CN117706403A - Intelligent rapid disassembly method and system for new energy lithium battery - Google Patents
Intelligent rapid disassembly method and system for new energy lithium battery Download PDFInfo
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- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 70
- 229910052744 lithium Inorganic materials 0.000 title claims abstract description 70
- 238000000034 method Methods 0.000 title claims abstract description 52
- 238000012544 monitoring process Methods 0.000 claims abstract description 109
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- 230000002457 bidirectional effect Effects 0.000 claims abstract description 29
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- 238000012163 sequencing technique Methods 0.000 claims description 2
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 abstract description 2
- 229910001416 lithium ion Inorganic materials 0.000 abstract description 2
- 230000008569 process Effects 0.000 description 13
- 238000004458 analytical method Methods 0.000 description 7
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- 239000000463 material Substances 0.000 description 3
- 238000004064 recycling Methods 0.000 description 3
- 238000005476 soldering Methods 0.000 description 3
- 238000012795 verification Methods 0.000 description 3
- 230000032677 cell aging Effects 0.000 description 2
- 238000004146 energy storage Methods 0.000 description 2
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Abstract
The invention relates to the technical field of lithium ion batteries, in particular to an intelligent rapid disassembly method and system for a new energy lithium battery, wherein the method comprises the following steps: acquiring power monitoring data and temperature monitoring data; acquiring a power time-frequency diagram of power monitoring data, and further acquiring a frequency offset coefficient and a peak bandwidth ratio; acquiring a power time-frequency disturbance coefficient according to the frequency deviation coefficient and the power time-frequency diagram, and further acquiring a power disturbed fundamental wave deviation factor; acquiring a battery cell power aging judgment factor according to the power disturbed fundamental wave offset factor, further acquiring a power judgment feature vector, and acquiring a temperature judgment feature vector; acquiring a bidirectional aging evaluation index of the battery cell according to the power judgment feature vector and the temperature judgment feature vector; and classifying and judging the disassembly mode of the battery cells according to the bidirectional aging evaluation index of the battery cells. The invention aims to solve the problem that the service condition of the lithium battery cannot be clarified due to misjudgment caused by the influence of uneven energy density and monitoring errors in the battery.
Description
Technical Field
The invention relates to the technical field of lithium ion batteries, in particular to an intelligent rapid disassembly method and system for a new energy lithium battery.
Background
The new energy lithium battery is an advanced battery technology and is widely applied to electric automobiles and energy storage systems. Compared with the traditional lead-acid battery, the lithium battery has higher energy density, more charge and discharge times and lighter weight, and can be used as one of main driving forces for energy storage and movement. The method is an important environmental protection measure aiming at the recovery of new energy batteries, aims at effectively recovering and treating waste lithium batteries, reduces the influence on the environment, and realizes the reutilization of valuable materials in the batteries.
The lithium battery in use at present often adopts a firm shell wrapping mode, so that the lithium battery can be well protected in the use process, but the firm shell improves the recycling difficulty of the lithium battery. And the lithium battery recovery is required to be subjected to validity test, and the main purpose is to ensure that the waste batteries are safely and efficiently treated and valuable materials are effectively recovered. At present, when the lithium battery is recovered, a mode of single monitoring or multiple monitoring and average value obtaining is adopted, misjudgment is easily caused by the influence of non-uniform energy density and monitoring errors in the battery, the service condition of the lithium battery cannot be clarified, deviation occurs in judgment of the recovery condition of the lithium battery, and the recovery and the recycling of the lithium battery are not facilitated. Aiming at the problems, the invention provides an intelligent rapid disassembly method and system for a new energy lithium battery, which can realize effective inspection of the service condition of the lithium battery.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide an intelligent rapid disassembly method and system for a new energy lithium battery, and the adopted technical scheme is as follows:
in a first aspect, an embodiment of the present invention provides an intelligent and rapid disassembly method for a new energy lithium battery, where the method includes the following steps:
acquiring lithium battery power monitoring data and temperature monitoring data;
acquiring a power time-frequency diagram of power monitoring data; acquiring a frequency offset coefficient and a peak bandwidth ratio according to a power time-frequency diagram; acquiring a power time-frequency disturbance coefficient according to the frequency offset coefficient and the power time-frequency diagram; acquiring a power disturbed fundamental wave offset factor according to the power time-frequency disturbance coefficient; acquiring a battery cell power aging judgment factor according to the power disturbed fundamental wave offset factor; acquiring a power judgment feature vector according to the battery cell power aging judgment factor, and acquiring a temperature judgment feature vector; acquiring a bidirectional aging evaluation index of the battery cell according to the power judgment feature vector and the temperature judgment feature vector;
and classifying and judging the disassembly mode of the battery cells according to the bidirectional aging evaluation index of the battery cells.
Further, the acquiring the power time-frequency diagram of the power monitoring data includes:
and clustering and dividing the power monitoring data in the time window by using a time sequence clustering algorithm to obtain time clusters, and processing the power monitoring data in each time cluster in the charging period by using wavelet transformation to obtain a power time-frequency diagram of each time cluster.
Further, the obtaining the frequency offset coefficient and the peak bandwidth ratio includes:
collecting maximum values and maximum values in energy values corresponding to all frequencies of power monitoring data corresponding to an ith sampling moment in a kth time cluster in a power time-frequency diagram, calculating a difference value of the maximum values and the maximum values as a first difference value, calculating a difference value of a frequency value corresponding to the maximum value and a frequency value corresponding to the maximum values as a second difference value, and calculating a sum value of absolute values of ratio values of all the first difference values and the second difference values as a frequency offset coefficient of the ith sampling moment in the kth time cluster;
and collecting bandwidths corresponding to the maximum values, calculating the ratio of the maximum values to the bandwidths, and taking the sum of all the ratio values as the peak bandwidth ratio of the ith sampling moment in the kth time cluster.
Further, the obtaining the power time-frequency disturbance coefficient includes:
obtaining the maximum value number in the energy values corresponding to all frequencies of the power monitoring data at the ith sampling moment in the kth time cluster, calculating the sum value of the frequency offset coefficient and the peak bandwidth ratio of the ith sampling moment in the kth time cluster, and calculating the ratio of the maximum value number to the sum value as the power time-frequency disturbance coefficient of the ith sampling moment in the kth time cluster.
Further, the acquiring the power disturbed fundamental offset factor includes:
calculating the absolute value of the difference value of the power time-frequency disturbance coefficient of the adjacent sampling time in the kth time cluster as a first absolute value of the difference value, and calculating the absolute value of the difference value of the fundamental wave frequency of the power monitoring data of the adjacent sampling time in the kth time cluster as a second absolute value of the difference value, wherein the fundamental wave frequency is the frequency corresponding to the energy maximum value of the power monitoring data in the power time-frequency diagram; and obtaining a calculation result of an exponential function taking a natural constant as a base and taking a second difference absolute value as an exponent, calculating products of the calculation result and the first difference absolute value at each adjacent sampling moment, and taking the sum of all the products as a power disturbed fundamental wave offset factor in a kth time cluster.
Further, the obtaining the battery cell power aging determination factor includes:
and forming a time-frequency matrix by arranging energy values in a power time-frequency diagram of the power monitoring data in the kth time cluster according to positions in the power time-frequency diagram, calculating norms of products of the time-frequency matrix of the kth time cluster and time-frequency matrices of other time clusters in the same time window, calculating sum values of all norms in the time window, calculating products of power disturbed fundamental wave offset factors in the kth time cluster and the number of the time clusters in the time window, and calculating the ratio of the products to the sum values as a battery cell power aging judgment factor in the kth time cluster.
Further, the obtaining the power determination feature vector according to the battery cell power aging determination factor includes:
and sequencing the battery cell power aging judgment factors of all the time clusters in the time window according to a time sequence to obtain a power judgment feature vector.
Further, the obtaining the bidirectional aging evaluation index of the battery cell includes:
calculating the average value of cosine similarity of the power judging feature vector and the temperature judging feature vector of all time windows in the charging period, fitting all power monitoring data in the charging period to obtain a charging power curve, calculating the integral of the power charging curve in the charging period, calculating the absolute value of the difference value of the preset theoretical capacitance and the average value, and calculating the ratio of the absolute value of the difference value and the integral result as a bidirectional aging evaluation index of the battery cell in the charging period.
Further, the classifying and judging the disassembly mode of the battery cell according to the bidirectional aging evaluation index of the battery cell comprises the following steps:
when the bidirectional aging evaluation index of the battery cell is larger than or equal to a preset aging threshold, the disassembly mode of the lithium battery is violent disassembly; and when the bidirectional aging evaluation index of the battery cell is smaller than a preset aging threshold, the disassembly mode of the lithium battery is electrolyte corrosion disassembly.
In a second aspect, an embodiment of the present invention further provides an intelligent rapid disassembly system for a new energy lithium battery, where the system includes a memory, a processor, and a computer program stored in the memory and running on the processor, where the processor implements the steps of any one of the methods described above when executing the computer program.
The invention has at least the following beneficial effects:
according to the invention, the new energy lithium battery is disassembled according to standard specification steps, validity test is carried out on the battery core, battery core monitoring data is obtained through power monitoring data and temperature monitoring data of the battery core in the charging process, time-frequency analysis is carried out on signals, time-frequency disturbance coefficients and disturbed fundamental wave deviation coefficients are obtained through analysis of differences in time clusters, finally a power aging judgment factor and a temperature aging judgment factor are obtained, a battery core bidirectional aging evaluation index is obtained through power and temperature bidirectional verification, and finally a specific recovery processing mode of the battery core is judged through a neural network. Compared with the traditional method for only monitoring the charging time length and carrying out validity test, the method can acquire the aging state of the battery cell more accurately by carrying out time-frequency analysis on the power and temperature monitoring data, ensures the accuracy of a judgment result by carrying out bidirectional verification on the power and the temperature, and finally improves the accuracy and completeness of the battery cell by setting an aging threshold to judge the disassembling mode of the battery cell, thereby solving the problem that the service condition of the lithium battery cannot be clarified due to misjudgment caused by the influence of uneven energy density and monitoring error in the battery, and achieving the purpose of reducing the environmental pollution of the lithium battery while maximizing the recycling of resources.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a step flowchart of an intelligent rapid disassembly method for a new energy lithium battery according to an embodiment of the present invention;
FIG. 2 is a battery disassembly flow chart;
fig. 3 is a schematic diagram of a charging phase.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following is a detailed description of specific implementation, structure, characteristics and effects of the intelligent rapid disassembly method and system for the new energy lithium battery according to the invention in combination with the accompanying drawings and the preferred embodiment. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, 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 belongs.
The invention provides a method and a system for intelligent and rapid disassembly of a new energy lithium battery, which are specifically described below with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of steps of an intelligent rapid disassembly method for a new energy lithium battery according to an embodiment of the invention is shown, the method includes the following steps:
and S001, dismantling the new energy lithium battery according to the standard dismantling step, and monitoring the lithium battery to obtain battery cell monitoring data.
The lithium battery occupies a large market share by virtue of the advantages of high energy density and light weight, but the performance of the battery gradually decreases with the increase of the service time and the service times of the lithium battery, mainly each time the battery is charged and discharged by internal chemical reaction, and the positive and negative electrode materials of the battery gradually degrade with the increase of the charge and discharge times to cause the battery to age. The operation of safety, reasonability and standardization is required in the process of disassembling the lithium battery, and certain potential safety hazards exist because harmful substances exist in the lithium battery.
In this embodiment, a new energy battery lithium battery is taken as an example, and a specific disassembly step is shown in fig. 2.
Since the interior of a lithium battery contains harmful substances, there may be a certain safety risk, and thus, disassembly of the lithium battery requires a strict process. Before the lithium battery is disassembled, the energy supply switch of the lithium battery in the new energy automobile needs to be disconnected, and the operation is carried out in an open and safe environment. The specific disassembly steps are as follows:
and (3) disassembling the shell: the battery shell needs to be carefully opened by using a professional disassembly tool, the shell of the lithium battery is usually made of metal and plastic, and the lithium battery shell is required to be completely detached by slowly heating through a hot air gun for some lithium batteries which are bonded with the shell through glue bonding.
Separation by a membrane: after the new energy lithium battery is disassembled, a diaphragm on the battery can be seen, and the diaphragm is mainly made of a plastic film and has the main effects of dividing the anode and the cathode of the lithium battery and preventing the short circuit of a battery core. The anti-static glove needs to be worn, the separator is carefully removed, and care needs to be taken that the process cannot damage the internal structure of the battery.
Taking out the battery cell: when the separator is removed, the main body of the lithium battery, namely the battery cells, is visible, the battery cells are usually in a stacked sheet structure and are often nested in a battery shell, the bottom of the battery cells is connected with a base of the shell, and therefore, the battery cells are required to be carefully separated from the base to prevent the outer surface of the battery cells from being damaged.
And (3) cell separation: for the lithium battery of the new energy automobile, the battery cell is generally composed of a battery cell group, the battery cells are connected through soldering tin, the battery cells are closely connected together to form the battery cell group together, the recovery mode is determined according to the use degree of the battery cells, the battery cells are required to be separated, the soldering tin of the battery cells is melted through a hot air gun in a slow heating mode, residual soldering tin is removed through a tin suction gun, and the whole cleanness of the outer surface of the battery cells is kept.
In the use process of a new energy automobile, energy is provided for the automobile through discharging of a lithium battery, and due to the influence of the use habit of the automobile, different battery core use conditions are possibly inconsistent, so that different recovery modes are adopted for different aging degrees of the battery core, and therefore the battery core needs to be subjected to validity test.
In this embodiment, the validity of the battery cell is monitored by performing charge-discharge test on a single battery cell, so that when the battery cell is charged, the charging power of the battery cell is obtained by the power sensor and is recorded as P t The method comprises the steps of carrying out a first treatment on the surface of the The charging temperature of the battery is obtained by a temperature sensor and is recorded as W t . In order to obtain the data of the battery cell more accurately, the embodiment sets the sampling interval of the sensor to 5ms. In addition, due to the charging power P of the battery cell t And a charging temperature W t The data size and unit of (a) are not uniform, thereby performing a normalization operation on the data.
The battery core of the new energy lithium battery is obtained through a standard disassembly step, and the power monitoring data and the temperature monitoring data of the battery core are obtained through charging the battery core.
Step S002, time-frequency analysis is carried out through the monitoring signals to obtain time-frequency signals, instantaneous power of the power monitoring data at a single sampling moment is used for obtaining a power time-frequency disturbance coefficient, fundamental wave differences at all sampling moments in a time cluster are combined to obtain power disturbed fundamental wave offset factors, a battery cell power aging judgment factor is obtained aiming at the power monitoring data time-frequency matrix and the offset factors of all the time clusters in a time window, the same processing mode is adopted for the temperature monitoring data, and a battery cell bidirectional aging evaluation index is obtained by combining the battery cell capacitance.
Next, the validity of the battery cell needs to be checked according to the monitoring data of the battery cell, and the specific steps are as follows:
the monitoring data of the charging of the battery cell is obtained in step S001, and the embodiment refers to a charging period from the start of charging to the end of charging. The charging of the battery cells in one charging cycle is mainly divided into three phases: the constant flow voltage limiting stage, the voltage limiting and constant flow stage and the trickle floating charging stage are shown in fig. 3. The battery cells have different performances in power and temperature in different charging stages, so that the battery cells need to be effectively analyzed by combining monitoring data of the battery cells in a charging period.
In order to acquire real-time charging data, the sampling interval is set to be 5ms, so that a larger data volume can be acquired in one charging period, and the analysis of the cell monitoring data is not facilitated. Therefore, according to the charging stage of the battery cell, each charging period is divided into three time windows, namely a constant-current voltage-limiting stage window, a voltage-limiting constant-current stage window and a trickle floating charging stage window.
The monitored data within the time window is analyzed. In the actual charging process, certain fluctuation occurs in the power monitoring data, so the embodiment clusters the sequence in the time window into a time cluster through a time sequence clustering DTC model, namely clusters the time window into N τ Time clusters. Because the DTC model is established as a well-known technique of time-series clustering, the embodiment is not described in detail.
In theory, the temperature monitoring data is similar to the power monitoring data in change, so that after the power monitoring data is divided according to the time clusters, the temperature monitoring data is divided in the same time cluster division mode, and the two monitoring data are in the same analysis mode.
The analysis is performed below using power monitoring data as an example.
In order to better acquire the time-frequency characteristic of the power monitoring data, the embodiment uses wavelet transformation to process the power monitoring data in the time cluster, and the input of the wavelet transformation is the power monitoring dataThe output of the wavelet transform is a power time-frequency diagram F representing the set of power monitoring data of the kth time cluster k Wherein, power time-frequency diagram F k The abscissa of (2) represents time information of the power monitoring data, the ordinate represents frequency information of the power monitoring data, and the dimension is N f ×N k Wherein N is f Representing power time-frequency diagram F k Frequency scale of middle ordinate, N k Representing power time-frequency diagram F k Time scale on the abscissa.
In power time-frequency diagram F k Each coordinate point comprises the energy value of the corresponding frequency of the power monitoring data at a certain time point, namely the power monitoring data at the ith sampling moment has a plurality of frequencies, and the frequency interval is set asIn power time-frequency diagram F k Energy values corresponding to all frequencies of the power monitoring data in the frequency interval of the power monitoring data in the ith sampling moment can be obtained>
Power time frequency diagram F k Frequency information contained in the power monitoring data at a single sampling instant can be acquired. The calculation process of wavelet transformation is a well-known technology and will not be described in detail herein.
Thereby combining the power frequency diagram F k Acquiring a power time-frequency disturbance coefficient:
in the method, in the process of the invention,a power time-frequency disturbance coefficient representing the ith sampling moment of the power monitoring data in the kth time cluster,/-, and>representing the number of maxima in the energy values corresponding to all frequencies of the power monitoring data at the ith sampling moment in the kth time cluster,/for>Peak bandwidth ratio, +.f, representing the i-th sampling instant of the power monitoring data in the k-th time cluster>Frequency offset coefficient representing the ith sampling instant of the power monitoring data in the kth time cluster,/for the power monitoring data>Representing a power frequency diagram F k The mth maximum value in the energy values corresponding to all frequencies of the power monitoring data corresponding to the ith sampling moment,/for the power monitoring data>A 3db bandwidth of the frequency corresponding to the m-th maximum value among the energy values corresponding to all frequencies of the power monitoring data representing the i-th sampling time,representing the ith acquisition in the kth time clusterMaximum value of energy values corresponding to all frequencies of the power monitoring data at the sampling time is used as fundamental wave energy value omega 0 And omega m The fundamental frequency and the mth frequency component value of the power monitoring data at the ith sampling time are respectively represented, wherein the fundamental frequency is a frequency value corresponding to the fundamental energy value, and the mth frequency component value is a frequency value corresponding to the mth maximum value in the energy values corresponding to all frequencies of the power monitoring data at the ith sampling time.
Formula logic: the theoretical power monitoring data should be maintained at a stable value in each time window as shown in fig. 3, but due to aging attenuation of the cells, attenuation fluctuation of frequency occurs, and for normal cells, only small fluctuation occurs, i.e. the energy of the power monitoring data is concentrated in the fundamental wave signal at a single sampling moment, and the peak bandwidth ratio is obtainedLarger, without influence of abnormal frequency components, whereby the frequency offset coefficient +.>The number of extreme points is greater only at the fundamental wave, thus +.>The value of (2) is smaller, and finally the power time-frequency disturbance coefficient +.>The number of (2) is small. Conversely, when the cell is severely aged, the power monitoring data has larger fluctuation, and redundant frequency components are generated, so that +.>Is increased.
Power time-frequency disturbance coefficientTime-frequency wave representing power monitoring data at single sampling momentDynamic conditions, there may be some variation in the fluctuation of the power monitoring data within a single time cluster, thereby constructing a power disturbed fundamental wave offset factor between sampling moments for a single time cluster:
in delta k Representing a power disturbed fundamental offset factor for a kth time cluster within the time window; n (N) k Representing the number of samples of the kth time cluster power monitoring data,and->Respectively representing the power time-frequency disturbance coefficients of the ith and the (i+1) th sampling moments of the power monitoring data in the kth time cluster,/for>And->The fundamental frequencies of the ith and the (i+1) th power monitoring data are represented, respectively.
Formula logic: in the ideal case, the charging power of the battery cell can be maintained at a relatively stable value during the charging process, so that the fundamental frequency of the power monitoring data can be kept consistent at each sampling time, therebyThe value of the power is smaller, the power time-frequency disturbance coefficient of each sampling time is closer, and finally the power disturbed fundamental wave offset factor delta is caused k Is reduced. In contrast, when the battery cell is seriously aged and damaged, the charging power can be greatly different at each sampling time, so that delta is obtained k Is increased.
Thus, the power monitoring can be performed according to each sampling moment in the time clusterThe change of the measured data obtains the power disturbed fundamental wave offset factor, and the larger the fluctuation of the power monitoring data is, the corresponding power disturbed fundamental wave offset factor delta is k The larger the value of (c) is, the difference of power monitoring data among each time cluster in the same time window is reflected, and delta is calculated k Analyzing the instantaneous frequency signal of the power monitoring data at a single sampling instant loses a portion of the frequency resolution. Thus combining power time-frequency diagram F of power monitoring data in time cluster k And a power disturbed fundamental offset factor delta k Obtaining a battery cell power aging judgment factor:
wherein ρ is k A battery cell power aging determination factor representing a kth time cluster within a time window; delta k A power disturbed fundamental offset factor, N, representing the kth time cluster within the time window τ Representing the number of time clusters divided in a time window, F k And F n Time-frequency matrix representing kth and nth time clusters of power monitoring data, respectively, () T Representing the transpose of the matrix, II F Representing the F-norm of the matrix. The method for calculating the norm is a well-known technique and will not be described in detail herein. The time-frequency matrix is formed by arranging energy values in a power time-frequency diagram of the power monitoring data in the time cluster according to positions in the power time-frequency diagram.
Formula logic: in an ideal case, the fluctuation change of the power monitoring data of each time cluster is consistent, so that the obtained power disturbed fundamental wave offset factor is smaller, and in addition, the correlation of the time-frequency matrix of the obtained power monitoring data is higher, namely the II (F k ) T ×F n ‖ F The value of (2) is larger, and finally the battery cell power aging judgment factor rho is obtained k The value of (2) is small. In contrast, when the battery cell is seriously aged, the fluctuation changes of the power monitoring data in different time clusters in the time window have larger difference, and finally the rho is caused k Is increased.
When all time clusters in the time window are traversedObtaining the battery cell power aging judgment factor rho corresponding to each cluster k Thereby jointly constructing the power determination feature vector for the window, usingAnd (3) representing.
In addition, the temperature monitoring data are analyzed in the same way, so that corresponding temperature judgment characteristic vectors can be obtained by usingAnd (3) representing.
In theory, the temperature monitoring data and the power monitoring data have similar change trend, and the battery cell generates heat more in the quick charging stage of the battery cell corresponding to the constant current voltage limiting stage, and the value of the battery cell temperature monitoring data is larger; the constant voltage current limiting stage corresponds to the stable charging of the battery cell, and the charging power is correspondingly reduced at the moment, so that the temperature value of the battery cell is correspondingly reduced; the trickle floating charge is used for protecting the battery core and fully filling the battery core, and the charging power is lower at the moment, and the value corresponding to the temperature monitoring data is lower.
In order to aged battery cells, the charging power can be greatly fluctuated, and in addition, the internal resistance of the battery cells is increased and the heating value of the battery cells is also increased due to the aging of materials in the battery cells. Meanwhile, the temperature monitoring data of each time cluster can be subjected to power monitoring data differential fluctuation, and the larger fluctuation difference is, the more serious the aging of the battery cell is, so that the bidirectional aging evaluation index of the battery cell is obtained by combining the charging monitoring data of the battery cell in a charging period:
in phi, phi T Representing a bidirectional aging evaluation index of the battery cell obtained in a single charging period;mean value of cosine similarity of power judgment feature vector and temperature judgment feature vector in all time windows in charging period, deltaQ represents theoretical capacitance of battery cell, Q T Representing the actual cell capacitance during the charging period +.>And->Representing a power decision feature vector and a temperature decision feature vector in a τ -th time window in a charging period, a>And fitting the sampled power monitoring data in the charging period by using a least square method to obtain a charging power curve, wherein T represents the ending time of the charging period. The fitting process of the least square method is a known technique, and will not be described in detail herein.
Formula logic: when the battery cell is seriously aged, the fluctuation change difference of the power monitoring data and the temperature monitoring data in the time window is larger, and the obtained power temperature bidirectional verification coefficient is obtainedThe value of (2) is small. The larger the difference between the actual capacitance and the theoretical capacitance of the outer core, i.e., |DeltaQ-Q T The larger the value of I is, the bidirectional aging evaluation index phi of the battery cell is finally obtained T The greater the value of (2). Conversely, when the battery cell is light in aging or the battery cell is not aged, the battery cell bidirectional aging evaluation index phi is obtained T The smaller the value of (2).
And S003, judging the decomposition mode of the battery cell according to the battery cell bidirectional aging evaluation index, and realizing the intelligent rapid disassembly method of the lithium battery.
According to step S002, the bidirectional aging evaluation index phi of the battery cell can be obtained T ，Φ T The larger the value of the battery cell is, the more serious the battery cell is aged, and the decomposition mode of the battery cell needs to be judged according to the specific use condition of the battery cell, so that the aim of rapidly disassembling the lithium battery is fulfilled. Thereby for phi T And (5) performing classification judgment:
wherein omega represents the decomposition mode of the current battery cell, O B The current disassembly mode of the battery cell is violent disassembly, O Y The current disassembly mode of the battery cell is electrolyte corrosion disassembly, theta Φ Representing an aging threshold value, setting θ based on empirical values Φ ＝3。
Bidirectional aging evaluation index phi of battery cell T The aging degree of the battery core can be reflected, and when the aging degree of the battery core is more than or equal to the aging threshold value theta Φ When the battery cell aging is severe, the energy density in the battery cell is low, so that the battery cell is rapidly disassembled through the pulverizer, and no safety accident occurs. When the aging degree of the battery cell is smaller than the aging threshold value theta Φ When the battery cell aging degree is light, the energy density in the battery cell is high, and at the moment, a large amount of heat is easily generated due to the oxidation reaction of electrolyte in the battery and air through violent disassembly, so that safety accidents such as fire disaster and the like can be caused, and therefore milder electrolyte is selected for corrosion disassembly.
The bidirectional aging evaluation index phi of the battery cell is obtained by analyzing the battery cell monitoring data in the charging period T Therefore, the disassembly mode of the battery cell is judged, the reasonable and safe disassembly of the battery cell is realized, and finally the aim of intelligent and rapid disassembly of the new energy lithium battery is fulfilled.
Based on the same inventive concept as the method, the embodiment of the invention also provides an intelligent rapid disassembly system for the new energy lithium battery, which comprises a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor realizes the steps of any one of the intelligent rapid disassembly methods for the new energy lithium battery when executing the computer program.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
The foregoing description of the preferred embodiments of the present invention is not intended to be limiting, but rather, any modifications, equivalents, improvements, etc. that fall within the principles of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. The intelligent rapid disassembly method for the new energy lithium battery is characterized by comprising the following steps of:
acquiring lithium battery power monitoring data and temperature monitoring data;
acquiring a power time-frequency diagram of power monitoring data; acquiring a frequency offset coefficient and a peak bandwidth ratio according to a power time-frequency diagram; acquiring a power time-frequency disturbance coefficient according to the frequency offset coefficient and the power time-frequency diagram; acquiring a power disturbed fundamental wave offset factor according to the power time-frequency disturbance coefficient; acquiring a battery cell power aging judgment factor according to the power disturbed fundamental wave offset factor; acquiring a power judgment feature vector according to the battery cell power aging judgment factor, and acquiring a temperature judgment feature vector; acquiring a bidirectional aging evaluation index of the battery cell according to the power judgment feature vector and the temperature judgment feature vector;
and classifying and judging the disassembly mode of the battery cells according to the bidirectional aging evaluation index of the battery cells.
2. The intelligent rapid disassembly method for the new energy lithium battery according to claim 1, wherein the obtaining of the power time-frequency diagram of the power monitoring data comprises the following steps:
and clustering and dividing the power monitoring data in the time window by using a time sequence clustering algorithm to obtain time clusters, and processing the power monitoring data in each time cluster in the charging period by using wavelet transformation to obtain a power time-frequency diagram of each time cluster.
3. The intelligent rapid disassembly method for new energy lithium battery as claimed in claim 1, wherein the obtaining of the frequency offset coefficient and the peak bandwidth ratio comprises:
collecting maximum values and maximum values in energy values corresponding to all frequencies of power monitoring data corresponding to an ith sampling moment in a kth time cluster in a power time-frequency diagram, calculating a difference value of the maximum values and the maximum values as a first difference value, calculating a difference value of a frequency value corresponding to the maximum value and a frequency value corresponding to the maximum values as a second difference value, and calculating a sum value of absolute values of ratio values of all the first difference values and the second difference values as a frequency offset coefficient of the ith sampling moment in the kth time cluster;
and collecting bandwidths corresponding to the maximum values, calculating the ratio of the maximum values to the bandwidths, and taking the sum of all the ratio values as the peak bandwidth ratio of the ith sampling moment in the kth time cluster.
4. The intelligent rapid disassembly method for the new energy lithium battery as claimed in claim 1, wherein the obtaining of the power time-frequency disturbance coefficient comprises the following steps:
obtaining the maximum value number in the energy values corresponding to all frequencies of the power monitoring data at the ith sampling moment in the kth time cluster, calculating the sum value of the frequency offset coefficient and the peak bandwidth ratio of the ith sampling moment in the kth time cluster, and calculating the ratio of the maximum value number to the sum value as the power time-frequency disturbance coefficient of the ith sampling moment in the kth time cluster.
5. The intelligent rapid disassembly method for the new energy lithium battery according to claim 1, wherein the obtaining of the power disturbed fundamental wave offset factor comprises the following steps:
calculating the absolute value of the difference value of the power time-frequency disturbance coefficient of the adjacent sampling time in the kth time cluster as a first absolute value of the difference value, and calculating the absolute value of the difference value of the fundamental wave frequency of the power monitoring data of the adjacent sampling time in the kth time cluster as a second absolute value of the difference value, wherein the fundamental wave frequency is the frequency corresponding to the energy maximum value of the power monitoring data in the power time-frequency diagram; and obtaining a calculation result of an exponential function taking a natural constant as a base and taking a second difference absolute value as an exponent, calculating products of the calculation result and the first difference absolute value at each adjacent sampling moment, and taking the sum of all the products as a power disturbed fundamental wave offset factor in a kth time cluster.
6. The method for intelligent and rapid disassembly of a new energy lithium battery according to claim 1, wherein the obtaining the battery cell power aging determination factor comprises:
and forming a time-frequency matrix by arranging energy values in a power time-frequency diagram of the power monitoring data in the kth time cluster according to positions in the power time-frequency diagram, calculating norms of products of the time-frequency matrix of the kth time cluster and time-frequency matrices of other time clusters in the same time window, calculating sum values of all norms in the time window, calculating products of power disturbed fundamental wave offset factors in the kth time cluster and the number of the time clusters in the time window, and calculating the ratio of the products to the sum values as a battery cell power aging judgment factor in the kth time cluster.
7. The method for intelligent and rapid disassembly of a new energy lithium battery according to claim 1, wherein the obtaining the power determination feature vector according to the battery cell power aging determination factor comprises:
and sequencing the battery cell power aging judgment factors of all the time clusters in the time window according to a time sequence to obtain a power judgment feature vector.
8. The method for intelligent and rapid disassembly of a new energy lithium battery according to claim 1, wherein the obtaining of the bidirectional aging evaluation index of the battery cell comprises the following steps:
calculating the average value of cosine similarity of the power judging feature vector and the temperature judging feature vector of all time windows in the charging period, fitting all power monitoring data in the charging period to obtain a charging power curve, calculating the integral of the power charging curve in the charging period, calculating the absolute value of the difference value of the preset theoretical capacitance and the average value, and calculating the ratio of the absolute value of the difference value and the integral result as a bidirectional aging evaluation index of the battery cell in the charging period.
9. The intelligent rapid disassembly method of the new energy lithium battery as claimed in claim 1, wherein the classifying and judging the disassembly mode of the battery cell according to the bidirectional aging evaluation index of the battery cell comprises the following steps:
when the bidirectional aging evaluation index of the battery cell is larger than or equal to a preset aging threshold, the disassembly mode of the lithium battery is violent disassembly; and when the bidirectional aging evaluation index of the battery cell is smaller than a preset aging threshold, the disassembly mode of the lithium battery is electrolyte corrosion disassembly.
10. An intelligent rapid disassembly system for a new energy lithium battery, comprising a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor realizes the steps of the intelligent rapid disassembly method for the new energy lithium battery according to any one of claims 1-9 when executing the computer program.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190381898A1 (en) * | 2018-06-15 | 2019-12-19 | Denso Corporation | Drive control apparatus for controlling vehicle drive system |
| US11340308B1 (en) * | 2021-04-27 | 2022-05-24 | Beta Air, Llc | System and method for state determination of a battery module configured for used in an electric vehicle |
| CN115389936A (en) * | 2022-08-24 | 2022-11-25 | 温州大学 | Online prediction method for continuous peak power capability of digital-analog hybrid driven lithium battery |
| CN115656830A (en) * | 2022-09-27 | 2023-01-31 | 内蒙古工业大学 | Low-temperature quick-charging lithium battery negative electrode lithium deposition detection method based on alternating-current impedance spectrum |
| CN116298929A (en) * | 2023-05-08 | 2023-06-23 | 合肥工业大学 | Battery pack cell consistency assessment method |
| CN116404186A (en) * | 2023-06-08 | 2023-07-07 | 西安黄河电子技术有限公司 | Power lithium-manganese battery production system |
| US20230258734A1 (en) * | 2021-11-15 | 2023-08-17 | Hei | System for estimating the state of health (soh) of battery, system and method for deriving parameters therefor |
| CN116973764A (en) * | 2023-07-18 | 2023-10-31 | 武汉动力电池再生技术有限公司 | Lithium battery consistency assessment method and device, electronic equipment and storage medium |
| CN117080619A (en) * | 2023-10-16 | 2023-11-17 | 上海北开科技发展有限公司 | New energy automobile battery heat dissipation management method and system |
| CN117233636A (en) * | 2023-09-21 | 2023-12-15 | 阿里云计算有限公司 | Method, device, equipment and storage medium for determining health state of lithium battery |
-
2023
- 2023-12-16 CN CN202311732315.0A patent/CN117706403B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190381898A1 (en) * | 2018-06-15 | 2019-12-19 | Denso Corporation | Drive control apparatus for controlling vehicle drive system |
| US11340308B1 (en) * | 2021-04-27 | 2022-05-24 | Beta Air, Llc | System and method for state determination of a battery module configured for used in an electric vehicle |
| US20230258734A1 (en) * | 2021-11-15 | 2023-08-17 | Hei | System for estimating the state of health (soh) of battery, system and method for deriving parameters therefor |
| CN115389936A (en) * | 2022-08-24 | 2022-11-25 | 温州大学 | Online prediction method for continuous peak power capability of digital-analog hybrid driven lithium battery |
| CN115656830A (en) * | 2022-09-27 | 2023-01-31 | 内蒙古工业大学 | Low-temperature quick-charging lithium battery negative electrode lithium deposition detection method based on alternating-current impedance spectrum |
| CN116298929A (en) * | 2023-05-08 | 2023-06-23 | 合肥工业大学 | Battery pack cell consistency assessment method |
| CN116404186A (en) * | 2023-06-08 | 2023-07-07 | 西安黄河电子技术有限公司 | Power lithium-manganese battery production system |
| CN116973764A (en) * | 2023-07-18 | 2023-10-31 | 武汉动力电池再生技术有限公司 | Lithium battery consistency assessment method and device, electronic equipment and storage medium |
| CN117233636A (en) * | 2023-09-21 | 2023-12-15 | 阿里云计算有限公司 | Method, device, equipment and storage medium for determining health state of lithium battery |
| CN117080619A (en) * | 2023-10-16 | 2023-11-17 | 上海北开科技发展有限公司 | New energy automobile battery heat dissipation management method and system |
Non-Patent Citations (2)
| Title |
|---|
| 阮一钊: "基于动态阻抗谱的锂离子电池健康状态评估方法研究", 中国优秀硕士学位论文全文数据库, no. 2, 15 February 2021 (2021-02-15) * |
| 陈佳: "基于机器学习的废旧动力电池安全拆解工艺路线优化方法研究", 中国优秀硕士学位论文全文数据库, no. 3, 15 March 2023 (2023-03-15) * |
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