battery to cause artificial micro-short circuit, and performing fault diagnosis in real time by using a nondestructive fault diagnosis mode; s4, monitoring internal thermal quantity information and characteristic gas information of the high-perceptibility battery while causing the high-perceptibility battery to fail, and completing the selection of a temperature deviation threshold value and a gas concentration threshold value; and S5, monitoring a high-perceptibility battery fault evolution rule in the whole process, dividing a fault emergency program, and constructing a fault database. According to the application, the battery fault evolution process is monitored in real time according to the high-perceptibility battery, so that the subsequent conventional battery fault evolution process prediction is realized.
Description
Technical Field
The application relates to the technical field of battery management, in particular to a method, medium and terminal for diagnosing faults and predicting dangers of a lithium battery energy storage system fused with power electronics.
Background
The evolution mechanism of battery failure is mainly divided into two types, one is battery failure caused by external emergency, and the other is failure caused by degradation of battery reliability to a certain extent due to battery performance degradation. The fault evolution of emergency situations comprises abnormal reaction of active components in the battery caused by vibration, extrusion, puncture, external short circuit, high temperature and the like, thereby causing overheat and further causing thermal runaway. Such an emergency fault should be considered in the design stage and its occurrence probability should be reduced as much as possible, and the fault caused by the decrease of the reliability of the battery due to the degradation of the battery performance should be controlled by fault diagnosis and hazard prediction means.
The battery energy storage system basically does not encounter extreme working conditions such as puncture, extrusion, falling, seawater soaking and the like in the operation process, the main fault sources of the battery energy storage system include two types, and one type of faults is caused by extra stress impact such as power grid impact overvoltage, external short circuit, dielectric breakdown and the like, and the faults are avoided or emergency protection is realized through reasonable design of a power converter and a protection circuit; the second type of faults mainly originate from diaphragm damage caused by dendrite growth and other reasons in the battery, so that micro short circuits are formed, the safety influence of the micro short circuit faults on the battery is a gradual change process, and if a positioning micro short circuit unit can be found in time, more abundant processing time can be reserved.
The search finds that:
the application discloses a method and a system for early fault diagnosis and safety advanced warning of an energy storage battery, which are disclosed in China patent application publication No. CN115219912A, and comprise the following steps: acquiring a voltage signal of an energy storage battery; based on the voltage signal, performing a preliminary fault diagnosis result by adopting a plurality of fault diagnosis methods; mapping the preliminary fault diagnosis results of the multiple fault diagnosis methods into a characteristic value sequence; performing convex function processing on the characteristic value sequence, and adding bias; and integrating the processed characteristic value sequence with respect to time to obtain an early fault diagnosis result of the energy storage battery. The fault diagnosis result is mapped into a characteristic value sequence, convex function processing is carried out, bias is added, time integration is carried out, the calculation is complex and has no physical meaning,
the application discloses a method for diagnosing short-circuit faults of an energy storage battery pack, which comprises the following steps of: acquiring a reference parameter required by fault detection; normalizing the maximum reference eigenvalue vector, setting a significance level, acquiring a short-circuit fault detection threshold value, further acquiring the minimum detectable fault estimation of the short-circuit fault, and determining the minimum detectable short-circuit resistance; constructing a standardized differential voltage measurement time sequence matrix according to the differential voltage time sequence vector of the energy storage battery pack based on the staggered voltage measurement topology, and calculating a corresponding real-time short circuit fault detection index; and when the short circuit fault detection index exceeds the fault detection threshold value, calculating the contribution degree of the differential voltage channels of the faults, and determining the occurrence position of the short circuit faults. The method uses a reference differential voltage mean vector of a reference differential measurement voltage time sequence sub-matrix based on an interleaved voltage measurement topology and a reference differential voltage standard deviation diagonal array, has complex calculation and no physical significance,
the application relates to a fault diagnosis method and device of a high-capacity battery energy storage system, and comprises the steps of obtaining to-be-diagnosed data of the battery energy storage system; and inputting the data to be diagnosed as a test sample into a pre-constructed BP neural network model for fault diagnosis, and outputting a fault diagnosis result. The application adopts the neural network, has higher time complexity and space complexity and has no physical significance.
The application discloses a method, a device, a system and a storage medium for diagnosing internal short-circuit faults of an energy storage battery, which are disclosed in China patent application with publication number of CN115524613A, and comprise the following steps: acquiring a first electrical characteristic of an energy storage battery, and predicting whether a suspected internal short circuit fault occurs in the energy storage battery according to the first electrical characteristic; calculating a current heat generation amount and an internal material temperature value set of the energy storage battery, and determining a heat accumulation position of the energy storage battery according to the current heat generation amount and the internal material temperature value set; and determining whether the internal short circuit fault occurs in the energy storage battery according to the suspected internal short circuit fault and the heat accumulation position. It does not discuss how to obtain the failure threshold.
Disclosure of Invention
Aiming at the defects in the prior art, the application aims to provide a fault diagnosis and danger prediction method for a lithium battery energy storage system fused with power electronics.
According to one aspect of the application, there is provided a power-electron-fused fault diagnosis and hazard prediction method for a lithium battery energy storage system, comprising:
constructing a high-perceptibility battery;
performing micro short circuit fault simulation of an actual battery unit, extracting fault expression characteristics, and determining a nondestructive diagnosis threshold;
performing an overcharge and overdischarge experiment on the high-perceptibility battery to cause artificial micro-short circuit, and performing fault diagnosis in real time by combining a nondestructive fault diagnosis mode with the nondestructive diagnosis threshold value;
monitoring internal thermal quantity information and characteristic gas information of the high-perceptibility battery while causing the high-perceptibility battery to fail, and completing the selection of a temperature deviation threshold value and a gas concentration threshold value;
and based on the nondestructive diagnosis threshold value, the temperature deviation threshold value and the gas concentration threshold value, monitoring the high-perceptibility battery fault evolution rule in the whole process, dividing a fault emergency program, and constructing a fault database.
Preferably, the constructing a high perceptibility battery includes:
selecting the specification of battery cells and modules of an energy storage system;
manufacturing a high-perceptibility battery cell, packaging, embedding a temperature sensor, a gas sensor and a pressure sensor in the battery cell, and arranging a transmission medium to realize information transmission in the battery cell;
performing a cyclic test on the packaged high-perceptibility battery cell and the selected battery cell of the energy storage system, and detecting battery parameters, wherein the battery parameters comprise capacity, internal resistance, open-circuit voltage and thermal resistance;
performing optimization iteration on the high-perceptibility battery cell to enable the high-perceptibility battery cell to be consistent with the battery parameter curve of the selected battery cell of the energy storage system;
according to the module specification of the energy storage system battery, the high-perceptibility battery cell is assembled and packaged in a modularized mode; and the module is packaged with a temperature sensor, a gas sensor and a pressure sensor, and a transmission medium is arranged to realize the internal information transmission of the module.
Preferably, the performing micro-short circuit fault simulation of the actual battery unit, extracting fault performance characteristics, and determining a nondestructive diagnosis threshold value includes:
selecting a conventional battery module with the same specification as that of a battery cell and a module of the energy storage system;
a plurality of short-circuit resistors with different resistance values are added on the electrode lugs of the battery core in the conventional battery module to form a micro-short-circuit battery module;
controlling an energy storage unit power interface converter to inject voltage ripples with specific frequency and waveform into the micro-short circuit battery module to perform battery cell and module level impedance online measurement, comparing the measured impedance with the impedance before fault simulation, and completing impedance information deviation comparison to obtain an impedance difference, obtaining the relation between the impedance difference and a short circuit resistance value, and determining a micro-short circuit fault deviation impedance threshold value according to the relation;
controlling an energy storage unit power interface converter to enable the micro-short circuit battery module to operate under the same working condition as an actual energy storage system, comparing the data reported by the converter and a battery management system with voltage current temperature data under normal working conditions, selecting a plurality of quantities with the change amplitude values arranged in front from large to small as characteristic quantities, obtaining the relation between the characteristic quantities and the resistance value of a short circuit resistor, and determining a voltage current deviation threshold;
and constructing a digital mirror image model of the energy storage system by utilizing an electrothermal coupling model of the energy storage unit, ensuring that the mirror image model and an actual physical model operate under the same temperature and current working conditions, performing state deviation monitoring on output of the mirror image model and the actual physical model, obtaining the relation between a voltage residual error and a short-circuit resistance value, and finishing voltage residual error threshold selection.
Preferably, the controlling the power conversion interface of the energy storage unit injects voltage ripple with specific frequency and waveform into the micro-short circuit battery module to perform on-line measurement of battery cell and module level impedance, including:
according to the required waveform and frequency of the excitation voltage, the duty ratio disturbance signals of the corresponding waveform and frequency are injected into the PWM control signals of the energy storage unit power interface converter, and the voltage ripple of the specific frequency and waveform is injected into the micro short circuit battery module;
sampling a battery cell and a module voltage and current signal;
carrying out Fourier operation on the battery voltage and current sampling signals to obtain the amplitude and phase of the voltage and current sampling signals under different frequencies;
calculating the battery impedance according to the battery voltage and the battery current, namely: extracting the voltage and current signal frequency Lω of the battery unit by using a Fourier algorithm 1 The magnitudes below are c respectively l_U And c l_I The phases are respectivelyAnd->The frequency lω can be calculated 1 Lower cell impedance information: />
Preferably, the performing an overcharge and overdischarge experiment on the high-perceptibility battery causes artificial micro-short circuit, and performs fault diagnosis in real time by combining a nondestructive fault diagnosis mode with the nondestructive diagnosis threshold value, including:
performing an overcharge and overdischarge experiment on the high-perceptibility battery, and performing micro-short circuit real-time monitoring by utilizing a plurality of fault diagnosis modes, and continuously recording the change of each fault characteristic quantity in the overcharge and overdischarge experiment;
and stopping the overcharge and overdischarge experiment when judging that the high-perceptibility battery has micro-short circuit according to the nondestructive diagnosis threshold value, and charging and discharging the high-perceptibility battery to enable the charge state to be in a 40% -60% interval.
Preferably, the monitoring the internal thermal quantity information and the characteristic gas information of the high-perceptibility battery while causing the high-perceptibility battery to fail, and completing the selection of the temperature deviation threshold and the gas concentration threshold, includes:
after nondestructive fault diagnosis is recorded, monitoring internal and external thermal quantity information of the high-perceptibility battery, and recording the temperature deviation at the moment as a fault threshold;
after nondestructive fault diagnosis is recorded, the change condition of the concentration of the characteristic gas inside and outside the high-perceptibility battery is monitored, and the concentration at the moment is recorded as a fault threshold value.
Preferably, the whole process monitors the fault evolution rule of the high-perceptibility battery, and performs fault emergency program division to construct a fault database, including:
continuously recording the change condition of fault characteristic quantities of various methods by operating the high-perceptibility battery after faults in a normal SOC range; meanwhile, a control test is set, the high-perceptibility battery after the fault is operated under the derating working condition, and the fault characteristic quantity change conditions of various methods are continuously recorded;
comparing fault evolution rules under the conditions of normal operation and derated operation, researching the influence of operation under different powers on micro-short-circuit evolution of the battery module, dividing fault evolution stages and fault emergency degree according to research results, and constructing a fault database.
According to a second aspect of the present application, there is provided a power-electron-fused lithium battery energy storage system fault diagnosis and hazard prediction system, comprising:
a sensing module that constructs a high perceptibility battery;
the nondestructive threshold module is used for carrying out micro-short circuit fault simulation on the actual battery unit, extracting fault expression characteristics and determining a nondestructive diagnosis threshold;
the micro-damage test module is used for conducting an overcharge and overdischarge test on the high-perceptibility battery to cause artificial micro-short circuit, and conducting fault diagnosis in real time by combining a nondestructive fault diagnosis mode with the nondestructive diagnosis threshold value;
the micro-damage threshold module monitors internal thermal quantity information and characteristic gas information of the high-perceptibility battery while causing the high-perceptibility battery to fail, and completes the selection of a temperature deviation threshold and a gas concentration threshold;
and the database module monitors the fault evolution rule of the high-perceptibility battery in the whole process, divides fault emergency programs and constructs a fault database.
According to a third aspect of the present application, there is provided a terminal comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor being operable to perform the method of diagnosing and predicting faults in a lithium battery energy storage system of a fusion power electronic or to run the system of diagnosing faults and predicting faults in a lithium battery energy storage system of a fusion power electronic when executing the program.
According to a fourth aspect of the present application, there is provided a computer readable storage medium having stored thereon a computer program which when executed by a processor is operative to perform the method of diagnosing and predicting a fault of a lithium battery energy storage system of a fusion power electronic or to run the system of diagnosing and predicting a fault of a lithium battery energy storage system of a fusion power electronic.
Compared with the prior art, the method has at least one of the following beneficial effects:
1. the battery reliability is reduced due to the battery performance attenuation, the fault process is longer, and the fault evolution rule is not closely related to external fault key feature quantity information. According to the method and the system for diagnosing faults and predicting risks of the lithium battery energy storage system integrating the power electronics, disclosed by the embodiment of the application, the battery fault evolution process is monitored in real time according to the high-perceptibility battery, and the subsequent conventional battery fault evolution process is realized.
2. According to the method and the system for diagnosing faults and predicting risks of the lithium battery energy storage system fused with the power electronics, the characteristic differences of battery current, voltage characteristic quantity, electrochemical impedance, internal temperature and battery gassing under normal and fault working conditions are evaluated, and a lossless and micro-damage two-stage fault diagnosis method is constructed; and establishing a fault diagnosis database containing different fault characteristic quantities, and establishing a judgment criterion when the different fault characteristic quantities are adopted for collaborative judgment through research, so that the fault judgment when the different fault characteristic diagnoses are different in the subsequent system operation is facilitated.
3. According to the method and the system for diagnosing faults and predicting risks of the lithium battery energy storage system integrating power electronics, disclosed by the embodiment of the application, the established battery multidimensional coupling model is utilized to construct a digital mirror image of the energy storage system, residual error analysis is utilized, a multistage fault diagnosis mode is combined, fault diagnosis and risk early warning based on the model are realized, a nondestructive diagnosis strategy is perfected, the fault identification speed is improved, and a sufficient window period is reserved for fault post-treatment.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the detailed description of non-limiting embodiments, given with reference to the accompanying drawings in which:
FIG. 1 is a schematic diagram of an overall flow chart of a method for diagnosing and predicting a failure of a lithium battery energy storage system integrated with power electronics according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of a high-perceptibility cell constructed according to a preferred embodiment of the present application;
FIG. 3 is a schematic diagram of an on-line measurement process of battery cell and module level impedance according to a preferred embodiment of the present application;
FIG. 4 is a schematic diagram of an on-line measurement structure of battery cell and module level impedance according to a preferred embodiment of the present application;
FIG. 5 is a schematic diagram of the fault characteristics of the non-destructive and destructive diagnosis according to a preferred embodiment of the present application.
Detailed Description
The present application will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the present application, but are not intended to limit the application in any way. It should be noted that variations and modifications could be made by those skilled in the art without departing from the inventive concept. These are all within the scope of the present application.
Fig. 1 is an overall flow chart of a method for diagnosing faults and predicting risks of a lithium battery energy storage system fused with power electronics according to an embodiment of the present application, which specifically includes the following steps:
s1, constructing a high-perceptibility battery;
s2, performing micro-short circuit fault simulation of an actual battery unit, extracting fault expression characteristics, and determining a nondestructive diagnosis threshold;
s3, performing an overcharge and overdischarge experiment on the high-perceptibility battery to cause artificial micro-short circuit, and performing fault diagnosis in real time by combining a nondestructive fault diagnosis mode with a nondestructive diagnosis threshold value;
s4, monitoring internal thermal quantity information and characteristic gas information of the high-perceptibility battery while causing the high-perceptibility battery to fail, and completing the selection of a temperature deviation threshold value and a gas concentration threshold value;
and S5, monitoring a high-perceptibility battery fault evolution rule in the whole process based on the nondestructive diagnosis threshold value, the temperature deviation threshold value and the gas concentration threshold value, dividing the fault emergency degree, and constructing a fault database.
In this embodiment, the high perceptibility means that in addition to the battery current, the battery terminal voltage, and the battery case temperature (including the battery tab that can measure the temperature) that can be measured by the conventional battery, physical/thermal quantities including, but not limited to, the following can be measured: the battery internal temperature, the cell tab potential, the cell internal pressure, the battery released gas concentration, and the like, since these amounts above cannot be obtained by measurement in the conventional battery, for this reason, in the application, the battery from which these amounts can be obtained by measurement is defined as a highly perceptible battery.
The actual battery unit is a battery with the specification consistent with the battery with high perceptibility, such as the volume, the capacity, the current multiplying power and the like, and the related specification parameters are consistent.
"non-destructive" refers to a manner in which diagnosis can be performed without conducting destructive testing. "micro damage" means that the battery is damaged by overcharge and overdischarge, but the damage is limited because the battery is not disassembled, so that the damage is called "micro damage" for distinguishing nondestructive diagnosis/detection "
In a preferred embodiment of the present application, S1 is executed, and the specifications of the battery cells and the modules of the energy storage system are selected, so as to perform the same-specification high-perceptibility cell and battery module design, which may include the following steps, as shown in fig. 2:
s1.1: performing battery cell trial production by using a full spectrum battery test platform, embedding sensors such as temperature, gas, pressure and the like in the battery cell trial production platform, and realizing internal information transmission of the battery cell by using transmission media such as inert materials, optical fibers and the like;
s1.2: after the battery cell packaging is completed, carrying out cyclic test on the battery cells with high perceptibility and the battery cells with the same specification, and detecting battery parameters such as battery capacity, internal resistance, open-circuit voltage, thermal resistance and the like;
s1.3: through iterative optimization of the cell structure, the basic consistency of the characteristics of the high-perceptibility cell and the actually used energy storage cell in capacity, internal resistance, open-circuit voltage curve and the like is realized;
s1.4: on the basis of completing the high-perceptibility battery cells, the high-perceptibility battery cells are assembled and packaged in a modularized mode according to the specifications of the selected battery modules, intelligent sensors (actually, the sensors are placed outside the battery cells and then packaged into modules after the battery cells are connected in series and parallel) are embedded in the same way before the module is packaged, and internal information transmission of the battery is realized through inert materials, optical fibers and other transmission media.
The high-perceptibility battery obtained by the embodiment can monitor the battery fault evolution process in real time, and is beneficial to realizing the follow-up conventional battery fault evolution process.
In a preferred embodiment of the present application, performing S2 may include the steps of:
s2.1: by adding a plurality of external short-circuit resistors (1 omega/10 omega/100 omega) with different resistance values on the battery cell lugs in the selected conventional battery module (the conventional battery module is the battery module which corresponds to the special battery module manufactured in S1.4 and does not do any other operation, note that the battery cells in the conventional battery module are also in the specification selected in S1) in the battery cell lugs at the moment, and simulating the micro-short-circuit condition of the energy storage unit;
s2.2: controlling an energy storage unit power interface converter to inject voltage ripples with specific frequency and waveform into a micro-short circuit battery module formed by S2.1 to perform battery cell and module level impedance on-line measurement, comparing the measured impedance with the impedance before fault simulation, completing impedance information deviation comparison, developing related research on the relation between the impedance difference and the short circuit resistance value, and determining a micro-short circuit fault deviation impedance threshold; the specific frequency refers to one or several frequencies within 0.1-1000 Hz, optionally 0.1Hz, 0.2Hz, 0.5Hz, 1Hz, 2Hz, 5Hz, 10Hz, 20Hz, 50Hz, 100Hz, 200Hz, 500Hz, 1000Hz.
Further, the method for controlling the power conversion interface of the energy storage unit to inject voltage ripples with specific frequency and waveform into the micro-short circuit battery module to perform on-line measurement of battery cell and module level impedance specifically comprises the following steps of:
s2.2.1: according to the required waveform and frequency of the excitation voltage, the duty ratio disturbance signals of the corresponding waveform and frequency are injected into the PWM control signals of the energy storage unit power interface converter, and voltage ripples of specific frequency and waveform are injected into the micro-short circuit battery module, as shown in fig. 4;
s2.2.2: sampling battery cell and module battery voltage and current signals;
s2.2.3: carrying out Fourier operation on the sampling signals of the battery voltage and the battery current to obtain the amplitude and the phase of the sampling signals of the voltage and the battery current under different frequencies;
s2.2.4: calculating battery impedance according to battery voltage and current, wherein the impedance is calculatedThe impedance modulus is equal to the ratio of the voltage to the current amplitude, the impedance phase angle is equal to the difference between the voltage and the current phase, e.g. by taking the frequency of the cell voltage current signal Lω using a Fourier algorithm 1 The magnitudes below are c respectively l_U And c l_I The phases are respectivelyAnd->The frequency lω can be calculated 1 Lower cell impedance information: />
S2.3: controlling an energy storage unit power interface converter to enable the micro-short circuit battery module to operate under the same working condition as an actual energy storage system, comparing the data reported by the converter and a battery management system with voltage current temperature data under normal working conditions, selecting a plurality of quantities with obvious change as characteristic quantities, researching the relation between the characteristic quantities and short circuit resistance, and determining an outlier factor deviation threshold;
s2.4: and constructing a digital mirror image model of the energy storage system by utilizing an electrothermal coupling model of the energy storage unit, ensuring that the mirror image model and an actual physical model operate under the same temperature and current working conditions, performing state deviation monitoring on output voltages of the mirror image model and the actual physical model, developing related researches on the relation between voltage residual errors and the resistance value of a short-circuit resistor, and completing the selection of voltage residual error thresholds.
In the embodiment, the established battery multidimensional coupling model is utilized to construct a digital image of the energy storage system, residual error analysis is utilized, a multistage fault diagnosis mode is combined, fault diagnosis and danger early warning based on the model are realized, a nondestructive diagnosis strategy is perfected, the fault identification speed is improved, and a sufficient window period is reserved for fault post-treatment.
In a preferred embodiment of the present application, performing S3, a non-destructive diagnostic schematic is shown in fig. 5, may include the steps of:
s3.1: performing an overcharge and overdischarge experiment on a battery module containing a high-perceptibility battery cell, and performing micro-short circuit real-time monitoring by utilizing a plurality of fault diagnosis modes in S2, and continuously recording the change of each fault characteristic quantity (namely impedance deviation, voltage and current deviation and voltage residual error) in the overcharge and overdischarge experiment;
s3.2: and (3) stopping the overcharge and overdischarge experiments when the battery is judged to have micro short circuit according to the fault diagnosis criterion obtained in the step (S2), and charging and discharging the battery module to enable the charge state to be in a 40% -60% interval.
In a preferred embodiment of the present application, performing S4, a micro-damage diagnosis schematic is shown in fig. 5, may include the steps of:
s4.1: after nondestructive fault diagnosis is recorded, the internal and external thermal quantity information of the high-perceptibility battery is recorded, and the temperature deviation at the moment is recorded as a fault threshold value;
s4.2: after nondestructive fault diagnosis is recorded, the concentration change condition of the characteristic gas inside and outside the high-perceptibility battery is recorded, and the concentration at the moment is recorded as a fault threshold value.
In a preferred embodiment of the present application, performing step 5 may include the steps of:
s5.1: the high-perceptibility battery after faults is operated in a normal SOC range, and the change conditions of the 5 fault characteristic quantities (impedance information deviation, voltage current characteristic quantity deviation, actual energy storage system voltage residual error deviation, temperature deviation and gas concentration deviation) obtained by various methods are continuously recorded; meanwhile, a control test is set, the high-perceptibility battery after the fault is operated under the derating working condition, and the fault characteristic quantity change conditions of various methods are continuously recorded;
furthermore, a working condition simulation experiment can be carried out on 3 high-perceptibility battery modules comprising micro-short circuit cells, wherein 1 module adopts a non-derating operation mode, and the other two modules respectively adopt a derating operation mode of 1/3 and a derating operation mode of 2/3;
further, the fault characteristic quantities of various methods comprise fault deviation impedance in S2, fault deviation voltage and current quantity, voltage residual quantity of a mirror image model, internal and external temperature deviation of a battery in S4 and characteristic gas concentration;
s5.2: comparing fault evolution rules under the conditions of normal operation and derated operation, researching the influence of operation under different powers on micro-short-circuit evolution of the battery module, dividing fault evolution stages and fault emergency degree according to research results, and constructing a fault database.
The fault database refers to a judgment criterion of different fault characteristic quantities, generally, the difference of the characteristic quantities of battery current, voltage characteristic quantities, electrochemical impedance, internal temperature and battery gassing is judged, and if the difference exceeds the acquired threshold value range, the corresponding fault is judged to occur. However, in actual situations, after the individual feature amount exceeds the threshold value, other feature amounts may not exceed the threshold value, and therefore, a judgment criterion for collaborative judgment needs to be established.
The judgment criterion in the collaborative judgment can be set according to the priority, for example, if the characteristic gas is collected, the fault is stated to have occurred, and the threshold value can be considered to be reduced if the other characteristic quantities do not exceed the threshold value. The determination can also be made with accuracy, i.e. which fault feature is more accurate to see.
The fault database established in this embodiment may be directly used when the same energy storage system is established, or may be used to provide a reference for other established energy storage systems.
In a preferred embodiment of the application, a fault database is provided for the impedance section, the internal short circuit impedance being > 300 omega; 100-300 omega; 10 to 100 omega; the first one, which is basically equivalent to normal, is considered to be slightly faulty, and can be turned into important monitoring, the third one, which is considered to be more faulty, requires derating, and the last one, which is already very dangerous, must be cut off immediately.
Based on the same inventive concept, in other embodiments of the present application, there is provided a power electronics-integrated fault diagnosis and hazard prediction system for a lithium battery energy storage system, comprising:
a sensing module that constructs a high perceptibility battery;
the nondestructive threshold module is used for carrying out micro-short circuit fault simulation on the actual battery unit, extracting fault expression characteristics and determining a nondestructive diagnosis threshold;
the micro-damage test module is used for conducting an overcharge and overdischarge test on the high-perceptibility battery to cause artificial micro-short circuit, and conducting fault diagnosis in real time by combining a nondestructive fault diagnosis mode with the nondestructive diagnosis threshold value;
the micro-damage threshold module monitors internal thermal quantity information and characteristic gas information of the high-perceptibility battery while causing the high-perceptibility battery to fail, and completes the selection of a temperature deviation threshold and a gas concentration threshold;
and the database module monitors the fault evolution rule of the high-perceptibility battery in the whole process, divides fault emergency programs and constructs a fault database.
The specific reference of each module/unit in the above embodiment of the present application may be the implementation technology of the steps corresponding to the method for diagnosing the fault and predicting the risk of the lithium battery energy storage system based on the fusion of power electronics in the above embodiment, which is not described herein.
Based on the same inventive concept, in other embodiments of the present application, a terminal is provided, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor is operable to execute the method for diagnosing and predicting a fault of the lithium battery energy storage system of the integrated power electronics or to execute the system for diagnosing a fault of the lithium battery energy storage system of the integrated power electronics and predicting a fault of the risk when the processor executes the program.
Based on the same inventive concept, in other embodiments of the present application, a computer-readable storage medium is provided, on which a computer program is stored, which program, when executed by a processor, is operative to perform the method of diagnosing and predicting a fault of a lithium battery energy storage system of a fusion power electronic, or to run the system of diagnosing a fault of a lithium battery energy storage system of a fusion power electronic.
This embodiment describes a specific embodiment of the present application. It is to be understood that the application is not limited to the particular embodiments described above, and that various changes and modifications may be made by one skilled in the art within the scope of the claims without affecting the spirit of the application. The above-described preferred features may be used in any combination without collision.
Claims (10)
1. The utility model provides a lithium battery energy storage system fault diagnosis and danger prediction method of fusion power electronics, which is characterized by comprising the following steps:
constructing a high-perceptibility battery;
performing micro short circuit fault simulation of an actual battery unit, extracting fault expression characteristics, and determining a nondestructive diagnosis threshold;
performing an overcharge and overdischarge experiment on the high-perceptibility battery to cause artificial micro-short circuit, and performing fault diagnosis in real time by combining a nondestructive fault diagnosis mode with the nondestructive diagnosis threshold value;
monitoring internal thermal quantity information and characteristic gas information of the high-perceptibility battery while causing the high-perceptibility battery to fail, and completing the selection of a temperature deviation threshold value and a gas concentration threshold value;
and based on the nondestructive diagnosis threshold value, the temperature deviation threshold value and the gas concentration threshold value, monitoring the high-perceptibility battery fault evolution rule in the whole process, dividing a fault emergency program, and constructing a fault database.
2. The method for diagnosing and predicting risk of a lithium battery energy storage system incorporating power electronics of claim 1, wherein said constructing a highly perceptible battery comprises:
selecting the specification of battery cells and modules of an energy storage system;
manufacturing a high-perceptibility battery cell, packaging, embedding a temperature sensor, a gas sensor and a pressure sensor in the battery cell, and arranging a transmission medium to realize external transmission of internal information of the battery cell;
performing a cyclic test on the packaged high-perceptibility battery cell and the selected battery cell of the energy storage system, and detecting battery parameters, wherein the battery parameters comprise capacity, internal resistance, open-circuit voltage and thermal resistance;
performing optimization iteration on the high-perceptibility battery cell to enable the high-perceptibility battery cell to be consistent with the battery parameter curve of the selected battery cell of the energy storage system;
according to the module specification of the energy storage system battery, the high-perceptibility battery cell is assembled and packaged in a modularized mode; and pre-embedding a temperature sensor, a gas sensor and a pressure sensor before packaging the module, and arranging a transmission medium to realize external transmission of the internal information of the module.
3. The method for diagnosing and predicting risk of lithium battery energy storage system integrated with power electronics according to claim 1, wherein the performing the micro-short fault simulation of the actual battery unit, extracting the fault performance characteristics, and determining the nondestructive diagnosis threshold value comprises:
selecting a conventional battery module with the same specification as that of a battery cell and a module of the energy storage system;
a plurality of short-circuit resistors with different resistance values are added on the electrode lugs of the battery core in the conventional battery module to form a micro-short-circuit battery module;
controlling an energy storage unit power interface converter to inject voltage ripples with specific frequency and waveform into the micro-short circuit battery module to perform battery cell and module level impedance online measurement, comparing the measured impedance with the impedance before fault simulation, and completing impedance information deviation comparison to obtain an impedance difference, obtaining the relation between the impedance difference and a short circuit resistance value, and determining a micro-short circuit fault deviation impedance threshold value according to the relation;
controlling an energy storage unit power interface converter to enable the micro-short circuit battery module to operate under the same working condition as an actual energy storage system, comparing the data reported by the converter and a battery management system with voltage current temperature data under normal working conditions, selecting a plurality of quantities with the change amplitude values arranged in front from large to small as characteristic quantities, obtaining the relation between the characteristic quantities and the resistance value of a short circuit resistor, and determining a voltage current temperature deviation threshold;
and constructing a digital mirror image model of the energy storage system by utilizing an electrothermal coupling model of the energy storage unit, ensuring that the mirror image model and an actual physical model operate under the same temperature and current working conditions, performing state deviation monitoring on output of the mirror image model and the actual physical model, obtaining the relation between a voltage residual error and a short-circuit resistance value, and finishing voltage residual error threshold selection.
4. The method for diagnosing faults and predicting risks of a lithium battery energy storage system fused with power electronics according to claim 3, wherein the controlling the energy storage unit power conversion interface to inject voltage ripples with specific frequency and waveform into the micro-short circuit battery module to perform on-line measurement of battery cell and module level impedance comprises the following steps:
according to the required waveform and frequency of the excitation voltage, the duty ratio disturbance signals of the corresponding waveform and frequency are injected into the PWM control signals of the energy storage unit power interface converter, and the voltage ripple of the specific frequency and waveform is injected into the micro short circuit battery module;
sampling a battery cell and a module voltage and current signal;
carrying out Fourier operation on the battery voltage and current sampling signals to obtain the amplitude and phase of the voltage and current sampling signals under different frequencies;
calculating the battery impedance according to the battery voltage and the battery current, namely: extracting the voltage and current signal frequency Lω of the battery unit by using a Fourier algorithm 1 The magnitudes below are c respectively l_U And c l_I The phases are respectivelyAnd->The frequency lω can be calculated 1 Lower cell impedance information: />
5. The method for diagnosing and predicting the risk of a lithium battery energy storage system integrated with power electronics according to claim 1, wherein the performing an overcharge and overdischarge experiment on the high-perceptibility battery causes artificial micro-shorting, and performing fault diagnosis in real time by combining a non-destructive fault diagnosis mode with the non-destructive diagnosis threshold, comprises:
performing an overcharge and overdischarge experiment on the high-perceptibility battery, and performing micro-short circuit real-time monitoring by utilizing a plurality of fault diagnosis modes, and continuously recording the change of each fault characteristic quantity in the overcharge and overdischarge experiment;
and stopping the overcharge and overdischarge experiment when judging that the high-perceptibility battery has micro-short circuit according to the nondestructive diagnosis threshold value, and charging and discharging the high-perceptibility battery to enable the charge state to be in a 40% -60% interval.
6. The method for diagnosing and predicting risk of lithium battery energy storage system integrated with power electronics according to claim 1, wherein the steps of monitoring the thermal information and the characteristic gas information in the high-perceptibility battery while causing the high-perceptibility battery to fail, and completing the selection of the temperature deviation threshold and the gas concentration threshold include:
after nondestructive fault diagnosis is recorded, monitoring internal and external thermal quantity information of the high-perceptibility battery, and recording the temperature deviation at the moment as a fault threshold;
after nondestructive fault diagnosis is recorded, the change condition of the concentration of the characteristic gas inside and outside the high-perceptibility battery is monitored, and the concentration at the moment is recorded as a fault threshold value.
7. The method for diagnosing and predicting the risk of the lithium battery energy storage system integrated with power electronics according to claim 1, wherein the whole process monitors the evolution law of the battery fault with high perceptibility, performs the emergency program division of the fault, and constructs a fault database, comprising:
continuously recording the change condition of fault characteristic quantities of various methods by operating the high-perceptibility battery after faults in a normal SOC range; meanwhile, a control test is set, the high-perceptibility battery after the fault is operated under the derating working condition, and the fault characteristic quantity change conditions of various methods are continuously recorded;
comparing fault evolution rules under the conditions of normal operation and derated operation, researching the influence of operation under different powers on micro-short-circuit evolution of the battery module, dividing fault evolution stages and fault emergency degree according to research results, and constructing a fault database.
8. A power electronics-integrated lithium battery energy storage system fault diagnosis and hazard prediction system, comprising:
a sensing module that constructs a high perceptibility battery;
the nondestructive threshold module is used for carrying out micro-short circuit fault simulation on the actual battery unit, extracting fault expression characteristics and determining a nondestructive diagnosis threshold;
the micro-damage test module is used for conducting an overcharge and overdischarge test on the high-perceptibility battery to cause artificial micro-short circuit, and conducting fault diagnosis in real time by combining a nondestructive fault diagnosis mode with the nondestructive diagnosis threshold value;
the micro-damage threshold module monitors internal thermal quantity information and characteristic gas information of the high-perceptibility battery while causing the high-perceptibility battery to fail, and completes the selection of a temperature deviation threshold and a gas concentration threshold;
and the database module monitors the fault evolution rule of the high-perceptibility battery in the whole process, divides fault emergency programs and constructs a fault database.
9. A terminal comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor is operable to perform the method of any one of claims 1-7 or to run the system of claim 8 when the program is executed by the processor.
10. A computer readable storage medium having stored thereon a computer program, which when executed by a processor is operative to perform the method of any one of claims 1-7 or to run the system of claim 8.
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