A battery pack matching method, system and computer

Abstract

This invention relates to the field of new energy technology, and provides a battery pack selection method, system, and computer. The battery pack selection method includes: acquiring several initial cell datasets and several initial auxiliary datasets from the battery pack to be repaired based on an online data platform; acquiring a correction coefficient table and updating the initial auxiliary datasets to updated auxiliary datasets; updating the initial cell datasets to updated cell datasets and calculating a benchmark dataset based on the updated cell datasets; acquiring several spare cell datasets and calculating a matching degree set based on the spare cell datasets, benchmark datasets, and updated auxiliary datasets; setting a comprehensive weight set and calculating a weighted comprehensive matching degree based on the matching degree set and the comprehensive weight set. By adopting the above method, the health status of the battery pack to be repaired and several spare battery packs is quantified and then matched for evaluation, realizing the use of spare battery packs to repair and group the battery pack to be repaired.

Description

Technical Field

[0001] This invention relates to the field of new energy technology, and in particular to a battery pack selection method, system, and computer. Background Technology

[0002] Against the backdrop of the booming development of the new energy industry, battery packs, as the core power components of electric vehicles, energy storage systems, and other equipment, are crucial to the operational efficiency and safety of the entire device. As the basic building block of the battery pack, the performance consistency of the battery cells has a decisive impact on the overall performance and lifespan of the battery pack. With the increase in the usage time of battery devices, the cells in after-sales battery packs will experience capacity decay and increased internal resistance due to repeated charge-discharge cycles, resulting in a significant decrease in their performance parameters compared to new cells.

[0003] In after-sales repair scenarios, using new cells to pair with faulty battery packs is not only difficult to optimize battery pack performance due to the significant performance differences between new and old cells, but also greatly increases repair costs, resulting in extremely low cost-effectiveness. Therefore, using other used cells with similar performance conditions for repair pairing is an important way to reduce costs and improve the overall performance of the battery pack.

[0004] However, the current lack of effective methods for performance evaluation and screening of used battery cells makes it impossible to quickly determine the health differences of cells within faulty battery packs and used battery packs, making it difficult to guarantee the performance and stability of the battery pack after matching. When individual cells in a battery pack fail and need to be replaced, using traditional methods to test the capacity of old cells before matching is costly in terms of both equipment and time. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the present invention aims to provide a battery pack selection method, system, and computer. This invention is based on real-time data acquisition and recording of battery pack data through an online data platform. The acquired cell data is cleaned and corrected, and a comprehensive matching degree is calculated to select battery packs from multiple spare battery packs that have a similar performance condition to the battery pack under repair. This invention aims to solve the technical problem of the lack of effective means in existing technologies to evaluate and screen the performance of used cells, resulting in the inability to quickly determine the health differences of cells within faulty battery packs and used battery packs.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solution:

[0007] A battery pack selection method includes the following steps:

[0008] Based on an online data platform, several initial cell datasets and several initial auxiliary datasets are obtained from the battery pack to be repaired. The initial cell datasets correspond to the initial auxiliary datasets. The initial cell datasets include the initial charging capacity and the initial discharging capacity. The initial auxiliary datasets include the charging temperature, the initial charging and discharging current, and the initial charging time.

[0009] Obtain a correction coefficient table, extract efficiency correction coefficients from the correction coefficient table based on the charging temperature, and update the initial charge / discharge current and the initial charging time to updated charge / discharge current and updated charging time respectively using the efficiency correction coefficients, so as to update the initial auxiliary dataset to updated auxiliary dataset.

[0010] Based on the updated auxiliary dataset, the initial cell dataset is updated to an updated cell dataset, and a baseline dataset is calculated based on several of the updated cell datasets;

[0011] Obtain several backup cell datasets from several backup battery packs. Based on the backup cell datasets and the benchmark dataset, calculate a matching degree set, which includes coulomb efficiency matching degree, charge acceptance rate matching degree, charge and discharge stress index matching degree, and monthly average capacity decay rate matching degree.

[0012] A comprehensive weight set is set up, and a comprehensive matching degree is calculated based on the matching degree set and the comprehensive weight set, so as to select a matching battery pack from a number of backup battery packs according to a number of comprehensive matching degrees.

[0013] Furthermore, the battery pack to be repaired includes several normal battery cells, and the step of obtaining several initial cell datasets and several initial auxiliary datasets from the battery pack to be repaired includes:

[0014] Collect data from several cells to be processed from the normal battery cells;

[0015] The Z-score method is used to detect and remove outliers from several cell data sets to be processed, resulting in several processed cell data sets. The processed cell data sets are then grouped according to the cell data type to form several initial cell datasets and several initial auxiliary datasets.

[0016] Furthermore, the initial auxiliary dataset also includes extreme currents. The step of updating the initial charge / discharge current and the initial charging time to updated charge / discharge current and updated charging time respectively using the efficiency correction coefficient, so as to update the initial auxiliary dataset to the updated auxiliary dataset, includes:

[0017] Based on the initial charge / discharge current and the initial charging time, an initial charge / discharge efficiency is obtained. The initial charge / discharge efficiency is then corrected to an updated charge / discharge efficiency using the efficiency correction coefficient. Finally, an updated charge / discharge current and an updated charging time are obtained based on the updated charge / discharge efficiency, the initial charge / discharge current, and the initial charging time.

[0018] The initial charge / discharge current in the initial auxiliary dataset is replaced with the updated charge / discharge current, the initial charging time is replaced with the updated charging time, and the updated charge / discharge current, the updated charge / discharge time, and the extreme current are combined to form an updated auxiliary dataset.

[0019] Furthermore, the initial cell dataset also includes relative self-discharge rate, internal resistance, and cumulative mileage. The step of updating the initial cell dataset to an updated cell dataset based on the updated auxiliary dataset includes:

[0020] Based on the updated charge / discharge current, the extreme current, and the updated charging time in the updated auxiliary dataset, the updated charge / discharge capacity is calculated, and the initial charge / discharge capacity is replaced with the updated charge / discharge capacity, so that the updated charge / discharge capacity, the relative self-discharge rate, the internal resistance, and the cumulative mileage are combined into an updated cell dataset.

[0021] Furthermore, both the initial cell dataset and the updated cell dataset include relative self-discharge rate, internal resistance, and cumulative mileage. The step of calculating a benchmark dataset based on several of the updated cell datasets includes:

[0022] Obtain several collection times corresponding to several updated battery cell datasets, and obtain several time weights corresponding to several collection times;

[0023] Based on several time weights and several updated charge / discharge capacities, the average charge / discharge capacity is calculated using a weighted average method. Based on several time weights and several relative self-discharge rates, the average relative self-discharge rate is calculated using a weighted average method. Based on several time weights and several internal resistances, the average internal resistance is calculated using a weighted average method. Based on several time weights and several cumulative mileages, the average cumulative mileage is calculated using a weighted average method. The average charge / discharge capacity, the average relative self-discharge rate, the average internal resistance, and the average cumulative mileage form a benchmark dataset.

[0024] Furthermore, the step of calculating the matching degree set based on the spare battery cell dataset and the benchmark dataset includes:

[0025] The backup battery charging scenario of the backup battery pack is obtained through the online data platform. Based on the backup cell dataset and the backup battery charging scenario, the backup coulombic efficiency, backup charge acceptance rate, backup charge and discharge stress index and backup monthly average capacity decay rate are obtained to form the first dataset.

[0026] The online data platform is used to obtain the charging scenario of the battery to be repaired in the charging pack to be repaired. Based on the benchmark dataset and the charging scenario of the battery to be repaired, the benchmark coulombic efficiency, benchmark charge acceptance rate, benchmark charge and discharge stress index, benchmark monthly average capacity decay rate and charge acceptance rate tolerance are obtained to form a second dataset.

[0027] The online data platform is used to obtain several stress index sub-item weights, coulomb efficiency tolerance, charge-discharge stress index tolerance, and monthly average capacity decay rate tolerance to form a third dataset. The matching degree set is then calculated based on the first dataset, the second dataset, and the third dataset.

[0028] Furthermore, the formula for the matching degree set is:

[0029]

[0030] in, Indicates the Coulomb efficiency matching degree. This indicates taking the maximum value. Indicates the spare coulomb efficiency. Indicates the baseline coulomb efficiency. This indicates the Coulomb efficiency tolerance. Indicates the acceptance rate of backup charging. Indicates the baseline charge acceptance rate. Indicates the charging acceptance tolerance. Indicates the standby charge / discharge stress index. Indicates the reference charge-discharge stress index. Indicates the charge / discharge stress index tolerance. This indicates the average monthly capacity decay rate of the reserve. This represents the baseline monthly average capacity decay rate. This indicates the monthly average capacity decay rate tolerance;

[0031]

[0032] in, , , , Indicates the weight of the stress index sub-item. Indicates the average charge and discharge current. Indicates the rated current. Indicates the peak charge / discharge current. This indicates an update to the charging time. Indicates reference charging time. Indicates the charging temperature. Indicates the reference charging temperature.

[0033] Furthermore, the comprehensive weight set includes coulombic efficiency weight, charge acceptance rate weight, charge / discharge stress index weight, and monthly average capacity decay rate weight. The coulombic efficiency weight is greater than or equal to the charge acceptance rate weight, the charge acceptance rate weight is greater than or equal to the monthly average capacity decay rate weight, the coulombic efficiency weight is greater than or equal to the charge / discharge stress index weight, and the charge / discharge stress index weight is greater than or equal to the monthly average capacity decay rate weight.

[0034] A battery pack selection system, employing the battery pack selection method described above, the system comprising:

[0035] The acquisition module is used to acquire several initial cell datasets and several initial auxiliary datasets from the battery pack to be repaired based on an online data platform. The initial cell datasets correspond to the initial auxiliary datasets. The initial cell datasets include initial charging capacity and initial discharging capacity. The initial auxiliary datasets include charging temperature, initial charging and discharging current, and initial charging time.

[0036] The correction module is used to obtain a correction coefficient table, extract an efficiency correction coefficient from the correction coefficient table based on the charging temperature, and update the initial charge / discharge current and the initial charging time to an updated charge / discharge current and an updated charging time using the efficiency correction coefficient, so as to update the initial auxiliary dataset to an updated auxiliary dataset.

[0037] An update module is used to update the initial cell dataset to an updated cell dataset based on the updated auxiliary dataset, and to calculate a benchmark dataset based on several of the updated cell datasets.

[0038] The matching module is used to obtain a dataset of several spare cells from several spare battery packs, and calculate a matching degree set based on the spare cell dataset and the benchmark dataset. The matching degree set includes coulomb efficiency matching degree, charge acceptance rate matching degree, charge and discharge stress index matching degree and monthly average capacity decay rate matching degree.

[0039] The comprehensive module is used to set a comprehensive weight set, calculate a comprehensive matching degree based on the matching degree set and the comprehensive weight set, and select a matching battery pack from a plurality of backup battery packs according to a plurality of comprehensive matching degrees.

[0040] A computer includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the battery pack selection method as described above.

[0041] Compared with the prior art, the beneficial effects of the present invention are as follows: By continuously collecting cell data from the battery pack through the online data platform, when it is necessary to group the battery pack under repair, the cell data of the battery pack under repair is retrieved, cleaned, and grouped for extraction to obtain the initial cell dataset and the initial auxiliary dataset; since the actual charging and discharging efficiency of the battery is affected by different temperatures, the efficiency correction coefficient is determined based on the charging temperature, and the initial auxiliary dataset is updated to obtain a more accurate updated auxiliary dataset. The initial cell dataset is then updated using the updated auxiliary dataset to obtain a more accurate updated cell dataset, which helps improve the accuracy of assessing the condition of the battery pack under repair; combined with the time... The benchmark dataset is weighted and calculated by incorporating the time factor into the long-term data collection of the battery pack to be repaired, combining recent and historical data to comprehensively assess the health status of the battery pack to be repaired. The benchmark dataset is used to filter for backup battery packs that match the battery pack to be repaired. By calculating the matching degree set and combining it with the comprehensive weight to calculate the comprehensive matching degree, the health status of the battery pack to be repaired and several backup battery packs is quantified and subjected to multi-dimensional, systematic matching evaluation. This allows for the full utilization of the backup battery packs that have been used and have the most similar performance to repair and group the battery pack to be repaired. This improves the performance and stability of the grouped battery pack and eliminates the need to test old cells, significantly saving costs. Attached Figure Description

[0042] Figure 1 This is a flowchart of the battery pack selection method in the first embodiment of the present invention;

[0043] Figure 2 This is a structural block diagram of the battery pack selection system in the second embodiment of the present invention;

[0044] The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation

[0045] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0046] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0047] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0048] Please see Figure 1 The battery pack selection method in the first embodiment of the present invention includes the following steps:

[0049] Step S10: Based on the online data platform, obtain several initial cell datasets and several initial auxiliary datasets from the battery pack to be repaired. The initial cell datasets correspond to the initial auxiliary datasets. The initial cell datasets include the initial charging capacity and the initial discharging capacity. The initial auxiliary datasets include the charging temperature, the initial charging and discharging current, and the initial charging time.

[0050] Preferably, the battery pack to be repaired includes several faulty cells and several normal cells. In this embodiment, the data of the normal cells is obtained by collecting the cell data of the battery pack over a long period of time through the online data platform, collecting and recording the data from multiple charge and discharge processes in the history of the battery pack. When assessing the health status and usage of the battery pack to be repaired, the data in the online data platform can be retrieved, saving the cost of testing the batteries once during the matching process.

[0051] Step S10 includes:

[0052] S110: Collect data from several cells to be processed from the normal battery cells;

[0053] S120: Several outliers are detected and removed from several cell data to be processed using the Z-score method to obtain several processed cell data. The processed cell data are then grouped according to the cell data type to form several initial cell datasets and several initial auxiliary datasets.

[0054] The normal battery cells can continue to be used. Based on the health status of several normal battery cells in the battery pack to be repaired, battery packs with cells in similar states are selected for grouping and repair. The faulty battery cells are then replaced. The Z-score method reflects the relative standard distance of a numerical value from the mean, which is used for outlier rejection. Understandably, processing a large amount of data on the battery cells to be repaired through data cleaning improves the accuracy of the condition assessment of the battery to be repaired and avoids the negative impact of outliers.

[0055] Step S20: Obtain the correction coefficient table, extract the efficiency correction coefficient from the correction coefficient table based on the charging temperature, and update the initial charge / discharge current and the initial charging time to the updated charge / discharge current and the updated charging time respectively using the efficiency correction coefficient, so as to update the initial auxiliary dataset to the updated auxiliary dataset.

[0056] The actual charging and discharging efficiency of a battery can be affected by high or low temperature environments. Therefore, the charging and discharging current data and time data are corrected and adjusted using the correction coefficient table to calculate a more accurate charging and discharging capacity.

[0057] In step S20, the initial auxiliary dataset further includes extreme currents, and step S20 includes:

[0058] S210: Based on the initial charge / discharge current and the initial charging time, obtain the initial charge / discharge efficiency, correct the initial charge / discharge efficiency to the updated charge / discharge efficiency using the efficiency correction coefficient, and obtain the updated charge / discharge current and updated charging time based on the updated charge / discharge efficiency, the initial charge / discharge current, and the initial charging time.

[0059] S220: Replace the initial charge / discharge current in the initial auxiliary dataset with the updated charge / discharge current, replace the initial charging time with the updated charging time, and combine the updated charge / discharge current, the updated charge / discharge time, and the extreme current into an updated auxiliary dataset.

[0060] Preferably, the updated charging time can be calculated based on the updated charging efficiency and the initial charging time, using the charging time calculation formula. Then, the updated charging current is further obtained based on the updated charging efficiency, the updated charging time, and the initial charging / discharging current, using the charging efficiency calculation formula. The correction coefficient table is shown below:

[0061]

[0062] Step S30: Based on the updated auxiliary dataset, update the initial cell dataset to an updated cell dataset, and calculate a baseline dataset based on several of the updated cell datasets;

[0063] Preferably, each normal battery cell corresponds to data collected at different times, i.e., there are several initial battery cell datasets. Based on several initial auxiliary datasets corresponding to the several initial battery cell datasets, the several initial battery cell datasets are corrected and adjusted one by one to obtain several updated battery cell datasets. The benchmark dataset is calculated based on the several updated battery cell datasets, and each benchmark dataset corresponds to one normal battery cell. Specifically, the battery cell data collected in the most recent 6 times can be selected, i.e., 6 initial battery cell datasets are used for processing and analysis. Understandably, the benchmark dataset can be used to evaluate the overall performance status of the normal battery cell over a period of time.

[0064] In step S30, the initial cell dataset further includes relative self-discharge rate, internal resistance, and cumulative mileage. Step S30 includes:

[0065] S310: Based on the updated charge and discharge current, the extreme current and the updated charging time in the updated auxiliary dataset, calculate the updated charge and discharge capacity, replace the initial charge and discharge capacity with the updated charge and discharge capacity, and combine the updated charge and discharge capacity, the relative self-discharge rate, the internal resistance and the cumulative mileage into an updated cell dataset.

[0066] S320: Obtain several collection times corresponding to several of the updated battery cell datasets, and obtain several time weights corresponding to several of the collection times;

[0067] S330: Based on several time weights and several updated charge / discharge capacities, calculate the average charge / discharge capacity using a weighted average method; based on several time weights and several relative self-discharge rates, calculate the average relative self-discharge rate using a weighted average method; based on several time weights and several internal resistances, calculate the average internal resistance using a weighted average method; based on several time weights and several cumulative mileages, calculate the average cumulative mileage using a weighted average method, so as to form a benchmark dataset using the average charge / discharge capacity, the average relative self-discharge rate, the average internal resistance, and the average cumulative mileage.

[0068] Preferably, the collection time corresponds to a number of collections. In this embodiment, data from 6 collections are used. The first collection is the earliest collection. Weights are assigned according to the order of the collection time, with the most recently collected cell data being assigned the highest weight. Specifically, the time weight corresponding to the first collection is 0.05, the time weight corresponding to the second collection is 0.1, the time weight corresponding to the third collection is 0.1, the time weight corresponding to the fourth collection is 0.15, the time weight corresponding to the fifth collection is 0.25, and the time weight corresponding to the sixth collection is 0.35. The time weights are applicable to the weighted average calculation of all types of data in the updated cell dataset.

[0069] Step S40: Obtain several backup cell datasets from several backup battery packs. Based on the backup cell datasets and the benchmark dataset, calculate a matching degree set, which includes coulomb efficiency matching degree, charge acceptance rate matching degree, charge and discharge stress index matching degree, and monthly average capacity decay rate matching degree.

[0070] Preferably, the backup cell dataset can reflect the performance status of the backup battery pack, which is a used battery pack. Understandably, by analyzing and calculating the data, the battery pack with the closest performance status to the battery pack to be repaired is selected from multiple used battery packs for matching and repair.

[0071] Step S40 includes:

[0072] S410: Obtain the backup battery charging scenario of the backup battery pack through the online data platform, and based on the backup cell dataset and the backup battery charging scenario, obtain the backup coulombic efficiency, backup charge acceptance rate, backup charge and discharge stress index and backup monthly average capacity decay rate to form the first dataset;

[0073] S420: Obtain the charging scenario of the battery to be repaired in the charging pack to be repaired through the online data platform. Based on the benchmark dataset and the charging scenario of the battery to be repaired, obtain the benchmark coulombic efficiency, benchmark charge acceptance rate, benchmark charge and discharge stress index, benchmark monthly average capacity decay rate and charge acceptance rate tolerance to form a second dataset.

[0074] S430: Obtain several stress index sub-item weights, coulomb efficiency tolerance, charge / discharge stress index tolerance, and monthly average capacity decay rate tolerance through the online data platform to form a third dataset. Calculate the matching degree set based on the first dataset, the second dataset, and the third dataset.

[0075] Preferably, the coulombic efficiency is calculated based on the charge / discharge amount, the charge acceptance rate is calculated based on the charge amount, rated battery capacity, and charging time, and the monthly average capacity decay rate is calculated based on the initial battery capacity, current battery capacity, and the number of months the battery has been used. If the charging scenario is a slow charging scenario, the charge acceptance rate tolerance is 0.08, and the baseline charge acceptance rate is 0.2. If the charging scenario is a fast charging scenario, the baseline charge acceptance rate ranges from 0.5 to 1, and the charge acceptance rate tolerance is adjusted accordingly.

[0076] The formula for the matching degree set is:

[0077]

[0078] in, Indicates the Coulomb efficiency matching degree. This indicates taking the maximum value. Indicates the spare coulomb efficiency. Indicates the baseline coulomb efficiency. This indicates the Coulomb efficiency tolerance. Indicates the acceptance rate of backup charging. Indicates the baseline charge acceptance rate. Indicates the charging acceptance tolerance. Indicates the standby charge / discharge stress index. Indicates the reference charge-discharge stress index. Indicates the charge / discharge stress index tolerance. This indicates the average monthly capacity decay rate of the reserve. This represents the baseline monthly average capacity decay rate. This indicates the monthly average capacity decay rate tolerance;

[0079]

[0080] in, , , , Indicates the weight of the stress index sub-item. Indicates the average charge and discharge current. Indicates the rated current. Indicates the peak charge / discharge current. This indicates an update to the charging time. Indicates reference charging time. Indicates the charging temperature. Indicates the reference charging temperature.

[0081] Preferably, the coulombic efficiency tolerance can be 1.6%, the charge acceptance rate is calculated based on the charged amount, effective charging time and battery rated capacity, and the sum of the weights of the four stress index sub-items is 1. Specifically, a can be 0.3, b can be 0.3, c can be 0.2 and d can be 0.2, the charge-discharge stress index tolerance can be 0.25, and the monthly average capacity decay rate tolerance can be 0.2%.

[0082] Step S50: Set a comprehensive weight set, and calculate a comprehensive matching degree based on the matching degree set and the comprehensive weight set, so as to select a matching battery pack from a number of backup battery packs according to a number of comprehensive matching degrees.

[0083] In step S50, the comprehensive weight set includes coulombic efficiency weight, charge acceptance rate weight, charge / discharge stress index weight, and monthly average capacity decay rate weight. The coulombic efficiency weight is greater than or equal to the charge acceptance rate weight, the charge acceptance rate weight is greater than or equal to the monthly average capacity decay rate weight, the coulombic efficiency weight is greater than or equal to the charge / discharge stress index weight, and the charge / discharge stress index weight is greater than or equal to the monthly average capacity decay rate weight.

[0084] Preferably, the weights of four indicators are set according to the importance of the backup battery pack matching. The four indicators are coulombic efficiency, charge acceptance rate, charge / discharge stress index, and monthly average capacity decay rate. Coulombic efficiency reflects the energy utilization efficiency of the battery during charging and discharging, which directly affects the range and performance. Charge acceptance rate measures the battery's ability to accept charging in actual charging, affecting charging speed and thermal management. Charge / discharge stress index reflects the load and temperature stress on the battery during use, which has a significant impact on battery life. Monthly average capacity decay rate reflects the long-term degradation trend of the battery, ensuring lifespan matching between batteries. Among them, coulombic efficiency directly determines the usable energy of the battery and is the most important. The weight of coulombic efficiency is set to 0.35. Charge acceptance rate and charge / discharge stress index affect short-term use performance and safety, and are less important than coulombic efficiency. The weight of charge acceptance rate is set to 0.25, the weight of charge / discharge stress index is set to 0.25, and the weight of monthly average capacity decay rate is used as a long-term health reference for the battery. The weight of monthly average capacity decay rate is set to 0.15. Understandably, quantifying the health status of the battery pack to be repaired and several of the backup battery packs and then performing a multi-dimensional and systematic matching evaluation can fully utilize the backup battery packs that have been used and have the most similar performance to repair and group the battery pack to be repaired, which is beneficial to improving the performance and stability of the grouped battery pack.

[0085] Please see Figure 2 The second embodiment of the present invention provides a battery pack selection system, which applies the battery pack selection method described in the first embodiment above. The system includes:

[0086] The acquisition module 10 is used to acquire several initial cell datasets and several initial auxiliary datasets in the battery pack to be repaired based on an online data platform. The initial cell datasets correspond to the initial auxiliary datasets. The initial cell datasets include initial charging capacity and initial discharging capacity. The initial auxiliary datasets include charging temperature, initial charging and discharging current and initial charging time.

[0087] The acquisition module 10 includes:

[0088] The first unit is used to collect data from several cells to be processed from the normal battery cells;

[0089] The second unit is used to detect and remove several outliers from several cell data to be processed using the Z-score method to obtain several processed cell data. The processed cell data is then grouped according to the cell data type to form several initial cell datasets and several initial auxiliary datasets.

[0090] Correction module 20 is used to obtain a correction coefficient table, extract an efficiency correction coefficient from the correction coefficient table based on the charging temperature, and update the initial charge / discharge current and the initial charging time to an updated charge / discharge current and an updated charging time respectively using the efficiency correction coefficient, so as to update the initial auxiliary dataset to an updated auxiliary dataset.

[0091] In the correction module 20, the initial auxiliary dataset further includes extreme currents, and the correction module 20 includes:

[0092] The third unit is used to obtain an initial charge-discharge efficiency based on the initial charge-discharge current and the initial charging time, correct the initial charge-discharge efficiency to an updated charge-discharge efficiency through the efficiency correction coefficient, and obtain an updated charge-discharge current and an updated charging time based on the updated charge-discharge efficiency, the initial charge-discharge current and the initial charging time.

[0093] The fourth unit is used to replace the initial charge-discharge current in the initial auxiliary dataset with the updated charge-discharge current, replace the initial charging time with the updated charging time, and combine the updated charge-discharge current, the updated charge-discharge time, and the extreme current into an updated auxiliary dataset.

[0094] The update module 30 is used to update the initial cell dataset to an updated cell dataset based on the updated auxiliary dataset, and to calculate a benchmark dataset based on several of the updated cell datasets.

[0095] In the update module 30, the initial cell dataset further includes relative self-discharge rate, internal resistance, and cumulative mileage. The update module 30 includes:

[0096] The fifth unit is used to calculate the updated charge-discharge capacity based on the updated charge-discharge current, the extreme current and the updated charging time in the updated auxiliary dataset, and replace the initial charge-discharge capacity with the updated charge-discharge capacity, so as to combine the updated charge-discharge capacity, the relative self-discharge rate, the internal resistance and the cumulative mileage into an updated cell dataset.

[0097] The sixth unit is used to obtain several collection times corresponding to several updated battery cell datasets, and to obtain several time weights corresponding to several collection times;

[0098] The seventh unit is used to calculate the average charge / discharge capacity using a weighted average method based on several time weights and several updated charge / discharge capacities; to calculate the average relative self-discharge rate using a weighted average method based on several time weights and several relative self-discharge rates; to calculate the average internal resistance using a weighted average method based on several time weights and several internal resistances; and to calculate the average cumulative mileage using a weighted average method based on several time weights and several cumulative mileages, so as to form a benchmark dataset using the average charge / discharge capacity, the average relative self-discharge rate, the average internal resistance, and the average cumulative mileage.

[0099] Matching module 40 is used to acquire several backup cell datasets from several backup battery packs, and calculate a matching degree set based on the backup cell datasets and the benchmark datasets. The matching degree set includes coulomb efficiency matching degree, charge acceptance rate matching degree, charge and discharge stress index matching degree, and monthly average capacity decay rate matching degree.

[0100] The matching module 40 includes:

[0101] The eighth unit is used to obtain the backup battery charging scenario of the backup battery pack through the online data platform, and based on the backup cell dataset and the backup battery charging scenario, to obtain the backup coulombic efficiency, backup charge acceptance rate, backup charge and discharge stress index and backup monthly average capacity decay rate to form the first dataset.

[0102] The ninth unit is used to obtain the charging scenario of the battery to be repaired in the charging pack to be repaired through the online data platform, and to obtain the benchmark coulombic efficiency, benchmark charge acceptance rate, benchmark charge and discharge stress index, benchmark monthly average capacity decay rate and charge acceptance rate tolerance based on the benchmark dataset and the charging scenario of the battery to be repaired, so as to form a second dataset.

[0103] The tenth unit is used to obtain several stress index sub-item weights, coulomb efficiency tolerance, charge-discharge stress index tolerance, and monthly average capacity decay rate tolerance through the online data platform to form a third dataset, and to calculate a matching degree set based on the first dataset, the second dataset, and the third dataset.

[0104] The formula for the matching degree set is:

[0105]

[0106] in, Indicates the Coulomb efficiency matching degree. This indicates taking the maximum value. Indicates the spare coulomb efficiency. Indicates the baseline coulomb efficiency. This indicates the Coulomb efficiency tolerance. Indicates the acceptance rate of backup charging. Indicates the baseline charge acceptance rate. Indicates the charging acceptance tolerance. Indicates the standby charge / discharge stress index. Indicates the reference charge-discharge stress index. Indicates the charge / discharge stress index tolerance. This indicates the average monthly capacity decay rate of the reserve. This represents the baseline monthly average capacity decay rate. This indicates the monthly average capacity decay rate tolerance;

[0107]

[0108] in, , , , Indicates the weight of the stress index sub-item. Indicates the average charge and discharge current. Indicates the rated current. Indicates the peak charge / discharge current. This indicates an update to the charging time. Indicates reference charging time. Indicates the charging temperature. Indicates the reference charging temperature.

[0109] The integration module 50 is used to set an integration weight set, calculate an integration matching degree based on the matching degree set and the integration weight set, and select a matching battery pack from a plurality of backup battery packs according to a plurality of integration matching degrees.

[0110] In the integrated module 50, the integrated weight set includes coulombic efficiency weight, charge acceptance rate weight, charge / discharge stress index weight, and monthly average capacity decay rate weight. The coulombic efficiency weight is greater than or equal to the charge acceptance rate weight, the charge acceptance rate weight is greater than or equal to the monthly average capacity decay rate weight, the coulombic efficiency weight is greater than or equal to the charge / discharge stress index weight, and the charge / discharge stress index weight is greater than or equal to the monthly average capacity decay rate weight.

[0111] The third embodiment of the present invention provides a computer, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the battery pack selection method as described in the first embodiment.

[0112] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0113] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for selecting and matching a battery pack, characterized in that, Includes the following steps: Based on an online data platform, several initial cell datasets and several initial auxiliary datasets are obtained from the battery pack to be repaired. The initial cell datasets correspond to the initial auxiliary datasets. The initial cell datasets include the initial charging capacity and the initial discharging capacity. The initial auxiliary datasets include the charging temperature, the initial charging and discharging current, and the initial charging time. Obtain a correction coefficient table, extract efficiency correction coefficients from the correction coefficient table based on the charging temperature, and update the initial charge / discharge current and the initial charging time to updated charge / discharge current and updated charging time respectively using the efficiency correction coefficients, so as to update the initial auxiliary dataset to updated auxiliary dataset. Based on the updated auxiliary dataset, the initial cell dataset is updated to an updated cell dataset, and a baseline dataset is calculated based on several of the updated cell datasets; Obtain several backup cell datasets from several backup battery packs. Based on the backup cell datasets and the benchmark dataset, calculate a matching degree set, which includes coulomb efficiency matching degree, charge acceptance rate matching degree, charge and discharge stress index matching degree, and monthly average capacity decay rate matching degree. The step of calculating the matching degree set based on the spare battery cell dataset and the benchmark dataset includes: The backup battery charging scenario of the backup battery pack is obtained through the online data platform. Based on the backup cell dataset and the backup battery charging scenario, the backup coulombic efficiency, backup charge acceptance rate, backup charge and discharge stress index and backup monthly average capacity decay rate are obtained to form the first dataset. The online data platform is used to obtain the charging scenario of the battery to be repaired in the charging pack to be repaired. Based on the benchmark dataset and the charging scenario of the battery to be repaired, the benchmark coulombic efficiency, benchmark charge acceptance rate, benchmark charge and discharge stress index, benchmark monthly average capacity decay rate and charge acceptance rate tolerance are obtained to form a second dataset. The online data platform is used to obtain several stress index sub-item weights, coulomb efficiency tolerance, charge and discharge stress index tolerance, and monthly average capacity decay rate tolerance to form a third dataset. The matching degree set is calculated based on the first dataset, the second dataset, and the third dataset. The formula for the matching degree set is: in, Indicates the Coulomb efficiency matching degree. This indicates taking the maximum value. Indicates the spare coulomb efficiency. Indicates the baseline coulomb efficiency. This indicates the Coulomb efficiency tolerance. Indicates the acceptance rate of backup charging. Indicates the baseline charge acceptance rate. Indicates the charging acceptance tolerance. Indicates the standby charge / discharge stress index. Indicates the reference charge-discharge stress index. Indicates the charge / discharge stress index tolerance. This indicates the average monthly capacity decay rate of the reserve. This represents the baseline monthly average capacity decay rate. This indicates the monthly average capacity decay rate tolerance; in, , , , Indicates the weight of the stress index sub-item. Indicates the average charge and discharge current. Indicates the rated current. Indicates the peak charge / discharge current. This indicates an update to the charging time. Indicates reference charging time. Indicates the charging temperature. Indicates the reference charging temperature; A comprehensive weight set is set up, and a comprehensive matching degree is calculated based on the matching degree set and the comprehensive weight set, so as to select a matching battery pack from a number of backup battery packs according to a number of comprehensive matching degrees.

2. The battery pack selection method according to claim 1, characterized in that, The battery pack to be repaired includes several normal battery cells. The step of obtaining several initial battery cell datasets and several initial auxiliary datasets from the battery pack to be repaired includes: Collect data from several cells to be processed from the normal battery cells; The Z-score method is used to detect and remove outliers from several cell data sets to be processed, resulting in several processed cell data sets. The processed cell data sets are then grouped according to the cell data type to form several initial cell datasets and several initial auxiliary datasets.

3. The battery pack selection method according to claim 1, characterized in that, The initial auxiliary dataset also includes extreme currents. The step of updating the initial charge / discharge current and the initial charging time to updated charge / discharge current and updated charging time respectively using the efficiency correction coefficient, so as to update the initial auxiliary dataset to the updated auxiliary dataset, includes: Based on the initial charge / discharge current and the initial charging time, an initial charge / discharge efficiency is obtained. The initial charge / discharge efficiency is then corrected to an updated charge / discharge efficiency using the efficiency correction coefficient. Finally, an updated charge / discharge current and an updated charging time are obtained based on the updated charge / discharge efficiency, the initial charge / discharge current, and the initial charging time. The initial charge / discharge current in the initial auxiliary dataset is replaced with the updated charge / discharge current, the initial charging time is replaced with the updated charging time, and the updated charge / discharge current, the updated charging time, and the extreme current are combined to form an updated auxiliary dataset.

4. The battery pack selection method according to claim 3, characterized in that, The initial cell dataset also includes relative self-discharge rate, internal resistance, and cumulative mileage. The step of updating the initial cell dataset to an updated cell dataset based on the updated auxiliary dataset includes: Based on the updated charge / discharge current, the extreme current, and the updated charging time in the updated auxiliary dataset, the updated charge / discharge capacity is calculated, and the initial charge / discharge capacity is replaced with the updated charge / discharge capacity, so that the updated charge / discharge capacity, the relative self-discharge rate, the internal resistance, and the cumulative mileage are combined into an updated cell dataset.

5. The battery pack selection method according to claim 4, characterized in that, Both the initial cell dataset and the updated cell dataset include relative self-discharge rate, internal resistance, and cumulative mileage. The step of calculating a baseline dataset based on several of the updated cell datasets includes: Obtain several collection times corresponding to several updated battery cell datasets, and obtain several time weights corresponding to several collection times; Based on several time weights and several updated charge / discharge capacities, the average charge / discharge capacity is calculated using a weighted average method. Based on several time weights and several relative self-discharge rates, the average relative self-discharge rate is calculated using a weighted average method. Based on several time weights and several internal resistances, the average internal resistance is calculated using a weighted average method. Based on several time weights and several cumulative mileages, the average cumulative mileage is calculated using a weighted average method. The average charge / discharge capacity, the average relative self-discharge rate, the average internal resistance, and the average cumulative mileage form a benchmark dataset.

6. The battery pack selection method according to claim 1, characterized in that, The comprehensive weight set includes coulombic efficiency weight, charge acceptance rate weight, charge / discharge stress index weight, and monthly average capacity decay rate weight. The coulombic efficiency weight is greater than or equal to the charge acceptance rate weight, the charge acceptance rate weight is greater than or equal to the monthly average capacity decay rate weight, the coulombic efficiency weight is greater than or equal to the charge / discharge stress index weight, and the charge / discharge stress index weight is greater than or equal to the monthly average capacity decay rate weight.

7. A battery pack selection system, employing the battery pack selection method as described in any one of claims 1 to 6, characterized in that, The system includes: The acquisition module is used to acquire several initial cell datasets and several initial auxiliary datasets from the battery pack to be repaired based on an online data platform. The initial cell datasets correspond to the initial auxiliary datasets. The initial cell datasets include initial charging capacity and initial discharging capacity. The initial auxiliary datasets include charging temperature, initial charging and discharging current, and initial charging time. The correction module is used to obtain a correction coefficient table, extract an efficiency correction coefficient from the correction coefficient table based on the charging temperature, and update the initial charge / discharge current and the initial charging time to an updated charge / discharge current and an updated charging time using the efficiency correction coefficient, so as to update the initial auxiliary dataset to an updated auxiliary dataset. An update module is used to update the initial cell dataset to an updated cell dataset based on the updated auxiliary dataset, and to calculate a benchmark dataset based on several of the updated cell datasets. The matching module is used to obtain a dataset of several spare cells from several spare battery packs, and calculate a matching degree set based on the spare cell dataset and the benchmark dataset. The matching degree set includes coulomb efficiency matching degree, charge acceptance rate matching degree, charge and discharge stress index matching degree and monthly average capacity decay rate matching degree. The matching module includes: The eighth unit is used to obtain the backup battery charging scenario of the backup battery pack through the online data platform, and based on the backup cell dataset and the backup battery charging scenario, to obtain the backup coulombic efficiency, backup charge acceptance rate, backup charge and discharge stress index and backup monthly average capacity decay rate to form the first dataset. The ninth unit is used to obtain the charging scenario of the battery to be repaired in the charging pack to be repaired through the online data platform, and to obtain the benchmark coulombic efficiency, benchmark charge acceptance rate, benchmark charge and discharge stress index, benchmark monthly average capacity decay rate and charge acceptance rate tolerance based on the benchmark dataset and the charging scenario of the battery to be repaired, so as to form a second dataset. The tenth unit is used to obtain several stress index sub-item weights, coulomb efficiency tolerance, charge and discharge stress index tolerance and monthly average capacity decay rate tolerance through the online data platform to form a third dataset, and to calculate the matching degree set based on the first dataset, the second dataset and the third dataset. The formula for the matching degree set is: in, Indicates the Coulomb efficiency matching degree. This indicates taking the maximum value. Indicates the spare coulomb efficiency. Indicates the baseline coulomb efficiency. This indicates the Coulomb efficiency tolerance. Indicates the acceptance rate of backup charging. Indicates the baseline charge acceptance rate. Indicates the charging acceptance tolerance. Indicates the standby charge / discharge stress index. Indicates the reference charge-discharge stress index. Indicates the charge / discharge stress index tolerance. This indicates the average monthly capacity decay rate of the reserve. This represents the baseline monthly average capacity decay rate. This indicates the monthly average capacity decay rate tolerance; in, , , , Indicates the weight of the stress index sub-item. Indicates the average charge and discharge current. Indicates the rated current. Indicates the peak charge / discharge current. This indicates an update to the charging time. Indicates reference charging time. Indicates the charging temperature. Indicates the reference charging temperature; The comprehensive module is used to set a comprehensive weight set, calculate a comprehensive matching degree based on the matching degree set and the comprehensive weight set, and select a matching battery pack from a plurality of backup battery packs according to a plurality of comprehensive matching degrees.

8. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the battery pack selection method as described in any one of claims 1 to 6.

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