CN106610478B - Energy storage battery characteristic evaluation method and system based on mass data - Google Patents
Energy storage battery characteristic evaluation method and system based on mass data Download PDFInfo
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- 238000011156 evaluation Methods 0.000 title claims abstract description 45
- 238000004146 energy storage Methods 0.000 title claims abstract description 33
- 238000012544 monitoring process Methods 0.000 claims abstract description 43
- 239000000178 monomer Substances 0.000 claims abstract description 37
- 238000004458 analytical method Methods 0.000 claims abstract description 33
- 238000000034 method Methods 0.000 claims abstract description 11
- 238000013500 data storage Methods 0.000 claims abstract description 9
- 238000012937 correction Methods 0.000 claims description 7
- 210000004027 cell Anatomy 0.000 description 11
- 238000005516 engineering process Methods 0.000 description 3
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- 238000010586 diagram Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 210000000352 storage cell Anatomy 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000013523 data management Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000008821 health effect Effects 0.000 description 1
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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]
- G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
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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
Abstract
The invention provides a method and a system for evaluating the characteristics of an energy storage battery based on mass data, wherein the method comprises the following steps: (1) acquiring monitoring data of each battery monomer, including voltage, current, SOC and temperature of the battery; (2) calculating the health characteristic index of each battery cell according to the monitoring data of each battery cell; (3) and (4) integrating the health characteristic indexes of each evaluation point of the monomer, calculating the overall health characteristic of the current monomer, and storing the analysis result. The system comprises a mass battery monitoring data storage subsystem, a battery characteristic analysis subsystem and a battery characteristic analysis result storage subsystem which are sequentially connected. The method can be suitable for rapid analysis of the characteristics of all the single batteries of the large-scale energy storage power station, and can reflect the battery characteristics more accurately.
Description
Technical Field
The invention relates to an energy storage battery evaluation method and system, in particular to an energy storage battery characteristic evaluation method and system based on mass data.
Background
The development of large-scale energy storage technology, especially battery energy storage technology, is a hot spot nowadays, and more practical applications appear in recent years. However, the construction and commissioning of energy storage power stations is only one beginning. The large-capacity battery energy storage system comprises a large number of single batteries and battery packs. With the application of the battery energy storage system, the characteristics of the battery cell, the battery pack and the battery energy storage system are changed. How to accurately know the current state of the battery and grasp the characteristics of the energy storage system and apply the characteristics to the operation maintenance and the management control of the battery energy storage system becomes an important research content of the energy storage system. And along with the continuous deepening and the promotion of the construction of the energy storage power station, the data volume of the energy storage power station monitoring system is exponentially increased, and massive data are formed. The energy storage battery characteristic analysis by using mass data management and processing technology becomes a research direction which is widely concerned.
However, in the aspect of monitoring the characteristics of the energy storage battery cell, the following difficulties exist: firstly, the types of energy storage batteries are multiple, the evaluation methods of different types of batteries are greatly different, and a unified evaluation system is lacked; secondly, in an energy storage power station system, the number of single batteries is large, which often reaches hundreds of thousands of scales, and accurate monitoring and positioning of the single batteries are very difficult; in addition, since the characteristics of the battery gradually change during the dynamic operation of the power station system, a feasible method is also needed for dynamically evaluating the characteristics of the battery in a relatively long time scale.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides an energy storage battery characteristic evaluation method based on mass data. The method can be suitable for rapid analysis of the characteristics of all the single batteries of the large-scale energy storage power station, and can reflect the battery characteristics more accurately.
In order to achieve the purpose of the invention, the invention adopts the following technical scheme:
a method for evaluating the characteristics of an energy storage battery based on mass data comprises the following steps:
(1) acquiring monitoring data of each battery monomer, including voltage, current, SOC and temperature of the battery;
(2) calculating the health characteristic index of each battery cell according to the monitoring data of each battery cell;
(3) and (4) integrating the health characteristic indexes of each evaluation point of the monomer, calculating the overall health characteristic of the current monomer, and storing the analysis result.
Preferably, the step (2) comprises the steps of:
step 2-1, dividing the monitoring data by taking the SOC variation trend of each monomer as a condition to form a group of evaluation points;
step 2-2, calculating the average variation of the voltage of the monomer at each evaluation point, and correcting the voltage variation according to the voltage, current, SOC and temperature data;
and 2-3, calculating the battery cell characteristics of each evaluation point according to the preset health characteristic classification indexes and the voltage change correction value of the cell at each evaluation point.
Preferably, in step 2-1, the evaluation point is a set of time intervals, and the length of the time interval exceeds 1 hour; the SOC changes monotonously in a time interval; the SOC variation range exceeds 30% in the time interval.
Preferably, in step 2-2, the formula for correcting the voltage variation is as follows:
where δ v is a voltage change amount calculated before correction,
is a corrected voltage variation, wsocCurrent battery charge quantity influence factor, wtTemperature influence factor, w, of the current batteryvCurrent battery voltage specification and current voltage impact factor, wiThe present charge and discharge current influences the factor.
Preferably, in step 2-3, the predetermined health characteristic classification index is a good, a medium, and a bad, and the corresponding voltage change intervals are set as [ a, b ], [ b, c ], and [ c, d ], respectively, where values of a, b, c, and d are adjusted according to factors of a battery type, an operation condition, and an actual demand, and the health characteristic classification index of the evaluation point is obtained according to the voltage change interval in which the voltage change correction value of the evaluation point falls.
Preferably, in the step (3), the health characteristics of all the evaluation points are counted, and the health characteristic with the largest number is taken as the overall health characteristic of the current individual.
Preferably, the system for evaluating the characteristics of the energy storage battery based on the mass data comprises: the battery characteristic analysis system comprises a mass battery monitoring data storage subsystem, a battery characteristic analysis subsystem and a battery characteristic analysis result storage subsystem which are sequentially connected; the mass battery monitoring data storage subsystem is used for storing dynamic data acquired by various types of batteries along with time, and the dynamic data comprises single battery voltage, current, SOC and temperature; the battery characteristic analysis subsystem calculates the health characteristic index of each battery monomer according to monitoring data, wherein the monitoring data comprises monomer voltage, current, SOC and temperature; the battery characteristic analysis result storage subsystem is used for storing the analysis result of each battery cell.
Compared with the prior art, the invention has the beneficial effects that:
the distributed storage and calculation framework is used, so that the rapid analysis method can be suitable for rapid analysis of the characteristics of all single batteries of the large-scale energy storage power station; various monitoring data of the battery monomer are comprehensively utilized, so that the battery characteristics can be more accurately reflected;
the method takes the reduced voltage variation as an evaluation index for battery characteristic classification, not only simplifies the evaluation difficulty, but also can adapt to different types of batteries, and can be suitable for electric power energy storage batteries, electric vehicle power batteries and the like.
Drawings
FIG. 1 is a flow chart of a method for evaluating the characteristics of energy storage cells based on mass data;
FIG. 2 is a schematic diagram of a system for evaluating the characteristics of energy storage cells based on mass data;
FIG. 3 is a schematic diagram of battery evaluation point partitioning;
fig. 4 is a schematic view of the evaluation characteristics of each evaluation point of the battery cell.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1, the present invention provides a method for evaluating characteristics of an energy storage battery based on mass data, which comprises the following steps:
step 1, acquiring monitoring data of each battery monomer, wherein the monitoring data comprises voltage, current, SOC and temperature of a battery;
and grouping the massive single data, and respectively inputting the grouped massive single data into different computing units of the distributed computing system for processing. The battery monitoring data adopts a distributed storage mode, and the storage content comprises monitoring points, monitoring moments and monitoring values of each single body.
Step 2, calculating the health characteristic index of each battery monomer according to the monitoring data of each battery monomer;
and 2-1, processing a group of monomer data by each computing unit, wherein the monomer data are years of data which take minutes as intervals and contain various monitoring points, and dividing the acquired data under the condition of SOC (state of charge) change trend to form a group of evaluation points. Each evaluation point is a time interval in which the SOC changes monotonously, and the SOC change width in the entire interval is within a defined range.
The following table intercepts cell sample data over a period of approximately 2 hours:
| SOC | 70％ | 60％ | 50％ | 40％ | 30％ |
| Time | 8:49 | 9:17 | 9:44 | 10:03 | 10:19 |
| electric current | 37.4 | 38.9 | 51 | 66.2 | 67.2 |
| Voltage of | 3.268 | 3.247 | 3.247 | 3.205 | 3.184 |
| Temperature of | 21 | 22 | 22 | 22 | 23 |
When δ soc is set to 10%, 4 evaluation points e may be formed in the period1、e2、e3And e4The corresponding time intervals are respectively as follows: [8:49,9:17]、[9:17,9:44]、[9:44,10:03]And [10:03,10:19 ]]As shown in fig. 3.
And 2-2, calculating the voltage variation of the monomer at each evaluation point. The voltage changes at several evaluation points in the time period shown in the table above are as follows:
| evaluation point | e1 | e2 | e3 | e4 |
| δv | 0.02 | 0 | 0.04 | 0.02 |
Since different monitoring data have a certain influence on the voltage variation, it is necessary to correct and set the voltage variation according to the data such as voltage, current, SOC, and temperature
wsocDepending on the current battery charge, wtDepending on the current battery temperature, wvDepending on the current battery voltage specification and the current voltage, wiDepending on the present charge and discharge current.
In the intercepted time period, the set influence factor and the corrected voltage change amount are respectively as follows:
and 2-3, calculating the battery monomer characteristics of the current monomer at each evaluation point according to the preset classification index and the voltage change correction value of the monomer at each evaluation point.
The battery health characteristics are set to [ excellent, medium, poor ], for example, the corresponding voltage change intervals may be set to [0,0.02], [0.02,0.05], and [0.05, ∞ ], respectively, and the battery health characteristics at 4 evaluation points in the above time period are excellent, medium, and excellent, respectively, as shown in the cell a shown in fig. 4.
And 3, integrating the health characteristics of each evaluation point in a single month to calculate the monthly health characteristics of the single body. The difference of different monomers can be compared from the transverse direction through the lunar health characteristics, and the variation trend of the monomer characteristics can be analyzed from the longitudinal direction. As shown in FIG. 4, monomer A has better health properties than monomer B.
As shown in fig. 2, the present invention provides a system for evaluating characteristics of a storage battery based on mass data, the system includes: the battery monitoring system comprises a mass battery monitoring data storage system, a battery characteristic analysis system and a battery characteristic analysis result storage system which are connected in sequence; the mass battery monitoring data storage system is used for storing dynamic data acquired by various types of batteries over time, wherein the dynamic data comprises single battery voltage, current, SOC and temperature; the battery characteristic analysis system calculates the health characteristic index of each battery monomer according to monitoring data, wherein the monitoring data comprises monomer voltage, current, SOC and temperature; the battery characteristic analysis result storage system is used for storing the analysis result of each battery cell.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (2)
1. An energy storage battery characteristic evaluation method based on mass data is disclosed, and an evaluation system for the energy storage battery characteristic evaluation method based on mass data comprises the following steps: the battery characteristic analysis system comprises a mass battery monitoring data storage subsystem, a battery characteristic analysis subsystem and a battery characteristic analysis result storage subsystem which are sequentially connected; the mass battery monitoring data storage subsystem is used for storing monitoring data acquired by various types of batteries along with time, and the monitoring data comprises single battery voltage, current, SOC and temperature; the battery characteristic analysis subsystem calculates the health characteristic index of each battery monomer according to monitoring data, wherein the monitoring data comprises monomer voltage, current, SOC and temperature; the battery characteristic analysis result storage subsystem is used for storing the analysis result of each battery cell;
characterized in that the method comprises the following steps:
(1) acquiring monitoring data of each battery monomer, including voltage, current, SOC and temperature of the battery;
(2) calculating the health characteristic index of each battery cell according to the monitoring data of each battery cell;
(3) the health characteristic index of each evaluation point of the monomer is integrated, the overall health characteristic of the current monomer is calculated, and the analysis result is stored;
the step (2) comprises the following steps:
step 2-1, dividing the monitoring data by taking the SOC variation trend of each monomer as a condition to form a group of evaluation points;
step 2-2, calculating the average variation of the voltage of the monomer at each evaluation point, and correcting the voltage variation according to the voltage, current, SOC and temperature data;
step 2-3, calculating the battery monomer characteristics of each evaluation point according to the preset health characteristic classification indexes and the voltage change correction value of the monomer at each evaluation point;
in the step 2-1, each evaluation point is a time interval, and the length of the time interval exceeds 1 hour; the SOC changes monotonously in a time interval; the SOC variation amplitude exceeds 30% in a time interval;
in step 2-2, the formula for correcting the voltage variation is as follows:
where δ v is a voltage change amount calculated before correction,
is a corrected voltage variation, wsocCurrent battery charge quantity influence factor, wtTemperature influence factor, w, of the current batteryvCurrent battery voltage specification and current voltage impact factor, wiCurrent charge-discharge current influence factor;
in the step 2-3, the predetermined health characteristic classification indexes are superior, medium and poor, and corresponding voltage change intervals are set as [ a, b ], [ b, c ] and [ c, d ] respectively, wherein the values of a, b, c and d are adjusted according to factors of battery type, operation condition and actual demand, and the health characteristic classification index of the evaluation point is obtained according to the voltage change interval in which the voltage change correction value of the evaluation point falls;
in the step (3), the health characteristics of all the evaluation points are counted, and the health characteristic with the largest quantity is taken as the overall health characteristic of the current single body.
2. An evaluation system for the method for evaluating the characteristics of the energy storage battery based on the mass data according to claim 1, wherein the system comprises: the battery characteristic analysis system comprises a mass battery monitoring data storage subsystem, a battery characteristic analysis subsystem and a battery characteristic analysis result storage subsystem which are sequentially connected; the mass battery monitoring data storage subsystem is used for storing monitoring data acquired by various types of batteries along with time, and the monitoring data comprises single battery voltage, current, SOC and temperature; the battery characteristic analysis subsystem calculates the health characteristic index of each battery monomer according to monitoring data, wherein the monitoring data comprises monomer voltage, current, SOC and temperature; the battery characteristic analysis result storage subsystem is used for storing the analysis result of each battery cell.
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| CN111584952B (en) * | 2020-04-17 | 2022-04-08 | 许继集团有限公司 | Method and system for online evaluation of electrochemical cells of energy storage power station |
| CN111856284B (en) * | 2020-06-11 | 2023-06-20 | 国网江苏省电力有限公司电力科学研究院 | Failure analysis method and device for energy storage power station battery |
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