A scene-based battery sorting method, device, equipment and storage medium

Abstract

The application relates to a scene-based battery sorting method, device, equipment and storage medium, which comprises the following steps: performing preliminary screening on all retired batteries according to preset requirements to obtain a plurality of to-be-sorted batteries; calculating a regression SOH according to the initial SOH and the initial discharge rate of all the to-be-sorted batteries; classifying the to-be-sorted batteries according to the regression SOH to determine the application scene of all the to-be-sorted batteries; calculating the charge-discharge energy curve sequence distance between any two to-be-sorted batteries under the same application scene; and sorting the to-be-sorted batteries under the same application scene based on the charge-discharge energy curve sequence distance. The application classifies the batteries according to scenes first, the battery states under the same scene are closer, the sorted batteries have better consistency, the charge-discharge energy curve sequence distance of the batteries under the same application scene is calculated, the sorting of the batteries is completed according to the charge-discharge energy curve sequence distance, and the accuracy of the battery sorting is improved.

Description

Technical Field

[0001] This invention relates to the field of battery sorting technology, and in particular to a scenario-based battery sorting method, apparatus, device, and storage medium. Background Technology

[0002] Due to differences in manufacturers, electric vehicle operating environments, and usage conditions, retired batteries exhibit extremely poor consistency. Directly reassembling them for reuse can lead to a series of problems and challenges, such as overcharging, over-discharging, and thermal runaway. Therefore, selecting appropriate battery characteristics and designing reasonable sorting methods to separate retired batteries with consistent overall performance for reassembly and reuse is of great significance for the tiered utilization of retired batteries.

[0003] The existing sorting method for retired batteries generally involves measuring the static and / or dynamic characteristic parameters (such as capacity, internal resistance, open circuit voltage, and charge-discharge characteristic curves) of the retired power battery cells, specifying the deviation range that the sorting parameters should meet, and dividing them into different groups according to the different ranges of the battery characteristic parameters.

[0004] However, these characteristic parameters in the existing technology can only ensure the consistency of battery characteristics at the initial moment or at a certain moment after sorting, but cannot guarantee their consistency over a long time scale. The resulting inconsistency problem will be aggravated as the battery's working time increases, causing irreversible damage to the battery, accelerating its scrapping process, and posing safety hazards. Summary of the Invention

[0005] In view of this, it is necessary to provide a scenario-based battery sorting method, apparatus, device and storage medium to solve the problem that the existing technology of sorting batteries solely by the characteristic parameters of retired batteries cannot guarantee their consistency over a long time scale, which can easily cause irreversible damage to the batteries and accelerate their scrapping process.

[0006] To achieve the above-mentioned technical objectives, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a scenario-based battery sorting method, comprising:

[0008] According to the preset requirements, a preliminary screening of all retired batteries was conducted to obtain a number of batteries to be sorted.

[0009] The regression SOH is calculated based on the initial SOH and initial discharge rate of all cells to be sorted;

[0010] The application scenarios of all batteries to be sorted are determined by classifying the batteries according to their application scenarios using regression SOH.

[0011] Calculate the distance between the charge and discharge energy curve sequences of any two batteries to be sorted under the same application scenario;

[0012] Batteries to be sorted under the same application scenario are sorted based on the distance of the charge and discharge energy curve sequence.

[0013] In some possible implementations, a preliminary screening of all retired batteries is performed according to preset requirements to obtain several batteries to be sorted, including:

[0014] Obtain the initial state of harm (SOH) and initial discharge rate of all retired batteries;

[0015] Retired batteries that meet the preset SOH and preset discharge rate ranges are selected based on the initial state of harm (SOH) and initial discharge rate, resulting in a number of batteries to be sorted.

[0016] In some possible implementations, the regression SOH is calculated based on the initial SOH and initial discharge rate of all cells to be sorted, including:

[0017] Calculate the average SOH and average discharge rate based on the initial SOH and initial discharge rate of all batteries to be sorted;

[0018] Based on the preset regression formula, the regression SOH is calculated according to the average SOH, average discharge rate, current initial SOH of the battery to be sorted, and current initial discharge rate of the battery to be sorted.

[0019] In some possible implementations, the application scenarios of all batteries to be sorted are determined by regressing the State of Occurrence (SOH) to classify the batteries into different application scenarios, including:

[0020] Based on the preset rule of corresponding discharge rate and SOH, the regression discharge rate is determined according to the regression SOH.

[0021] Based on the preset rules of matching discharge rate with application scenario, the application scenario of all batteries to be sorted is determined according to the regression discharge rate.

[0022] In some possible implementations, the distance between the charge / discharge energy curve sequences of any pair of batteries to be sorted under the same application scenario is calculated, including:

[0023] All batteries to be sorted are processed to obtain a sequence of charge and discharge energy curves for all batteries to be sorted;

[0024] The longest common subsequence length is calculated based on the charge and discharge energy curve sequences of any two pairs of batteries to be sorted under the same application scenario.

[0025] The sum of the feature sequence lengths is determined based on the charge and discharge energy curve sequences of any two pairs of batteries to be sorted under the same application scenario.

[0026] The distance between the charge / discharge energy curve sequences is calculated based on the sum of the longest common subsequence length and the characteristic sequence length.

[0027] In some possible implementations, all batteries to be sorted are processed to obtain a sequence of charge-discharge energy curves for all batteries to be sorted, including:

[0028] Charge and discharge tests were performed on the batteries to be sorted to obtain the charge and discharge energy curves of the batteries to be sorted.

[0029] The charge-discharge energy curves are normalized to obtain the target energy curves of the batteries to be sorted.

[0030] The charge and discharge energy curve sequence of the batteries to be sorted is determined based on the target energy curve.

[0031] In some possible implementations, the longest common subsequence length is calculated based on the charge / discharge energy curve sequences of any pair of batteries to be sorted under the same application scenario, including:

[0032] Calculate the mode matching distance between two charge-discharge energy curve sequences based on the charge-discharge energy curve sequence;

[0033] Set similarity and mismatch parameters;

[0034] The length of the longest common subsequence is calculated based on the pattern matching distance, similarity parameter, and mismatch parameter.

[0035] Secondly, the present invention also provides a scenario-based battery sorting device, comprising:

[0036] The initial screening module is used to perform an initial screening of all retired batteries according to preset requirements to obtain a number of batteries to be sorted.

[0037] The regression module is used to calculate the regression SOH based on the initial SOH and initial discharge rate of all batteries to be sorted.

[0038] The scenario classification module is used to classify the application scenarios of all batteries to be sorted by regression SOH;

[0039] The calculation module is used to calculate the distance between the charge and discharge energy curve sequences of any two batteries to be sorted under the same application scenario;

[0040] The sorting module is used to sort batteries under the same application scenario based on the distance of the charge and discharge energy curve sequence.

[0041] Thirdly, the present invention also provides a scenario-based battery sorting device, including a memory and a processor, wherein,

[0042] Memory, used to store programs;

[0043] The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the scenario-based battery sorting method in any of the above implementations.

[0044] Fourthly, the present invention also provides a computer-readable storage medium for storing a computer-readable program or instruction, which, when executed by a processor, can implement the steps in the scenario-based battery sorting method described above.

[0045] The beneficial effects of the above embodiments are as follows: The present invention relates to a scenario-based battery sorting method, apparatus, device, and storage medium. The method includes: performing initial screening on all retired batteries according to preset requirements to obtain a number of batteries to be sorted; calculating regression SOH based on the initial SOH and initial discharge rate of all batteries to be sorted; classifying the batteries to be sorted into application scenarios based on the regression SOH to determine the application scenarios of all batteries to be sorted; calculating the charge-discharge energy curve sequence distance between any two batteries to be sorted in the same application scenario; and sorting the batteries to be sorted in the same application scenario based on the charge-discharge energy curve sequence distance. This invention first calculates the regression SOH of the batteries to be sorted, and then classifies the batteries into scenarios based on the regression SOH. Batteries in the same scenario are more similar in state and have better consistency. Next, the distance between the charge and discharge energy curve sequences of batteries in the same application scenario is calculated, and the batteries are sorted based on the distance between the charge and discharge energy curve sequences. By sorting the batteries in the same scenario, the batteries to be sorted have better consistency, thereby improving the accuracy of battery sorting and avoiding a series of problems caused by the inability to guarantee long-term consistency after battery sorting. The batteries sorted by this invention are more suitable for long-term operation. Attached Figure Description

[0046] Figure 1 This is a flowchart illustrating an embodiment of the scenario-based battery sorting method provided by the present invention.

[0047] Figure 2 Provided by the present invention Figure 1 A flowchart illustrating an embodiment of step S104;

[0048] Figure 3 Provided by the present invention Figure 2 A flowchart illustrating an embodiment of step S201;

[0049] Figure 4 Provided by the present invention Figure 2 A flowchart illustrating an embodiment of step S202;

[0050] Figure 5A schematic diagram of the structure of an embodiment of the scenario-based battery sorting device provided by the present invention;

[0051] Figure 6 This is a schematic diagram of the structure of a scenario-based battery sorting device provided in an embodiment of the present invention. Detailed Implementation

[0052] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0053] Before describing the embodiments of the present invention, the relevant terms are explained as follows:

[0054] SOH stands for State of Health, which indicates the battery's health status. It can be understood as the percentage of the battery's current capacity relative to its factory-set capacity.

[0055] Discharge rate is a measure of how fast a battery discharges; it refers to the current intensity required for a battery to release its rated capacity within a specified time.

[0056] In the description of this application, "multiple" means two or more, unless otherwise expressly and specifically defined.

[0057] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0058] This invention provides a scenario-based battery sorting method, apparatus, device, and storage medium, which are described below.

[0059] Please see Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of the scenario-based battery sorting method provided by the present invention. A specific embodiment of the present invention discloses a scenario-based battery sorting method, comprising:

[0060] S101. According to the preset requirements, a preliminary screening of all retired batteries is performed to obtain a number of batteries to be sorted.

[0061] S102. Calculate the regression SOH based on the initial SOH and initial discharge rate of all batteries to be sorted;

[0062] S103. By regressing SOH, the application scenarios of all batteries to be sorted are classified and determined.

[0063] S104. Calculate the distance between the charge and discharge energy curve sequences of any two batteries to be sorted under the same application scenario.

[0064] S105. Based on the distance of the charge and discharge energy curve sequence, sort the batteries to be sorted under the same application scenario.

[0065] In the above embodiments, the state of retired batteries is not uniform, and there are even large differences in the state between some retired batteries. Not all retired batteries can be sorted and utilized, and some retired batteries may face the situation of being scrapped. Therefore, it is necessary to first screen the retired batteries to select those with sorting and utilization value, thus obtaining a number of batteries to be sorted.

[0066] The initial SOH and initial discharge rate of the batteries to be sorted refer to the SOH and discharge rate measured after these batteries have been decommissioned. They can reflect the current condition of the batteries to be sorted to a certain extent. The regression SOH can be calculated from the initial SOH and initial discharge rate of the batteries to be sorted. The regression SOH can be used for subsequent analysis to classify the decommissioned batteries according to their scenarios.

[0067] By using the regression SOH of the batteries to be sorted, the range of regression discharge rate of the batteries to be sorted can be determined. Thus, the application scenario of the batteries to be sorted can be determined based on the regression discharge rate of the batteries to be sorted, thereby achieving the initial sorting of the batteries to be sorted. Batteries to be sorted under the same application scenario have good consistency.

[0068] When calculating the distance between charge and discharge energy curve sequences, two batteries to be sorted under the same application scenario are arbitrarily selected as a group. The distance between the charge and discharge energy curve sequences is calculated from their charge and discharge energy curves. The smaller the distance between the charge and discharge energy curve sequences, the longer the common subsequence of the two curves and the more similar the characteristics of the charge and discharge energy curves, so that the batteries to be sorted can be accurately sorted.

[0069] By setting the range of distance between charge and discharge energy curve sequences, batteries to be sorted under the same application scenario can be sorted. Specifically, multiple ranges of distance between charge and discharge energy curve sequences can be set according to actual needs, and the specific values ​​of the ranges of distance between charge and discharge energy curve sequences can also be set according to actual needs. The batteries to be sorted are sorted according to the range of distance between charge and discharge energy curve sequences.

[0070] Compared with existing technologies, this embodiment provides a scenario-based battery sorting method, which includes: performing initial screening on all retired batteries according to preset requirements to obtain several batteries to be sorted; calculating regression SOH based on the initial SOH and initial discharge rate of all batteries to be sorted; classifying the batteries to be sorted into application scenarios based on the regression SOH to determine the application scenarios of all batteries to be sorted; calculating the charge-discharge energy curve sequence distance between any two batteries to be sorted in the same application scenario; and sorting the batteries to be sorted in the same application scenario based on the charge-discharge energy curve sequence distance. This invention first calculates the regression SOH of the batteries to be sorted, and then classifies the batteries into scenarios based on the regression SOH. Batteries in the same scenario are more similar in state and have better consistency. Next, the distance between the charge and discharge energy curve sequences of batteries in the same application scenario is calculated, and the batteries are sorted based on the distance between the charge and discharge energy curve sequences. By sorting the batteries in the same scenario, the batteries to be sorted have better consistency, thereby improving the accuracy of battery sorting and avoiding a series of problems caused by the inability to guarantee long-term consistency after battery sorting. The batteries sorted by this invention are more suitable for long-term operation.

[0071] In some embodiments of the present invention, a preliminary screening of all retired batteries is performed according to preset requirements to obtain a number of batteries to be sorted, including:

[0072] Obtain the initial state of harm (SOH) and initial discharge rate of all retired batteries;

[0073] Retired batteries that meet the preset SOH and preset discharge rate ranges are selected based on the initial state of harm (SOH) and initial discharge rate, resulting in a number of batteries to be sorted.

[0074] In the above embodiments, when initially screening retired batteries, they can also be screened based on their appearance. Retired batteries with bulges, damage, or other defects can be filtered out. Such retired batteries are damaged and cannot be reused through sorting.

[0075] The initial state of harm (SOH) and initial discharge rate of all retired batteries can be directly measured. The specific measurement method can be directly implemented by existing technology, which will not be elaborated on in this invention. The initial SOH and initial discharge rate are the initial states of the retired batteries at this time. The initial SOH and initial discharge rate are used for subsequent sorting of retired batteries.

[0076] As a preferred embodiment, the preset SOH range and preset discharge rate range are [0.4, 0.8] and (0, 0.5], respectively. Selecting retired batteries whose initial SOH and initial discharge rate are within the preset SOH range and preset discharge rate range results in a more moderate discharge rate for secondary use, which is beneficial for the long-term use of retired batteries and slows down their aging.

[0077] In some embodiments of the present invention, the regression SOH is calculated based on the initial SOH and initial discharge rate of all batteries to be sorted, including:

[0078] Calculate the average SOH and average discharge rate based on the initial SOH and initial discharge rate of all batteries to be sorted;

[0079] Based on the preset regression formula, the regression SOH is calculated according to the average SOH, average discharge rate, current initial SOH of the battery to be sorted, and current initial discharge rate of the battery to be sorted.

[0080] In the above embodiments, the calculation of the average SOH and average discharge rate based on the initial SOH and initial discharge rate of all batteries to be sorted needs to be calculated based on all batteries to be sorted in the current batch, and each battery to be sorted also has its own inherent parameters.

[0081] The preset regression formula is as follows:

[0082] return

[0083] in, C represents the average discharge rate. i Let be the initial discharge rate of the i-th battery to be sorted. For average SOH, SO1 i Let b be the initial SOH of the i-th cell to be sorted. i Let be the inherent parameters of the i-th battery to be sorted.

[0084] In some embodiments of the present invention, the application scenarios of all batteries to be sorted are determined by classifying the application scenarios through regression SOH, including:

[0085] Based on the preset rule of corresponding discharge rate and SOH, the regression discharge rate is determined according to the regression SOH.

[0086] Based on the preset rules of matching discharge rate with application scenario, the application scenario of all batteries to be sorted is determined according to the regression discharge rate.

[0087] In the above embodiments, the preset discharge rate and SOH correspondence rules can be determined according to actual needs. The present invention does not impose further restrictions on the preset discharge rate and SOH correspondence rules. The corresponding return discharge rate range can be obtained by looking up the discharge rate range corresponding to the return SOH in Table 1 based on the calculated return SOH.

[0088] As a preferred embodiment, the correspondence between the preset discharge rate and SOH in this invention is shown in Table 1:

[0089] Table 1. Correspondence between discharge rate and SOH

[0090]

[0091] Similarly, the rules for matching the preset discharge rate with the application scenario can also be set according to actual needs. This invention does not impose any further restrictions on the rules for matching the preset discharge rate with the application scenario.

[0092] Please see Figure 2 , Figure 2 Provided by the present invention Figure 1 A flowchart illustrating an embodiment of step S104. In some embodiments of the present invention, calculating the distance between the charge / discharge energy curve sequences of any two batteries to be sorted under the same application scenario includes:

[0093] S201. Process all the batteries to be sorted to obtain a sequence of charge and discharge energy curves for all the batteries to be sorted.

[0094] S202. Calculate the length of the longest common subsequence based on the charge and discharge energy curve sequences of any two pairs of batteries to be sorted under the same application scenario.

[0095] S203. Determine the sum of the feature sequence lengths based on the charge and discharge energy curve sequences of any two pairs of batteries to be sorted under the same application scenario;

[0096] S204. Calculate the distance between the charge and discharge energy curve sequences based on the sum of the longest common subsequence length and the characteristic sequence length.

[0097] In the above embodiments, the charge-discharge energy curves of the batteries to be sorted can be obtained by performing charge-discharge tests on the batteries to be sorted. The processed charge-discharge energy curve sequence can reflect the energy status of the batteries to be sorted, and multiple charge-discharge energy curve sequences of batteries to be sorted in different situations can be obtained through processing.

[0098] Batteries to be sorted under the same application scenario have good consistency. Further sorting of batteries under the same application scenario requires calculating the length of the longest common subsequence. In this embodiment, the length of the longest common subsequence is calculated by an improved LCS algorithm. After the LCS algorithm is improved, the conditions for the value of its independent variable are more reasonable.

[0099] Determining the sum of the characteristic sequence lengths of any pair of charge-discharge energy curves of batteries to be sorted under the same application scenario requires determining the characteristic sequence of the two energy curves based on their energies at different positions. The energy distribution characteristic formula is: EP(r)=Σ(x i ) / TE, where x iThe data points represent the first r% of energy, where TE is the total energy and EP is the energy percentage. As a preferred embodiment, the energy from the first 10% or the middle 50% of the charge / discharge energy curve can be used.

[0100] To reduce the impact of data time distance, the distance between charge and discharge energy curve sequences is calculated as follows:

[0101] D(X,y)=en(N d )- LCS (X,U);

[0102] Where len(N) d The sum of the characteristic sequence lengths of the charge-discharge energy curves, where D(X,Y) represents the distance between the X and Y sequences of the charge-discharge energy curves, and f LCC (X,Y) represents the length of the longest common subsequence of the charge and discharge energy curves X and Y.

[0103] Please see Figure 3 , Figure 3 Provided by the present invention Figure 2 A schematic flowchart of one embodiment of step S201. In some embodiments of the present invention, processing all batteries to be sorted to obtain a sequence of charge-discharge energy curves for all batteries to be sorted includes:

[0104] S301. Perform charge and discharge tests on the batteries to be sorted to obtain the charge and discharge energy curves of the batteries to be sorted.

[0105] S302. Normalize the charge and discharge energy curves to obtain the target energy curves of the batteries to be sorted.

[0106] S303. Determine the charge and discharge energy curve sequence of the batteries to be sorted based on the target energy curve.

[0107] In the above embodiments, the charge / discharge energy curve sequence can be represented as X = [x1, x2, ..., x...]. p ] and Y = [y1, y2, ..., y q The charging and discharging energy curves of the batteries to be sorted, obtained by charging and discharging tests, the normalization of the charging and discharging energy curves, and the determination of the charging and discharging energy curve sequence of the batteries to be sorted based on the target energy curves can all be directly achieved by existing technologies, and this invention does not impose further limitations on these aspects.

[0108] Please see Figure 4 , Figure 4 Provided by the present invention Figure 2A flowchart illustrating an embodiment of step S202. In some embodiments of the present invention, the longest common subsequence length is calculated based on the charge-discharge energy curve sequences of any pair of batteries to be sorted under the same application scenario, including:

[0109] S401. Calculate the mode matching distance between two charge-discharge energy curve sequences based on the charge-discharge energy curve sequence.

[0110] S402. Set similarity parameters and mismatch parameters;

[0111] S403. Calculate the length of the longest common subsequence based on the pattern matching distance, similarity parameter, and mismatch parameter.

[0112] In the above embodiments, the formula for calculating the mode matching distance between the two charge-discharge energy curve sequences is as follows:

[0113]

[0114] Where, x i and y j These are sequences from the charge / discharge energy curve sequences X and Y, respectively.

[0115] The similarity parameter ζ is used to measure the similarity of the curves, and the mismatch parameter τ is used to prevent mismatches. In a preferred embodiment, the value of the similarity parameter ζ is set to 0.04, and the value of the mismatch parameter τ is set to 4.

[0116] The improved LCS algorithm uses the following formula to calculate the length of the longest common subsequence:

[0117]

[0118] Where m and n represent the lengths of the two charge-discharge energy curve sequences X and Y, respectively.

[0119] To better implement the scenario-based battery sorting method in the embodiments of the present invention, based on the scenario-based battery sorting method, please refer to the corresponding... Figure 5 , Figure 5 This is a schematic diagram of a scenario-based battery sorting device according to an embodiment of the present invention. The embodiment of the present invention provides a scenario-based battery sorting device 500, comprising:

[0120] The initial screening module 510 is used to perform initial screening on all retired batteries according to preset requirements to obtain a number of batteries to be sorted.

[0121] The regression module 520 is used to calculate the regression SOH based on the initial SOH and initial discharge rate of all batteries to be sorted.

[0122] The scenario classification module 530 is used to classify the application scenarios of all batteries to be sorted by regression SOH;

[0123] Calculation module 540 is used to calculate the distance between the charge and discharge energy curve sequences of any two batteries to be sorted under the same application scenario;

[0124] The sorting module 550 is used to sort batteries under the same application scenario based on the distance of the charge and discharge energy curve sequence.

[0125] It should be noted that the device 500 provided in the above embodiments can implement the technical solutions described in the above method embodiments. The specific implementation principles of the above modules or units can be found in the corresponding content in the above method embodiments, and will not be repeated here.

[0126] Please see Figure 6 , Figure 6 This is a schematic diagram of the structure of a scenario-based battery sorting device provided in an embodiment of the present invention. Based on the above-described scenario-based battery sorting method, the present invention also provides a scenario-based battery sorting device, which can be a computing device such as a mobile terminal, desktop computer, laptop, handheld computer, or server. The scenario-based battery sorting device 600 includes a processor 610, a memory 620, and a display 630. Figure 6 Only some components of the scenario-based battery sorting device are shown; however, it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0127] In some embodiments, memory 620 can be an internal storage unit of the scene-based battery sorting device 600, such as a hard disk or memory of the scene-based battery sorting device 600. In other embodiments, memory 620 can also be an external storage device of the scene-based battery sorting device 600, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the scene-based battery sorting device 600. Furthermore, memory 620 can include both internal and external storage units of the scene-based battery sorting device 600. Memory 620 is used to store application software and various types of data installed on the scene-based battery sorting device 600, such as the program code of the scene-based battery sorting device 600. Memory 620 can also be used to temporarily store data that has been output or will be output. In one embodiment, a scenario-based battery sorting program 640 is stored on the memory 620. The scenario-based battery sorting program 640 can be executed by the processor 610 to implement the scenario-based battery sorting method of the various embodiments of this application.

[0128] In some embodiments, processor 610 may be a central processing unit (CPU), microprocessor, or other data processing chip, used to run program code stored in memory 620 or process data, such as executing a scenario-based battery sorting method.

[0129] In some embodiments, display 630 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. Display 630 is used to display information from the scene-based battery sorting device 600 and to display a visual user interface. Components 610-630 of the scene-based battery sorting device 600 communicate with each other via a system bus.

[0130] In one embodiment, when the processor 610 executes the scenario-based battery sorting program 640 in the memory 620, the steps in the scenario-based battery sorting method described above are implemented.

[0131] This embodiment also provides a computer-readable storage medium storing a scenario-based battery sorting program, which, when executed by a processor, performs the following steps:

[0132] According to the preset requirements, a preliminary screening of all retired batteries was conducted to obtain a number of batteries to be sorted.

[0133] The regression SOH is calculated based on the initial SOH and initial discharge rate of all cells to be sorted;

[0134] The application scenarios of all batteries to be sorted were determined by classifying the batteries using SOH regression.

[0135] Calculate the distance between the charge and discharge energy curve sequences of any two batteries to be sorted under the same application scenario;

[0136] Batteries to be sorted under the same application scenario are sorted based on the distance of the charge and discharge energy curve sequence.

[0137] In summary, this embodiment provides a scenario-based battery sorting method, apparatus, device, and storage medium. The method includes: performing initial screening on all retired batteries according to preset requirements to obtain a number of batteries to be sorted; calculating a regression SOH based on the initial SOH and initial discharge rate of all batteries to be sorted; classifying the batteries to be sorted into application scenarios using the regression SOH to determine the application scenarios of all batteries to be sorted; calculating the charge-discharge energy curve sequence distance between any two batteries to be sorted in the same application scenario; and sorting the batteries to be sorted in the same application scenario based on the charge-discharge energy curve sequence distance. This invention first calculates the regression SOH of the batteries to be sorted, and then classifies the batteries into scenarios based on the regression SOH. Batteries in the same scenario are more similar in state and have better consistency. Next, the distance between the charge and discharge energy curve sequences of batteries in the same application scenario is calculated, and the batteries are sorted based on the distance between the charge and discharge energy curve sequences. By sorting the batteries in the same scenario, the batteries to be sorted have better consistency, thereby improving the accuracy of battery sorting and avoiding a series of problems caused by the inability to guarantee long-term consistency after battery sorting. The batteries sorted by this invention are more suitable for long-term operation.

[0138] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A scenario-based battery sorting method, characterized in that, include: According to the preset requirements, a preliminary screening of all retired batteries was conducted to obtain a number of batteries to be sorted. The regression SOH is calculated based on the initial SOH and initial discharge rate of all cells to be sorted; The application scenarios of all batteries to be sorted are determined by classifying them using the regression SOH. Calculate the distance between the charge and discharge energy curve sequences of any two batteries to be sorted under the same application scenario; Based on the distance of the charge / discharge energy curve sequence, batteries to be sorted under the same application scenario are sorted. The calculation of the distance between the charge / discharge energy curve sequences of any two batteries to be sorted under the same application scenario includes: Process all the batteries to be sorted to obtain a sequence of charge and discharge energy curves for all the batteries to be sorted; The longest common subsequence length is calculated based on the charge-discharge energy curve sequence of any two pairs of batteries to be sorted under the same application scenario; The sum of the feature sequence lengths is determined based on the charge-discharge energy curve sequence of any two pairs of batteries to be sorted under the same application scenario; The distance between the charge / discharge energy curve sequences is calculated based on the sum of the longest common subsequence length and the characteristic sequence length. The calculation of the longest common subsequence length based on the charge / discharge energy curve sequence of any pair of batteries to be sorted under the same application scenario includes: The pattern matching distance between the two charge-discharge energy curve sequences is calculated based on the charge-discharge energy curve sequence. Set similarity and mismatch parameters; The longest common subsequence length is calculated based on the pattern matching distance, the similarity parameter, and the mismatch parameter.

2. The scenario-based battery sorting method according to claim 1, characterized in that, The preliminary screening of all retired batteries according to preset requirements yields several batteries to be sorted, including: Obtain the initial state of harm (SOH) and the initial discharge rate for all retired batteries; Based on the initial state of harm (SOH) and the initial discharge rate, retired batteries that meet the preset SOH range and preset discharge rate range are selected, resulting in a number of batteries to be sorted.

3. The scenario-based battery sorting method according to claim 1, characterized in that, The calculation of regression SOH based on the initial SOH and initial discharge rate of all batteries to be sorted includes: Calculate the average SOH and average discharge rate based on the initial SOH and initial discharge rate of all batteries to be sorted; Based on a preset regression formula, the regression SOH is calculated according to the average SOH, the average discharge rate, the current initial SOH of the battery to be sorted, and the current initial discharge rate of the battery to be sorted.

4. The scenario-based battery sorting method according to claim 1, characterized in that, The process of classifying the application scenarios of all batteries to be sorted using the regression SOH to determine the application scenarios of all batteries to be sorted includes: Based on the preset discharge rate and SOH correspondence rule, the regression discharge rate is determined according to the regression SOH; Based on the preset rules for corresponding discharge rates and application scenarios, the application scenarios of all the batteries to be sorted are determined according to the regression discharge rate.

5. The scenario-based battery sorting method according to claim 1, characterized in that, The process of processing all the batteries to be sorted to obtain a sequence of charge-discharge energy curves for all the batteries to be sorted includes: The batteries to be sorted were subjected to charge-discharge tests to obtain charge-discharge energy curves of the batteries to be sorted. The charge-discharge energy curves are normalized to obtain the target energy curves of the batteries to be sorted. The charge / discharge energy curve sequence of the battery to be sorted is determined based on the target energy curve.

6. A scenario-based battery sorting device, characterized in that, The apparatus is used to implement the retired battery sorting method according to any one of claims 1 to 5, the apparatus comprising: The initial screening module is used to perform an initial screening of all retired batteries according to preset requirements to obtain a number of batteries to be sorted. The regression module is used to calculate the regression SOH based on the initial SOH and initial discharge rate of all batteries to be sorted. The scenario classification module is used to classify the application scenarios of the batteries to be sorted by the regression SOH to determine the application scenarios of all batteries to be sorted. The calculation module is used to calculate the distance between the charge and discharge energy curve sequences of any two batteries to be sorted under the same application scenario; The sorting module is used to sort batteries under the same application scenario based on the distance of the charge and discharge energy curve sequence.

7. A scenario-based battery sorting device, characterized in that, Including memory and processor, among which, The memory is used to store programs; The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the scenario-based battery sorting method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, Used to store computer-readable programs or instructions, which, when executed by a processor, are capable of implementing the steps in the scenario-based battery sorting method according to any one of claims 1 to 5.

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