CN116666799A - Retired battery reorganization method and system - Google Patents
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- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 5
- 229910052744 lithium Inorganic materials 0.000 description 5
- 229910001416 lithium ion Inorganic materials 0.000 description 5
- 238000002474 experimental method Methods 0.000 description 4
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- 239000010926 waste battery Substances 0.000 description 1
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- Y02W—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO WASTEWATER TREATMENT OR WASTE MANAGEMENT
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Abstract
The invention provides a retired battery reorganization method and a retired battery reorganization system, wherein the method comprises the following steps: disassembling the retired module, and screening out battery monomers with bad appearance and liquid leakage; selecting various characteristic parameters to carry out multidimensional screening on retired monomers; carrying out data processing on the detected characteristic parameters; solving an objective function through a fuzzy C-means algorithm and a Lagrange multiplier algorithm to realize grouping of retired monomers. The invention solves the technical problems of lower production efficiency and higher recombination cost.
Description
Technical Field
The invention relates to the technical field of lithium ion batteries, in particular to a retired battery reorganization method and system.
Background
In order to improve and solve the problems of environmental pollution and energy shortage, the state supports electric vehicles, which are rapidly developed and become an important development direction of the automobile industry. Power lithium ion batteries have been used in large amounts as Electric Vehicle (EV) on-board energy sources in recent years because of their high energy density, light weight, long cycle life, high power capability, and the like. However, the performance of lithium batteries gradually deteriorates during use. Due to the safety requirements of electric vehicles, the electric vehicles are retired when the power battery capacity is reduced to 80% of the rated capacity. With the popularization of electric automobiles, a large number of retired power lithium batteries exist. The retired lithium battery still has larger capacity, potential application value of the retired lithium battery can be utilized in a screening and recombination mode, gradient utilization is realized, and the retired lithium battery becomes a low-cost energy storage scheme.
In the long-term use process of the battery pack, the degree of dispersion of the performance indexes of the single batteries is increased continuously, and the inconsistency is increased. To avoid problems of over-charge and over-discharge and circulation of the battery cells due to inconsistency, the initial assembly is ensured to be consistent with the characteristics of the cells as much as possible or an advanced battery management system is provided. Parameters to be detected for the characteristics of the battery cells include capacity, self-discharge, internal resistance and the like, and a large amount of time and resources are consumed; on the other hand, the equalization by using an external circuit has problems of slow equalization speed and energy waste. For example, in the prior patent application publication CN114200320a, a method for reorganizing retired power battery based on SOC consistency matching, the prior scheme not only needs time to measure the SOC of a single battery, but also evaluates the performance of the battery by only one parameter of SOC, and may have deviation. For example, the prior patent application publication No. CN115602949A discloses a screening method for gradient use of waste batteries, the prior scheme divides a capacity section through an available capacity optimal principle, screens a battery pack meeting reuse in advance, then divides the battery pack with the overall capacity lower than a threshold value into battery cell split battery cell groups, and then reorganizes the battery cells with very similar characteristics, thereby shortening gradient utilization screening time among a plurality of battery cell groups, but has the problems of less careful screening and low efficiency.
In conclusion, the prior art has the technical problems of lower production efficiency and higher recombination cost.
Disclosure of Invention
The invention aims to solve the technical problems of lower production efficiency and higher recombination cost in the prior art.
The invention adopts the following technical scheme to solve the technical problems: a retired battery reorganization method comprises the following steps:
s1, disassembling the retired module to screen out unsuitable retired battery monomers in the retired module;
s2, detecting and acquiring at least 2 kinds of characteristic data, and carrying out dynamic voltage analysis according to the characteristic data in a preset time period through charging and discharging operations in the preset time period to establish a Thevenin model, so as to determine the characteristic parameters, and screening retired battery monomers in a multi-dimensional manner to obtain screening characteristic parameters, wherein the characteristic data comprise: single open circuit voltage, electrochemical impedance spectrum characteristic parameter;
s3: processing the characteristic parameters by preset logic to obtain normalized characteristic parameters;
and S4, solving an objective function according to the normalized characteristic parameters by using a fuzzy C-means algorithm and a Lagrange multiplier algorithm to determine a single clustering center and the membership degree of the battery single to each single clustering center so as to obtain retired battery single clustering data, and reorganizing the retired battery single.
According to the invention, the obsolete batteries are screened by combining a fuzzy C-means clustering algorithm with various battery characteristic parameters, so that the consistency of battery monomers can be ensured, and the screening recombination time can be greatly shortened. The efficiency and the clustering accuracy of the recombination of the retired batteries are improved.
In a more specific technical solution, step S2 includes:
s21, detecting single open circuit voltage V of retired battery single body 0 ；
S22, determining electrochemical impedance spectrum characteristic parameters R and DeltaIm by measuring electrochemical impedance spectrums of retired battery monomers;
s23, according to the single open circuit voltage V 0 And electrochemical impedance spectrum characteristic parameters R and DeltaIm are subjected to dynamic voltage analysis to obtain electrochemical analysis data;
s24, establishing a Thevenin model according to preset logic according to electrochemical analysis data, and determining a characteristic parameter R according to the Thevenin model 1c And R is 1d ；
And S25, mapping all the characteristic parameters to a preset interval to generate a box diagram of the characteristic parameters, and screening out abnormal retired battery monomers according to the box diagram.
According to the invention, the retired module is disassembled and screened out, then the battery is comprehensively evaluated, and the multi-dimensional screening is conveniently carried out by measuring and selecting various characteristic parameters to reflect the multi-aspect performance of the battery monomer, so that the effect of the retired battery reorganization operation is improved. The invention screens out obviously abnormal batteries by utilizing the characteristic parameters of the box diagram processing, prevents the abnormal parameters caused by abnormal faults or detection of the batteries, and ensures the final clustering effect.
In a more specific embodiment, in step S24, the characteristic parameter R is determined using the following logic 1c And R is 1d ：
Wherein V is 1 For the initial voltage of the discharge process, V 2 In order to cut-off the voltage during the discharge process,
wherein V is 3 To charge the process initiation voltage, V 4 For the charging process cut-off voltage, I is the charge-discharge current.
In a more specific technical solution, step S4 includes:
s41, calculating the membership degree of each monomer cluster center, and dividing the monomer cluster centers into corresponding clusters with the largest membership degree;
s42, establishing an objective function according to the corresponding cluster by using preset logic;
s43, establishing an optimization problem by preset logic according to an objective function;
s44, solving an optimization problem according to a Lagrange multiplier algorithm, obtaining membership grade solving logic according to membership grade processing, substituting the membership grade solving logic into the Lagrange multiplier algorithm, and calculating to obtain a monomer clustering center;
s45, initializing a membership degree matrix U according to membership degrees to calculate a clustering center matrix C, and continuing iteration until a preset convergence condition is met.
In a more specific technical solution, in step S42, according to the corresponding cluster, an objective function J is established with the following logic:
wherein n is the total number of clustered objects, c is the number of clustered object subsets, and x i Represents the ith monomer data, v j Represents the j-th cluster center data, u ij The weighted index of membership is controlled for the membership of the ith object x to the jth class, m being [1, + ].
According to the invention, various characteristic parameters are selected and the relevance between the characteristic parameters and the battery health state is determined through different experimental methods, so that the consistency state of the battery can be comprehensively evaluated, the grouping accuracy is ensured, the time cost is greatly reduced through data processing and a fuzzy C-means algorithm, and the detection cost for echelon utilization is saved for manufacturers.
In a more specific solution, in step S43, according to the objective function, an optimization problem with constraint conditions is established with the following logic:
j is the determination of the objective function of equation (1),to determine the constraints of the minimum of the objective function, s.t. refers to subject to, which is limited.
In a more specific technical solution, step S44 includes:
s441, constructing a Lagrange function according to a Lagrange multiplier algorithm by using the following logicSolving an optimization problem:
lambda in i The lagrangian multiplier representing the i-th object,
s442, using the formula (3) to determine the membership degree u ij Partial derivative is calculated according to membership u ij Deriving the following membership degree calculation logic:
s443, substituting the membership degree calculation logic into the formula (3), and using the formula (3) to calculate the number of the clustering object subsets c j Obtaining partial derivatives to obtain monomer clustering centers:
in a more specific embodiment, in step S45, the membership matrix U satisfies the membership U ij Is a constraint on (c).
In a more specific technical solution, in step S45, the preset convergence condition includes: i J (t+1) -J (t) |<Epsilon or up to a preset maximum number of iterations, where t represents the number of iterations, J (t+1) 、J (t) And expressing the t+1 th and t th iteration objective function values, wherein epsilon is a relative error limit and is determined according to the target requirement precision.
In a more specific aspect, a retired battery reorganization system includes:
the retirement module primary screening module is used for disassembling the retirement module to screen out unsuitable retired battery monomers in the retirement module;
the single characteristic parameter obtaining module is used for detecting and obtaining at least 2 kinds of characteristic data, carrying out dynamic voltage analysis according to the characteristic data in a preset time period through charging and discharging operations in the preset time period so as to establish a Thevenin model, determining the characteristic parameters according to the dynamic voltage analysis, and screening retired battery single bodies in a multi-dimensional mode to obtain screening characteristic parameters, wherein the characteristic data comprise: and the single open circuit voltage and the electrochemical impedance spectrum characteristic parameters. The monomer characteristic parameter obtaining module is connected with the retired module primary screening module;
the feature data processing module is used for processing the feature parameters through preset logic to obtain normalized feature parameters, and is connected with the monomer feature parameter solving module;
the recombination solving module is used for solving the objective function according to the normalized characteristic parameters by utilizing a fuzzy C-means algorithm and a Lagrange multiplier algorithm to determine single clustering centers and membership of the battery single to each single clustering center so as to obtain retired battery single clustering data, so that the retired battery single is recombined, and the recombination solving module is connected with the characteristic data processing module.
Compared with the prior art, the invention has the following advantages: according to the invention, the obsolete batteries are screened by combining a fuzzy C-means clustering algorithm with various battery characteristic parameters, so that the consistency of battery monomers can be ensured, and the screening recombination time can be greatly shortened. The efficiency and the clustering accuracy of the recombination of the retired batteries are improved.
According to the invention, the retired module is disassembled and screened out, then the battery is comprehensively evaluated, and the multi-dimensional screening is conveniently carried out by measuring and selecting various characteristic parameters to reflect the multi-aspect performance of the battery monomer, so that the effect of the retired battery reorganization operation is improved. The invention screens out obviously abnormal batteries by utilizing the characteristic parameters of the box diagram processing, prevents the abnormal parameters caused by abnormal faults or detection of the batteries, and ensures the final clustering effect.
According to the invention, various characteristic parameters are selected and the relevance between the characteristic parameters and the battery health state is determined through different experimental methods, so that the consistency state of the battery can be comprehensively evaluated, the grouping accuracy is ensured, the time cost is greatly reduced through data processing and a fuzzy C-means algorithm, and the detection cost for echelon utilization is saved for manufacturers.
The invention solves the technical problems of lower production efficiency and higher recombination cost in the prior art.
Drawings
FIG. 1 is a schematic diagram showing basic steps of a method for reorganizing retired batteries according to embodiment 1 of the present invention;
FIG. 2 is a schematic diagram of an equivalent circuit model of a method for reorganizing retired batteries according to embodiment 1 of the present invention;
FIG. 3 is a schematic view showing the selection of characteristic parameters of the electrochemical impedance spectrum according to embodiment 1 of the present invention;
FIG. 4 is a schematic diagram showing the variation of the charge-discharge voltage and current of the monomer according to embodiment 1 of the present invention;
FIG. 5 is a diagram showing the case diagram of the embodiment 1 of the present invention for screening abnormal values;
FIGS. 6a to 6d are diagrams showing four normalized module characteristic parameters according to embodiment 1 of the present invention;
FIGS. 7a to 7d are schematic diagrams illustrating the case diagram of the embodiment 1 of the present invention for screening out abnormal parameters;
FIGS. 8a to 8d are distribution diagrams of the capacity of the monomer in the reorganizing module in the reorganizing method of the retired battery according to embodiment 1 of the present invention;
fig. 9 is a schematic diagram of a basic module of a retired battery reorganization system according to embodiment 3 of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions in the embodiments of the present invention will be clearly and completely described in the following in conjunction with the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
As shown in FIG. 1, the method for reorganizing retired batteries provided by the invention comprises the following basic steps:
s1: disassembling the retired module, and screening out battery monomers with bad appearance and liquid leakage;
s2: selecting various characteristic parameters to carry out multidimensional screening on retired monomers;
in the present embodiment, as shown in fig. 2, the open circuit voltage of the cell after the disassembly of the battery module is detected as the characteristic parameter V 0 . The electrochemical impedance spectrum characteristic parameters are R and DeltaIm determined by measuring the electrochemical impedance spectrum of the lithium ion battery cell. R is the difference between the real part and the imaginary part at 0.1Hz frequency and the real part when the imaginary part is 0, and DeltaIm is the difference between the 0.258Hz impedance and the 0.1Hz impedance, so as to describe the impedance change in the low frequency region. Short-time dynamic voltage analysis is carried out through short-time charge and discharge, a Thevenin model is established, and characteristic parameters R are determined 1c And R is 1d 。R 1c The expression of (2) is:
wherein V is 1 To start the discharge process therein, V 2 In order to cut-off the voltage during the discharge process,
in the present embodiment, the characteristic parameter R 1d The expression of (2) is:
wherein V is 3 To charge the process initiation voltage, V 4 For the charging process cut-off voltage, I is the charge-discharge current.
In this embodiment, all feature parameters are mapped to [0,1]. The screening is to process the battery with obvious abnormal characteristic parameters by using the box diagram, and the battery has abnormal parameters caused by abnormal faults or detection, so that the final clustering effect is affected.
S3: carrying out data processing on the detected characteristic parameters;
in this embodiment, the data is normalized to utilize the formula:
and S4, solving an objective function through a fuzzy C-means algorithm and a Lagrange multiplier algorithm to realize grouping of retired monomers.
In this embodiment, the purpose of the whole retired battery screening is to group batteries, and the characteristic approximation among the battery cells in each group is achieved by determining the cluster centers and the membership degree of the battery cells to each cluster center.
In this embodiment, the clustering problem is converted into the optimization problem by calculating the membership degree of each monomer in different subsets, and dividing the monomers into corresponding clusters with the largest membership degree. Establishing an objective function:
wherein n is the total number of clustered objects, c is the number of clustered object subsets, and x i Represents the ith monomer data, v j Represents the j-th cluster center data, u ij For the membership of the ith object x to the jth class, m is [1, + ] and the weighted index of membership is controlled, in this embodiment, the value of m includes: 2.
in this embodiment, an optimization problem with constraints is established:
in this embodiment, the optimization problem is solved using the Lagrangian multiplier method:
in the present embodimentIn the process, the liquid crystal display device comprises a liquid crystal display device,for u ij Taking the partial derivative and making it equal to 0, according to u ij The constraint of (2) may derive a membership calculation formula:
substituting the above formula (4) into formula (3),pair c j Taking the partial derivative and making it equal to 0, deriving the clustering center:
in this embodiment, a satisfying u is randomly initialized ij And (5) a membership matrix U of the constraint condition, and calculating a clustering center matrix C. And stopping iteration until the iteration times are reached or convergence conditions are met by continuous iterative computation.
The convergence condition may be set as, for example:
|J (t+1) -J (t) |<epsilon or the maximum number of iterations is reached.
The output is the optimal solution of the objective function meeting the constraint condition and is also the optimal solution of battery reorganization.
Example 2
As shown in fig. 3, 4 and 5, in the present embodiment, the module is disassembled, the remarkably abnormal battery cells are screened out, and the open circuit voltage V of the cells is measured 0 . Then, electrochemical impedance detection and short-time charge-discharge experiments are performed on each cell, and characteristic parameters R, Δim and R are determined in FIG. 3 and FIG. 4 1c 、R 1d . The obtained data were normalized and the abnormal data were screened out via a box plot in fig. 5.
As shown in fig. 6 to 6d, in the present embodiment, four different module feature parameters are normalized. And a small part of battery characteristic parameters are obviously separated from most batteries, and meanwhile, the battery parameters in the module have a clustering phenomenon. The number of the monomers with characteristic parameters far away from most of the monomers is small, and the state of the monomers is different from that of most of the batteries, so that screening is performed at the beginning of cluster sorting, and interference to subsequent clusters is avoided.
In the present embodiment, as shown in FIGS. 7a to 7d, the intra-module monomers R, ΔIm, R are 1c 、R 1d And V 0 And (3) screening out the monomers of the abnormal parameters by using the box diagram from small to large. Wherein c can take the values of, for example: 0.8.
as shown in fig. 8a to 8d, in this embodiment, four module remaining monomers are clustered sequentially by using a fuzzy C-means algorithm to obtain a proposed recombination monomer set.
According to the specific embodiment, the retired module is disassembled firstly, the battery monomer with obvious defects is screened out, and then the characteristic parameters for recombination analysis are obtained by measuring open-circuit voltage, analyzing electrochemical impedance spectrum and short-time charge-discharge curve. And normalizing the characteristic parameters and screening out abnormal box diagram, and solving an objective function through a fuzzy C-means algorithm and a Lagrange multiplier algorithm to realize grouping of retired monomers. The method only takes about 6 minutes, the monomer with stronger consistency in the retired module can be effectively and rapidly screened out, and the reorganized module can meet the use occasion of the low-power retired battery.
Example 3
As shown in fig. 9, based on embodiment 1, the invention further provides a retired battery reorganization system based on a fuzzy C-means algorithm and a lagrangian multiplier algorithm, which comprises the following basic modules:
the primary screening module 1 is disassembled for the purpose of disassembling the module and screening out the monomers with obvious bad appearance.
The characteristic parameter detection module 2 is used for detecting and collecting lithium ion battery performance parameters required by battery recombination calculation;
the data preprocessing module 3 is used for identifying and processing the abnormal characteristic parameters;
and the battery reorganization module 4 is used for determining an objective function, a membership function and a clustering center of the fuzzy C-means algorithm.
In this embodiment, the feature parameter detection module is configured to detect feature parameters: v (V) 0 、R、ΔIm、R 1c 、R 1d 。
In this embodiment, the open circuit voltage of the disassembled battery module is detected as the characteristic parameter V 0 . The electrochemical impedance spectrum characteristic parameters are R and DeltaIm determined by measuring the electrochemical impedance spectrum of the lithium ion battery cell.
In this embodiment, R is, for example, the difference between the real part and the imaginary part at 0.1Hz, and Δim is the difference between the imaginary part of the impedance at 0.258Hz and 0.1Hz, so as to describe the impedance change in the low frequency region. And carrying out short-time dynamic voltage analysis through short-time charge and discharge.
In conclusion, the invention screens retired batteries by combining a fuzzy C-means clustering algorithm with various battery characteristic parameters, so that the consistency of battery monomers can be ensured, and the screening recombination time can be greatly shortened. The efficiency and the clustering accuracy of the recombination of the retired batteries are improved.
According to the invention, the retired module is disassembled and screened out, then the battery is comprehensively evaluated, and the multi-dimensional screening is conveniently carried out by measuring and selecting various characteristic parameters to reflect the multi-aspect performance of the battery monomer, so that the effect of the retired battery reorganization operation is improved. The invention screens out obviously abnormal batteries by utilizing the characteristic parameters of the box diagram processing, prevents the abnormal parameters caused by abnormal faults or detection of the batteries, and ensures the final clustering effect.
According to the invention, various characteristic parameters are selected and the relevance between the characteristic parameters and the battery health state is determined through different experimental methods, so that the consistency state of the battery can be comprehensively evaluated, the grouping accuracy is ensured, the time cost is greatly reduced through data processing and a fuzzy C-means algorithm, and the detection cost for echelon utilization is saved for manufacturers.
The invention solves the technical problems of lower production efficiency and higher recombination cost in the prior art.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A method of retired battery reorganization, the method comprising:
s1, disassembling a retired module to screen out unsuitable retired battery monomers in the retired module;
s2, detecting and acquiring at least 2 kinds of characteristic data, and carrying out dynamic voltage analysis according to the characteristic data in the preset time through charging and discharging operations of the preset time so as to establish a Thevenin model, thereby determining characteristic parameters, and screening the retired battery cells in a multi-dimensional manner to obtain screening characteristic parameters, wherein the characteristic data comprise: single open circuit voltage, electrochemical impedance spectrum characteristic parameter;
s3: processing the characteristic parameters by preset logic to obtain normalized characteristic parameters;
and S4, solving an objective function according to the normalized characteristic parameters by using a fuzzy C-means algorithm and a Lagrange multiplier algorithm to determine a single clustering center and membership of the battery single to each single clustering center so as to obtain retired battery single clustering data, and reorganizing the retired battery single.
2. The method according to claim 1, wherein the step S2 comprises:
s21, detecting single open circuit voltage V of retired battery single body 0 ；
S22, determining characteristic parameters R and DeltaIm of the electrochemical impedance spectrum by measuring the electrochemical impedance spectrum of the retired battery cell;
s23, according to the single open circuit voltage V 0 And the electrochemical impedance spectrum characteristic parameters R and DeltaIm are subjected to dynamic voltage analysis to obtain electrochemical analysis data;
s24, establishing a Thevenin model according to preset logic according to the electrochemical analysis data, and determining the characteristic parameter R according to the Thevenin model 1c And R is 1d ；
And S25, mapping all the characteristic parameters to a preset interval to generate a box diagram of the characteristic parameters, and screening out abnormal retired battery monomers according to the box diagram.
3. The method according to claim 2, wherein in step S24, the characteristic parameter R is determined by using the following logic 1c And R is 1d ：
Wherein V is 1 For the initial voltage of the discharge process, V 2 In order to cut-off the voltage during the discharge process,
wherein V is 3 To charge the process initiation voltage, V 4 For the charging process cut-off voltage, I is the charge-discharge current.
4. The method according to claim 1, wherein the step S4 comprises:
s41, calculating the membership degree of each monomer cluster center, and dividing the monomer cluster centers into corresponding clusters with the largest membership degree;
s42, establishing an objective function according to the corresponding cluster by preset logic;
s43, establishing an optimization problem by preset logic according to the objective function;
s44, solving the optimization problem according to the Lagrangian multiplier algorithm, obtaining membership degree solving logic according to the membership degree processing, and substituting the membership degree solving logic into the Lagrangian multiplier algorithm to calculate and obtain the monomer clustering center;
s45, initializing a membership degree matrix U according to the membership degree to calculate a clustering center matrix C, and continuing iteration until a preset convergence condition is met.
5. The method according to claim 1, wherein in step S42, the objective function is established according to the corresponding cluster by the following logic:
wherein n is the total number of clustered objects, c is the number of clustered object subsets, and x i Represents the ith monomer data, v j Represents the j-th cluster center data, u ij The weighted index of membership is controlled for the membership of the ith object x to the jth class, m being [1, + ].
6. The method according to claim 1, wherein in step S43, the optimization problem with constraint conditions is established according to the objective function by the following logic:
wherein J is the objective function determined in the formula (1),to determine the constraint of the minimum of the objective function, s.t. meaningRefer to subject to (limited to).
7. The method of claim 1, wherein step S44 comprises:
s441, solving the optimization problem according to the Lagrangian multiplier algorithm by using the following logic:
wherein lambda is i The lagrangian multiplier representing the i-th object,
s442, using the formula (3) to apply the membership degree u ij Obtaining partial derivative according to the membership degree u ij Deriving the following membership degree calculation logic:
s443, substituting the membership degree obtaining logic into the formula (3), and using the formula (3) to obtain the clustering object subset number c j Obtaining partial derivatives to obtain the monomer clustering center:
8. the method according to claim 1, wherein in step S45, the membership matrix U satisfies the membership U ij Is a constraint on (c).
9. The method for reorganizing retired battery according to claim 1In step S45, the preset convergence condition includes: i J (t+1) -J (t) |<Epsilon or up to a preset maximum number of iterations, where t represents the number of iterations, J (t+1) 、J (t) And expressing the t+1 th and t th iteration objective function values, wherein epsilon is a relative error limit and is determined according to the target requirement precision.
10. A retired battery reorganization system, the system comprising:
the retirement module primary screening module is used for disassembling the retirement module to screen out unsuitable retired battery monomers in the retirement module;
the single characteristic parameter obtaining module is used for detecting and obtaining at least 2 kinds of characteristic data, and carrying out dynamic voltage analysis according to the characteristic data in a preset time period through charging and discharging operations of the preset time period so as to establish a Thevenin model, so that the characteristic parameters are determined, and the retired battery single is screened in a multi-dimensional mode to obtain screened characteristic parameters, wherein the characteristic data comprise: and the single open circuit voltage and the electrochemical impedance spectrum characteristic parameters. The monomer characteristic parameter obtaining module is connected with the retired module primary screening module;
the characteristic data processing module is used for processing the characteristic parameters by preset logic to obtain normalized characteristic parameters, and is connected with the monomer characteristic parameter solving module;
and the recombination solving module is used for solving an objective function according to the normalized characteristic parameters by using a fuzzy C-means algorithm and a Lagrange multiplier algorithm to determine a single clustering center and the membership degree of each single clustering center of the battery single to obtain retired battery single clustering data so as to recombine the retired battery single, and is connected with the characteristic data processing module.
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| CN116780002A (en) * | 2023-08-21 | 2023-09-19 | 合肥工业大学 | Retired battery module reorganization method, system, equipment and storage medium |
| CN117054902A (en) * | 2023-09-06 | 2023-11-14 | 斯润天朗(合肥)科技有限公司 | Lithium battery voltage sequencing abnormality detection method and device, electronic equipment and medium |
| CN117276706A (en) * | 2023-10-20 | 2023-12-22 | 珠海中力新能源科技有限公司 | Battery management method, device, electronic equipment and storage medium |
| CN117410609A (en) * | 2023-12-15 | 2024-01-16 | 山西迪诺思新能源科技有限公司 | Echelon utilization method of waste power battery of new energy automobile |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116780002A (en) * | 2023-08-21 | 2023-09-19 | 合肥工业大学 | Retired battery module reorganization method, system, equipment and storage medium |
| CN117054902A (en) * | 2023-09-06 | 2023-11-14 | 斯润天朗(合肥)科技有限公司 | Lithium battery voltage sequencing abnormality detection method and device, electronic equipment and medium |
| CN117054902B (en) * | 2023-09-06 | 2024-03-19 | 斯润天朗(合肥)科技有限公司 | Lithium battery voltage sequencing abnormality detection method and device, electronic equipment and medium |
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| CN117276706B (en) * | 2023-10-20 | 2024-02-20 | 珠海中力新能源科技有限公司 | Battery management method, device, electronic equipment and storage medium |
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