CN117458010B - Lithium battery energy storage monitoring system based on data analysis - Google Patents
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- 229910052744 lithium Inorganic materials 0.000 title claims abstract description 264
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 262
- 238000012544 monitoring process Methods 0.000 title claims abstract description 49
- 238000004146 energy storage Methods 0.000 title claims abstract description 38
- 238000007405 data analysis Methods 0.000 title claims abstract description 13
- 238000001514 detection method Methods 0.000 claims abstract description 115
- 230000007547 defect Effects 0.000 claims abstract description 114
- 238000011156 evaluation Methods 0.000 claims abstract description 78
- 230000015556 catabolic process Effects 0.000 claims abstract description 67
- 238000006731 degradation reaction Methods 0.000 claims abstract description 67
- 238000004458 analytical method Methods 0.000 claims abstract description 56
- 238000012797 qualification Methods 0.000 claims abstract description 10
- 238000000034 method Methods 0.000 claims description 56
- 230000008569 process Effects 0.000 claims description 47
- 238000005259 measurement Methods 0.000 claims description 43
- 230000007613 environmental effect Effects 0.000 claims description 29
- 238000012423 maintenance Methods 0.000 claims description 24
- 238000012360 testing method Methods 0.000 claims description 24
- 238000004364 calculation method Methods 0.000 claims description 18
- 230000005611 electricity Effects 0.000 claims description 16
- 238000003912 environmental pollution Methods 0.000 claims description 10
- 238000007726 management method Methods 0.000 claims description 9
- 230000003068 static effect Effects 0.000 claims description 9
- 230000005670 electromagnetic radiation Effects 0.000 claims description 5
- 238000004891 communication Methods 0.000 claims description 4
- 230000005856 abnormality Effects 0.000 claims 2
- 230000000750 progressive effect Effects 0.000 abstract description 5
- 230000002159 abnormal effect Effects 0.000 description 5
- 230000009286 beneficial effect Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000011835 investigation Methods 0.000 description 2
- 238000010223 real-time analysis Methods 0.000 description 2
- 239000000243 solution Substances 0.000 description 2
- 206010011906 Death Diseases 0.000 description 1
- 229910000733 Li alloy Inorganic materials 0.000 description 1
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 1
- 239000010405 anode material Substances 0.000 description 1
- 239000010406 cathode material Substances 0.000 description 1
- 239000000428 dust Substances 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000001989 lithium alloy Substances 0.000 description 1
- 229910001416 lithium ion Inorganic materials 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000011255 nonaqueous electrolyte Substances 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Classifications
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- 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
- H01M10/425—Structural combination with electronic components, e.g. electronic circuits integrated to the outside of the casing
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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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- 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
- H01M10/425—Structural combination with electronic components, e.g. electronic circuits integrated to the outside of the casing
- H01M2010/4271—Battery management systems including electronic circuits, e.g. control of current or voltage to keep battery in healthy state, cell balancing
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- 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
- H01M10/425—Structural combination with electronic components, e.g. electronic circuits integrated to the outside of the casing
- H01M2010/4278—Systems for data transfer from batteries, e.g. transfer of battery parameters to a controller, data transferred between battery controller and main controller
Abstract
The invention belongs to the technical field of lithium battery management and control, and particularly relates to a lithium battery energy storage monitoring system based on data analysis, which comprises a processor, a lithium battery availability detection module, a lithium battery performance degradation detection module, a lithium battery defect evaluation module and a lithium battery omnibearing monitoring module; according to the invention, the availability detection module is used for carrying out availability analysis on the corresponding lithium battery to generate a high availability signal or a low availability signal, the performance degradation condition of the corresponding lithium battery is analyzed to generate a performance degradation disqualification signal or a performance degradation qualification signal when the high availability signal is generated, and the corresponding lithium battery is scanned and subjected to defect identification analysis by the lithium battery defect evaluation module when the performance degradation qualification signal is generated, so that multi-angle and gradual progressive analysis is realized, the energy storage risk and the use risk of the lithium battery can be comprehensively and comprehensively evaluated, the energy storage safety of the lithium battery is improved, the stable and efficient operation of the lithium battery is ensured, the intelligent degree is high, and the supervision difficulty of the lithium battery is reduced.
Description
Technical Field
The invention relates to the technical field of lithium battery management and control, in particular to a lithium battery energy storage monitoring system based on data analysis.
Background
The lithium battery is divided into two major types, namely a lithium metal battery and a lithium ion battery, is a battery which uses lithium metal or lithium alloy as an anode/cathode material and uses nonaqueous electrolyte solution, has the advantages of high energy density, long service life, low self-discharge rate, environmental protection and the like, and is often applied to the fields of electronic products, electric vehicles and the like; along with the transformation of energy structures and the upgrading of power systems, the lithium battery energy storage technology is widely applied;
however, when energy is stored by the lithium battery, the existing lithium battery monitoring system is generally single in function, the energy storage risk and the use risk of the lithium battery cannot be comprehensively and comprehensively estimated through multi-angle and gradual progressive analysis, real-time monitoring and feedback early warning on the operation of the lithium battery are difficult to realize, intelligent monitoring on the lithium battery is difficult to realize, and the energy storage safety of the lithium battery is not improved and stable and efficient operation of the lithium battery is guaranteed;
in view of the above technical drawbacks, a solution is now proposed.
Disclosure of Invention
The invention aims to provide a lithium battery energy storage monitoring system based on data analysis, which solves the problems that the prior art cannot comprehensively evaluate the energy storage risk and the use risk of a lithium battery through multi-angle and gradual progressive analysis, is difficult to realize real-time monitoring and feedback early warning of the operation of the lithium battery, is not beneficial to improving the energy storage safety of the lithium battery, ensures the stable and efficient operation of the lithium battery and has great management difficulty of the lithium battery.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a lithium battery energy storage monitoring system based on data analysis comprises a processor, a lithium battery availability detection module, a lithium battery performance degradation detection module, a lithium battery defect evaluation module and a lithium battery omnibearing monitoring module; the lithium battery availability detection module analyzes the availability of the corresponding lithium battery, generates a high availability signal or a low availability signal through analysis, sends the high availability signal to the lithium battery availability drop detection module through the processor, and sends the low availability signal to the lithium battery omnibearing monitoring module through the processor;
when the lithium battery performance degradation detection module receives the high availability signal, analyzing the performance degradation condition of the corresponding lithium battery to generate a performance degradation disqualification signal or a performance degradation qualification signal, sending the performance degradation qualification signal to the lithium battery defect evaluation module through the processor, and sending the performance degradation disqualification signal to the lithium battery omnibearing monitoring module through the processor;
the lithium battery defect evaluation module scans the corresponding lithium battery, acquires an external image of the corresponding lithium battery, performs defect identification analysis on the external image, generates a defect evaluation qualified signal or a defect evaluation unqualified signal through analysis, and sends the defect evaluation qualified signal or the defect evaluation unqualified signal to the lithium battery omnibearing monitoring module through the processor; and when the lithium battery omnibearing monitoring module receives the low-availability signal, the performance degradation disqualification signal or the defect evaluation disqualification signal, corresponding early warning information is generated, and the early warning information is sent to an intelligent terminal of a manager.
Further, the specific analysis process of the availability analysis includes:
acquiring the service life of the type of the corresponding lithium battery, acquiring the service time of the corresponding lithium battery, and calculating the ratio of the service time to the service life to obtain a battery service life end table value; collecting the charging times of the corresponding lithium battery, and calculating the ratio of the charging times to a preset charging times threshold value of the lithium battery of the type to obtain a battery charging frequency detection value; collecting maintenance times of the lithium battery in a historical process, marking the maintenance times as a battery maintenance frequency table value, and summing up maintenance time lengths of all maintenance processes to obtain a battery maintenance time table value;
performing numerical calculation on a battery life end table value, a battery charging frequency detection value, a battery maintenance frequency table value and a battery maintenance time table value to obtain an availability detection value, performing numerical comparison on the availability detection value and a preset availability detection threshold value, and generating a low availability signal if the availability detection value exceeds the preset availability detection threshold value; and if the availability detection value does not exceed the preset availability detection threshold value, generating a high availability signal.
Further, the specific operation process of the lithium battery performance degradation detection module comprises the following steps:
acquiring electricity storage performance data and electricity storage consumption data of a corresponding lithium battery in unit time, acquiring battery recharging value of the corresponding lithium battery through charge evaluation summary analysis, performing numerical calculation on the energy storage performance data, the electricity storage consumption data and the battery recharging value to obtain a battery performance degradation value, performing numerical comparison on the battery performance degradation value and a preset battery performance degradation threshold value, and generating a performance degradation detection failure signal if the battery performance degradation value exceeds the preset battery performance degradation threshold value; and if the battery performance degradation value does not exceed the preset battery performance degradation threshold value, generating a performance degradation detection qualified signal.
Further, the specific analysis procedure of the charge evaluation summary analysis is as follows:
collecting real-time charging speed of the lithium battery in the charging process of the lithium battery, calculating a difference value between the real-time charging speed and a preset standard charging speed, taking an absolute value to obtain charging speed deviation data, and calculating a mean value and a variance of all charging speed deviation data of the corresponding lithium battery in the corresponding charging process to obtain a charging speed deviation measurement value and a charging speed wave measurement value;
respectively carrying out numerical comparison on the charging speed deviation measurement value and the charging speed wave measurement value as well as a preset charging speed deviation measurement threshold value and a preset charging speed wave measurement threshold value, and if the charging speed deviation measurement value exceeds the preset charging speed deviation measurement threshold value or the charging speed wave measurement value exceeds the preset charging speed wave measurement threshold value, giving a charging judgment symbol CF-1 to the corresponding charging process; if the charging speed deviation measurement value does not exceed the preset charging speed deviation measurement threshold value and the charging speed wave measurement value does not exceed the preset charging speed wave measurement threshold value, a charging judgment symbol CF-2 is given to the corresponding charging process;
the method comprises the steps of obtaining the number of charging processes corresponding to a charging judgment symbol CF-1 in unit time and marking the number as a negative charging performance value, obtaining the number of charging processes corresponding to a charging judgment symbol CF-2 in unit time and marking the number as a positive charging performance value, and calculating the ratio of the negative charging performance value to the positive charging performance value to obtain the battery charging measurement value.
Further, the omnibearing lithium battery monitoring module is also used for comprehensively monitoring the operation process of the lithium battery, retrieving the operation performance data and the environment performance data of the lithium battery from the processor, respectively comparing the operation performance data and the environment performance data with a preset operation performance data threshold value and a preset environment performance data threshold value in numerical value, generating a monitoring abnormal signal if the operation performance data or the environment performance data exceeds the corresponding preset threshold value, and sending the monitoring abnormal signal to the intelligent terminal of the manager through the processor.
Further, the specific operation process of the lithium battery defect evaluation module comprises the following steps:
comparing an external image of the lithium battery with a standard image thereof, identifying a bulge concave defect and a crack damage defect of the lithium battery shell, judging whether a super-abnormal bulge concave region or a super-abnormal crack damage region exists through analysis, and generating a defect evaluation disqualification signal if the super-abnormal bulge concave region or the super-abnormal crack damage region exists;
if the super-abnormal bulge concave area or the super-abnormal crack damage area does not exist, acquiring the area and the value of the bulge concave defect, marking the area and the value as the face value of the non-flat area, and acquiring the number of the crack damage defects and marking the number as the crack damage detection value; performing numerical calculation on the non-flat area face value and the crack damage detection value to obtain a battery defect value, performing numerical comparison on the battery defect value and a preset battery defect threshold value, and generating a defect evaluation disqualification signal if the battery defect value exceeds the preset battery defect threshold value; and if the battery defect value does not exceed the preset battery defect threshold value, generating a defect evaluation qualified signal.
Further, the specific analysis process for judging whether the super-abnormal bulge concave area or the super-abnormal crack damage area exists by analysis is as follows:
collecting the area of the corresponding bulge indent defect, marking the area as a defect surface measured value, collecting the deepest indent distance or the farthest protruding distance of the corresponding bulge indent defect, marking the deepest indent distance or the farthest protruding distance as a concave-convex distance measured value, weighting and summing the defect surface measured value and the concave-convex distance measured value to obtain a bulge indent detected value, comparing the bulge indent detected value with a preset bulge indent detected threshold value, and marking the corresponding bulge indent defect as an ultra-abnormal bulge indent region if the bulge indent detected value exceeds the preset bulge indent detected threshold value;
acquiring crack extension length data corresponding to the crack damage defect, acquiring a crack depth amplitude value and a crack width value corresponding to the crack damage defect, and performing numerical calculation on the crack extension length data, the crack depth amplitude value and the crack width value to obtain a crack depth analysis value; and (3) comparing the crack deep analysis value with a preset crack deep analysis threshold value, and marking the corresponding crack damage defect as a super-abnormal crack damage region if the crack deep analysis value exceeds the preset crack deep analysis threshold value.
Further, the processor is in communication connection with the lithium battery pipe transporting module, the lithium battery pipe transporting module analyzes the operation condition and the environmental condition of the lithium battery to acquire operation performance data and environmental performance data of the lithium battery, and the operation performance data and the environmental performance data of the lithium battery are sent to the processor for storage; the specific operation process of the lithium battery pipe-transporting module is as follows:
collecting the operation temperature, the operation voltage and the operation current of the lithium battery during operation, calculating the difference value between the operation temperature and the median value of a preset operation temperature range, taking an absolute value to obtain an operation temperature test value, and obtaining an operation voltage test value and an operation current test value in the same way; the operation temperature check value, the operation voltage check value, the operation current check value and the instantaneous impact data are respectively compared with corresponding preset thresholds in numerical values, and if the operation temperature check value, the operation voltage check value, the operation current check value or the instantaneous impact data exceed the corresponding preset thresholds, the lithium battery is judged to be in an operation danger state;
acquiring the time length of the lithium battery in the operation risk state in the detection period, marking the time length as a battery operation risk time detection value, marking the operation temperature detection value with the largest numerical value in the detection period as an operation temperature detection value, and acquiring the operation voltage detection value and the operation current detection value in the same way; performing numerical calculation on the battery dangerous operation detection value, the battery temperature operation detection value, the battery voltage operation detection value and the battery current operation detection value to obtain operation expression data;
collecting the ambient temperature and the ambient humidity of the environment where the lithium battery is operated, calculating the difference value between the ambient temperature and the median value of a preset ambient temperature range, taking an absolute value to obtain an ambient temperature test value, acquiring an ambient temperature test value in a similar way, calculating the average value of all ambient temperature test values in a detection period to obtain an ambient temperature evaluation value, and acquiring an ambient temperature evaluation value in a similar way; the environmental pollution data, the environmental electromagnetic radiation data and the environmental static data of the environment where the lithium battery is operated are acquired, the average value of all the environmental pollution data in the detection period is calculated to obtain an environmental pollution evaluation value, and the electromagnetic evaluation value and the static evaluation value are acquired in the same way; and carrying out numerical calculation on the ring temperature evaluation value, the ring humidity evaluation value, the ring pollution evaluation value, the electromagnetic evaluation value and the static evaluation value to obtain environmental performance data.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the invention, the corresponding lithium battery is subjected to availability analysis through the lithium battery availability detection module to generate a high availability signal or a low availability signal, the performance degradation condition of the corresponding lithium battery is analyzed through the lithium battery availability degradation detection module to generate a performance degradation disqualification signal or a performance degradation qualification signal when the high availability signal is generated, the corresponding lithium battery is scanned and subjected to defect identification analysis through the lithium battery defect evaluation module when the performance degradation qualification signal is generated, the energy storage risk and the use risk of the lithium battery are comprehensively and comprehensively evaluated through multi-angle and gradual progressive analysis, the energy storage safety of the lithium battery is improved, the stable and efficient operation of the lithium battery is ensured, the intelligent degree is high, and the supervision difficulty of the lithium battery is reduced;
2. according to the invention, the operation condition and the environmental condition of the lithium battery are analyzed through the lithium battery management module so as to accurately feed back the potential risk of the operation of the lithium battery in real time, and data support is provided for the real-time analysis process of the omnibearing monitoring module of the lithium battery; the comprehensive monitoring module of the lithium battery comprehensively monitors the operation process of the lithium battery, and judges the operation potential risk degree of the lithium battery in real time based on the operation performance data and the environment performance data, so that a manager timely performs reason investigation and adaptively regulates and controls the operation parameters or the environment of the lithium battery, the operation risk of the lithium battery is further reduced, and the management difficulty of the lithium battery is reduced.
Drawings
For the convenience of those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
FIG. 1 is a system block diagram of a first embodiment of the present invention;
fig. 2 is a system block diagram of the second and third embodiments of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but 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.
Embodiment one: as shown in fig. 1, the lithium battery energy storage monitoring system based on data analysis provided by the invention comprises a processor, a lithium battery availability detection module, a lithium battery performance degradation detection module, a lithium battery defect evaluation module and a lithium battery omnibearing monitoring module, wherein the processor is in communication connection with the lithium battery availability detection module, the lithium battery performance degradation detection module, the lithium battery defect evaluation module and the lithium battery omnibearing monitoring module; the lithium battery availability detection module analyzes the availability of the corresponding lithium battery, generates a high availability signal or a low availability signal through analysis, and can accurately feed back the availability condition of the lithium battery so that management staff can grasp the life state of the lithium battery in detail and send the low availability signal to the lithium battery omnibearing monitoring module through the processor; the specific analysis procedure for the usability analysis is as follows:
acquiring the service life of the type of the corresponding lithium battery, acquiring the service time of the corresponding lithium battery, and calculating the ratio of the service time to the service life to obtain a battery service life end table value; wherein, the larger the value of the battery life end-of-life table value is, the more the lithium battery tends to be scrapped; collecting the charging times of the corresponding lithium battery, and calculating the ratio of the charging times to a preset charging times threshold value of the lithium battery of the type to obtain a battery charging frequency detection value; the larger the value of the battery charging detection value is, the more the lithium battery tends to be scrapped; collecting maintenance times of the lithium battery in a historical process, marking the maintenance times as a battery maintenance frequency table value, and summing up maintenance time lengths of all maintenance processes to obtain a battery maintenance time table value;
performing numerical calculation on the battery life end table value KP, the battery charging frequency detection value KF, the battery maintenance frequency table value KR and the battery maintenance time table value KW through a formula KY= (ew 1+ew2 KF)/(ew 3+ew4 KW) to obtain an availability detection value KY, performing numerical comparison on the availability detection value KY and a preset availability detection threshold value, and generating a low availability signal if the availability detection value KY exceeds the preset availability detection threshold value, wherein the availability of the lithium battery is poor and the use risk is large; and if the usability detection value KY does not exceed the preset usability detection threshold value, indicating that the usability of the lithium battery is good, generating a high usability signal.
The lithium battery availability detection module sends a high availability signal to the lithium battery availability drop detection module through the processor, when the lithium battery availability drop detection module receives the high availability signal, the performance drop state of the corresponding lithium battery is analyzed to generate an unacceptable performance drop signal or an acceptable performance drop signal, and the unacceptable performance drop signal is sent to the lithium battery omnibearing monitoring module through the processor, so that the energy storage performance drop state of the lithium battery can be accurately fed back when the lithium battery is in the high availability state, and a manager can grasp the performance state of the lithium battery in detail so as to reduce the use risk of the lithium battery; the specific operation process of the lithium battery performance degradation detection module is as follows:
the battery charging value of the corresponding lithium battery is obtained through charging evaluation summary analysis, and the method specifically comprises the following steps: collecting real-time charging speed of the lithium battery in the charging process of the lithium battery, calculating a difference value between the real-time charging speed and a preset standard charging speed, taking an absolute value to obtain charging speed deviation data, and calculating a mean value and a variance of all charging speed deviation data of the lithium battery in the corresponding charging process to obtain a charging speed deviation measurement value and a charging speed wave measurement value; the larger the value of the charging speed deviation measurement value is, the more the charging speed of the lithium battery in the corresponding charging process is not in accordance with the requirement; the larger the value of the charging wave measurement value is, the more unstable the charging speed of the lithium battery in the corresponding charging process is, and the greater the charging risk is;
respectively carrying out numerical comparison on the charging speed deviation measurement value and the charging speed wave measurement value with a preset charging speed deviation measurement threshold value and a preset charging speed wave measurement threshold value, and if the charging speed deviation measurement value exceeds the preset charging speed deviation measurement threshold value or the charging speed wave measurement value exceeds the preset charging speed wave measurement threshold value, indicating that the charging performance of the corresponding charging process of the lithium battery is poor, giving a charging judgment symbol CF-1 to the corresponding charging process; if the charging speed deviation measurement value does not exceed the preset charging speed deviation measurement threshold value and the charging speed wave measurement value does not exceed the preset charging speed wave measurement threshold value, indicating that the charging performance of the corresponding charging process of the lithium battery is good, giving a charging judgment symbol CF-2 to the corresponding charging process;
acquiring the number of charging processes corresponding to a charging judgment symbol CF-1 in unit time, marking the number as a negative charging performance value, acquiring the number of charging processes corresponding to a charging judgment symbol CF-2 in unit time, marking the number as a positive charging performance value, and calculating the ratio of the negative charging performance value to the positive charging performance value to obtain a battery charging measurement value; acquiring electricity storage performance data and electricity storage consumption data of a corresponding lithium battery in unit time, wherein the electricity storage performance data is a data value representing the difference value between the theoretical electricity storage capacity and the actual electricity storage capacity of the lithium battery, and the larger the value of the electricity storage performance data is, the worse the energy storage performance is; the electricity storage self-consumption data is a data value representing the self-electricity consumption speed of the lithium battery in an undischarged state, and the larger the value of the electricity storage self-consumption data is, the worse the energy storage performance is;
performing numerical calculation on energy storage performance data XF, electricity storage consumable data XK and battery charge value XR through a formula XJ=ty 1 xF+ty2 xK+ty3 xXR to obtain a battery performance degradation value XJ, wherein ty1, ty2 and ty3 are preset proportionality coefficients, and ty3 is more than 1 and more than 0.5; and the larger the value of the battery performance degradation value XJ is, the larger the degree of the energy storage performance degradation of the lithium battery is, the larger the use risk is, and the use effect is poorer; comparing the battery performance degradation value XJ with a preset battery performance degradation threshold value, and if the battery performance degradation value XJ exceeds the preset battery performance degradation threshold value, generating a performance degradation detection failure signal, wherein the performance degradation degree of the lithium battery is larger; if the battery performance degradation value XJ does not exceed the preset battery performance degradation threshold value, the energy storage performance of the lithium battery is less degraded, and a performance degradation detection qualified signal is generated.
The lithium battery performance degradation detection module sends performance degradation qualified signals to the lithium battery defect evaluation module through the processor, the lithium battery defect evaluation module scans corresponding lithium batteries, acquires external images corresponding to the lithium batteries, performs defect identification analysis on the external images, generates defect evaluation qualified signals or defect evaluation unqualified signals through analysis, and sends the defect evaluation qualified signals or defect evaluation unqualified signals to the lithium battery omnibearing monitoring module through the processor, so that the appearance condition of the lithium batteries can be subjected to defect identification and potential risks can be accurately judged, the energy storage safety and the operation safety of the lithium batteries are further ensured, the intelligent degree is high, and the supervision difficulty of the lithium batteries is reduced; the lithium battery omnibearing monitoring module generates corresponding early warning information when receiving a low availability signal, a performance degradation disqualification signal or a defect evaluation disqualification signal, and sends the early warning information to an intelligent terminal of a manager; the specific operation process of the lithium battery defect evaluation module is as follows:
comparing the external image of the lithium battery with the standard image thereof, identifying the bulge concave defect (namely bulge defect and concave defect) and crack damage defect of the lithium battery shell, and judging whether a super-abnormal bulge concave area or a super-abnormal crack damage area exists or not through analysis, wherein the method specifically comprises the following steps: collecting the area of the corresponding bulge concave defect, marking the area as a defect surface measured value, and collecting the deepest concave distance or the farthest convex distance of the corresponding bulge concave defect, and marking the area as a concave-convex distance measured value;
weighting and summing a defect surface measured value GM and a concave-convex distance measured value GJ through a formula GB=tq1, GM+tq2, and calculating to obtain a bulge concave detection value GB, wherein tq1 and tq2 are preset weight coefficients, and tq2 is more than tq1 and more than 0; and, the larger the value of the bulge indent detection value GB, the larger the potential risk of the corresponding bulge indent defect is indicated; comparing the bulge indent detection value GB with a preset bulge indent detection threshold value, and marking the corresponding bulge indent defect as an ultra-abnormal bulge indent region if the bulge indent detection value GB exceeds the preset bulge indent detection threshold value, which indicates that the potential risk of the corresponding bulge indent defect is large;
acquiring crack extension length data corresponding to a crack damage defect, and acquiring a crack depth amplitude value and a crack width amplitude value corresponding to the crack damage defect, wherein the crack depth amplitude value is a data value representing the average depth of the crack, the crack width amplitude value is a data value representing the average width of the crack, and the larger the numerical values of the crack depth amplitude value and the crack width amplitude value are, the larger the potential risk brought by the corresponding crack damage defect is indicated;
performing numerical calculation on the crack extension length data LK, the crack depth amplitude value LD and the crack width value LQ through a formula LX= (fy1+fy2+LD+fy3+LQ)/3 to obtain a crack depth analysis value LX; wherein, fy1, fy2 and fy3 are preset proportionality coefficients, and the values of fy1, fy2 and fy3 are all larger than zero; and, the larger the numerical value of the crack depth analysis value LX is, the larger the potential risk of the corresponding crack damage defect is; comparing the crack deep analysis value LX with a preset crack deep analysis threshold value, and marking the corresponding crack damage defect as an ultra-abnormal crack damage region if the crack deep analysis value LX exceeds the preset crack deep analysis threshold value, which shows that the potential risk of the corresponding crack damage defect is large;
if the super-abnormal bulge concave area or the super-abnormal crack damage area exists, generating a defect evaluation disqualification signal; if the super-abnormal bulge concave area or the super-abnormal crack damage area does not exist, acquiring the area and the value of the bulge concave defect, marking the area and the value as the face value of the non-flat area, and acquiring the number of the crack damage defects and marking the number as the crack damage detection value; the larger the values of the face value and the crack damage detection value of the non-flat area are, the worse the appearance of the lithium battery is, and the larger the safety risk is;
performing numerical calculation on the non-flat area value FR and the crack damage detection value FY through a formula FX=gy1+gy2×FY to obtain a battery defect value FX, wherein gy1 and gy2 are preset weight coefficients, and gy2 is larger than gy1 and larger than 0; and the larger the value of the defect value FX of the battery is, the larger the potential risk of the lithium battery is, and the worse the energy storage safety and the use safety are; comparing the battery defect value FX with a preset battery defect threshold value, and if the battery defect value FX exceeds the preset battery defect threshold value, indicating that the potential risk of the lithium battery is large, and the energy storage safety and the use safety are poor, generating a defect evaluation disqualification signal; if the battery defect value FX does not exceed the preset battery defect threshold, the potential risk of the lithium battery is smaller, the energy storage safety and the use safety are better, and a defect evaluation qualification signal is generated.
Embodiment two: as shown in fig. 2, the difference between this embodiment and embodiment 1 is that the omnibearing lithium battery monitoring module is further configured to comprehensively monitor the operation process of the lithium battery, retrieve the operation performance data and the environmental performance data of the lithium battery from the processor, respectively perform numerical comparison on the operation performance data and the environmental performance data with a preset operation performance data threshold and a preset environmental performance data threshold, and if the operation performance data or the environmental performance data exceeds the corresponding preset threshold, it indicates that the current use risk of the lithium battery is relatively high, generate a monitoring abnormal signal, and send the monitoring abnormal signal to the intelligent terminal of the manager through the processor, where the manager should timely perform a cause investigation and adaptively regulate the operation parameters or the environment where the lithium battery is located when receiving the monitoring abnormal signal, so as to reduce the operation risk of the lithium battery.
Embodiment III: as shown in fig. 2, the difference between the present embodiment and embodiments 1 and 2 is that the processor is in communication connection with the lithium battery management module, and the lithium battery management module analyzes the operation condition and the environmental condition of the lithium battery to obtain the operation performance data and the environmental performance data of the lithium battery, and sends the operation performance data and the environmental performance data of the lithium battery to the processor for storage, so that the operation risk condition and the environmental risk condition of the lithium battery can be fed back accurately in real time, which is beneficial for the manager to make corresponding improvement measures timely and quickly to ensure the safe and stable operation of the lithium battery, and also provides data support for the real-time analysis process of the omnibearing lithium battery monitoring module; the specific operation process of the lithium battery pipe-transporting module is as follows:
collecting the operation temperature, the operation voltage and the operation current of the lithium battery during operation, calculating the difference value between the operation temperature and the median value of a preset operation temperature range, taking an absolute value to obtain an operation temperature test value, and obtaining an operation voltage test value and an operation current test value in the same way; the method comprises the steps of collecting instantaneous impact data when a lithium battery runs, wherein the instantaneous impact data is a data value representing the vibration degree of the lithium battery caused by external force impact; respectively comparing the temperature test value, the voltage test value, the current test value and the instantaneous impact data with corresponding preset thresholds, and judging that the lithium battery is in an operation danger state if the temperature test value, the voltage test value, the current test value or the instantaneous impact data exceeds the corresponding preset thresholds, which indicates that the operation risk of the lithium battery at the corresponding moment is larger;
acquiring the time length of the lithium battery in the operation risk state in the detection period, marking the time length as a battery operation risk time detection value, marking the operation temperature detection value with the largest numerical value in the detection period as an operation temperature detection value, and acquiring the operation voltage detection value and the operation current detection value in the same way; performing numerical calculation on the battery risk time detection value YR, the battery temperature detection value YW, the battery voltage detection value YK and the battery current detection value YG through a formula YB=a1×YR+a2×YW+a3×YK+a4×YG to obtain operation performance data YB; wherein a1, a2, a3, a4 are preset proportionality coefficients, and a1, a2, a3, a4 are positive numbers; and, the larger the value of the operation performance data YB, the worse the operation condition of the lithium battery in the detection period, the greater the operation risk;
collecting the ambient temperature and the ambient humidity of the environment where the lithium battery is operated, calculating the difference value between the ambient temperature and the median value of a preset ambient temperature range, taking an absolute value to obtain an ambient temperature test value, acquiring an ambient temperature test value in a similar way, calculating the average value of all ambient temperature test values in a detection period to obtain an ambient temperature evaluation value, and acquiring an ambient temperature evaluation value in a similar way; the environmental pollution data, the environmental electromagnetic radiation data and the environmental static data of the environment where the lithium battery is operated are acquired, the average value of all the environmental pollution data in the detection period is calculated to obtain an environmental pollution evaluation value, and the electromagnetic evaluation value and the static evaluation value are acquired in the same way;
the environmental pollution data is a data value indicating the concentration of dust particles in the environment where the lithium battery is located; the environmental electromagnetic radiation data is a data magnitude representing the intensity of electromagnetic radiation in the environment in which the lithium battery is located; the environmental static data is a data value representing the severity of static electricity in the environment where the lithium battery is located; by the formulaPerforming numerical calculation on the ring temperature evaluation value HW, the ring humidity evaluation value HY, the ring pollution evaluation value HP, the electromagnetic evaluation value HK and the electrostatic evaluation value HD to obtain environmental performance data HB; wherein c1, c2, c3, c4, c5 are preset proportionality coefficients, and c1, c2, c3, c4, c5 are positive numbers; and, the larger the numerical value of the environmental performance data HB, the more unfavorable the environmental condition of the detection period is for the safe operation of the lithium battery, and the more needs to be correspondingly regulated and controlled in time for the environment where the lithium battery is located.
The working principle of the invention is as follows: when the method is used, the availability analysis is carried out on the corresponding lithium battery through the lithium battery availability detection module, the high availability signal or the low availability signal is generated through analysis, the availability condition of the lithium battery can be accurately fed back, the performance degradation condition of the corresponding lithium battery is analyzed through the lithium battery availability degradation detection module when the high availability signal is generated to generate an energy storage performance degradation condition of the lithium battery, the energy storage performance degradation condition of the lithium battery can be accurately fed back when the lithium battery is in the high availability state, the corresponding lithium battery is scanned through the lithium battery defect evaluation module when the performance degradation condition is generated, the external image of the corresponding lithium battery is acquired and subjected to defect recognition analysis, the defect evaluation qualification signal or the defect evaluation failure signal is generated through analysis, the appearance condition of the lithium battery can be subjected to defect recognition and the potential risk of the lithium battery can be accurately judged, the energy storage risk and the use risk of the lithium battery are comprehensively evaluated through multi-angle progressive analysis, the energy storage safety of the lithium battery is improved, the stable and efficient operation of the lithium battery is guaranteed, the intelligent degree of the lithium battery is high, and the supervision and the risk of the lithium battery is reduced.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation. The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (6)
1. The lithium battery energy storage monitoring system based on data analysis is characterized by comprising a processor, a lithium battery availability detection module, a lithium battery performance degradation detection module, a lithium battery defect evaluation module and a lithium battery omnibearing monitoring module; the lithium battery availability detection module analyzes the availability of the corresponding lithium battery, generates a high availability signal or a low availability signal through analysis, sends the high availability signal to the lithium battery availability drop detection module through the processor, and sends the low availability signal to the lithium battery omnibearing monitoring module through the processor;
when the lithium battery performance degradation detection module receives the high availability signal, analyzing the performance degradation condition of the corresponding lithium battery to generate a performance degradation disqualification signal or a performance degradation qualification signal, sending the performance degradation qualification signal to the lithium battery defect evaluation module through the processor, and sending the performance degradation disqualification signal to the lithium battery omnibearing monitoring module through the processor;
the lithium battery defect evaluation module scans the corresponding lithium battery, acquires an external image of the corresponding lithium battery, performs defect identification analysis on the external image, generates a defect evaluation qualified signal or a defect evaluation unqualified signal through analysis, and sends the defect evaluation qualified signal or the defect evaluation unqualified signal to the lithium battery omnibearing monitoring module through the processor; the lithium battery omnibearing monitoring module generates corresponding early warning information when receiving a low availability signal, a performance degradation disqualification signal or a defect evaluation disqualification signal, and sends the early warning information to an intelligent terminal of a manager;
the specific operation process of the lithium battery performance degradation detection module comprises the following steps:
acquiring the electricity storage performance data and the electricity storage consumption data of the corresponding lithium batteries in unit time, acquiring battery recharging values of the corresponding lithium batteries through charging evaluation summary analysis, performing numerical calculation on the energy storage performance data, the electricity storage consumption data and the battery recharging values to obtain battery performance degradation values, and if the battery performance degradation values exceed a preset battery performance degradation threshold value, generating performance degradation detection failure signals; if the battery performance degradation value does not exceed the preset battery performance degradation threshold value, generating a performance degradation detection qualified signal;
the specific analysis procedure for the charge evaluation summary analysis is as follows:
collecting real-time charging speed of the lithium battery in the charging process of the lithium battery, calculating a difference value between the real-time charging speed and a preset standard charging speed, taking an absolute value to obtain charging speed deviation data, and calculating a mean value and a variance of all charging speed deviation data of the corresponding lithium battery in the corresponding charging process to obtain a charging speed deviation measurement value and a charging speed wave measurement value;
if the charging speed deviation measurement value exceeds a preset charging speed deviation measurement threshold value or the charging speed wave measurement value exceeds a preset charging speed wave measurement threshold value, a charging judgment symbol CF-1 is given to the corresponding charging process; if the charging speed deviation measurement value does not exceed the preset charging speed deviation measurement threshold value and the charging speed wave measurement value does not exceed the preset charging speed wave measurement threshold value, a charging judgment symbol CF-2 is given to the corresponding charging process;
the method comprises the steps of obtaining the number of charging processes corresponding to a charging judgment symbol CF-1 in unit time and marking the number as a negative charging performance value, obtaining the number of charging processes corresponding to a charging judgment symbol CF-2 in unit time and marking the number as a positive charging performance value, and calculating the ratio of the negative charging performance value to the positive charging performance value to obtain the battery charging measurement value.
2. The lithium battery energy storage monitoring system based on data analysis according to claim 1, wherein the specific analysis process of the availability analysis comprises:
acquiring the service life of the type of the corresponding lithium battery, acquiring the service time of the corresponding lithium battery, and calculating the ratio of the service time to the service life to obtain a battery service life end table value; collecting the charging times of the corresponding lithium battery, and calculating the ratio of the charging times to a preset charging times threshold value of the lithium battery of the type to obtain a battery charging frequency detection value; collecting maintenance times of the lithium battery in a historical process, marking the maintenance times as a battery maintenance frequency table value, and summing up maintenance time lengths of all maintenance processes to obtain a battery maintenance time table value;
performing numerical calculation on the battery life end table value KP, the battery charging frequency detection value KF, the battery maintenance frequency table value KR and the battery maintenance time table value KW through a formula KY= (ew1+ew2 KF)/(ew3+ew4 KW) to obtain an availability detection value KY; wherein, the w1, the w2, the w3 and the w4 are preset proportionality coefficients, and the w1, the w2, the w3 and the w4 are positive numbers; if the availability detection value exceeds a preset availability detection threshold, generating a low availability signal; and if the availability detection value does not exceed the preset availability detection threshold value, generating a high availability signal.
3. The lithium battery energy storage monitoring system based on data analysis according to claim 1, wherein the lithium battery omnibearing monitoring module is further used for comprehensively monitoring the operation process of the lithium battery, retrieving the operation performance data and the environment performance data of the lithium battery from the processor, generating a monitoring abnormality signal if the operation performance data or the environment performance data exceeds a corresponding preset threshold, and sending the monitoring abnormality signal to the intelligent terminal of the manager through the processor.
4. The lithium battery energy storage monitoring system based on data analysis according to claim 1, wherein the specific operation process of the lithium battery defect evaluation module comprises:
comparing an external image of the lithium battery with a standard image thereof, identifying a bulge concave defect and a crack damage defect of the lithium battery shell, judging whether a super-abnormal bulge concave region or a super-abnormal crack damage region exists through analysis, and generating a defect evaluation disqualification signal if the super-abnormal bulge concave region or the super-abnormal crack damage region exists;
if the super-abnormal bulge concave area or the super-abnormal crack damage area does not exist, acquiring the area and the value of the bulge concave defect, marking the area and the value as the face value of the non-flat area, and acquiring the number of the crack damage defects and marking the number as the crack damage detection value; performing numerical calculation on the non-flat area value and the crack damage detection value to obtain a battery defect value, and generating a defect evaluation disqualification signal if the battery defect value exceeds a preset battery defect threshold value; and if the battery defect value does not exceed the preset battery defect threshold value, generating a defect evaluation qualified signal.
5. The lithium battery energy storage monitoring system based on data analysis according to claim 4, wherein the specific analysis process for judging whether the super-abnormal bulge concave area or the super-abnormal crack damage area exists by analysis is as follows:
collecting the area of the corresponding bulge indent defect, marking the area as a defect surface measured value, collecting the deepest indent distance or the farthest protruding distance of the corresponding bulge indent defect, marking the deepest indent distance or the farthest protruding distance as a concave-convex distance measured value, weighting and summing the defect surface measured value and the concave-convex distance measured value to obtain a bulge indent detected value, and marking the corresponding bulge indent defect as a super-abnormal bulge indent area if the bulge indent detected value exceeds a preset bulge indent detected threshold value;
acquiring crack extension length data corresponding to the crack damage defect, acquiring a crack depth amplitude value and a crack width value corresponding to the crack damage defect, and performing numerical calculation on the crack extension length data, the crack depth amplitude value and the crack width value to obtain a crack depth analysis value; and if the crack deep analysis value exceeds a preset crack deep analysis threshold, marking the corresponding crack damage defect as a super-abnormal crack damage region.
6. The lithium battery energy storage monitoring system based on data analysis according to claim 3, wherein the processor is in communication connection with a lithium battery management module, the lithium battery management module analyzes the operation condition and the environmental condition of the lithium battery to obtain operation performance data and environmental performance data of the lithium battery, and the operation performance data and the environmental performance data of the lithium battery are sent to the processor for storage; the specific operation process of the lithium battery pipe-transporting module is as follows:
collecting the operation temperature, the operation voltage and the operation current of the lithium battery during operation, calculating the difference value between the operation temperature and the median value of a preset operation temperature range, taking an absolute value to obtain an operation temperature test value, and obtaining an operation voltage test value and an operation current test value in the same way; the instantaneous impact data of the lithium battery during operation is collected, and if the operation temperature check value, the operation voltage check value, the operation current check value or the instantaneous impact data exceed corresponding preset thresholds, the lithium battery is judged to be in an operation risk state; acquiring the time length of the lithium battery in the operation risk state in the detection period, marking the time length as a battery operation risk time detection value, marking the operation temperature detection value with the largest numerical value in the detection period as an operation temperature detection value, and acquiring the operation voltage detection value and the operation current detection value in the same way; performing numerical calculation on the battery dangerous operation detection value, the battery temperature operation detection value, the battery voltage operation detection value and the battery current operation detection value to obtain operation expression data;
collecting the ambient temperature and the ambient humidity of the environment where the lithium battery is operated, calculating the difference value between the ambient temperature and the median value of a preset ambient temperature range, taking an absolute value to obtain an ambient temperature test value, acquiring an ambient temperature test value in a similar way, calculating the average value of all ambient temperature test values in a detection period to obtain an ambient temperature evaluation value, and acquiring an ambient temperature evaluation value in a similar way; the environmental pollution data, the environmental electromagnetic radiation data and the environmental static data of the environment where the lithium battery is operated are acquired, the average value of all the environmental pollution data in the detection period is calculated to obtain an environmental pollution evaluation value, and the electromagnetic evaluation value and the static evaluation value are acquired in the same way;
by the formulaPerforming numerical calculation on the ring temperature evaluation value HW, the ring humidity evaluation value HY, the ring pollution evaluation value HP, the electromagnetic evaluation value HK and the electrostatic evaluation value HD to obtain environmental performance data HB; wherein c1, c2, c3, c4, c5 are preset proportionality coefficients, and c1, c2, c3, c4, c5 are positive numbers.
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