CN117420452B - Monitoring and early warning system for lithium battery energy storage - Google Patents
Monitoring and early warning system for lithium battery energy storage Download PDFInfo
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- CN117420452B CN117420452B CN202311741954.3A CN202311741954A CN117420452B CN 117420452 B CN117420452 B CN 117420452B CN 202311741954 A CN202311741954 A CN 202311741954A CN 117420452 B CN117420452 B CN 117420452B
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- 229910052744 lithium Inorganic materials 0.000 title claims abstract description 127
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 125
- 238000012544 monitoring process Methods 0.000 title claims abstract description 89
- 238000004146 energy storage Methods 0.000 title claims abstract description 17
- 230000005856 abnormality Effects 0.000 claims abstract description 51
- 238000004458 analytical method Methods 0.000 claims abstract description 39
- 238000001514 detection method Methods 0.000 claims abstract description 16
- 239000007789 gas Substances 0.000 claims description 86
- 238000000034 method Methods 0.000 claims description 72
- 238000007599 discharging Methods 0.000 claims description 66
- 230000002159 abnormal effect Effects 0.000 claims description 46
- ZAMOUSCENKQFHK-UHFFFAOYSA-N Chlorine atom Chemical compound [Cl] ZAMOUSCENKQFHK-UHFFFAOYSA-N 0.000 claims description 43
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 claims description 43
- 239000000460 chlorine Substances 0.000 claims description 43
- 229910052801 chlorine Inorganic materials 0.000 claims description 43
- 239000001257 hydrogen Substances 0.000 claims description 43
- 229910052739 hydrogen Inorganic materials 0.000 claims description 43
- 230000032683 aging Effects 0.000 claims description 17
- 238000004519 manufacturing process Methods 0.000 claims description 15
- 238000013139 quantization Methods 0.000 claims description 15
- 238000013500 data storage Methods 0.000 claims description 12
- 239000000428 dust Substances 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 description 10
- 239000000284 extract Substances 0.000 description 7
- 238000004880 explosion Methods 0.000 description 3
- 239000000779 smoke Substances 0.000 description 3
- 239000000463 material Substances 0.000 description 2
- 238000007792 addition Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000002485 combustion reaction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
Classifications
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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]
- G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
-
- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
- H01M10/48—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/10—Energy storage using batteries
Abstract
The invention relates to the technical field of lithium battery monitoring, in particular to a monitoring and early warning system for lithium battery energy storage, which is used for solving the problems that the existing monitoring and early warning system is low in monitoring accuracy of a lithium battery, cannot monitor the state of the lithium battery in real time and timely, finds abnormality in time and cannot guarantee the safe use of the lithium battery; the monitoring and early warning system comprises the following modules: the system comprises a surface detection module, a parameter analysis module, an early warning grading module, a gas monitoring module, a state monitoring module and a grading alarm module; the monitoring and early warning system can monitor the lithium battery from multiple aspects, improves the monitoring accuracy of the lithium battery, can monitor the state of the lithium battery in real time and in time, timely finds abnormality, and ensures the safe use of the lithium battery.
Description
Technical Field
The invention relates to the technical field of lithium battery monitoring, in particular to a monitoring and early warning system for lithium battery energy storage.
Background
Along with the development of new energy technology, lithium batteries have become a mainstream product for battery energy storage, and lithium metal is very active due to its chemical characteristics, so that safety accidents are very easy to occur in the use process, and serious explosion accidents are possibly caused. However, in the prior art, the safety monitoring and early warning method for the lithium battery can only monitor and early warn individual parameters in the charging process, and mainly detect temperature and smoke generation. However, when the lithium metal detects smoke, it is often too late, with consequent burning and explosion, not leaving enough time for the user to perform an emergency treatment.
According to practical observation and research, when the battery is deteriorated, high temperature and smoke are generated, then combustion and explosion come immediately, and the time is short, so that a monitoring and early warning system for lithium battery energy storage is needed to be capable of timely sending out a safety monitoring signal before abnormality occurs to the lithium battery, and the monitoring and early warning system is an urgent task of the current lithium battery vehicle.
How to improve the monitoring accuracy of the existing monitoring and early warning system on the lithium battery is not high, the state of the lithium battery cannot be monitored in real time and timely, abnormality can be found timely, and the safe use of the lithium battery cannot be guaranteed.
Disclosure of Invention
In order to overcome the technical problems, the invention aims to provide a monitoring and early warning system for lithium battery energy storage: the surface state of the lithium battery is detected through the surface detection module, the abnormal parameters of the lithium battery are obtained, the surface abnormal coefficient is obtained through the parameter analysis module according to the surface abnormal parameters, the serious danger alarm signal is generated through the early warning classification module according to the surface abnormal coefficient, the gas concentration generated in the charging and discharging process of the lithium battery is detected through the gas monitoring module, the gas abnormal parameters of the lithium battery are obtained, the state of the lithium battery is detected through the state monitoring module, the state abnormal parameters of the lithium battery are obtained through the parameter analysis module according to the gas abnormal parameters, the state abnormal coefficient is obtained through the parameter analysis module according to the state abnormal parameters, the serious danger alarm signal or the mild danger alarm signal or the no danger signal is generated through the early warning classification module according to the gas abnormal coefficient and the state abnormal coefficient, and the serious danger alarm signal, the mild danger alarm signal and the no danger signal are displayed in a classified mode, and the problem that the monitoring accuracy of the lithium battery is not high, the state of the lithium battery cannot be monitored in real time and the safety use of the lithium battery cannot be guaranteed is solved through the conventional monitoring and early warning system.
The aim of the invention can be achieved by the following technical scheme:
a monitoring and early warning system for lithium battery energy storage, comprising:
the surface detection module is used for detecting the surface state of the lithium battery, acquiring abnormal parameters of the lithium battery and sending the surface abnormal parameters to the parameter analysis module; wherein the surface anomaly parameters include ash values HS, deformation values BX, and crack values LW;
the parameter analysis module is used for obtaining a surface abnormality coefficient BY according to the surface abnormality parameters and sending the surface abnormality coefficient BY to the early warning classification module; the gas abnormality detection module is also used for obtaining a gas abnormality coefficient QY according to the gas abnormality parameter and sending the gas abnormality coefficient QY to the early warning classification module; the system is also used for obtaining a state anomaly coefficient ZY according to the state anomaly parameters and sending the state anomaly coefficient ZY to the early warning classification module;
the early warning grading module is used for generating a gas monitoring instruction and a state monitoring instruction according to the surface anomaly coefficient BY, or generating a severe danger alarm signal, sending the gas monitoring instruction to the gas monitoring module, sending the state monitoring instruction to the state monitoring module and sending the severe danger alarm signal to the grading alarm module; the system is also used for generating a moderate-risk alarm signal or a mild-risk alarm signal or a no-risk signal according to the gas anomaly coefficient QY and the state anomaly coefficient ZY and sending the moderate-risk alarm signal, the mild-risk alarm signal and the no-risk signal to the grading alarm module;
the gas monitoring module is used for detecting the gas concentration generated in the charging and discharging process of the lithium battery, acquiring the gas abnormal parameters of the lithium battery and sending the gas abnormal parameters to the parameter analysis module; wherein, the gas anomaly parameters comprise a hydrogen value QQ, a chlorine value LQ, a hydrogen initial value QC and a chlorine initial value LC;
the state monitoring module is used for detecting the state of the lithium battery, acquiring state abnormal parameters of the lithium battery and sending the state abnormal parameters to the parameter analysis module; the state anomaly parameters comprise a temperature value WD, a time value SJ, an aging value LH and an equilibrium value JH;
and the grading alarm module is used for receiving the severe danger alarm signal, the moderate danger alarm signal, the mild danger alarm signal and the no danger signal and grading and displaying the same.
As a further scheme of the invention: the specific process of acquiring the abnormal parameters by the surface detection module is as follows:
detecting the surface state of the lithium battery, obtaining the total amount of dust which is larger than a preset diameter in the unit area of the surface of the lithium battery, and marking the total amount of dust as an ash value HS;
obtaining the appearance shape of the lithium battery, marking the appearance shape as an actual model, obtaining the appearance shape of the lithium battery when leaving the factory from a data storage module, marking the appearance shape as an original model, performing superposition comparison on the actual model and the original model, obtaining the volume of a part where the actual model and the original model cannot be completely superposed, and marking the volume as a deformation value BX;
obtaining the total number of cracks and the total area of the cracks on the surface of the lithium battery, marking the total number of the cracks and the total area of the cracks as a crack value LS and a crack value LM respectively, carrying out quantization treatment on the crack value LS and the crack value LM, extracting the values of the crack value LS and the crack value LM, substituting the values into a formula for calculation, and calculating according to the formulaObtaining a crack value LW, wherein w1 and w2 are preset proportional coefficients corresponding to a set crack value LS and a preset crack value LM respectively, w1 and w2 meet w1+w2=1, 0 < w2 < w1 < 1, w1=0.58 and w2=0.42;
the ash value HS, the deformation value BX and the crack value LW are sent to a parameter analysis module.
As a further scheme of the invention: the specific process of obtaining the surface anomaly coefficient BY BY the parameter analysis module is as follows:
quantizing the ash value HS, the deformation value BX and the crack value LW, extracting the values of the ash value HS, the deformation value BX and the crack value LW, substituting the values into a formula for calculation, and calculating according to the formulaObtaining a surface anomaly coefficient BY, wherein e is a mathematical constant, b1, b2 and b3 are preset weight factors corresponding to a set ash value HS, a set deformation value BX and a set crack value LW respectively, b1, b2 and b3 meet the condition that b2 > b3 > b1 > 1.355, b1=1.49 is taken, b2=2.32, and b3=1.88;
and sending the surface anomaly coefficient BY to an early warning grading module.
As a further scheme of the invention: the specific process of the gas monitoring module for acquiring the gas abnormal parameters is as follows:
detecting the gas concentration generated in the charging and discharging process of the lithium battery after receiving the gas monitoring instruction, obtaining the hydrogen concentration generated in the charging process of the lithium battery, marking the hydrogen concentration as a hydrogen value QQ, obtaining the chlorine concentration generated in the discharging process of the lithium battery, and marking the chlorine concentration as a chlorine value LQ;
the method comprises the steps of obtaining the concentration of hydrogen generated in the first charging process of a lithium battery from a data storage module, marking the concentration of hydrogen as a hydrogen initial value QC, obtaining the concentration of chlorine generated in the first discharging process of the lithium battery, and marking the concentration of chlorine as a chlorine initial value LC;
and sending the hydrogen value QQ, the chlorine value LQ, the hydrogen initial value QC and the chlorine initial value LC to a parameter analysis module.
As a further scheme of the invention: the specific process of obtaining the gas anomaly coefficient QY by the parameter analysis module is as follows:
quantizing the hydrogen value QQ, the chlorine value LQ, the hydrogen initial value QC and the chlorine initial value LC, extracting the values of the hydrogen value QQ, the chlorine value LQ, the hydrogen initial value QC and the chlorine initial value LC, substituting the values into a formula for calculation, and calculating according to the formulaObtaining a gas anomaly coefficient QY, wherein e is a mathematical constant, q1 and q2 are preset weight factors corresponding to a set hydrogen value QQ and a set chlorine value LQ respectively, q1 and q2 meet q1 & gtq 2 & gt1.241, q1=1.58 is taken, and q2=1.29;
and sending the gas anomaly coefficient QY to an early warning grading module.
As a further scheme of the invention: the specific process of the state monitoring module for acquiring the state abnormal parameters is as follows:
detecting the state of the lithium battery after receiving a state monitoring instruction, acquiring the maximum operating temperature in the charging process, acquiring the maximum operating temperature in the first charging process from a data storage module, acquiring the temperature difference between the maximum operating temperature and the maximum operating temperature in the discharging process, acquiring the maximum operating temperature in the first discharging process from the data storage module, acquiring the temperature difference between the maximum operating temperature and the maximum operating temperature, and marking the maximum operating temperature as the temperature charging WCFor the temperature value WF, the temperature value WC and the temperature value WF are quantized, the numerical values of the temperature value WC and the temperature value WF are extracted and substituted into a formula for calculation, and the numerical values are calculated according to the formulaObtaining a temperature value WD, wherein d1 and d2 are preset proportional coefficients corresponding to a set temperature recharging WC and a set temperature discharging WF respectively, d1 and d2 meet d1+d2=1, 0 < d2 < d1 < 1, d1=0.63 and d2=0.37;
obtaining the production time and the current time of the lithium battery, obtaining the time difference between the production time and the current time, marking the time difference as a production time value CS, obtaining the total charging and discharging time of the lithium battery, marking the total charging and discharging time as an operation time value YS, carrying out quantization treatment on the production time value CS and the operation time value YS, extracting the values of the production time value CS and the operation time value YS, substituting the values into a formula for calculation, and obtaining the total charging and discharging time of the lithium battery according to the formulaObtaining a time value SJ, wherein s1 and s2 are preset proportionality coefficients of a set production value CS and an operation value YS respectively, s1 and s2 meet s1+s2=1, 0 < s1 < s2 < 1, s1=0.35 and s2=0.65;
obtaining the charging times of a lithium battery, marking the charging times as charging values CC, obtaining the maximum charging capacity in the charging process of the lithium battery and the maximum charging capacity in the first charging process, obtaining the capacity difference between the maximum charging capacity and the maximum charging capacity, marking the capacity difference as a capacity value RL, carrying out quantization treatment on the charging values CC and the capacity value RL, extracting the values of the charging values CC and the capacity value RL, substituting the values into a formula for calculation, and obtaining the capacity difference between the maximum charging capacity and the maximum charging capacity in the first charging process of the lithium battery according to the formulaObtaining an aging value LH, wherein h1 and h2 are preset proportionality coefficients of a set recharging CC and a capacity value RL respectively, h1 and h2 meet the condition that h1+h2=1, 0 < h1 < h2 < 1, and taking h1=0.42 and h2=0.58;
obtaining the maximum charging voltage and the minimum charging voltage in the charging process, obtaining the voltage difference between the maximum charging voltage and the minimum charging voltage, marking the voltage difference as a charging value CY, obtaining the maximum discharging voltage and the minimum discharging voltage in the discharging process, and obtaining the voltage difference between the maximum discharging voltage and the minimum discharging voltageMarking the current difference as a discharge value FY, obtaining the sum of the charge value CY and the discharge value FY, marking the sum as a voltage value DY, obtaining the maximum charge current and the minimum charge current in the charging process, obtaining the current difference between the charge value CY and the minimum charge current, obtaining the maximum discharge current and the minimum discharge current in the discharging process, obtaining the current difference between the charge value CL and the minimum discharge current, marking the current difference as a discharge value FL, obtaining the sum of the charge value CL and the discharge value FL, marking the sum as a current value DL, carrying out quantization treatment on the voltage value DY and the current value DL, extracting the values of the voltage value DY and the current value DL, substituting the values into a formula for calculation according to the formulaObtaining an equalizing value JH, wherein j1 and j2 are preset proportional coefficients corresponding to a set voltage value DY and a current value DL respectively, j1 and j2 meet the condition that j1+ j2 = 1,0 < j1 < j2 < 1, j1 = 0.47 and j2 = 0.53;
and sending the temperature value WD, the time value SJ, the aging value LH and the balance value JH to a parameter analysis module.
As a further scheme of the invention: the specific process of obtaining the state anomaly coefficient ZY by the parameter analysis module is as follows:
quantizing the temperature WD, time SJ, aging LH and balance JH, extracting the values of the temperature WD, time SJ, aging LH and balance JH, substituting them into the formula, and calculating according to the formulaObtaining a state anomaly coefficient ZY, wherein e is a mathematical constant, z1, z2, z3 and z4 are preset weight factors corresponding to a set temperature value WD, a time value SJ, an aging value LH and an equalization value JH respectively, z1, z2, z3 and z4 meet the condition that z4 > z1 > z3 > z2 > 1.741, z1=2.12, z2=1.85, z3=1.97 and z4=2.40 are taken;
and sending the state anomaly coefficient ZY to an early warning grading module.
The invention has the beneficial effects that:
according to the monitoring and early warning system for lithium battery energy storage, the surface state of a lithium battery is detected through a surface detection module, abnormal parameters of the lithium battery are obtained, a surface abnormal coefficient is obtained through a parameter analysis module according to the surface abnormal parameters, a severe danger alarm signal is generated through an early warning and grading module according to the surface abnormal coefficient, the gas concentration generated in the charging and discharging process of the lithium battery is detected through a gas monitoring module, the gas abnormal parameters of the lithium battery are obtained, the state of the lithium battery is detected through a state monitoring module, the state abnormal parameters of the lithium battery are obtained, the gas abnormal coefficient is obtained through a parameter analysis module according to the state abnormal parameters, a moderate danger alarm signal or a mild danger alarm signal or a no danger signal is generated through an early warning and grading alarm module according to the gas abnormal coefficient and the state abnormal coefficient, and the serious danger alarm signal, the moderate danger alarm signal and the no danger signal are received through a grading alarm module for grading display; the monitoring and early warning system firstly detects the surface of the lithium battery, the surface abnormality degree of the lithium battery can be comprehensively measured according to the surface abnormality coefficient obtained by the surface abnormality parameter, the smaller the surface abnormality coefficient is, the higher the abnormality degree is, the external state difference of the lithium battery is described, then the gas concentration generated in the charging and discharging process of the lithium battery and the state of the lithium battery are detected, the gas abnormality degree of the charging and discharging of the lithium battery can be comprehensively measured according to the gas abnormality coefficient obtained by the gas abnormality parameter, the higher the abnormality degree is described on the one hand, the internal state difference of the lithium battery is described on the other hand, the operating state abnormality degree of the lithium battery can be comprehensively measured according to the state abnormality coefficient obtained by the state abnormality parameter, the higher the abnormality degree is described on the other hand, and therefore the abnormal situation of the lithium battery can be classified and early warned through the three parts; the monitoring and early warning system can monitor the lithium battery from multiple aspects, improves the monitoring accuracy of the lithium battery, can monitor the state of the lithium battery in real time and in time, timely finds abnormality, and ensures the safe use of the lithium battery.
Drawings
The invention is further described below with reference to the accompanying drawings.
Fig. 1 is a schematic block diagram of a monitoring and early warning system for lithium battery energy storage in the present invention.
Detailed Description
The technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1:
referring to fig. 1, the present embodiment is a monitoring and early warning system for energy storage of a lithium battery, including the following modules: the system comprises a surface detection module, a parameter analysis module, an early warning grading module, a gas monitoring module, a state monitoring module and a grading alarm module;
the surface detection module is used for detecting the surface state of the lithium battery, acquiring abnormal parameters of the lithium battery and sending the surface abnormal parameters to the parameter analysis module; wherein the surface anomaly parameters include ash values HS, deformation values BX, and crack values LW;
the parameter analysis module is used for obtaining a surface abnormality coefficient BY according to the surface abnormality parameters and sending the surface abnormality coefficient BY to the early warning classification module; the gas abnormality detection module is also used for obtaining a gas abnormality coefficient QY according to the gas abnormality parameter and sending the gas abnormality coefficient QY to the early warning classification module; the system is also used for obtaining a state anomaly coefficient ZY according to the state anomaly parameters and sending the state anomaly coefficient ZY to the early warning classification module;
the early warning grading module is used for generating a gas monitoring instruction and a state monitoring instruction according to the surface anomaly coefficient BY, or generating a severe danger alarm signal, sending the gas monitoring instruction to the gas monitoring module, sending the state monitoring instruction to the state monitoring module and sending the severe danger alarm signal to the grading alarm module; the system is also used for generating a moderate-risk alarm signal or a mild-risk alarm signal or a no-risk signal according to the gas anomaly coefficient QY and the state anomaly coefficient ZY and sending the moderate-risk alarm signal, the mild-risk alarm signal and the no-risk signal to the grading alarm module;
the gas monitoring module is used for detecting the gas concentration generated in the charging and discharging process of the lithium battery, acquiring the gas abnormal parameters of the lithium battery and sending the gas abnormal parameters to the parameter analysis module; wherein, the gas anomaly parameters comprise a hydrogen value QQ, a chlorine value LQ, a hydrogen initial value QC and a chlorine initial value LC;
the state monitoring module is used for detecting the state of the lithium battery, acquiring state abnormal parameters of the lithium battery and sending the state abnormal parameters to the parameter analysis module; the state anomaly parameters comprise a temperature value WD, a time value SJ, an aging value LH and an equilibrium value JH;
the grading alarm module is used for receiving the severe danger alarm signal, the moderate danger alarm signal, the mild danger alarm signal and the no danger signal and carrying out grading display on the severe danger alarm signal, the moderate danger alarm signal, the mild danger alarm signal and the no danger signal.
Example 2:
referring to fig. 1, the present embodiment is a working method of a monitoring and early warning system for energy storage of a lithium battery, including the following steps:
step s1: the surface detection module detects the surface state of the lithium battery, acquires the total amount of dust which is larger than a preset diameter in the unit area of the surface of the lithium battery, and marks the total amount of dust as an ash value HS;
step s2: the surface detection module obtains the appearance shape of the lithium battery, marks the appearance shape as an actual acquisition model, obtains the appearance shape of the lithium battery when leaving the factory from the data storage module, marks the appearance shape as an original model, performs superposition comparison on the actual acquisition model and the original model, obtains the volume of a part where the actual acquisition model and the original model cannot be completely superposed, and marks the volume as a deformation value BX;
step s3: the surface detection module obtains the total number of cracks and the total number of cracks on the surface of the lithium batteryThe areas are marked as a crack value LS and a crack value LM respectively, the crack value LS and the crack value LM are quantized, the values of the crack value LS and the crack value LM are extracted and substituted into a formula for calculation, and the values are calculated according to the formulaObtaining a crack value LW, wherein w1 and w2 are preset proportional coefficients corresponding to a set crack value LS and a preset crack value LM respectively, w1 and w2 meet w1+w2=1, 0 < w2 < w1 < 1, w1=0.58 and w2=0.42;
step s4: the surface detection module sends the ash value HS, the deformation value BX and the crack value LW to the parameter analysis module;
step s5: the parameter analysis module carries out quantization processing on the ash value HS, the deformation value BX and the crack value LW, extracts the values of the ash value HS, the deformation value BX and the crack value LW, substitutes the values into a formula for calculation, and calculates according to the formulaObtaining a surface anomaly coefficient BY, wherein e is a mathematical constant, b1, b2 and b3 are preset weight factors corresponding to a set ash value HS, a set deformation value BX and a set crack value LW respectively, b1, b2 and b3 meet the condition that b2 > b3 > b1 > 1.355, b1=1.49 is taken, b2=2.32, and b3=1.88;
step s6: the parameter analysis module sends the surface anomaly coefficient BY to the early warning classification module;
step s7: the early warning classification module compares the surface anomaly coefficient BY with a preset surface anomaly threshold BYy:
if the surface anomaly coefficient BY is more than or equal to the surface anomaly threshold BYy, generating a gas monitoring instruction and a state monitoring instruction, sending the gas monitoring instruction to a gas monitoring module, and sending the state monitoring instruction to a state monitoring module;
if the surface anomaly coefficient BY is smaller than the surface anomaly threshold BYy, generating a severe danger alarm signal, and sending the severe danger alarm signal to the grading alarm module;
step s8: the gas monitoring module detects the gas concentration generated in the charging and discharging process of the lithium battery after receiving the gas monitoring instruction, obtains the hydrogen concentration generated in the charging process of the lithium battery, marks the hydrogen concentration as a hydrogen value QQ, obtains the chlorine concentration generated in the discharging process of the lithium battery, and marks the chlorine concentration as a chlorine value LQ;
step s9: the gas monitoring module obtains the concentration of hydrogen generated in the first charging process of the lithium battery from the data storage module, marks the concentration of hydrogen as a hydrogen initial value QC, obtains the concentration of chlorine generated in the first discharging process of the lithium battery, and marks the concentration of chlorine as a chlorine initial value LC;
step s10: the gas monitoring module sends a hydrogen value QQ, a chlorine value LQ, a hydrogen initial value QC and a chlorine initial value LC to the parameter analysis module;
step s11: the state monitoring module detects the state of the lithium battery after receiving a state monitoring instruction, acquires the maximum operating temperature in the charging process, acquires the maximum operating temperature in the first charging process from the data storage module, acquires the temperature difference between the maximum operating temperature and the maximum operating temperature in the discharging process, acquires the maximum operating temperature in the first discharging process from the data storage module, acquires the temperature difference between the maximum operating temperature and the maximum operating temperature in the first discharging process, marks the maximum operating temperature as a temperature release value WF, carries out quantization processing on the temperature release value WC and the temperature release value WF, extracts the numerical values of the temperature release value WF and the temperature release value WC, and substitutes the numerical values into a formula to calculate according to the formulaObtaining a temperature value WD, wherein d1 and d2 are preset proportional coefficients corresponding to a set temperature recharging WC and a set temperature discharging WF respectively, d1 and d2 meet d1+d2=1, 0 < d2 < d1 < 1, d1=0.63 and d2=0.37;
step s12: the state monitoring module obtains the production time and the current time of the lithium battery, obtains the time difference between the production time and the current time, marks the time difference as a time production value CS, obtains the total charge and discharge time of the lithium battery, marks the time difference as an operation value YS, carries out quantization processing on the time production value CS and the operation value YS, extracts the values of the time production value CS and the operation value YS, substitutes the values into a formula to calculate, and calculates according to the formulaObtaining a time value SJ, wherein s1 and s2 are preset proportionality coefficients of a set production value CS and an operation value YS respectively, s1 and s2 meet s1+s2=1, 0 < s1 < s2 < 1, s1=0.35 and s2=0.65;
step s13: the state monitoring module obtains the charging times of the lithium battery, marks the charging times as charging values CC, obtains the maximum charging capacity in the charging process of the lithium battery and the maximum charging capacity in the first charging process, obtains the capacity difference value between the charging times and the maximum charging capacity, marks the capacity difference value as a capacity value RL, carries out quantization treatment on the charging values CC and the capacity value RL, extracts the values of the charging values CC and the capacity value RL, substitutes the values into a formula to calculate, and calculates according to the formulaObtaining an aging value LH, wherein h1 and h2 are preset proportionality coefficients of a set recharging CC and a capacity value RL respectively, h1 and h2 meet the condition that h1+h2=1, 0 < h1 < h2 < 1, and taking h1=0.42 and h2=0.58;
step s14: the state monitoring module obtains the maximum charging voltage and the minimum charging voltage in the charging process, obtains the voltage difference between the maximum charging voltage and the minimum charging voltage, marks the voltage difference as a charging value CY, obtains the maximum discharging voltage and the minimum discharging voltage in the discharging process, obtains the voltage difference between the maximum discharging voltage and the minimum discharging voltage, marks the voltage difference as a discharging value FY, obtains the sum of the charging value CY and the discharging value FY, obtains the maximum charging current and the minimum charging current in the charging process, obtains the current difference between the maximum charging current and the minimum charging current in the charging process, marks the current difference between the maximum discharging current and the minimum discharging current in the discharging process as a discharging value FL, obtains the sum of the charging current CL and the discharging current FL, marks the sum of the maximum discharging voltage and the minimum discharging voltage as a discharging value DL, carries out quantization treatment on the voltage value DY and the discharging value DL, extracts the numerical value of the voltage value DY and the current value DL, and substitutes the numerical value into a formula to calculate, obtains the numerical value according to the formula, obtains the current difference between the valuesObtaining an equalizing value JH, wherein j1 and j2 are preset proportional coefficients corresponding to a set voltage value DY and a set current value DL respectively, and j1,j2 satisfies j1+j2=1, 0 < j1 < j2 < 1, j1=0.47, j2=0.53;
step s15: the state monitoring module sends a temperature value WD, a time value SJ, an aging value LH and an equilibrium value JH to the parameter analysis module;
step s16: the parameter analysis module carries out quantization treatment on the hydrogen value QQ, the chlorine value LQ, the hydrogen initial value QC and the chlorine initial value LC, extracts the values of the hydrogen value QQ, the chlorine value LQ, the hydrogen initial value QC and the chlorine initial value LC, substitutes the values into a formula to calculate, and calculates according to the formulaObtaining a gas anomaly coefficient QY in a formula, wherein e is a mathematical constant, q1 and q2 are preset weight factors corresponding to a set hydrogen value QQ and a set chlorine value LQ respectively, q1 and q2 meet q1 & gtq 2 & gt1.241, q1=1.58 and q2=1.29;
step s17: the parameter analysis module carries out quantization processing on the temperature value WD, the time value SJ, the aging value LH and the balance value JH, extracts the values of the temperature value WD, the time value SJ, the aging value LH and the balance value JH, substitutes the values into a formula for calculation, and calculates according to the formulaObtaining a state anomaly coefficient ZY, wherein e is a mathematical constant, z1, z2, z3 and z4 are preset weight factors corresponding to a set temperature value WD, a time value SJ, an aging value LH and an equalization value JH respectively, z1, z2, z3 and z4 meet the condition that z4 > z1 > z3 > z2 > 1.741, z1=2.12, z2=1.85, z3=1.97 and z4=2.40 are taken;
step s18: the parameter analysis module sends the gas anomaly coefficient QY and the state anomaly coefficient ZY to the early warning classification module;
step s19: the early warning classification module compares the gas anomaly coefficient QY and the state anomaly coefficient ZY with a preset gas anomaly threshold value QYy and a preset state anomaly threshold value ZYy respectively:
if the gas abnormality coefficient QY is smaller than the gas abnormality threshold QYy and the state abnormality coefficient ZY is smaller than the state abnormality threshold ZYy, generating a moderate danger alarm signal and sending the moderate danger alarm signal to the grading alarm module;
if the gas abnormality coefficient QY is smaller than the gas abnormality threshold QYy or the state abnormality coefficient ZY is smaller than the state abnormality threshold ZYy, generating a mild danger alarm signal and sending the mild danger alarm signal to the grading alarm module;
if the gas abnormality coefficient QY is more than or equal to the gas abnormality threshold QYy and the state abnormality coefficient ZY is more than or equal to the state abnormality threshold ZYy, generating a non-danger signal and sending the non-danger signal to the hierarchical alarm module;
step s20: the grading alarm module receives the severe danger alarm signal, the moderate danger alarm signal, the mild danger alarm signal and the no danger signal and displays the severe danger alarm signal, the moderate danger alarm signal, the mild danger alarm signal and the no danger signal in a grading manner.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing is merely illustrative and explanatory of the invention, as various modifications and additions may be made to the particular embodiments described, or in a similar manner, by those skilled in the art, without departing from the scope of the invention or exceeding the scope of the invention as defined in the claims.
Claims (5)
1. A monitoring early warning system for lithium cell energy storage, characterized by comprising:
the surface detection module is used for detecting the surface state of the lithium battery, acquiring abnormal parameters of the lithium battery and sending the surface abnormal parameters to the parameter analysis module; wherein the surface anomaly parameters include ash values HS, deformation values BX, and crack values LW;
the parameter analysis module is used for obtaining a surface abnormality coefficient BY according to the surface abnormality parameters and sending the surface abnormality coefficient BY to the early warning classification module; the gas abnormality detection module is also used for obtaining a gas abnormality coefficient QY according to the gas abnormality parameter and sending the gas abnormality coefficient QY to the early warning classification module; the system is also used for obtaining a state anomaly coefficient ZY according to the state anomaly parameters and sending the state anomaly coefficient ZY to the early warning classification module;
the early warning grading module is used for generating a gas monitoring instruction and a state monitoring instruction according to the surface anomaly coefficient BY, or generating a severe danger alarm signal, sending the gas monitoring instruction to the gas monitoring module, sending the state monitoring instruction to the state monitoring module and sending the severe danger alarm signal to the grading alarm module; the system is also used for generating a moderate-risk alarm signal or a mild-risk alarm signal or a no-risk signal according to the gas anomaly coefficient QY and the state anomaly coefficient ZY and sending the moderate-risk alarm signal, the mild-risk alarm signal and the no-risk signal to the grading alarm module;
the gas monitoring module is used for detecting the gas concentration generated in the charging and discharging process of the lithium battery, acquiring the gas abnormal parameters of the lithium battery and sending the gas abnormal parameters to the parameter analysis module; wherein, the gas anomaly parameters comprise a hydrogen value QQ, a chlorine value LQ, a hydrogen initial value QC and a chlorine initial value LC;
the state monitoring module is used for detecting the state of the lithium battery, acquiring state abnormal parameters of the lithium battery and sending the state abnormal parameters to the parameter analysis module; the state anomaly parameters comprise a temperature value WD, a time value SJ, an aging value LH and an equilibrium value JH;
the grading alarm module is used for receiving the severe danger alarm signal, the moderate danger alarm signal, the mild danger alarm signal and the no danger signal and carrying out grading display on the severe danger alarm signal, the moderate danger alarm signal, the mild danger alarm signal and the no danger signal;
the specific process of acquiring the abnormal parameters by the surface detection module is as follows:
detecting the surface state of the lithium battery, obtaining the total amount of dust which is larger than a preset diameter in the unit area of the surface of the lithium battery, and marking the total amount of dust as an ash value HS;
obtaining the appearance shape of the lithium battery, marking the appearance shape as an actual model, obtaining the appearance shape of the lithium battery when leaving the factory from a data storage module, marking the appearance shape as an original model, performing superposition comparison on the actual model and the original model, obtaining the volume of a part where the actual model and the original model cannot be completely superposed, and marking the volume as a deformation value BX;
obtaining the total number of cracks and the total area of the cracks on the surface of the lithium battery, marking the total number of the cracks and the total area of the cracks as a crack value LS and a crack value LM respectively, carrying out quantization treatment on the crack value LS and the crack value LM, and according to a formulaObtaining a crack value LW, wherein w1 and w2 are preset proportional coefficients corresponding to the set crack value LS and the crack value LM respectively;
transmitting the ash value HS, the deformation value BX and the crack value LW to a parameter analysis module;
the specific process of the state monitoring module for acquiring the state abnormal parameters is as follows:
detecting the state of the lithium battery after receiving a state monitoring instruction, acquiring the maximum operating temperature in the charging process, acquiring the maximum operating temperature in the first charging process from a data storage module, acquiring the temperature difference between the maximum operating temperature and the maximum operating temperature, marking the maximum operating temperature as a temperature charging WC, acquiring the maximum operating temperature in the discharging process from the data storage module, acquiring the maximum operating temperature in the first discharging process, acquiring the temperature difference between the maximum operating temperature and the maximum operating temperature as a temperature discharging WF, quantifying the temperature charging WC and the temperature discharging WF according to a formulaObtaining a temperature value WD, wherein d1 and d2 are preset proportional coefficients corresponding to a set temperature recharging WC and a set temperature discharging WF respectively, d1 and d2 meet d1+d2=1, and 0 < d2 < d1 < 1;
acquiring the production time and the current time of the lithium battery, acquiring the time difference between the production time and the current time, marking the time difference as a time generation value CS, acquiring the total charge and discharge time of the lithium battery, marking the total charge and discharge time as an operation value YS, carrying out quantization processing on the time generation value CS and the operation value YS, and according to a formulaObtaining a time value SJ, wherein s1 and s2 are preset proportionality coefficients of a set production value CS and an operation value YS respectively;
acquiring the charging times of a lithium battery, marking the charging times as charging times CC, acquiring the maximum charging capacity in the charging process of the lithium battery and the maximum charging capacity in the first charging process, acquiring the capacity difference between the maximum charging capacity and the maximum charging capacity, marking the capacity difference as capacity value RL, carrying out quantization treatment on the charging times CC and the capacity value RL, and carrying out quantization treatment according to a formulaObtaining an aging value LH, wherein h1 and h2 are preset proportion coefficients of a set recharging CC and a capacity value RL respectively;
obtaining the maximum charging voltage and the minimum charging voltage in the charging process, obtaining the voltage difference between the maximum charging voltage and the minimum charging voltage, marking the maximum discharging voltage and the minimum discharging voltage in the discharging process, obtaining the voltage difference between the maximum discharging voltage and the minimum discharging voltage, marking the voltage difference as a discharging value FY, obtaining the sum of the charging value CY and the discharging value FY, marking the voltage value DY, obtaining the maximum charging current and the minimum charging current in the charging process, obtaining the current difference between the maximum charging current and the minimum charging current, marking the current difference as a charging value CL, obtaining the maximum discharging current and the minimum discharging current in the discharging process, obtaining the current difference between the maximum discharging current FL, obtaining the sum of the charging current CL and the discharging current FL, marking the sum of the charging current value FL and the discharging value FL as a current value DL, quantifying the voltage value DY and the current value DL according to a formulaObtaining an equalizing value JH, wherein j1 and j2 are preset proportional coefficients corresponding to a set voltage value DY and a current value DL respectively;
and sending the temperature value WD, the time value SJ, the aging value LH and the balance value JH to a parameter analysis module.
2. The monitoring and early warning system for lithium battery energy storage according to claim 1, wherein the specific process of obtaining the surface anomaly coefficient BY the parameter analysis module is as follows:
the ash value HS, the deformation value BX and the crack value LW are quantized according to the formulaObtaining a surface anomaly coefficient BY, wherein e is a mathematical constant, and b1, b2 and b3 are preset weight factors corresponding to a set ash value HS, a set deformation value BX and a set crack value LW respectively;
and sending the surface anomaly coefficient BY to an early warning grading module.
3. The monitoring and early warning system for lithium battery energy storage according to claim 1, wherein the specific process of acquiring the abnormal gas parameters by the gas monitoring module is as follows:
detecting the gas concentration generated in the charging and discharging process of the lithium battery after receiving the gas monitoring instruction, obtaining the hydrogen concentration generated in the charging process of the lithium battery, marking the hydrogen concentration as a hydrogen value QQ, obtaining the chlorine concentration generated in the discharging process of the lithium battery, and marking the chlorine concentration as a chlorine value LQ;
the method comprises the steps of obtaining the concentration of hydrogen generated in the first charging process of a lithium battery from a data storage module, marking the concentration of hydrogen as a hydrogen initial value QC, obtaining the concentration of chlorine generated in the first discharging process of the lithium battery, and marking the concentration of chlorine as a chlorine initial value LC;
and sending the hydrogen value QQ, the chlorine value LQ, the hydrogen initial value QC and the chlorine initial value LC to a parameter analysis module.
4. The monitoring and early warning system for lithium battery energy storage according to claim 1, wherein the specific process of obtaining the gas anomaly coefficient QY by the parameter analysis module is as follows:
quantitatively processing the hydrogen value QQ, the chlorine value LQ, the hydrogen initial value QC and the chlorine initial value LC according to the formulaObtaining a gas anomaly coefficient QY, wherein e is a mathematical constant, and q1 and q2 are preset weight factors corresponding to a set hydrogen value QQ and a set chlorine value LQ respectively;
and sending the gas anomaly coefficient QY to an early warning grading module.
5. The monitoring and early warning system for lithium battery energy storage according to claim 1, wherein the specific process of obtaining the state anomaly coefficient ZY by the parameter analysis module is as follows:
quantifying the temperature WD, time SJ, aging LH and balance JH according to the formulaObtaining a state anomaly coefficient ZY, wherein e is a mathematical constant, and z1, z2, z3 and z4 are preset weight factors corresponding to a set temperature value WD, a time value SJ, an aging value LH and an equalization value JH respectively;
and sending the state anomaly coefficient ZY to an early warning grading module.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114284589A (en) * | 2022-03-01 | 2022-04-05 | 深圳市海雷新能源有限公司 | Lithium battery intelligent monitoring management system based on internet |
| CN114678581A (en) * | 2022-04-06 | 2022-06-28 | 深圳市海雷新能源有限公司 | Lithium battery fireproof heat insulation structure and early warning system |
| CN115903659A (en) * | 2023-03-09 | 2023-04-04 | 深圳市星火数控技术有限公司 | Intelligent monitoring method and system for numerical control equipment |
| CN116093459A (en) * | 2023-03-20 | 2023-05-09 | 苏州时代华景新能源有限公司 | Intelligent monitoring method and system for lithium battery energy storage box |
| CN116094094A (en) * | 2022-12-19 | 2023-05-09 | 广州市力驰微电子科技有限公司 | Lithium battery pack monitoring system for monitoring long string serial connection |
| CN117072884A (en) * | 2023-09-15 | 2023-11-17 | 江西省天然气管道有限公司运营分公司 | Monitoring and early warning system for natural gas and oil gas pipeline |
| CN117196315A (en) * | 2023-10-09 | 2023-12-08 | 广东开放大学（广东理工职业学院） | Building structure safety evaluation monitoring system based on BIM after flood disaster |
| CN117233622A (en) * | 2023-10-31 | 2023-12-15 | 中扬储存设备（广德）有限公司 | New energy automobile power lithium battery performance detection test method |
-
2023
- 2023-12-18 CN CN202311741954.3A patent/CN117420452B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114284589A (en) * | 2022-03-01 | 2022-04-05 | 深圳市海雷新能源有限公司 | Lithium battery intelligent monitoring management system based on internet |
| CN114678581A (en) * | 2022-04-06 | 2022-06-28 | 深圳市海雷新能源有限公司 | Lithium battery fireproof heat insulation structure and early warning system |
| CN116094094A (en) * | 2022-12-19 | 2023-05-09 | 广州市力驰微电子科技有限公司 | Lithium battery pack monitoring system for monitoring long string serial connection |
| CN115903659A (en) * | 2023-03-09 | 2023-04-04 | 深圳市星火数控技术有限公司 | Intelligent monitoring method and system for numerical control equipment |
| CN116093459A (en) * | 2023-03-20 | 2023-05-09 | 苏州时代华景新能源有限公司 | Intelligent monitoring method and system for lithium battery energy storage box |
| CN117072884A (en) * | 2023-09-15 | 2023-11-17 | 江西省天然气管道有限公司运营分公司 | Monitoring and early warning system for natural gas and oil gas pipeline |
| CN117196315A (en) * | 2023-10-09 | 2023-12-08 | 广东开放大学（广东理工职业学院） | Building structure safety evaluation monitoring system based on BIM after flood disaster |
| CN117233622A (en) * | 2023-10-31 | 2023-12-15 | 中扬储存设备（广德）有限公司 | New energy automobile power lithium battery performance detection test method |
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