CN114429050A - Sorting method for gradient utilization of retired power batteries - Google Patents

Sorting method for gradient utilization of retired power batteries Download PDF

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CN114429050A
CN114429050A CN202210219250.9A CN202210219250A CN114429050A CN 114429050 A CN114429050 A CN 114429050A CN 202210219250 A CN202210219250 A CN 202210219250A CN 114429050 A CN114429050 A CN 114429050A
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retired power
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王存
丘意书
王亦伟
蒋方明
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Guangzhou Institute of Energy Conversion of CAS
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Abstract

The invention discloses a sorting method for gradient utilization of retired power batteries, which comprises the following steps: step 1: carrying out preliminary appearance screening on N retired power batteries to be tested to select M batteries with good appearances, wherein M is less than or equal to N; step 2: testing the capacity of each single battery; and step 3: testing the internal resistance of each single battery under X% SOC, wherein X is more than 0; and 4, step 4: testing the self-discharge rate of each single battery; and 5: based on an LOF algorithm, taking the battery capacity, the internal resistance and the self-discharge rate as input, and outputting LOFs of M retired power batteries; step 6: and setting a corresponding threshold value for the LOF to complete the classified screening of the retired power battery. The method simplifies the multi-parametric standard during the screening of the retired power battery, evaluates the aging parameters of the plurality of batteries by using an LOF algorithm, can realize the inconsistency evaluation of the retired power battery only through the LOF, and can realize the multi-stage screening of the retired power battery by setting different LOFs.

Description

Sorting method for gradient utilization of retired power batteries
Technical Field
The invention relates to the technical field of retired power battery gradient utilization, in particular to a sorting method for retired power battery gradient utilization.
Background
Currently, the power battery carried by a new energy automobile which is firstly put into the market in China is facing the critical period of retirement, and the total amount of the retired power battery accumulated in 2020 exceeds 20 ten thousand tons. When the power battery capacity declines to 80% of its rated capacity, it is decommissioned from the electric vehicle. If the waste of resources is caused by direct scrapping treatment, the environment pollution is also caused, and the retired battery can still be used in the use scenes with lower requirements on the battery, such as a household energy storage power supply, a power grid energy storage and a communication base station, although the demand of the power battery cannot be met. In 2 months of 2017, the temporary method for recycling and managing the new energy automobile power storage battery mentioned in the state of coming out encourages to develop multi-level and multi-purpose reasonable utilization of the waste power storage battery according to the principle of first echelon utilization and then recycling.
The health states of the retired power batteries are inconsistent, and the key of the gradient utilization of the power batteries is how to ensure the consistency of the recombined batteries. The aging parameters of the lithium ion batteries are numerous, the batteries are sequentially screened on the basis of a plurality of aging parameters of the batteries in the traditional screening method, however, the screening standards are required to be set for each aging parameter, and the screening standards of different types of batteries may have differences, so that the screening standards of the batteries are not uniform, and a lot of difficulties are brought to the echelon utilization of retired power batteries.
Patent document CN108199109A discloses a screening method for gradient utilization of retired power battery packs, which is based on four factors of cell performance, including capacity, internal resistance, power, and voltage, and starts to screen the sensitivity of the four factors to the cell performance from large to small, so as to improve the consistency of the retired batteries to a certain extent, however, this method still needs to set a corresponding screening standard for each aging parameter of the retired batteries.
Disclosure of Invention
In order to solve at least one technical problem existing in the background technology, the invention provides a sorting method for the gradient utilization of retired power batteries.
In order to realize the purpose, the technical scheme of the invention is as follows:
a sorting method for gradient utilization of retired power batteries comprises the following steps:
acquiring parameters representing battery aging;
based on an LOF algorithm, taking parameters representing battery aging as input, and outputting the LOF of the retired power battery;
and setting a corresponding threshold value for the LOF to complete the classified screening of the retired power battery.
Further, before obtaining the parameters for characterizing the battery aging, the method further comprises the following steps:
and carrying out preliminary appearance screening on the N retired power batteries to be tested, and selecting M batteries with intact appearances, wherein M is less than or equal to N, and N, M is a positive integer.
Further, the parameters for representing the battery aging comprise battery capacity, internal resistance and self-discharge rate.
Further, the preliminary appearance screening is performed on the N ex-service power batteries to be tested, and the sub-step of selecting M batteries with good appearances includes:
101: observing whether the appearance of the battery is intact;
102: observing whether the surface of the battery is flat or not, whether bulging exists or not, whether liquid leakage exists or not and whether the phenomenon of abrasion deformation exists or not;
103: cells that do not meet the requirements of sub-steps 101 and 102 are initially excluded.
Therefore, the battery with good appearance can be quickly and accurately screened through the sub-steps.
Further, the battery capacity is measured by:
201: the battery is charged at 0.8C0Charging current to a first set voltage, and constant-voltage charging to a current of 0.05C0Stopping, standing for 1 hr, and adding 0.8C0Electric current ofDischarging at constant current to a second set voltage, standing for 1 hr, wherein C0The rated capacity of the ex-service power battery when leaving the factory;
202: and charging and discharging the battery for 10 circles according to the charging and discharging program of the substep 201, and taking the discharging capacity of the last circle as the real capacity C of the battery.
Thus, the capacity of each single battery can be accurately tested through the sub-steps.
Further, the internal resistance of the battery is measured by: :
301: charging the battery to a first set voltage by 1C current, then charging at constant voltage until the current is 0.05C, stopping standing for 1 hour, then discharging at constant current of 1C until the battery is at 50% SOC, and standing for 1 hour;
302: the internal resistance of the retired power battery is calculated through a mixed pulse performance test which is performed by a 10-second discharge current pulse I d40 second rest and 10 second charging current pulse IcComposition, the internal resistance was calculated by the change amount Δ U of the voltage in 10 seconds of discharge. Wherein Δ U ═ U1-U2，U1The voltage of the power battery is retired at the discharge starting moment; u shape2For the voltage at the end of the discharge pulse, the battery internal resistance calculation formula is: r ═ U1-U2)/Id。
Therefore, the internal resistance of each single battery can be accurately tested through the sub-steps.
Further, the battery capacity is measured by:
401: charging the battery to a first set voltage with 1C current, and then constant-voltage charging to a current of less than or equal to 0.05C cutoff
402: standing at 25 deg.C for 7 days;
403: discharging to a second set voltage by adopting 1C current to measure the discharge capacity C of the batterysd；
404: and calculating the self-discharge rate of the battery.
Therefore, the self-discharge rate of the battery can be accurately tested through the sub-steps.
Further, it is characterized bySelf-discharge rate R of the batterysdThe calculation formula of (c) is:
Rsd＝(C-Csd)/C*100％。
further, based on an LOF algorithm, a parameter for representing battery aging is used as an input, and the LOF for outputting the retired power battery comprises the following substeps:
501: calculating the distance between all data points and other points, and finding the kth distance d of any point ok(o)，dk(o) is defined as the distance between point o and the point k-th from it;
502: solving for the kth distance neighborhood N of each point ok(o)，Nk(o) is defined as all distances to the point o being less than or equal to dk(o) the set of points;
503: the k-th reachable distance reach _ dist from any two points o to pk(p, o) satisfies reach _ distk(p，o)＝max{dk(o)，d(o，p)}，max{dk(o), d (o, p) } denotes the larger of the kth distance from the point o and the distance from the point o to the point p;
504: calculating the kth local reachable density of each point p;
505: calculating the kth local abnormal factor of each p point;
506: and outputting LOF corresponding to each battery.
Further, the k-th local reachable density of each point p is calculated by:
Figure BDA0003536334980000031
further, the k-th local anomaly factor of each p-point is calculated by the following formula:
Figure BDA0003536334980000032
compared with the prior art, the invention has the beneficial effects that:
the invention provides a graded utilization and sorting method for retired power batteries, which has the advantages that compared with the traditional sorting method, the method simplifies the multi-parametric standard during retired power battery screening, utilizes an LOF algorithm to evaluate aging parameters of a plurality of batteries, can realize the inconsistency evaluation of the retired power batteries only through the LOF, and can realize the multi-level screening of the retired power batteries by setting different LOFs. Compared with patent document CN108199109A, the present invention does not realize consistency of batteries by successively screening aging parameters of retired power batteries, but based on a Local external Factor (LOF) algorithm, uses parameters representing battery aging as input, outputs the LOF of retired power batteries, and only sets different LOF thresholds, can realize hierarchical screening of retired power batteries, thereby greatly simplifying the parameter setting problem in the traditional retired power battery screening, and improving screening efficiency.
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FIG. 1 is a flow chart of a sorting method for echelon utilization of retired power batteries according to an embodiment of the present invention;
FIG. 2 is a capacity diagram of a retired power battery according to an embodiment of the invention;
FIG. 3 is a schematic diagram of internal resistance testing in an embodiment of the present invention;
FIG. 4 is a graph of internal resistance of a decommissioned power cell at 50% SOC in an embodiment of the present invention;
FIG. 5 is a self-discharge diagram of a retired power cell in an embodiment of the invention;
FIG. 6 is a schematic of the LOF algorithm in an embodiment of the present invention;
fig. 7 shows the screening result of the retired power battery in the embodiment of the present invention, where LOF is 1.08 as the screening standard.
Detailed Description
Example (b):
the technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
Referring to fig. 1, the sorting method for the gradient utilization of the retired power battery provided by this embodiment includes the following steps:
step 1: performing preliminary appearance screening on N retired power batteries to be tested to select M batteries with good appearances, wherein M is less than or equal to N and is a positive integer;
and 2, step: testing the capacity of each single battery;
and step 3: testing the internal resistance of each single battery at 50% SOC;
and 4, step 4: the self-discharge rate of each cell was tested.
And 5: based on a Local external Factor (LOF) algorithm, taking the battery capacity, the internal resistance and the self-discharge rate as input, and outputting LOFs of M retired power batteries;
step 6: and setting a corresponding threshold value for the LOF, and then finishing the classified screening of the retired power battery.
Therefore, through the steps, the aging parameters of the batteries are evaluated by using the LOF algorithm, the inconsistency evaluation of the retired power battery can be realized only through the LOF, and the multistage screening of the retired power battery can be realized by setting different LOFs. The aging parameters of the battery selected in the invention are the battery capacity, the internal resistance and the self-discharge rate, but the aging parameters of the battery are not limited to the parameters, and any parameters capable of representing the aging of the battery (for example, the position of a peak in a capacity increment curve, the strength of the peak and the area of the peak in a battery charge-discharge curve can represent the aging state of the battery) can be used as input parameters of the method, and the parameters are within the range included by the invention.
Specifically, the step 1 specifically includes the following substeps:
101: observing whether the appearance of the battery is intact;
102: observing whether the surface of the battery is flat or not, whether bulging exists or not, whether liquid leakage exists or not and whether the phenomenon of abrasion deformation exists or not;
103: cells that do not meet the requirements of sub-steps 101 and 102 are initially excluded.
Specifically, the step 2 specifically includes the following substeps:
201: the battery is charged at 0.8C0Charging current to a first set voltage, and constant-voltage charging to a current of 0.05C0Stopping, standing for 1 hr, and adding 0.8C0Discharging to a second set voltage with constant current, standing for 1 hr, wherein0For retirement of power batteriesRated capacity at the time of delivery;
202: and charging and discharging the battery for 10 circles according to the charging and discharging program of the substep 201, and taking the discharging capacity of the last circle as the real capacity C of the battery.
Specifically, the step 3 specifically includes the following substeps:
301: charging the battery to a first set voltage by 1C current, then charging at constant voltage until the current is 0.05C, stopping standing for 1 hour, then discharging at constant current of 1C until the battery is at 50% SOC, and standing for 1 hour;
302: the internal resistance of the retired power battery is calculated through a mixed pulse performance test which is performed by a 10-second discharge current pulse Id40 second rest and 10 second charging current pulse IcComposition, the internal resistance was calculated by the change amount Δ U of the voltage in 10 seconds of discharge. Wherein Δ U ═ U1-U2，U1The voltage of the power battery is retired at the discharge starting moment; u shape2For the voltage at the end of the discharge pulse, the battery internal resistance calculation formula is: r ═ U1-U2)/Id；
Specifically, the step 4 specifically includes the following sub-steps:
401: charging the battery to a first set voltage by 1C current, and then charging the battery at constant voltage until the current is less than or equal to 0.05C and cutting off;
402: standing at 25 deg.C for 7 days;
403: discharging to a second set voltage by adopting 1C current to measure the discharge capacity C of the batterysd
404: calculating the self-discharge rate of the battery, wherein the self-discharge rate RsdThe calculation formula of (2) is as follows:
Rsd＝(C-Csd)/C*100％
specifically, the step 5 of calculating the local abnormality factor of each cell has the following substeps:
501: calculating the distance between all data points and other points, and finding the kth distance d of any point ok(o)，dk(o) is defined as the distance between point o and the point k-th from it;
502: determining the kth distance of each point oNeighborhood Nk(o)，Nk(o) is defined as all distances to the point o being less than or equal to dkThe set of points of (o).
503: the k-th reachable distance reach _ dist from any two points o to pk(p, o) satisfies reach _ distk(p，o)＝max{dk(o)，d(o，p)}，max{dk(o), d (o, p) } denotes the larger of the kth distance from the point o and the distance from the point o to the point p;
504: calculate the kth local achievable density for each point p:
Figure BDA0003536334980000061
505: calculating the k local anomaly factor of each p point:
Figure BDA0003536334980000062
506: and outputting LOF corresponding to each battery.
The steps of the method are verified and explained below by taking a certain retired lithium iron phosphate power battery as an example, and the delivery capacity of the power battery is 5.2Ah (C)05.2Ah), the concrete steps are as follows:
step 1: selecting 60 ex-service ion batteries for preliminary appearance screening, screening out batteries with incomplete appearance, swelling and liquid leakage, and finally obtaining 48 ex-service power batteries with good appearance;
step 2: the capacity of the single battery is tested, fig. 2 shows the capacity of the 48 retired power batteries, and the specific sub-steps are as follows:
201: the battery is charged at 0.8C0Charging to 3.6V with current, and constant-voltage charging to 0.05C or less0Stopping, standing for 1 hr, and adding 0.8C0Discharging to 2.5V with constant current, and standing for 1 hour;
202: and charging and discharging the battery for 10 circles according to the charging and discharging program of the substep 201, and taking the discharging capacity of the last circle as the real capacity C of the battery.
And step 3: testing the internal resistance of the single battery under 50% SOC, and referring to FIG. 3, the internal resistance of 48 retired power batteries under 50% SOC comprises the following specific sub-steps:
charging the battery to 3.6V by 1C current, then charging at constant voltage until the current is less than or equal to 0.05C, standing for 1 hour, then discharging to 50% SOC by 1C current constant current, standing for 1 hour, testing the internal resistance of the battery under SOC by adopting a mixed pulse performance test method, wherein the mixed pulse performance test comprises a 10-second discharge pulse Id(8A) 40 second rest and 10 second charging pulse Ic(6A) FIG. 4 is a graph illustrating the voltage variation with time and the internal resistance calculation in the mixed pulse performance test;
and 4, step 4: the self-discharge of the single battery is tested, and the specific sub-steps are as follows:
401: charging the battery to 3.6V at a current of 1C, and then performing constant-voltage charging until the current is less than or equal to 0.05C and cutting off;
402: standing at 25 deg.C for 7 days;
403: discharging to 2.5V by adopting 1C current, and measuring the discharge capacity C of the batterysd；
404: according to the formula Rsd＝(C-Csd) The self-discharge rate of the cells was calculated at/C100% and the results are shown in fig. 5;
and 5: taking the battery capacity, the internal resistance and the self-discharge as data input, respectively calculating LOFs of 48 retired power batteries based on an LOF algorithm, wherein in the example, the k value is 30, and the method specifically comprises the following substeps:
501: calculating the distance between all data points and the rest points, and calculating d of any point o30(o)；
502: finding N for each point o30(o)；
503: calculating reach _ dist of any two points o to p30(p，o)；
504: calculating the local achievable density of each point p:
Figure BDA0003536334980000071
505: calculate local anomaly factor for each data point:
Figure BDA0003536334980000072
506: outputting LOF corresponding to each battery;
step 6: the graded screening of the retired power battery can be completed by setting corresponding threshold values for the LOF, and the specific screening flow is shown in fig. 6, and the result is shown in fig. 7.
The above embodiments are only for illustrating the technical concept and features of the present invention, and the purpose thereof is to enable those skilled in the art to understand the contents of the present invention and implement the present invention accordingly, and not to limit the protection scope of the present invention accordingly. All equivalent changes or modifications made in accordance with the spirit of the present disclosure are intended to be covered by the scope of the present disclosure.

Claims (10)

1. A sorting method for gradient utilization of retired power batteries is characterized by comprising the following steps:
acquiring parameters representing battery aging;
based on an LOF algorithm, taking parameters representing battery aging as input, and outputting the LOF of the retired power battery;
and setting a corresponding threshold value for the LOF to complete the classified screening of the retired power battery.
2. The method for sorting out-of-service power cell echelon utilization of claim 1, further comprising, prior to obtaining parameters characterizing cell aging:
and carrying out preliminary appearance screening on the N retired power batteries to be tested, and selecting M batteries with intact appearances, wherein M is less than or equal to N, and N, M is a positive integer.
3. The method for sorting out retired power battery echelon utilization of claim 1 or 2, wherein the parameters characterizing battery aging include battery capacity, internal resistance, and self-discharge rate.
4. The method for sorting retired power battery echelon utilization according to claim 2, wherein the step of performing preliminary appearance screening on the N retired power batteries to be tested to select M batteries with good appearances comprises the following substeps:
101: observing whether the appearance of the battery is intact;
102: observing whether the surface of the battery is flat or not, whether bulging exists or not, whether liquid leakage exists or not and whether the battery has the phenomena of abrasion deformation or not;
103: cells that do not meet the requirements of sub-steps 101 and 102 are initially excluded.
5. The method for sorting out-of-service power battery echelon utilization of claim 1, wherein the battery capacity is measured by:
201: the battery is charged at 0.8C0Charging current to a first set voltage, and constant-voltage charging to a current of 0.05C0Stopping, standing for 1 hr, and adding 0.8C0Discharging to a second set voltage with constant current, standing for 1 hr, wherein0The rated capacity of the ex-service power battery when leaving the factory;
202: and charging and discharging the battery for 10 circles according to the charging and discharging program of the substep 201, and taking the discharging capacity of the last circle as the real capacity C of the battery.
6. The method for sorting out-of-service power battery echelon utilization of claim 4, wherein the internal resistance of the battery is measured by:
301: charging the battery to a first set voltage by 1C current, then charging at constant voltage until the current is 0.05C, stopping standing for 1 hour, then discharging at constant current of 1C until the battery is at 50% SOC, and standing for 1 hour;
302: the internal resistance of the retired power battery is calculated through a mixed pulse performance test which is performed by a 10-second discharge current pulse Id40 second rest and 10 second charging current pulse IcComposition, the internal resistance was calculated by the change amount Δ U of the voltage in 10 seconds of discharge. Wherein Δ U ═ U1-U2，U1Decommissioning for discharge start timeThe voltage of the power battery; u shape2For the voltage at the end of the discharge pulse, the battery internal resistance calculation formula is: r ═ U1-U2)/Id。
7. The method for sorting out-of-service power cell echelon utilization of claim 1, wherein the self-discharge rate is measured by:
401: charging the battery to a first set voltage by 1C current, and then performing constant voltage charging until the current is less than or equal to 0.05C and cutting off;
402: standing at 25 deg.C for 7 days;
403: discharging to a second set voltage by adopting 1C current to measure the discharge capacity C of the batterysd；
404: and calculating the self-discharge rate of the battery.
8. The method for sorting graded usage of retired power batteries according to claim 7, wherein the batteries have a self-discharge rate RsdThe calculation formula of (2) is as follows:
Rsd＝(C-Csd)/C*100％。
9. the method for sorting the ex-service power battery echelon utilization according to claim 1, wherein the LOF algorithm is used for inputting parameters for representing battery aging, and the LOF algorithm for outputting the ex-service power battery comprises the following sub-steps:
501: calculating the distance between all data points and other points, and finding the kth distance d of any point ok(o)，dk(o) is defined as the distance between point o and the point k-th from it;
502: solving for the kth distance neighborhood N of each point ok(o)，Nk(o) is defined as all distances to point o are less than or equal to dk(o) the set of points;
503: the k-th reachable distance reach _ dist from any two points o to pk(p, o) satisfies reach _ distk(p，o)＝max{dk(o)，d(o，p)}，max{dk(o), d (o, p) } denotes the kth distance from the point o and the distance from the point o to the point pThe larger of these;
504: calculating the kth local reachable density of each point p;
505: calculating the kth local abnormal factor of each p point;
506: and outputting LOF corresponding to each battery.
10. The method for sorting retired power battery echelon utilization according to claim 9, wherein the k-th local reachable density of each point p is calculated by the formula:
Figure FDA0003536334970000021
the calculation formula of the k-th local abnormal factor of each p point is as follows:
Figure FDA0003536334970000031
CN202210219250.9A 2022-03-08 2022-03-08 Sorting method for gradient utilization of retired power batteries Pending CN114429050A (en)

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CN117410598A (en) * 2023-12-15 2024-01-16 山西迪诺思新能源科技有限公司 Retired battery module combination method based on index similarity
CN117410598B (en) * 2023-12-15 2024-02-23 山西迪诺思新能源科技有限公司 Retired battery module combination method based on index similarity
CN117577977A (en) * 2024-01-17 2024-02-20 深圳市杰成镍钴新能源科技有限公司 Whole-pack recycling control method for retired battery pack
CN117577977B (en) * 2024-01-17 2024-04-19 深圳市杰成镍钴新能源科技有限公司 Whole-pack recycling control method for retired battery pack
CN117970159A (en) * 2024-04-02 2024-05-03 深圳深汕特别合作区乾泰技术有限公司 Method, system and medium for evaluating availability of waste battery based on big data

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