Edge value evaluation-based echelon utilization battery screening and recombination method and system

Abstract

The invention belongs to the technical field of retired battery recycling, and particularly provides an echelon utilization battery screening and recombination method and system based on surplus value evaluation, and the method mainly comprises the steps: obtaining the specific voltage point discharge time of a battery at a high discharge rate and the specific voltage point discharge time of the battery at a low discharge rate, and calculating the ratio, the rate discharge platform retention rate representing the high rate performance of the battery is obtained; and calculating the capacity retention rate of the battery, associating the rate discharge platform retention rate with the capacity retention rate of the battery, and generating a capacity rate characteristic comprehensive slope reflecting the comprehensive health state of the battery. The method effectively solves the problems that the battery pack is worsened in consistency and short in service cycle after long-term operation due to the fact that historical use difference and aging mechanism differentiation of the batteries are easily neglected in echelon recycling of the retired batteries, the comprehensive state of the batteries is accurately evaluated, the performance of the recombined battery pack is matched with the value, the operation stability is improved, and the service life of the battery pack is prolonged. And the service cycle of the echelon utilization scene is prolonged.

Description

Technical Field

[0001] This application belongs to the field of retired battery recycling technology, and specifically relates to a method and system for screening and reorganizing batteries for secondary use based on residual value assessment. Background Technology

[0002] In the recycling and reuse of retired batteries, current practices mostly rely on the battery's currently measurable performance parameters, such as capacity and internal resistance, to match and group them by comparing parameter consistency. Some methods combine simple discharge performance tests to optimize the grouping.

[0003] However, the capacity and internal resistance in the above schemes mostly reflect the instantaneous state of the battery during testing, and easily overlook the individual differences formed by its past use. These differences will differentiate the internal aging mechanism of the battery and affect the subsequent performance change trend. For example, even if the current parameters of the grouped battery pack are consistent, the individual degradation rate will be different after long-term operation, which can easily lead to uneven energy output efficiency, thereby affecting the overall operating performance of the battery pack and shortening the service life. Summary of the Invention

[0004] This application provides a method and system for screening and recombining batteries for secondary use based on residual value assessment. This effectively solves the problem that the differences in historical use and aging mechanisms of batteries are easily overlooked in the secondary recycling of retired batteries, which leads to the deterioration of consistency and short service life of battery packs after long-term operation. It achieves accurate assessment of the overall condition of batteries, makes the performance and value of recombined battery packs match, improves operational stability, and extends the service life of secondary use scenarios.

[0005] To achieve the above objectives, this application adopts the following technical solution: In a first aspect, this application provides a method for screening and reorganizing batteries for cascade utilization based on residual value assessment, including: The discharge time at a specific voltage point under high discharge rate and the discharge time at a specific voltage point under low discharge rate are obtained and the ratio is calculated to obtain the rate discharge plateau retention rate, which characterizes the high-rate performance of the battery.

[0006] Calculate the battery's capacity retention rate, correlate the rate discharge plateau retention rate with the battery's capacity retention rate, and generate a comprehensive slope of the capacity rate characteristic that reflects the overall health status of the battery.

[0007] By combining the rate requirement and capacity rate characteristics of the target tiered utilization scenario, the estimated total residual value of the battery in that scenario is predicted and quantified.

[0008] Based on the estimated total residual value and the overall slope, the battery group is dynamically clustered and optimized for consistency, resulting in an optimized initial battery group that matches performance and value.

[0009] The initial battery grouping was used to generate a battery recombination scheme, and the final battery recombination scheme was obtained through simulation verification.

[0010] Secondly, this application provides a battery screening and recombination system based on residual value assessment, comprising: Data acquisition module: Acquires the discharge time at a specific voltage point under high discharge rate and the discharge time at a specific voltage point under low discharge rate, and calculates the ratio to obtain the rate discharge plateau retention rate, which characterizes the high-rate performance of the battery.

[0011] Comprehensive Health Status Module: Calculates the battery's capacity retention rate, correlates the rate discharge plateau retention rate with the battery's capacity retention rate, and generates a comprehensive slope of the capacity rate characteristic that reflects the battery's overall health status.

[0012] Value estimation module: Combining the rate requirement and capacity rate characteristics of the target tiered utilization scenario, predict and quantify the estimated total residual value of the battery in that scenario.

[0013] Based on the estimated total residual value and the overall slope, the battery group is dynamically clustered and optimized for consistency, resulting in an optimized initial battery group that matches performance and value.

[0014] Simulation verification module: Performs simulation verification on the battery reconfiguration scheme generated from the initial battery grouping to obtain the final battery reconfiguration scheme.

[0015] Thirdly, this application provides a battery screening and reorganization device based on residual value assessment, which includes a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program to implement the steps of the battery screening and reorganization method based on residual value assessment.

[0016] Fourthly, this application provides a readable storage medium storing computer program instructions, which are read and executed by a processor to perform the steps of a method for screening and reorganizing batteries for secondary use based on residual value assessment.

[0017] Fifthly, this application provides a computer program product, including a computer program or instructions, which, when executed by a processor, implements the steps of a method for screening and reorganizing batteries for secondary use based on residual value assessment.

[0018] The beneficial effects of this application are: This application calculates the rate performance index by obtaining the discharge time at a specific voltage point under different rates of the battery, and generates comprehensive health parameters by combining the capacity retention rate. Based on this, the remaining value is estimated and the grouping is optimized, and finally a recombination scheme is generated. This effectively solves the problem that the differences in the historical use and aging mechanism of batteries are easily overlooked in the cascade recycling of retired batteries, which leads to the deterioration of the consistency of the battery pack after long-term operation and the short service life. It realizes the accurate assessment of the comprehensive status of the battery, makes the performance and value of the recombined battery pack match, improves the operational stability, and extends the service life of the cascade utilization scenario.

[0019] Other features and advantages of this application will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures pointed out in the description and the accompanying drawings. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0021] Figure 1 A flowchart illustrating the battery screening and reorganization method based on residual value assessment according to this application is shown. Figure 2 A graph showing the correlation between battery health status and rate performance of this application is presented. Detailed Implementation

[0022] To address the problems raised in the background technology, this application calculates the rate performance index by obtaining the discharge time at a specific voltage point under different rates of the battery, combines it with the capacity retention rate to generate comprehensive health parameters, predicts the remaining value and optimizes the grouping, and finally generates a recombination scheme.

[0023] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] In some embodiments, such as Figure 1 As shown, this application provides a method for screening and reorganizing batteries for cascade utilization based on residual value assessment, including the following steps: S1. Obtain the discharge time at a specific voltage point under high discharge rate and the discharge time at a specific voltage point under low discharge rate, and calculate the ratio to obtain the rate discharge plateau retention rate, which characterizes the high-rate performance of the battery.

[0025] S2. Calculate the battery's capacity retention rate, correlate the rate discharge plateau retention rate with the battery's capacity retention rate, and generate a comprehensive slope of the capacity rate characteristic that reflects the overall health status of the battery.

[0026] S3. Combine the rate requirement and capacity rate characteristics of the target tiered utilization scenario to predict and quantify the estimated total residual value of the battery in that scenario.

[0027] S4. Based on the estimated total residual value and the overall slope, perform dynamic clustering and consistency optimization on the battery group to form an optimized initial battery group that matches performance and value.

[0028] S5. Simulate and verify the battery recombination scheme generated from the initial battery grouping to obtain the final battery recombination scheme.

[0029] In some embodiments, obtaining the discharge time at a specific voltage point under a high discharge rate and the discharge time at a specific voltage point under a low discharge rate, and calculating the ratio, yields the rate discharge plateau retention rate, which characterizes the high-rate performance of the battery, including: Complete voltage-time discharge curve data of the battery at two different discharge rates (high and low) are obtained. The discharge curve data is then aligned on the time axis and the starting point of voltage drop is located to obtain a set of multi-rate discharge curves.

[0030] Constant current discharge tests were performed on the same group of retired batteries using battery testing equipment. High discharge rate and low discharge rate were set respectively, and complete data on the change of battery terminal voltage over time was recorded throughout the entire process from full charge to discharge cutoff voltage, resulting in two voltage-time discharge curves.

[0031] A high discharge rate can specifically be a discharge rate with a discharge current rate of not less than 0.5C, such as 1C; a low discharge rate can specifically be a discharge current rate of not more than 0.3C; and a high discharge rate can be such as 0.2C.

[0032] After aligning the two voltage-time curves, analyze each aligned curve to pinpoint the starting point where the voltage begins to decrease linearly from the open-circuit voltage or rated voltage. Specifically, this can be achieved by finding the maximum value of the first derivative of the voltage curve.

[0033] From each curve in the multi-rate discharge curve set, extract the discharge time corresponding to when the voltage reaches a preset characteristic voltage value to obtain the characteristic voltage discharge time at high rates. Characteristic voltage discharge time at low rates .

[0034] During the constant current discharge process of chemical systems such as lithium-ion batteries, the change curve of their terminal voltage over time does not decrease linearly, but usually presents three typical stages: the initial voltage rapid decrease stage, the voltage plateau stage, and the voltage sharp decrease stage.

[0035] The initial rapid voltage drop phase indicates the start of discharge. Due to the voltage drop caused by the internal ohmic resistance of the battery, the voltage rapidly decreases from the open-circuit voltage to a relatively stable range.

[0036] The voltage plateau stage represents the period when the battery voltage fluctuates within a narrow range and decreases slowly. It mainly corresponds to the stage when the battery active materials undergo major electrochemical reactions, and the voltage is relatively stable.

[0037] The phase of a sharp voltage drop indicates that when the active material is nearly exhausted, the voltage will rapidly drop to the discharge cutoff voltage.

[0038] Among them, the characteristic voltage value is located in the voltage plateau stage and can more sensitively reflect the difference in voltage caused by different discharge rates. The characteristic voltage value can be a specific percentage of the battery's rated voltage, such as 98.6% of the rated voltage, or a fixed offset, such as the rated voltage minus 0.05V.

[0039] Calculate the characteristic voltage and discharge time at high discharge rates. Characteristic voltage and discharge time at low discharge rates The ratio of the two values ​​is used to obtain the rate discharge plateau retention rate R.

[0040] In some embodiments, the capacity retention rate of the battery is calculated, the rate discharge plateau retention rate is correlated with the battery's capacity retention rate, and a comprehensive slope of the capacity rate characteristic reflecting the overall health of the battery is generated, including: The battery's discharge curve data at low discharge rates is integrated in ampere-hours to calculate the total capacity, thus obtaining the battery's current actual capacity. Reference formula: ;in, Represents constant current discharge current. This represents the total time from full charge to cutoff voltage.

[0041] Calculate the percentage of the battery's current actual capacity to its rated capacity to obtain the battery's capacity retention rate. .

[0042] With the rate discharge plateau retention rate R as the abscissa, the capacity retention rate Using the vertical axis as the ordinate, a two-dimensional scatter plot is drawn to obtain a correlation diagram between battery health status and rate performance. Each data point in the plot corresponds to the R and R values ​​of a battery. It can intuitively display the distribution of battery groups in terms of both rate performance health and capacity health.

[0043] For each data point in the correlation graph, the slope of the line connecting that point and the origin is calculated to obtain the comprehensive slope K of the capacity rate characteristic, which reflects the relative relationship between the battery's capacity retention capability and rate performance retention capability, so as to effectively identify the dominant aging mode inside the battery.

[0044] In some embodiments, S3, combining the rate requirement and overall slope of the target application scenario to predict and quantify the estimated total residual value of the battery in that scenario, includes: Identify the target scenarios for the reuse of retired batteries, such as specific applications for retired batteries in energy storage power stations, low-speed electric vehicles, and backup power supplies.

[0045] Obtain the typical operating discharge rate for each target scenario applicable to the aforementioned batteries. For example, the typical operating discharge rate of the battery can be obtained through the technical specifications. And select a standard test magnification. As a benchmark, for example, 0.5C is used to distinguish between low-to-medium rate and high-rate applications for lithium-ion batteries.

[0046] Will and In comparison, if Greater than If the target scene is found to be in the high magnification range, it is determined to be in the low magnification range otherwise, thus obtaining a clear target scene magnification range identifier.

[0047] Based on the target scenario rate range identifier and the numerical range of the comprehensive slope, one of the preset high-rate attenuation models and low-rate attenuation models is selected to obtain the selected lifetime prediction model.

[0048] We first used a large amount of aging test data from retired batteries under different tiered utilization scenarios. Using this data, we established a mapping relationship through curve fitting or data binning, and built two empirical models: a high-rate degradation model and a low-rate degradation model.

[0049] The high-rate decay model describes the rate discharge plateau retention rate R and the remaining cycle life under stress in high-rate cascade utilization scenarios. The correspondence.

[0050] Low-rate decay model describes capacity retention Remaining cycle life under stress in low-rate tiered utilization scenarios The correspondence.

[0051] The range of the dominant battery degradation is preset. For example, the 33% and 66% quantiles of the battery's K value are calculated. If the K value is less than 33%, it means that the battery degradation is mainly dominated by the rate performance. If the K value is greater than 66%, it means that the battery degradation is mainly dominated by the capacity loss. Otherwise, it means that the battery degradation is the result of the combined effect of capacity loss and rate performance deterioration.

[0052] If the target scenario is a high-rate range and the current battery's K value falls within the range where rate performance dominates the degradation, then it is determined that the battery's degradation in the high-rate scenario is mainly determined by its rate performance, and a high-rate degradation model based on the rate discharge platform retention rate R is selected.

[0053] If the target scenario is a low-rate range, and the current battery's K value falls within the range where capacity-driven degradation is the primary factor, then it is determined that the battery's degradation in low-rate scenarios is mainly determined by its capacity degradation. Therefore, a value based on capacity retention is chosen. The low-rate decay model.

[0054] When battery degradation is caused by the combined effects of capacity loss and rate performance deterioration, if the target scenario is in the high rate range, it is inferred that high rate stress will accelerate its rate performance-related degradation, and the high rate degradation model is preferred. If the target scenario is in the low rate range, it is inferred that capacity degradation is the main problem affecting its lifespan, and the low rate degradation model is preferred.

[0055] Maintain battery capacity Alternatively, the rate discharge plateau retention rate R can be input into the selected lifetime prediction model for calculation, yielding the predicted remaining cycle count of the battery under the target scenario. .

[0056] The economic benefit that a target battery can generate from completing one effective cycle is determined and used as the unit cycle value. Predict the remaining number of cycles for the battery in the target scenario. Multiply by the unit cyclic value in the scenario To obtain the estimated total surplus value .

[0057] In some embodiments, S4 involves dynamically clustering and optimizing the battery group based on the estimated total residual value and the overall slope to form an optimized initial battery grouping that matches performance and value, including: For the estimated total residual value The slope K of the combined capacity ratio characteristic is normalized to estimate the total residual value. Using the primary sorting key and the overall slope K as the secondary sorting key, all batteries are sorted to obtain a priority sorting list of batteries.

[0058] The priority list of batteries is divided into several equal parts using the equal frequency binning method, so that the number of batteries in each group is approximately equal, thus obtaining the initial value grouping.

[0059] A preset dynamic threshold is based on the overall slope distribution of all batteries. For each initial value group, calculate the range of the overall slope K of each battery within the group. ,like Exceed Then the group is split until the range of each group is reached. None more than The optimized initial battery grouping results in batteries in the same group having similar current economic value and highly consistent future performance degradation trajectories, thereby improving the overall life cycle stability and service life of the recombined battery pack.

[0060] Among them, dynamic threshold Set to an integer multiple of the standard deviation of the overall slope of all batteries, such as twice the standard deviation.

[0061] In some embodiments, S5 simulates and verifies the battery recombination scheme generated from the initial battery grouping to obtain the final battery recombination scheme, including: Obtain the battery list and DC internal resistance data of a specific group in the optimized initial battery group, sort them in ascending order of internal resistance value, and then pair them in a head-to-tail manner to form a preliminary battery series module scheme.

[0062] The battery parameters from the initial battery series module design are input into circuit simulation software. A constant current discharge is set, with the current set according to the typical rate of the target scenario. The simulation is run, and the voltage range at the end of the discharge is observed. ,like If the value is less than the effective range of the BMS equalization circuit, the solution is feasible, and the final battery reconfiguration solution is obtained.

[0063] In some embodiments, this application provides a battery screening and reconfiguration system based on residual value assessment, comprising: Data acquisition module: Acquires the discharge time at a specific voltage point under high discharge rate and the discharge time at a specific voltage point under low discharge rate, and calculates the ratio to obtain the rate discharge plateau retention rate, which characterizes the high-rate performance of the battery.

[0064] Comprehensive Health Status Module: Calculates the battery's capacity retention rate, correlates the rate discharge plateau retention rate with the battery's capacity retention rate, and generates a comprehensive slope of the capacity rate characteristic that reflects the battery's overall health status.

[0065] Value estimation module: Combining the rate requirement and capacity rate characteristics of the target tiered utilization scenario, predict and quantify the estimated total residual value of the battery in that scenario.

[0066] Based on the estimated total residual value and the overall slope, the battery group is dynamically clustered and optimized for consistency, resulting in an optimized initial battery group that matches performance and value.

[0067] Simulation verification module: Performs simulation verification on the battery reconfiguration scheme generated from the initial battery grouping to obtain the final battery reconfiguration scheme.

[0068] In some embodiments, this application provides a battery screening and reorganization apparatus based on residual value assessment, which includes a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program to implement the steps of the battery screening and reorganization method based on residual value assessment.

[0069] In some embodiments, this application provides a readable storage medium storing computer program instructions that are read and executed by a processor to perform the steps of a method for screening and reorganizing batteries for reuse based on residual value assessment.

[0070] In some embodiments, this application provides a computer program product, including a computer program or instructions, wherein when the computer program or instructions are executed by a processor, the steps of a method for screening and reorganizing batteries for secondary use based on residual value assessment are implemented.

[0071] It should be noted that, in this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0072] Any references to memory, storage, database, or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory.

[0073] Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for screening and reorganizing a cascade utilization battery based on residual value assessment, characterized in that, The method comprises the following steps: obtaining the discharge time of a battery at a specific voltage point under high discharge rate and the discharge time of the battery at the specific voltage point under low discharge rate, and calculating the ratio to obtain the discharge rate platform retention rate representing the high rate performance of the battery; calculating the capacity retention rate of the battery, correlating the discharge rate platform retention rate with the capacity retention rate of the battery, and generating the capacity-discharge rate characteristic comprehensive slope reflecting the comprehensive health state of the battery; combining the discharge rate requirement of the target application scenario with the capacity-discharge rate characteristic comprehensive slope to predict and quantify the estimated total residual value of the battery in the scenario; performing dynamic clustering and consistency optimization on the battery group according to the estimated total residual value and the comprehensive slope to form an optimized initial battery grouping with matched performance and value; generating a battery reorganization scheme for the initial battery grouping and performing simulation verification to obtain a final battery reorganization scheme.

2. The method of claim 1, wherein, The method comprises the following steps: obtaining the discharge time of a battery at a specific voltage point under high discharge rate and the discharge time of the battery at the specific voltage point under low discharge rate, and calculating the ratio to obtain the discharge rate platform retention rate representing the high rate performance of the battery; obtaining the complete voltage-time discharge curve data of the battery under high and low discharge rates, performing time axis alignment processing on the discharge curve data, and locating the voltage drop starting point to obtain a plurality of multi-rate discharge curves; extracting the discharge time corresponding to the voltage reaching a preset characteristic voltage value from each curve in the multi-rate discharge curve set to obtain the characteristic voltage discharge time under high and low rates; 3. The method of claim 1, wherein, calculating the ratio of the characteristic voltage discharge time under high discharge rate to the characteristic voltage discharge time under low discharge rate to obtain the discharge rate platform retention rate. The method comprises the following steps: calculating the capacity retention rate of the battery, correlating the discharge rate platform retention rate with the capacity retention rate of the battery, and generating the capacity-discharge rate characteristic comprehensive slope reflecting the comprehensive health state of the battery; integrating the ampere-hours of the discharge curve data of the battery under low discharge rate to calculate the total capacity to obtain the current actual capacity of the battery; calculating the percentage of the current actual capacity of the battery to the rated capacity of the battery to obtain the capacity retention rate of the battery; 4. The method of claim 3, wherein, distributing and displaying the discharge rate platform retention rate and the capacity retention rate of the battery as two-dimensional coordinate points to obtain the correlation graph of the health state and the discharge rate characteristic of the battery; 5. The method of claim 1, wherein, calculating the slope of the line connecting each data point in the correlation graph and the coordinate origin to obtain the capacity-discharge rate characteristic comprehensive slope. The low discharge rate can be a discharge current rate of not more than 0.3C. The method comprises the following steps: obtaining the specification requirements of the target application scenario and extracting the typical working discharge rate, comparing it with the standard test rate to perform interval division, and obtaining the target scenario discharge rate interval identifier; selecting one of the preset high-rate decay model and low-rate decay model according to the target scenario discharge rate interval identifier and the numerical range of the comprehensive slope to obtain the selected life prediction model; inputting the capacity retention rate of the battery or the discharge rate platform retention rate into the selected life prediction model to calculate the predicted residual cycle number of the battery in the target scenario; The predicted remaining cycle number of the battery under the target scenario is multiplied by the unit cycle value under the scenario to obtain an estimated total remaining value.

6. The method of claim 5, wherein, One of the preset high-rate attenuation model and low-rate attenuation model is selected to obtain a selected life prediction model, and the selected life prediction model is specifically: If the target scenario rate interval identifier is a high-rate interval, and the value of the capacity rate characteristic comprehensive slope falls within a preset range indicating rate performance dominant attenuation, a high-rate model is selected; if the target scenario rate interval identifier is a low-rate interval, and the value of the capacity rate characteristic comprehensive slope falls within a preset range indicating capacity dominant attenuation, a low-rate model is selected; The high-rate model is established by analyzing the corresponding relationship between the rate discharge platform retention rate of a historical battery and the cycle life under high-rate stress; and the low-rate model is established by analyzing the corresponding relationship between the capacity retention rate of a historical battery and the cycle life under low-rate stress.

7. The method of claim 6, wherein, According to the estimated total remaining value and the comprehensive slope, a battery group is dynamically clustered and consistency optimized to form an optimized initial battery grouping that matches performance and value, including: The estimated total remaining values and the comprehensive slopes of all batteries are normalized, and the estimated total remaining value is used as the main sorting key and the comprehensive slope is used as the secondary sorting key to sort all batteries to obtain a priority sorting list of the batteries; The priority sorting list of the batteries is divided by using an equal frequency binning method to obtain a preliminary value grouping; The range of the comprehensive slopes of the batteries in the preliminary value grouping is calculated, and if the range exceeds a dynamic threshold preset based on the comprehensive slope distribution of all batteries, the grouping is split to obtain an optimized initial battery grouping.

8. The method of claim 7, wherein, The dynamic threshold is set as an integer multiple of the standard deviation of the comprehensive slope of all batteries.

9. The method of claim 1, wherein, An initial battery grouping is generated to simulate and verify a battery recombination scheme to obtain a final battery recombination scheme, including: The battery list and DC resistance data of a specific grouping in the optimized initial battery grouping are obtained, and the batteries are paired in ascending order of resistance value in a head-to-tail manner to form a preliminary battery series module scheme; The battery parameters in the preliminary battery series module scheme are input into a circuit simulation software, a target scenario typical working rate is set for constant current discharge simulation, and voltage consistency is verified to obtain a final battery recombination scheme.

10. A system for screening and reorganizing batteries for their cascade utilization based on their residual value assessment, characterized by, including: A data acquisition module: obtains a specific voltage point discharge time under a high discharge rate of a battery and a specific voltage point discharge time under a low discharge rate, calculates a ratio, and obtains a rate discharge platform retention rate representing the high-rate performance of the battery; A comprehensive health state module: calculates the capacity retention rate of the battery, associates the rate discharge platform retention rate with the capacity retention rate of the battery, and generates a capacity rate characteristic comprehensive slope reflecting the comprehensive health state of the battery; A value estimation module: combines the rate requirement of a target ladder utilization scenario with the capacity rate characteristic comprehensive slope to predict and quantify the estimated total remaining value of the battery under the scenario; According to the estimated total residual value and the comprehensive slope, the battery group is dynamically clustered and consistency optimized, and an optimized initial battery grouping with matched performance and value is formed; A simulation verification module is configured to simulate and verify the battery reorganization scheme generated by the initial battery grouping, and obtain a final battery reorganization scheme.

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