A method for reorganizing second-life battery packs with large data sorting capacity

Abstract

The present invention discloses a method for reorganizing echelon battery packs with large data sorting capacity, which searches for the SOH value of the battery pack to be screened. When the SOH value of a battery pack is found to be less than a set value, the judgment criterion for the echelon battery pack is determined based on the SOH value of the found battery pack; the average value N of the SOH value of the battery pack to be screened is calculated, and the relationship between the average value N and the judgment criterion is used to determine whether the corresponding battery pack meets the echelon conditions; when the number of battery packs that meet the echelon conditions reaches the set number, the battery pack screening is completed, and the echelon battery pack reorganization is performed based on the screened battery packs. The present invention can achieve fast, efficient and accurate consistency screening and grouping, greatly save the testing cost of battery echelon utilization, improve production rhythm, and reduce equipment and manpower investment.

Description

Technical Field

[0001] The present invention belongs to the technical field of power batteries, and in particular relates to a method for reorganizing a battery pack with large data sorting capacity. Background Art

[0002] Second-use refers to the process of continuous use of a product that has reached its original design life and then fully or partially restores its functions through other means. This process is basically a way of same-level or downgraded use. The greatest social significance of second-use is that it maximizes the use of products and extends their design cycle. While creating economic value for society, it also reduces waste emissions for society. It is a circular, low-carbon lifestyle that modern society should vigorously promote.

[0003] The existing solution for the cascade utilization of retired batteries requires disassembling the battery pack, obtaining the battery cells, and then conducting capacity testing, OCV grouping, DCR testing, and re-grouping. The testing process is cumbersome and energy-intensive, making cascade utilization difficult.

[0004] A method for rapid consistency screening of retired power batteries for cascade utilization (CN 116736159A) uses the high current discharge energy of batteries with different performance to amplify the differences between battery capacity and internal resistance, and achieves rapid consistency screening of retired power batteries before cascade utilization. The method is implemented by disassembly and grouping → capacity sampling → OCV and IMP grouping → DCR grouping under high current to achieve consistency screening of retired power batteries. This patent has the following disadvantages:

[0005] 1. Capacity testing is required, and the charge and discharge capacity test will consume the full charge energy of the battery pack. During large-scale production, the energy consumption is high.

[0006] 2. Single capacity test has many sensitive factors and low accuracy, such as ambient temperature and consistency errors. Summary of the invention

[0007] The purpose of the present invention is to solve the shortcomings of the above-mentioned background technology and to provide a method for reorganizing battery packs with large data sorting capacity, which can achieve fast, efficient and accurate consistency screening and grouping.

[0008] The technical solution adopted by the present invention is: a method for reorganizing a battery pack with a large data sorting capacity, searching for the SOH value of the battery pack to be screened, and when the SOH value of a battery pack is found to be less than a set value, determining the judgment criterion of the battery pack with a cascade of storage based on the SOH value of the battery pack found;

[0009] Calculate the average SOH value N of the battery pack to be screened, and determine whether the corresponding battery pack meets the tier conditions based on the relationship between the average value N and the judgment standard;

[0010] When the number of battery packs that meet the cascade conditions reaches the set number, the battery pack screening is completed, and the cascade battery pack reorganization is performed based on the screened battery packs.

[0011] Furthermore, the process of determining the judgment criterion of the used battery pack is: obtaining the most recent M SOH values ​​of the found battery pack, calculating the average value N0 and variance σ of the M SOH values, and the judgment criterion is [N0-A*σ, N0+A*σ].

[0012] Furthermore, the value of M is 5-20.

[0013] Furthermore, the value of A is 1-10.

[0014] Further, when the average SOH value N of the battery pack to be screened is within the range of the determination criterion, it is determined that the battery pack meets the tier condition.

[0015] Furthermore, each time a battery pack is determined to satisfy the echelon condition, the count is increased by one, and then the next battery pack is determined to satisfy the echelon condition.

[0016] Furthermore, the SOH value is obtained based on data monitored by a big data monitoring platform, and the monitored data includes the remaining battery power, the remaining battery capacity, the highest single cell voltage, and the lowest single cell voltage during each charge and discharge of the battery pack.

[0017] Furthermore, the SOH value is determined by the following formula:

[0018]

[0019] Among them, Battsoc0 and Batttelec0 are the remaining power of the battery pack and the remaining capacity of the battery pack at the starting point of each charging, Battsoc1 and Batttelec1 are the remaining power of the battery pack and the remaining capacity of the battery pack at the end point of each charging, and c0 is the rated capacity of the battery pack.

[0020] Furthermore, the charging start point determination condition is that the battery has been left to stand for more than half an hour and the remaining power is lower than the first set power, or the lowest cell voltage during the discharge process is lower than the discharge cut-off voltage.

[0021] Furthermore, the determination condition of the charging endpoint is that the battery has been left to stand for more than half an hour and the remaining power is higher than the second set power, or the highest single cell voltage during the charging process is higher than the charging cut-off voltage.

[0022] The beneficial effects of the present invention are:

[0023] The present invention determines (searches or calculates) the SOH value of the battery pack based on the platform big data, and performs tiered screening and reorganization of the battery pack according to the SOH value, thereby solving the capacity screening problem in the tiered reorganization of battery-swap vehicle models, saving charging and discharging time, and improving the production line production rhythm.

[0024] The present invention analyzes the SOH value results of big data for multiple times in the past and performs weighted averaging to reduce consistency errors and better perform group matching.

[0025] The present invention can directly calculate the battery capacity using past charge and discharge history data without the need for laboratory testing resources, and intelligently reorganize the battery pack through big data screening, thus saving time and providing the possibility for cascade utilization of the battery pack.

[0026] The present invention can realize fast, efficient and accurate consistency screening and grouping, greatly save the testing cost of battery recycling, improve production rhythm, and reduce equipment and manpower investment. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 The present invention is a flow chart of the reorganization of the cascade battery pack.

[0028] Figure 2 This is a flow chart for determining the SOH value of the battery pack of the present invention. DETAILED DESCRIPTION

[0029] The specific embodiments of the present invention are further described below in conjunction with the accompanying drawings. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0030] like Figure 1 As shown, the present invention provides a method for reorganizing a battery pack with large data sorting capacity, comprising the following steps:

[0031] Step 1: When the charging and storage station has an order demand (i.e., there is a demand for tiered reorganization), the SOH value of the battery pack to be screened is searched or calculated from the big data. When the SOH value of a battery pack is found to be less than the set value (i.e., the first battery pack whose SOH value is less than the set value), the judgment criterion for the tiered battery pack is determined based on the SOH value of the battery pack found.

[0032] Specifically, the process of determining the judgment criterion for a used battery pack is as follows: obtain the most recent M SOH values ​​of the battery pack whose first SOH value is less than a set value, calculate the average value N0 and variance σ of the M SOH values, then the judgment criterion is [N0-A*σ, N0+A*σ], the value of M is 5-20, preferably 10; the value of A is 1-10, preferably 6.

[0033] The SOH results of the battery pack of the battery swap model fully charged 10 times at the battery swap station are as follows: N1, N2, N3, N4, N5, N6, N7, N8, N9, N10, with an average value of N and a variance of σ. The variance is the average of the squares of the deviations of each value in the data group from its mean. It can better reflect the degree of discreteness of the data and is the most widely used discreteness measure in practice. The smaller the variance, the smaller the average distance between the data value and the mean, and the better the representativeness of the mean.

[0034] The average value N0 of the first battery pack whose SOH value is less than the set value is N0 = (N1 + N2 + N3 + N4 + N5 + N6 + N7 + N8 + N9 + N10) / 10, and the corresponding variance is σ. The judgment basis is [N0-6*σ, N0+6*σ].

[0035] Step 2, based on the above method for determining the average SOH value, determine the average SOH value N of the battery pack to be screened, and judge whether the corresponding battery pack meets the tier conditions based on the relationship between the average value N and the judgment standard, that is, when the average SOH value N of the battery pack to be screened is in [N0-6*σ, N0+6*σ], it is determined that the tier conditions are met and reorganization can be carried out, otherwise it is not met.

[0036] Each time a battery pack is judged to meet the echelon condition, the count is increased by one, and then the next battery pack is judged to meet the echelon condition.

[0037] Step 3, when the number of battery packs that meet the echelon conditions reaches the set number, the battery pack screening is completed, and the echelon battery packs are reorganized based on the screened battery packs. The set number is determined based on the order requirements of the storage and charging station, generally 4 or 6.

[0038] For new energy vehicle battery-swap models, as a battery holding company, the battery-related charging and swapping data can be detected in real time and uploaded to the cloud platform to form a big data monitoring platform for subsequent search and use. The monitored data includes the remaining battery power, remaining battery capacity, highest cell voltage, and lowest cell voltage during each charge and discharge of the battery pack, so as to calculate the current SOH value. The SOH value can be calculated in advance in the big data monitoring platform and directly searched when needed, or it can be calculated after calling the data.

[0039] The big data monitoring fields and descriptions used for screening are as follows:

[0040]

[0041]

[0042] Based on the monitored data, the SOH value is determined by the following formula

[0043]

[0044] Among them, Battsoc0 and Batttelec0 are the remaining power of the battery pack and the remaining capacity of the battery pack at the starting point of each charging, Battsoc1 and Batttelec1 are the remaining power of the battery pack and the remaining capacity of the battery pack at the end point of each charging, and c0 is the rated capacity of the battery pack.

[0045] The determination condition for the charging starting point is that the battery has been left to stand for more than half an hour and the remaining power is lower than the first set power, or the lowest single cell voltage during the discharge process is lower than the discharge cut-off voltage.

[0046] The determination condition of the charging end point is that the battery has been left to stand for more than half an hour and the remaining power is higher than the second set power, or the highest single cell voltage during the charging process is higher than the charging cut-off voltage.

[0047] The discharge cut-off voltage is the voltage value at which the battery pack stops discharging when the voltage drops to a certain level during the discharge process; the charge cut-off voltage is the voltage value at which the battery pack stops charging when the voltage rises to a certain level during the charging process.

[0048] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in the field.

Claims

1. A method for reorganizing a battery pack with large data sorting capacity, characterized in that: Find the SOH value of the battery pack to be screened, and when the SOH value of a battery pack is found to be less than a set value, determine the judgment criterion of the battery pack based on the SOH value of the battery pack found; The process of determining the judgment criterion of the battery pack is as follows: obtaining the SOH values ​​of the battery pack found for the most recent M times, calculating the average value N0 and variance σ of the M times of SOH values, and then the judgment criterion is [N0-A*σ, N0+A*σ]; Determine the average SOH value N of the battery pack to be screened, and judge whether the corresponding battery pack meets the tier conditions based on the relationship between the average value N and the judgment criterion; When the number of battery packs that meet the echelon conditions reaches the set number, the battery pack screening is completed, and the echelon battery pack reorganization is performed based on the screened battery packs; The SOH value is obtained based on data monitored by a big data monitoring platform, wherein the monitored data includes the remaining battery power, the remaining battery capacity, the highest cell voltage, and the lowest cell voltage during each charge and discharge of the battery pack; The SOH value is determined by the following formula Among them, Battsoc0 and Batttelec0 are the remaining power of the battery pack and the remaining capacity of the battery pack at the starting point of each charging, Battsoc1 and Batttelec1 are the remaining power of the battery pack and the remaining capacity of the battery pack at the end point of each charging, and c0 is the rated capacity of the battery pack.

2. The method for reorganizing a battery pack with large data sorting capacity according to claim 1, characterized in that: The value of M is 5-20.

3. The method for reassembling secondary battery packs with large data sorting capacity according to claim 1, characterized in that: The value of A is 1-10.

4. The method for reassembling secondary battery packs with large data sorting capacity according to claim 1, characterized in that: When the average SOH value N of the battery pack to be screened is within the range of the judgment criterion, it is determined that the battery pack meets the tier conditions.

5. The method for reorganizing secondary battery packs with large data sorting capacity according to claim 1, characterized in that: Each time a battery pack is judged to meet the echelon condition, the count is increased by one, and then the next battery pack is judged to meet the echelon condition.

6. The method for reassembling secondary battery packs with large data sorting capacity according to claim 1, characterized in that: The determination condition for the charging starting point is that the battery has been left to stand for more than half an hour and the remaining power is lower than the first set power, or the lowest single cell voltage during the discharge process is lower than the discharge cut-off voltage.

7. The method for reassembling secondary battery packs with large data sorting capacity according to claim 1, characterized in that: The determination condition of the charging end point is that the battery has been left to stand for more than half an hour and the remaining power is higher than the second set power, or the highest single cell voltage during the charging process is higher than the charging cut-off voltage.

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