CN116699446A - Method, device, equipment and storage medium for rapidly sorting retired batteries - Google Patents
Method, device, equipment and storage medium for rapidly sorting retired batteries Download PDFInfo
- Publication number
- CN116699446A CN116699446A CN202310611583.0A CN202310611583A CN116699446A CN 116699446 A CN116699446 A CN 116699446A CN 202310611583 A CN202310611583 A CN 202310611583A CN 116699446 A CN116699446 A CN 116699446A
- Authority
- CN
- China
- Prior art keywords
- battery
- charging
- sorting
- interval
- capacity
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 50
- 238000007600 charging Methods 0.000 claims abstract description 222
- 230000008859 change Effects 0.000 claims abstract description 42
- 238000012216 screening Methods 0.000 claims description 77
- 230000002159 abnormal effect Effects 0.000 claims description 21
- 238000010280 constant potential charging Methods 0.000 claims description 7
- 238000007599 discharging Methods 0.000 claims description 7
- 238000001514 detection method Methods 0.000 abstract description 2
- 230000008569 process Effects 0.000 description 11
- 230000003068 static effect Effects 0.000 description 11
- 238000010586 diagram Methods 0.000 description 8
- 238000004891 communication Methods 0.000 description 6
- 238000012360 testing method Methods 0.000 description 5
- 230000005856 abnormality Effects 0.000 description 4
- 230000032683 aging Effects 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 238000010276 construction Methods 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 230000008521 reorganization Effects 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 238000004146 energy storage Methods 0.000 description 2
- 239000002360 explosive Substances 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000005286 illumination Methods 0.000 description 2
- GELKBWJHTRAYNV-UHFFFAOYSA-K lithium iron phosphate Chemical compound [Li+].[Fe+2].[O-]P([O-])([O-])=O GELKBWJHTRAYNV-UHFFFAOYSA-K 0.000 description 2
- 238000010277 constant-current charging Methods 0.000 description 1
- 238000013211 curve analysis Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
Classifications
-
- 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
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
- G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
-
- 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]
- G01R31/389—Measuring internal impedance, internal conductance or related variables
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
Abstract
The invention belongs to the technical field of power battery detection, and discloses a method, a device, equipment and a storage medium for rapidly sorting retired batteries. According to the invention, a charging voltage curve and a capacity increment curve are obtained according to the charging parameters, a target voltage interval is determined according to the charging voltage curve, an interval charging capacity in the target voltage interval is obtained according to the target voltage interval and the charging voltage curve, a charging time corresponding to the target voltage interval is obtained according to the charging voltage curve, a battery capacity change rate is obtained according to the interval charging capacity and the target voltage interval, the interval charging capacity, the battery capacity change rate and the charging parameters are input into a battery sorting model, and a battery sorting result is obtained.
Description
Technical Field
The invention relates to the technical field of power battery detection, in particular to a method, a device, equipment and a storage medium for rapidly sorting retired batteries.
Background
With the rapid development of electric vehicles, the number of retired batteries is increased in an explosive manner, when the residual capacity of the power battery is lower than 80% of the nominal capacity, the power battery can be retired, and the retired batteries still have high capacity and can be utilized in a gradient manner, such as a power supply for an electric bicycle, a general living illumination power supply, or electric energy storage and the like. Because the historical use time, environment and mode of the retired battery are different, the capacity, voltage and internal resistance of each single battery after retirement are not consistent. Therefore, before the cascade utilization, it is necessary to sort the unit cells to improve uniformity after the cell reorganization.
The conventional battery sorting method is to directly test basic parameters such as internal resistance, capacity, energy and the like of single batteries to sort the batteries, or extract characteristic parameters capable of representing SOH performance of the batteries according to test data such as current, voltage and the like of the single batteries so as to sort retired batteries.
The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present invention and is not intended to represent an admission that the foregoing is prior art.
Disclosure of Invention
The invention mainly aims to provide a rapid sorting method, device and equipment for retired batteries and a storage medium, and aims to solve the technical problem that the sorting efficiency of retired batteries is low in the prior art.
In order to achieve the above object, the present invention provides a method for rapidly sorting retired batteries, comprising the steps of:
acquiring charging parameters of the retired battery, and acquiring a charging voltage curve and a capacity increment curve according to the charging parameters;
determining a target voltage interval according to the charging voltage curve;
obtaining a section charging capacity in the target voltage section according to the target voltage section and the charging voltage curve;
obtaining a charging duration corresponding to the target voltage interval according to the charging voltage curve, and obtaining a battery capacity change rate according to the interval charging capacity and the target voltage interval;
and inputting the interval charging capacity, the battery capacity change rate and the charging parameters into a battery sorting model to obtain a battery sorting result.
Optionally, the inputting the interval charging capacity, the battery capacity change rate, and the charging parameter into a battery sorting model to obtain a battery sorting result includes:
Taking the internal resistance, the voltage and the interval charging capacity in the charging parameters as screening parameters;
inputting the screening parameters into the battery screening model to obtain a battery screening result;
and matching corresponding sorting parameters according to the battery screening result, and inputting the sorting parameters into the battery sorting model to obtain a battery sorting result.
Optionally, the obtaining the charging duration corresponding to the target voltage interval according to the charging voltage curve, and obtaining the battery capacity change rate according to the interval charging capacity and the target voltage interval, includes:
determining a target voltage interval in the charging voltage curve, and obtaining charging time length corresponding to the target voltage interval according to the charging voltage curve and the target voltage interval;
and comparing the interval charging capacity with the target voltage interval to obtain the battery capacity change rate.
Optionally, the inputting the screening parameter to the battery screening model to obtain a battery screening result includes:
inputting the screening parameters into the battery screening model to obtain discrete points with the same quantity as the screening parameters;
calculating the Kth distance between the discrete points, and obtaining a neighborhood according to the Kth distance;
Obtaining a data set according to the Kth distance;
calculating the reachable distance of each discrete point in the data set;
obtaining the reachable density of the neighborhood according to the reachable distance;
obtaining an anomaly factor according to the reachable density;
and obtaining a battery screening result according to the abnormal factor.
Optionally, the obtaining the battery screening result according to the abnormality factor includes:
when the abnormal factor is larger than a preset abnormal threshold, judging the retired battery corresponding to the abnormal factor as an abnormal battery;
and deleting the abnormal battery, and taking the rest retired battery as a battery screening result.
Optionally, the inputting the sorting parameter into the battery sorting model to obtain a battery sorting result includes:
obtaining the number of clusters, and selecting a corresponding number of class center points from the sorting parameters according to the number of clusters;
obtaining the Euclidean distance between the class center point and the sorting parameter according to the class center point;
classifying the sorting parameters according to the Euclidean distance to obtain a classification set;
updating the class center point according to the average value of the sorting parameters;
and repeating the step of obtaining the Euclidean distance between the class center point and the sorting parameter according to the class center point until the class center point is not changed.
Optionally, the obtaining the charging parameter of the retired battery, and obtaining the charging voltage curve and the capacity increment curve according to the charging parameter includes:
discharging the retired battery to a first voltage with a constant current at a first multiplying power, standing for a first time, and recording the charging parameters;
charging the retired battery to a second voltage with a constant current at a first multiplying power, converting the charging mode into constant voltage charging, enabling the current to reach the second multiplying power, standing for a second time, and recording the charging parameters;
and obtaining a charging voltage curve and a capacity increment curve according to the charging parameters.
In addition, in order to achieve the above object, the present invention also provides a rapid sorting apparatus for retired batteries, comprising:
the battery parameter acquisition module is used for acquiring the charging parameters of the retired battery and acquiring a charging voltage curve and a capacity increment curve according to the charging parameters;
the voltage interval determining module is used for determining a target voltage interval according to the charging voltage curve;
the charging capacity determining module is used for obtaining the interval charging capacity in the target voltage interval according to the target voltage interval and the charging voltage curve;
The capacity change determining module is used for obtaining the charging duration corresponding to the target voltage interval according to the charging voltage curve and obtaining the battery capacity change rate according to the interval charging capacity and the target voltage interval;
and the battery sorting module is used for inputting the interval charging capacity, the battery capacity change rate and the charging parameters into a battery sorting model to obtain a battery sorting result.
In addition, in order to achieve the above object, the present invention also provides a retired battery rapid sorting apparatus, including: a memory, a processor, and a retired battery quick sort program stored on the memory and executable on the processor, the retired battery quick sort program configured to implement the steps of the retired battery quick sort method as described above.
In addition, in order to achieve the above object, the present invention also proposes a storage medium having stored thereon a retired battery quick sorting program which, when executed by a processor, implements the steps of the retired battery quick sorting method as described above.
According to the invention, a charging voltage curve and a capacity increment curve are obtained according to the charging parameters, a target voltage interval is determined according to the charging voltage curve, an interval charging capacity in the target voltage interval is obtained according to the target voltage interval and the charging voltage curve, a charging time corresponding to the target voltage interval is obtained according to the charging voltage curve, a battery capacity change rate is obtained according to the interval charging capacity and the target voltage interval, the interval charging capacity, the battery capacity change rate and the charging parameters are input into a battery sorting model, and a battery sorting result is obtained.
Drawings
FIG. 1 is a schematic diagram of a fast retired battery sorting apparatus for a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a flow chart of a first embodiment of a method for rapidly sorting retired batteries according to the present invention;
FIG. 3 is a schematic diagram of a charging voltage curve and a capacity increment curve according to an embodiment of a method for rapidly sorting retired batteries of the present invention;
FIG. 4 is a flow chart of a second embodiment of the method for rapidly sorting retired batteries according to the present invention;
FIG. 5 is a schematic diagram of a fast sorting flow of an embodiment of a fast sorting method for retired batteries according to the present invention;
fig. 6 is a block diagram showing the construction of a first embodiment of the retired battery quick sorting device according to the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a retired battery rapid sorting device in a hardware running environment according to an embodiment of the present invention.
As shown in fig. 1, the retired battery rapid sorting apparatus may include: a processor 1001, such as a central processing unit (Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a Wireless interface (e.g., a Wireless-Fidelity (Wi-Fi) interface). The Memory 1005 may be a high-speed random access Memory (Random Access Memory, RAM) Memory or a stable nonvolatile Memory (NVM), such as a disk Memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
It will be appreciated by those skilled in the art that the configuration shown in fig. 1 is not limiting of the retired battery rapid sorting apparatus and may include more or fewer components than shown, or some components in combination, or a different arrangement of components.
As shown in fig. 1, an operating system, a network communication module, a user interface module, and a retired battery quick sort program may be included in a memory 1005 as one type of storage medium.
In the retired battery rapid sorting apparatus shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the retired battery rapid sorting device can be arranged in the retired battery rapid sorting device, and the retired battery rapid sorting device invokes a retired battery rapid sorting program stored in the memory 1005 through the processor 1001 and executes the retired battery rapid sorting method provided by the embodiment of the invention.
The embodiment of the invention provides a quick sorting method for retired batteries, and referring to fig. 2, fig. 2 is a schematic flow chart of a first embodiment of the quick sorting method for retired batteries.
The rapid development of electric vehicles is that the number of retired batteries is increased in an explosive manner, the retired batteries refer to that the residual capacity of the batteries is lower than 80% of the calibrated capacity, and after the residual capacity of the batteries is lower than the calibrated capacity, the power batteries are retired, but the power batteries are left with higher battery capacity, so that the power batteries can be screened and recycled, the retired batteries can be utilized in a gradient manner, such as for electric bicycles, general living illumination power supplies, or ionization energy storage batteries, and the like, and the capacity, the voltage and the internal resistance of each single battery after retired are greatly inconsistent due to different historical use time, environment and modes of the retired batteries. The consistency of battery reorganization is a key safety guarantee in the battery cascade utilization process, so before cascade utilization, single batteries are required to be sorted to improve the consistency of the batteries after reorganization. Currently, common battery sorting methods can be divided into two main types, namely direct sorting and indirect sorting. The direct sorting is to sort the batteries directly by testing basic parameters such as internal resistance, capacity, energy and the like of the single batteries, and the indirect screening is to extract characteristic parameters capable of representing SOH performance of the batteries according to test data such as current, voltage and the like of the single batteries, so as to sort the retired batteries. The classification of the sorting parameters can be classified into static sorting and dynamic sorting. The static screening refers to the process of sorting by taking static parameters such as internal resistance, capacity and the like of the battery as indexes, and the dynamic sorting is completed according to curves such as voltage, capacity, temperature and the like in the charging and discharging processes of the battery, wherein the static sorting parameters are simple, but the dynamic consistency of the battery cannot be ensured.
In this embodiment, the method for rapidly sorting retired batteries includes the following steps:
step S10: and acquiring charging parameters of the retired battery, and acquiring a charging voltage curve and a capacity increment curve according to the charging parameters.
It should be noted that, the execution main body of the embodiment is a retired battery rapid sorting device, where the retired battery rapid sorting device has functions of data processing, data communication, program running, etc., and the retired battery rapid sorting device may be an integrated controller, a control computer, etc., and may of course be other devices with similar functions, which is not limited in this embodiment.
It can be understood that the charging parameters of the retired battery refer to the inherent properties of the battery and the charging properties, including the remaining capacity, the internal resistance, the energy, the charged current voltage, etc., the charging curve refers to the corresponding relationship between the charging voltage and the capacity increment in the charging process, reflecting the increment of the battery capacity under different charging voltages, and the capacity increment curve refers to the variation curve of the battery capacity in the retired battery in the charging process.
In a specific implementation, referring to fig. 3, fig. 3 is a schematic diagram of a charging voltage curve and a capacity increment curve. When the retired battery is charged, the charging voltage of the retired battery and the residual capacity of the battery can be detected, the charging voltage of the retired battery and the battery capacity at the moment are obtained at a fixed monitoring frequency, the increment of the battery capacity can be obtained according to the battery capacity, the recorded charging voltage and the battery capacity are drawn into a graph, and a charging voltage curve and a capacity increment curve are obtained and are used for representing the relation between the battery capacity and the charging voltage.
Further, the obtaining the charging parameter of the retired battery, and obtaining a charging voltage curve and a capacity increment curve according to the charging parameter includes:
discharging the retired battery to a first voltage with a constant current at a first multiplying power, standing for a first time, and recording the charging parameters;
charging the retired battery to a second voltage with a constant current at a first multiplying power, converting the charging mode into constant voltage charging, enabling the current to reach the second multiplying power, standing for a second time, and recording the charging parameters;
and obtaining a charging voltage curve and a capacity increment curve according to the charging parameters.
The first multiplying power refers to the charging and discharging of the battery with a double of the charging current and the discharging current when the battery is charged and discharged, the second multiplying power refers to the multiplying power of the current when the battery is charged to a certain voltage and the constant voltage charging is switched to the switching value, and the current gradually decreases when the battery is charged to the constant voltage charging, so that the first multiplying power is larger than the second multiplying power. The first voltage is the discharge voltage of the battery when the capacity of the battery changes after the battery passes through the first section, the second voltage refers to the voltage value reached by the battery after the battery is charged, so that the second voltage value can be known to be larger than the first voltage value, the first time and the second time are the rest time of the battery, the first time and the second time are set according to actual conditions, and the magnitude and the relation of the first time and the second time are not limited in this embodiment.
In a specific implementation, in this embodiment, a 32650 lithium iron phosphate battery is taken as an example, the rated capacity of the lithium iron phosphate battery is 5.5Ah, and the charge and discharge tests are performed on 80 batteries, and the specific test method is as follows:
discharging the battery to 2.5V at constant current with the rate of 1C, and standing for 10min;
and (3) constant current charging is carried out on the battery to 3.65V at the rate of 1C, the constant voltage charging is carried out until the current is reduced to 0.05C, and the battery is left for 30min.
Step S20: and determining a target voltage interval according to the charging voltage curve.
The target voltage interval refers to a continuous voltage interval, and the amount of increase of the battery capacity in the voltage interval is in a rapid increase stage.
In a specific implementation, the charging voltage curve analysis of 80 retired batteries shows that the main peak of the charging voltage curve is in the voltage interval of 3.35V-3.5V, and when the charging voltage curve is in the voltage interval of 3.35V-3.5V, the charging voltage curve of the batteries with different attenuation degrees has the largest curve difference degree, so the charging voltage curve can be used as the target voltage interval.
Step S30: and obtaining the interval charging capacity in the target voltage interval according to the target voltage interval and the charging voltage curve.
It should be noted that, the charging voltage curve refers to the remaining capacity of the battery in the period of the voltage increase along with the voltage increase during the charging process, and the remaining capacity of the battery is continuously increased due to the charging state of the battery.
In a specific implementation, since the difference exists between the batteries, the residual capacity of the battery cannot be used to determine the quality of the battery, so the battery can be charged and discharged in a certain voltage interval to determine the interval charging capacity in the voltage interval, for example, in a voltage interval of 3.35V-3.5V, the battery is in a charging state, so only the battery capacity values at the voltages of 3.35V and 3.5V need to be read at this time, and the battery capacity read at the voltage of 3.35V can be C 1 The battery capacity read at 3.5V was C 2 At this time, the section charge capacity Δc=c in the voltage section 2 -C 1 。
Step S40: and obtaining the charging duration corresponding to the target voltage interval according to the charging voltage curve, and obtaining the battery capacity change rate according to the interval charging capacity and the target voltage interval.
The battery capacity change rate refers to an average increase in battery capacity in a voltage range, that is, a ratio of the range charge capacity Δc to the range voltage, that is, the battery capacity change rate K.
In a specific implementation, by analyzing the charging voltage curve, since the state of the battery needs to be detected by the charging voltage curve, when a special state is reached, the voltage and the battery capacity of the state are recorded, and meanwhile, the current moment is recorded, when the battery is completely detected, the consumed time in the process can be obtained, and the time corresponds to the charging time length T, so that the charging length from the lower boundary of the target voltage interval to the upper boundary of the target voltage interval can be obtained, and the charging time length T of the target interval is used as a dynamic index of sorting. When the battery capacity change rate is obtained, the interval length of the target voltage interval, namely the span of the target voltage interval, is required to be obtained according to the target voltage interval, the battery capacity increment in the target voltage interval is compared with the voltage interval length to obtain the battery capacity change rate, the charging capacity of the retired battery is reflected, and the formula for calculating the battery capacity change rate is as follows:
Wherein V is 2 Represents the upper bound of the target voltage interval, V 1 Representing the lower bound of the target voltage interval.
The battery capacity change rate K reflects the capacity change condition of the battery capacity, and therefore, is one of the important indicators in the classification.
Step S50: and inputting the interval charging capacity, the battery capacity change rate and the charging parameters into a battery sorting model to obtain a battery sorting result.
The battery sorting model is a model for sorting batteries according to the interval charge capacity of the retired batteries, the battery capacity change rate, the battery charging parameters and the like, and distinguishing the batteries according to the battery parameters, and the sorting result refers to an index reflecting whether the retired batteries can be used in a cascade.
In the specific implementation, the battery sorting model is used for sorting the batteries, the retired batteries capable of being used in a echelon manner are selected, and the static parameters and the dynamic parameters of the batteries are input into the battery sorting model to jointly determine the current battery state of the retired batteries.
According to the method, the charging parameters of the retired battery are obtained, a charging voltage curve and a capacity increment curve are obtained according to the charging parameters, a target voltage interval is determined according to the charging voltage curve, the interval charging capacity in the target voltage interval is obtained according to the target voltage interval and the charging voltage curve, the charging duration corresponding to the target voltage interval is obtained according to the charging voltage curve, the battery capacity change rate is obtained according to the interval charging capacity and the target voltage interval, the interval charging capacity, the battery capacity change rate and the charging parameters are input into a battery sorting model, and a battery sorting result is obtained.
Referring to fig. 4, fig. 4 is a flowchart illustrating a second embodiment of a method for rapidly sorting retired batteries according to the present invention.
Based on the first embodiment, the method for rapidly sorting retired batteries according to the present embodiment includes:
step S501: and taking the internal resistance, the voltage and the interval charging capacity in the charging parameters as screening parameters.
Step S502: and inputting the screening parameters into the battery screening model to obtain a battery screening result.
Step S503: and matching corresponding sorting parameters according to the battery screening result, and inputting the sorting parameters into the battery sorting model to obtain a battery sorting result.
It should be noted that, the screening parameters refer to static characteristics of the battery, where the static characteristics include internal resistance R, voltage V and interval charging capacity Δc of the battery, the screening parameters are parameter indexes for performing primary screening on the retired battery, the screening result refers to that the battery which cannot meet the requirement of the cascade utilization is removed, and the remaining batteries which can be used in the cascade utilization are all batteries, but in the screening result, the retired battery is not further subdivided, and the current battery state of the retired battery cannot be accurately positioned.
It will be appreciated that the values of the sorting parameters are dynamic characteristics of the battery, including the charge duration T of the battery, the main peak center voltage V, within the target voltage interval m And a battery capacity increase rate K of a ratio of the section charge capacity to the section voltage, the above-described parameter can quantitatively analyze characteristics of the battery aging mechanism, and thus the above-described parameter is set as a sorting parameter.
It should be understood that the battery sorting model includes a battery screening model, for the screening of the retired batteries, after the screening parameters of the retired batteries are input into the screening model, the retired batteries which do not meet the gradient utilization are rejected, and the sorting parameters of the rest retired batteries are input into the battery sorting model, so as to obtain sorting results, wherein the sorting results divide the retired batteries into a plurality of classes, and the retired batteries in the same class have consistency.
In a specific implementation, referring to fig. 5, fig. 5 is a schematic diagram of a fast sorting flow. The inconsistent nature of the battery is due to different levels of aging inside, while static parameters such as internal resistance R, voltage V, etc. are external manifestations of aging. Therefore, when the retired batteries are sorted, the retired batteries are firstly screened for the first time, abnormal batteries which do not accord with the gradient utilization are removed, and the screening parameters need to intuitively reflect the current performance of the batteries, such as voltage, internal resistance and the like, but the residual capacity of the batteries is not easy to acquire, so that the inconsistency degree of the batteries can be described by adopting the interval charging capacity delta C, and therefore, when the initial screening is carried out, the static characteristics of the current performance of the batteries can be intuitively reflected by the internal resistance R, the voltage V and the interval charging capacity delta C are selected as the screening parameters. After confirming the screening parameters, the screening parameters can be input into a battery screening model, and the retired batteries are screened through the battery screening model to eliminate the retired batteries which do not meet gradient utilization.
After rejecting the retired batteries which do not meet the gradient utilization, the rest of retired batteries can meet the basic requirement of the gradient utilization, so that the rest of retired batteries are classified for full utilization, different types of gradient utilization are performed, and the retired batteries are selected according to the corresponding sorting parameters of the rest of retired batteries, wherein the retired parameters comprise: charging duration T, main peak center voltage V m And the battery capacity increase rate K of the ratio of the interval charging capacity to the interval voltage is used as a sorting parameter for quantitatively analyzing the aging characteristics of the battery, and sorting results of the rest retired batteries are obtained by inputting the sorting parameter into a battery sorting model.
Further, the obtaining the charging duration corresponding to the target voltage interval according to the charging voltage curve, and obtaining the battery capacity change rate according to the interval charging capacity and the target voltage interval, includes:
determining a target voltage interval in the charging voltage curve, and obtaining charging time length corresponding to the target voltage interval according to the charging voltage curve and the target voltage interval;
and comparing the interval charging capacity with the target voltage interval to obtain the battery capacity change rate.
In a specific implementation, the position of a target voltage section is determined in a charging voltage curve, the time when the voltage value arrives is determined according to the voltage value corresponding to the target voltage section, the charging time corresponding to the target section voltage can be obtained according to the time when the upper boundary and the lower boundary of the target voltage section correspond to each other, on the other hand, after the target voltage section is determined, the section charging capacity in the target voltage section can be obtained according to the capacity change curve, and the battery capacity change rate can be obtained according to the ratio of the section charging capacity to the target charging section.
Further, the inputting the screening parameter to the battery screening model to obtain a battery screening result includes:
inputting the screening parameters into the battery screening model to obtain discrete points with the same quantity as the screening parameters;
calculating the Kth distance between the discrete points, and obtaining a neighborhood according to the Kth distance;
obtaining a data set according to the Kth distance;
calculating the reachable distance of each discrete point in the data set;
obtaining the reachable density of the neighborhood according to the reachable distance;
obtaining an anomaly factor according to the reachable density;
and obtaining a battery screening result according to the abnormal factor.
In a specific implementation, in a battery screening model, an input end of the model can receive transmitted screening parameters including internal resistance R, voltage V and interval charging capacity deltac. When screening the screening parameters, LOF algorithm can be adopted for carrying outModel construction, e.g., inputting screening parameters of 80 retired batteries into a battery screening model, the battery screening model fits the 80 parameters to 80 points, incumbent points o, calculates the Kth distance dK (o) of points o, determines the Kth distance neighborhood N of points o K Where dK (o) is the distance d (o, p), N between point o and point p where point o is the Kth closest K For the set of points where all points o within the dataset are no more distant than dK (o), in this embodiment k=5 is taken, which is not limiting in this embodiment. Thus, there is the following relationship:
wherein (X) o ,Y o ,Z o ) Is the coordinates of point o, (X) p ,Y p ,Z p ) Is the coordinates of point p.
Calculating the reachable distance rd between the point o and any point q in the data set K (o, q). When the K-th distance of the point o is larger than the distance from the point o to the point q, the reachable distance between the point o and the point q is d K (o), otherwise d (o, q), calculated as in formulas (2), (3).
rd K (o,q)＝max{d K (0),d(o,q)} (2)
Wherein (X) q ,Y q ,Z q ) Coordinates of point q
Calculating the Kth local reachable density ldr of the point o K (o), i.e., the inverse of the average reachable distance from all data points to point o in the kth distance neighborhood, reflects the degree of closeness of the data points in a certain space, and the denser the point o is with surrounding points, the greater its value.
In I N K And I is the number of data points in the K-th distance neighborhood of the point o.
Calculating the Kth local anomaly factor of point o
In the formula, LOF K (o) represents the Kth local anomaly factor, ldr, of data point o K (o) is the locally reachable density of points q.
The abnormal factor values are ordered, the larger the factor value is, the higher the outlier degree is, the smaller the factor value is, and the lower the outlier degree is.
The LOF value of 80 batteries is between 0.5 and 5 calculated by the LOF algorithm, wherein the LOF value of the battery with the largest category is concentrated between 0.5 and 1.8, so that the threshold value can be set at 1.8, namely, when the LOF value is greater than 1.8, the abnormal battery is judged.
Further, the inputting the sorting parameters into the battery sorting model to obtain a battery sorting result includes:
obtaining the number of clusters, and selecting a corresponding number of class center points from the sorting parameters according to the number of clusters;
obtaining the Euclidean distance between the class center point and the sorting parameter according to the class center point;
classifying the sorting parameters according to the Euclidean distance to obtain a classification set;
updating the class center point according to the average value of the sorting parameters;
and repeating the step of obtaining the Euclidean distance between the class center point and the sorting parameter according to the class center point until the class center point is not changed.
In a specific implementation, after batteries which cannot be subjected to gradient utilization are removed through a battery screening model, corresponding sorting parameters are obtained according to the rest of retired batteries, wherein the sorting parameters comprise charging time length T, main peak center voltage V1 and a ratio K of interval charging capacity to interval voltage. In the battery sorting parameters, an unsupervised clustering algorithm can be adopted as a basis to construct a battery sorting model, and the embodiment takes a K-means algorithm as an exampleThe present embodiment is not particularly limited to this. After the battery sorting model obtains the charging time length T, the main peak center voltage V1 and the ratio K of the interval charging capacity to the interval voltage, sample data P is generated i ，P i ＝{X i ,Y i ,Z i And determines the number of clusters K, which is exemplified by k=3 in this embodiment, but is not limited thereto. If 74 sample data remain after screening, 3 samples can be randomly selected from the 74 samples as the sample center point U of each class K ，U K ＝{X K ,Y K ,Z K And calculating Euclidean distances d from all sample points to the 3 clustering center points, wherein the calculation formula is as follows:
and respectively calculating the average value of each type of sample data as a new center point, wherein the calculation formula is as follows:
wherein X is K X coordinate value for the Kth cluster center, for Y K ,Z K The calculation method of (1) is same as X K N is the number in each type of sample data.
After the center point of the new cluster center is obtained, the Euclidean distance from all samples to the center point is calculated by the new cluster center point, the center point is updated until the position of the center point is not changed any more, and at the moment, the clustering result of each cluster center is output to obtain the sorting result of the retired battery. The classification results are shown in table 1:
table 1 sorting results
According to the method, the static parameters and the dynamic parameters of the retired battery are acquired, the abnormal battery is screened out according to the static characteristics, the abnormal battery is not classified in the follow-up classification, the consistency of classification results is guaranteed, on the other hand, when the retired battery is classified according to the dynamic parameters, the retired battery in the same state can be clustered, the retired battery can be directly classified into a plurality of different classifications according to the performance of the battery, and then different time gradient utilization can be carried out according to the obtained classification results.
In addition, the embodiment of the invention also provides a storage medium, wherein the storage medium is stored with a retired battery rapid sorting program, and the retired battery rapid sorting program realizes the steps of the retired battery rapid sorting method when being executed by a processor.
Referring to fig. 6, fig. 6 is a block diagram showing the construction of a first embodiment of the retired battery quick sorting device according to the present invention.
As shown in fig. 6, the device for rapidly sorting retired batteries according to the embodiment of the present invention includes:
the battery parameter acquisition module 10 is used for acquiring the charging parameters of the retired battery and obtaining a charging voltage curve and a capacity increment curve according to the charging parameters;
a voltage interval determining module 20, configured to determine a target voltage interval according to the charging voltage curve;
a charge capacity determination module 30, configured to obtain a section charge capacity within the target voltage section according to the target voltage section and the capacity increment curve;
a capacity change determining module 40, configured to obtain a charging duration corresponding to the target voltage interval according to the charging voltage curve, and obtain a battery capacity change rate according to the interval charging capacity and the target voltage interval;
the battery sorting module 50 is configured to input the interval charging capacity, the battery capacity change rate, and the charging parameter to a battery sorting model to obtain a battery sorting result.
In the embodiment, a charging voltage curve and a capacity increment curve are obtained according to the charging parameters, a target voltage interval is determined according to the charging voltage curve, an interval charging capacity in the target voltage interval is obtained according to the target voltage interval and the charging voltage curve, a charging duration corresponding to the target voltage interval is obtained according to the charging voltage curve, a battery capacity change rate is obtained according to the interval charging capacity and the target voltage interval, the interval charging capacity, the battery capacity change rate and the charging parameters are input into a battery sorting model to obtain a battery sorting result, and the invention sorts the retired battery from multiple dimensions by constructing a sorting model, so that compared with the prior art, the method has higher sorting efficiency and sorting precision according to single parameter sorting
In an embodiment, the battery sorting module 50 is further configured to take the internal resistance, the voltage and the interval charging capacity of the charging parameters as screening parameters; inputting the screening parameters into the battery screening model to obtain a battery screening result; and matching corresponding sorting parameters according to the battery screening result, and inputting the sorting parameters into the battery sorting model to obtain a battery sorting result.
In an embodiment, the battery sorting module 50 is further configured to determine a target voltage interval in the capacity increment curve, and obtain a charging duration corresponding to the target voltage interval according to the capacity increment curve and the target voltage interval; and comparing the interval charging capacity with the target voltage interval to obtain the battery capacity change rate.
In one embodiment, the battery sorting module 50 is further configured to input the screening parameters into the battery screening model to obtain discrete points with the same number as the screening parameters; calculating the Kth distance between the discrete points, and obtaining a neighborhood according to the Kth distance; obtaining a data set according to the Kth distance;
calculating the reachable distance of each discrete point in the data set; obtaining the reachable density of the neighborhood according to the reachable distance; obtaining an anomaly factor according to the reachable density; and obtaining a battery screening result according to the abnormal factor.
In an embodiment, the battery sorting module 50 is further configured to determine, when the abnormality factor is greater than a preset abnormality threshold, a retired battery corresponding to the abnormality factor as an abnormal battery; and deleting the abnormal battery, and taking the rest retired battery as a battery screening result.
In an embodiment, the battery sorting module 50 is further configured to obtain a number of clusters, and select a corresponding number of class center points from the sorting parameters according to the number of clusters; obtaining the Euclidean distance between the class center point and the sorting parameter according to the class center point; classifying the sorting parameters according to the Euclidean distance to obtain a classification set; updating the class center point according to the average value of the sorting parameters; and repeating the step of obtaining the Euclidean distance between the class center point and the sorting parameter according to the class center point until the class center point is not changed.
In an embodiment, the battery parameter obtaining module 10 is further configured to discharge the retired battery to a first voltage with a constant current at a first rate, stand for a first time, and record the charging parameter; charging the retired battery to a second voltage with a constant current at a first multiplying power, converting the charging mode into constant voltage charging, enabling the current to reach the second multiplying power, standing for a second time, and recording the charging parameters; and obtaining a charging voltage curve and a capacity increment curve according to the charging parameters.
It should be understood that the foregoing is illustrative only and is not limiting, and that in specific applications, those skilled in the art may set the invention as desired, and the invention is not limited thereto.
It should be noted that the above-described working procedure is merely illustrative, and does not limit the scope of the present invention, and in practical application, a person skilled in the art may select part or all of them according to actual needs to achieve the purpose of the embodiment, which is not limited herein.
Furthermore, it should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. Read Only Memory)/RAM, magnetic disk, optical disk) and including several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (10)
1. The rapid retired battery sorting method is characterized by comprising the following steps of:
acquiring charging parameters of the retired battery, and acquiring a charging voltage curve and a capacity increment curve according to the charging parameters;
determining a target voltage interval according to the charging voltage curve;
obtaining a section charging capacity in the target voltage section according to the target voltage section and the charging voltage curve;
obtaining a charging duration corresponding to the target voltage interval according to the charging voltage curve, and obtaining a battery capacity change rate according to the interval charging capacity and the target voltage interval;
and inputting the interval charging capacity, the battery capacity change rate and the charging parameters into a battery sorting model to obtain a battery sorting result.
2. The method of claim 1, wherein said inputting the interval charge capacity, the battery capacity change rate, and the charge parameter into a battery sorting model to obtain a battery sorting result comprises:
Taking the internal resistance, the voltage and the interval charging capacity in the charging parameters as screening parameters;
inputting the screening parameters into the battery screening model to obtain a battery screening result;
and matching corresponding sorting parameters according to the battery screening result, and inputting the sorting parameters into the battery sorting model to obtain a battery sorting result.
3. The method of claim 2, wherein the obtaining the charging duration corresponding to the target voltage interval according to the charging voltage curve, and obtaining the battery capacity change rate according to the interval charging capacity and the target voltage interval, comprises:
determining a target voltage interval in the charging voltage curve, and obtaining charging time length corresponding to the target voltage interval according to the charging voltage curve and the target voltage interval;
and comparing the interval charging capacity with the target voltage interval to obtain the battery capacity change rate.
4. The method of claim 2, wherein said inputting the screening parameters into the battery screening model results in battery screening results, comprising:
inputting the screening parameters into the battery screening model to obtain discrete points with the same quantity as the screening parameters;
Calculating the Kth distance between the discrete points, and obtaining a neighborhood according to the Kth distance;
obtaining a data set according to the Kth distance;
calculating the reachable distance of each discrete point in the data set;
obtaining the reachable density of the neighborhood according to the reachable distance;
obtaining an anomaly factor according to the reachable density;
and obtaining a battery screening result according to the abnormal factor.
5. The method of claim 4, wherein the obtaining battery screening results based on the anomaly factors comprises:
when the abnormal factor is larger than a preset abnormal threshold, judging the retired battery corresponding to the abnormal factor as an abnormal battery;
and deleting the abnormal battery, and taking the rest retired battery as a battery screening result.
6. The method of claim 2, wherein said inputting the sorting parameters into the battery sorting model results in a battery sorting result, comprising:
obtaining the number of clusters, and selecting a corresponding number of class center points from the sorting parameters according to the number of clusters;
obtaining the Euclidean distance between the class center point and the sorting parameter according to the class center point;
classifying the sorting parameters according to the Euclidean distance to obtain a classification set;
Updating the class center point according to the average value of the sorting parameters;
and repeating the step of obtaining the Euclidean distance between the class center point and the sorting parameter according to the class center point until the class center point is not changed.
7. The method of claim 1, wherein the obtaining the charging parameters of the retired battery, and obtaining the charging voltage curve and the capacity delta curve according to the charging parameters, comprises:
discharging the retired battery to a first voltage with a constant current at a first multiplying power, standing for a first time, and recording the charging parameters;
charging the retired battery to a second voltage with a constant current at a first multiplying power, converting the charging mode into constant voltage charging, enabling the current to reach the second multiplying power, standing for a second time, and recording the charging parameters;
and obtaining a charging voltage curve and a capacity increment curve according to the charging parameters.
8. Quick sorting unit of retired battery, its characterized in that, quick sorting unit of retired battery includes:
the battery parameter acquisition module is used for acquiring the charging parameters of the retired battery and acquiring a charging voltage curve and a capacity increment curve according to the charging parameters;
the voltage interval determining module is used for determining a target voltage interval according to the charging voltage curve;
The charging capacity determining module is used for obtaining the interval charging capacity in the target voltage interval according to the target voltage interval and the charging voltage curve;
the capacity change determining module is used for obtaining the charging duration corresponding to the target voltage interval according to the charging voltage curve and obtaining the battery capacity change rate according to the interval charging capacity and the target voltage interval;
and the battery sorting module is used for inputting the interval charging capacity, the battery capacity change rate and the charging parameters into a battery sorting model to obtain a battery sorting result.
9. A retired battery rapid sorting apparatus, the apparatus comprising: a memory, a processor, and a retired battery quick sort program stored on the memory and executable on the processor, the retired battery quick sort program configured to implement the steps of the retired battery quick sort method of any one of claims 1 to 7.
10. A storage medium having stored thereon a retired battery quick sort program which, when executed by a processor, implements the steps of the retired battery quick sort method of any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310611583.0A CN116699446A (en) | 2023-05-26 | 2023-05-26 | Method, device, equipment and storage medium for rapidly sorting retired batteries |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310611583.0A CN116699446A (en) | 2023-05-26 | 2023-05-26 | Method, device, equipment and storage medium for rapidly sorting retired batteries |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116699446A true CN116699446A (en) | 2023-09-05 |
Family
ID=87830426
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310611583.0A Pending CN116699446A (en) | 2023-05-26 | 2023-05-26 | Method, device, equipment and storage medium for rapidly sorting retired batteries |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116699446A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116886453A (en) * | 2023-09-08 | 2023-10-13 | 湖北华中电力科技开发有限责任公司 | Network flow big data analysis method |
| CN117463643A (en) * | 2023-12-28 | 2024-01-30 | 四川帝威能源技术有限公司 | Retired power lithium battery capacity sorting method, retired power lithium battery capacity sorting system, electronic equipment and medium |
-
2023
- 2023-05-26 CN CN202310611583.0A patent/CN116699446A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116886453A (en) * | 2023-09-08 | 2023-10-13 | 湖北华中电力科技开发有限责任公司 | Network flow big data analysis method |
| CN116886453B (en) * | 2023-09-08 | 2023-11-24 | 湖北华中电力科技开发有限责任公司 | Network flow big data analysis method |
| CN117463643A (en) * | 2023-12-28 | 2024-01-30 | 四川帝威能源技术有限公司 | Retired power lithium battery capacity sorting method, retired power lithium battery capacity sorting system, electronic equipment and medium |
| CN117463643B (en) * | 2023-12-28 | 2024-03-26 | 四川帝威能源技术有限公司 | Retired power lithium battery capacity sorting method, retired power lithium battery capacity sorting system, electronic equipment and medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116699446A (en) | Method, device, equipment and storage medium for rapidly sorting retired batteries | |
| CN109613440B (en) | Battery grading method, device, equipment and storage medium | |
| CN111816936B (en) | Battery echelon utilization grouping method and system, terminal equipment and storage medium | |
| Xiong et al. | A data-driven method for extracting aging features to accurately predict the battery health | |
| JP7450741B2 (en) | Lithium battery SOC estimation method, device and computer readable storage medium | |
| CN109633448B (en) | Method and device for identifying battery health state and terminal equipment | |
| CN111420898B (en) | Retired battery sorting method and system applied by same | |
| CN109800446B (en) | Method and device for estimating voltage inconsistency in discharging process of lithium ion battery | |
| CN113887601A (en) | Retired power battery recombination method based on cluster sorting | |
| CN112287980B (en) | Power battery screening method based on typical feature vector | |
| CN115248393A (en) | Battery consistency sorting method, device, equipment and storage medium | |
| CN109768340B (en) | Method and device for estimating voltage inconsistency in battery discharge process | |
| CN114720899A (en) | Retired battery echelon utilization and sorting method and system, electronic equipment and storage medium | |
| CN114114039A (en) | Method and device for evaluating consistency of single battery cells of battery system | |
| CN112084459A (en) | Method and device for predicting battery charge-discharge cycle life, electronic terminal and storage medium | |
| CN112180258B (en) | Method, device, medium, terminal and system for measuring average coulombic efficiency of battery | |
| CN117110884A (en) | Lithium battery remaining service life prediction method based on multi-core correlation vector machine | |
| CN111178383A (en) | Battery monomer quality classification method based on multi-core support vector machine | |
| CN115656834A (en) | Battery capacity prediction method and device and electronic equipment | |
| CN112485694B (en) | Battery pack detection method and device | |
| EP4300386A1 (en) | Method, apparatus and device for predicting power battery capacity | |
| CN115248394A (en) | Dynamic battery sorting method, device, equipment and storage medium | |
| CN114818936A (en) | Retired battery rapid comprehensive sorting method and system based on K-means clustering algorithm | |
| JP7276928B1 (en) | Secondary battery capacity estimation system | |
| Liu et al. | Fast sorting method of retired batteries based on multi-feature extraction from partial charging segment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |