CN-122025899-A - Battery comprehensive utilization method and system based on multi-parameter collaborative optimization

CN122025899ACN 122025899 ACN122025899 ACN 122025899ACN-122025899-A

Abstract

The invention belongs to the technical field of battery sorting, and provides a battery comprehensive utilization method and system based on multi-parameter collaborative optimization, which are used for acquiring capacity and internal resistance parameters of a battery, enhancing the capacity and internal resistance parameters to generate an enhanced data set, analyzing the enhanced data set, removing abnormal fault batteries according to data distribution density, and dividing healthy batteries into high-consistency clusters by optimizing initial center clusters; and establishing a module capacity and internal resistance analysis formula based on the analyzed relation and the divided battery cluster group, calculating the dispersion degree of the capacity and internal resistance among the monomers in the battery module under the combination of different connection modes and parameter distribution, constructing consistency evaluation indexes, analyzing the inhibition effect of each battery module reconstruction scheme on the barrel effect, and determining the optimal battery module reconstruction scheme. The invention improves the accuracy of battery sorting.

Inventors

DUAN BIN
SUN HUAYI
LIU XUEFENG
MA CHEN
LI YICHAO
RONG HAILIN

Assignees

山东大学

Dates

Publication Date: 20260512
Application Date: 20260410

Claims (10)

1. The battery comprehensive utilization method based on the multi-parameter collaborative optimization is characterized by comprising the following steps of: Acquiring capacity and internal resistance parameters of a battery, and enhancing to generate an enhanced data set; Analyzing the enhanced data set, removing abnormal fault batteries according to the data distribution density, and dividing the healthy batteries into high-consistency clusters by optimizing the clustering of the initial center; determining a reconstruction scheme of the battery module according to the connection mode and the parameter distribution mode of the single bodies in the battery pack; Based on the analyzed relation and the divided battery cluster groups, establishing a module capacity and internal resistance analysis formula to quantitatively evaluate the performance of each battery module reconstruction scheme in terms of module total capacity and equivalent internal resistance; Calculating the dispersion degree of the capacity and the internal resistance among the monomers in the battery modules under different connection modes and parameter distribution combinations, constructing consistency evaluation indexes, analyzing the inhibition effect of each battery module reconstruction scheme on the barrel effect, and determining the optimal battery module reconstruction scheme.
2. The comprehensive battery utilization method based on multi-parameter collaborative optimization according to claim 1 is characterized in that the process of obtaining capacity and internal resistance parameters of a battery and generating enhancement data by utilizing two-dimensional Weibull distribution comprises the steps of obtaining static capacity and direct-current internal resistance of a retired lithium ion battery as basic characteristic parameters, introducing a two-dimensional Weibull distribution model to fit the combined distribution of the capacity and the internal resistance of the battery, and generating an enhancement data set conforming to the distribution characteristics of original data through Monte Carlo simulation.
3. The comprehensive battery utilization method based on multi-parameter collaborative optimization according to claim 1, wherein the process of eliminating abnormal fault batteries according to the data distribution density comprises the steps of adopting a density-based outlier detection algorithm to identify and eliminate the fault batteries with abnormal characteristics of micro short circuit, lithium precipitation or capacity jump.
4. The comprehensive battery utilization method based on multi-parameter collaborative optimization according to claim 1, wherein the process of dividing healthy batteries into clusters with high consistency by optimizing initial center clustering comprises the steps of applying an initial center optimized clustering algorithm, dividing the healthy batteries with high consistency into a plurality of clusters according to multi-dimensional characteristics of battery capacity and internal resistance, and concretely comprises the steps of randomly selecting one point as a cluster center, calculating the shortest distance between each sample point in a data set and the current cluster center, calculating the probability that each sample is selected as the next cluster center, selecting the next cluster center by using a wheel disc method until the cluster centers meeting the preset number K are selected, calculating the distances from each sample point to the K cluster centers, dividing the cluster centers into nearest clusters, and recalculating the average value of all points in each cluster as the new cluster center until the cluster centers are not changed any more.
5. The comprehensive battery utilization method based on multi-parameter collaborative optimization according to claim 1, wherein in the process of determining a reconstruction scheme of a battery module according to a connection mode and a parameter distribution mode of single bodies in a battery pack, the connection mode of the single bodies in the battery pack comprises two modes of parallel connection, serial connection and parallel connection, and the parameter distribution mode comprises various arrangement modes with different orders according to capacity or internal resistance.
6. The battery comprehensive utilization method based on multi-parameter collaborative optimization according to claim 1, wherein the process of determining the battery module reconstruction scheme specifically comprises the steps of defining a module with a basic component consisting of n units connected in series as OnS and defining a module with a basic component consisting of m units connected in parallel as OmP according to the connection mode and parameter distribution mode of the single bodies in the battery pack; Setting up n multiplied by m batteries in a battery pack according to increment arrangement of capacity or resistance, and correspondingly increasing the capacity or resistance along with the increase of battery indexes, wherein the single distribution mode of the n multiplied by m batteries is divided into two types according to the increasing direction of battery parameters, wherein the first type is that the battery parameters are horizontally increased from left to right in modules OmP and OnS between the groups and are distributed in an H mode, the second type is that the battery parameters are increased in modules OmP and OnS between the groups in an S mode and are distributed in an S mode, and according to the two distribution modes, the connection modes of mPnS and nSmP are combined to obtain four battery reconstruction schemes which are respectively marked as H-mPNS, H-nSmP, S-mPnS and S-nSmP; The capacity and resistance of the battery are calculated as follows: the capacity and resistance of OmP modules are as follows: ; Wherein i of C OmP,i and R OmP,i represent the capacity and resistance of the i OmP module, respectively, and C i,j and R i,j represent the capacity and resistance of the j-th cell of the module, respectively; The resistance and capacity of the OnS modes are expressed as: ; Wherein, C OnS,j and R OnS,j respectively represent the capacity and the resistance of the jth OnS mode; mPnS the battery consists of n OmP modules in series, the capacity and resistance of which are respectively noted as C mPnS and R mPnS : ; The capacity and resistance of a nSmP battery consisting of m OnS modules in parallel are denoted C nSmP and R nSmP , respectively: ; the capacity and resistance of the connection mode of the battery pack can be obtained by the parameters of the battery cells thereof.
7. The method for comprehensively utilizing the battery based on the multi-parameter collaborative optimization according to claim 1, wherein the process of establishing a module capacity and internal resistance analysis formula based on the analyzed relationship and the divided battery clusters comprises the following steps of, for an H-mPnS reconstruction scheme, expressing the battery capacity as: ; Wherein Q 1 、Q 2 …Q nm is the capacity of the corresponding numbered battery cells in the module with n×m battery cells respectively; q H-mPnS is the capacity of the battery reconstructed in the H-mPnS scheme; q H-nSmP is the capacity of the battery reconstructed in the H-nSmP scheme; Q S-mPnS is the capacity of the battery reconstructed in the S-mPnS scheme; Q S-nSm P is the capacity of the battery reconstructed in the S-nSmP scheme; for the H-nSmP reconstruction scheme, the battery capacity is expressed as: ; for the S-mPnS reconstruction scheme, the battery capacity is expressed as: ; For the S-nSmP reconstruction scheme, the battery capacity is expressed as: ; the capacity of the various reconstruction schemes is ordered as follows, Q S-mPnS >Q S-nSmP >Q H-mPnS =Q H-nSmP , with the battery capacity increasing with increasing battery index.
8. The comprehensive battery utilization method based on multi-parameter collaborative optimization according to claim 1, wherein the process of calculating the dispersion degree of the capacity and the internal resistance among the battery modules under the combination of different connection modes and parameter distribution and constructing the consistency evaluation index comprises the steps of carrying out comprehensive evaluation of the consistency of the battery capacity and the internal resistance by combining a dispersion coefficient and a cumulative distribution function, calculating the capacity variation coefficient according to the capacity sequence of the battery modules aiming at each reconstruction scheme, calculating the dispersion coefficient value of each reconstruction scheme according to the sequential distribution of the battery cell capacities forming each serial-parallel connection module, and sequencing the dispersion coefficient values.
9. The comprehensive battery utilization method based on multi-parameter collaborative optimization according to claim 1, wherein the process of analyzing the inhibition effect of each battery module reconstruction scheme on the barrel effect and determining the optimal battery module reconstruction scheme comprises the steps of screening a preferred arrangement mode which enables the module capacity to be maximum and the internal resistance to be minimum, comprehensively considering the sorting of discrete coefficient values, and selecting an arrangement mode with the discrete coefficient value smaller than a set value.
10. A battery comprehensive utilization system based on multi-parameter collaborative optimization is characterized by comprising: the battery data testing module is configured to acquire capacity and internal resistance parameters of the battery, strengthen the capacity and internal resistance parameters and generate an enhanced data set; The two-stage sorting module is configured to analyze the enhanced data set, reject abnormal fault batteries according to the data distribution density, and divide healthy batteries into high-consistency clusters by optimizing the clustering of the initial center; The reconstruction scheme design module is configured to determine a reconstruction scheme of the battery module according to the connection mode and the parameter distribution mode of the single bodies in the battery pack; The performance quantitative analysis module is configured to establish a module capacity and internal resistance analysis formula based on the analyzed relation and the divided battery cluster groups so as to quantitatively evaluate the performance of each battery module reconstruction scheme in terms of the module total capacity and the equivalent internal resistance; The multi-index comprehensive evaluation module is configured to calculate the dispersion degree of the capacity and the internal resistance among the single bodies in the battery modules under different connection modes and parameter distribution combinations, construct consistency evaluation indexes, analyze the inhibition effect of each battery module reconstruction scheme on the barrel effect and determine the optimal battery module reconstruction scheme.

Description

Battery comprehensive utilization method and system based on multi-parameter collaborative optimization Technical Field The invention belongs to the technical field of comprehensive battery utilization, and particularly relates to a comprehensive battery utilization method and system based on multi-parameter collaborative optimization. Background The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art. The long-term use of the battery can lead to gradual degradation of battery parameters, when the capacity of the battery is reduced to below 80% of rated capacity, the endurance capacity and the performance are obviously reduced, the problem of retirement is faced, and if the battery cannot be effectively and comprehensively utilized, the battery is not only economically lost, but also serious environmental problems are caused. The retired battery is reused, so that the residual value of the retired battery can be furthest mined, the resource waste and the environmental impact can be effectively reduced, and the retired battery is an important path for realizing sustainable development of new energy industry. The lithium battery sorting is to remove or cluster the battery monomers with obvious differences through the differential analysis, which is the basis of comprehensive utilization, and the fault battery has potential safety hazard to the retired battery pack, so that an abnormal battery screening step is required to be added before sorting. The existing comprehensive utilization framework only carries out simple and random combination on the batteries in the same cluster, so that the distribution of the monomers in the battery pack is disordered, the battery pack is easy to be restricted by a wooden barrel effect, the performance is limited, and potential safety hazards are brought. Disclosure of Invention In order to solve the problems, the invention provides a battery comprehensive utilization method and a system based on multi-parameter collaborative optimization, which improve the accuracy of battery comprehensive utilization and realize comprehensive evaluation reconstruction. According to some embodiments, the present invention employs the following technical solutions: a battery comprehensive utilization method based on multi-parameter collaborative optimization comprises the following steps: Acquiring capacity and internal resistance parameters of a battery, and enhancing to generate an enhanced data set; Analyzing the enhanced data set, removing abnormal fault batteries according to the data distribution density, and dividing the healthy batteries into high-consistency clusters by optimizing the clustering of the initial center; determining a reconstruction scheme of the battery module according to the connection mode and the parameter distribution mode of the single bodies in the battery pack; Based on the analyzed relation and the divided battery cluster groups, establishing a module capacity and internal resistance analysis formula to quantitatively evaluate the performance of each battery module reconstruction scheme in terms of module total capacity and equivalent internal resistance; Calculating the dispersion degree of the capacity and the internal resistance among the monomers in the battery modules under different connection modes and parameter distribution combinations, constructing consistency evaluation indexes, analyzing the inhibition effect of each battery module reconstruction scheme on the barrel effect, and determining the optimal battery module reconstruction scheme. The method comprises the steps of obtaining static capacity and direct current internal resistance of a retired lithium ion battery as basic characteristic parameters, introducing a two-dimensional Weibull distribution model to fit the combined distribution of the capacity and the internal resistance of the battery, and generating an enhancement data set conforming to the distribution characteristics of original data through Monte Carlo simulation. In an alternative embodiment, the process of rejecting abnormal faulty batteries according to the data distribution density comprises the steps of identifying and rejecting faulty batteries with abnormal features such as micro-short circuits, lithium precipitation or capacity jumps by adopting a density-based outlier detection algorithm. The process of dividing the healthy batteries into the clusters with high consistency by optimizing the clustering of the initial center comprises the steps of applying an initial center optimized clustering algorithm and dividing the healthy batteries with high consistency into a plurality of clusters according to the multidimensional characteristics of the battery capacity and the internal resistance. As a further limiting embodiment, the process of dividing healthy batteries with high consistency in performance into a plurality of cluster gro