CN109768340B - Method and device for estimating voltage inconsistency in battery discharge process - Google Patents
Method and device for estimating voltage inconsistency in battery discharge process Download PDFInfo
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Abstract
The invention provides a method and a device for estimating voltage inconsistency in a battery discharge process, wherein the method comprises the following steps: acquiring a probability distribution cloud chart of the discharge voltage of the battery changing along with the capacity before the battery is grouped; carrying out statistical analysis on the probability distribution cloud picture by using a Weibull probability model to obtain the variation trend of the three parameters along with the discharge process of the battery; and acquiring the variation rule of the voltage distribution discreteness and symmetry of different single batteries along with the discharge process according to the variation trend of the three parameters along with the discharge process of the batteries. The method can clearly describe the change process of the voltage distribution in the battery discharge interval by adopting a three-parameter Weibull probability model. Compared with the traditional inconsistency estimation method, the method can describe not only the discrete change process of the voltage, but also the symmetrical change process of the voltage, and the statistical result is used as one of the performance indexes of the battery pack, so that the establishment of a battery pack state estimation algorithm can be effectively guided, each single battery in the battery pack is ensured to be in a monitoring range, and the monitoring dead angle is avoided.
Description
Technical Field
The invention relates to the technical field of lithium ion batteries, in particular to a method and a device for estimating voltage inconsistency in a battery discharging process.
Background
In order to meet the requirements of high energy and high power for vehicles, the lithium ion batteries need to be connected in series and parallel in groups to be used for getting on the vehicle. However, the performance of the battery pack is lower than that of the unit cells due to the difference in performance between the unit cells, which not only affects the detection accuracy of the BMS but also shortens the life span of the battery pack or raises a safety problem. The problem of inconsistency after the single batteries are grouped is inevitable and only aggravated along with the circulation process. The most important manifestation of the inconsistency between the single cells in the battery pack is the inconsistency of voltage and capacity, which directly influences the accuracy of the BMS system on the battery state estimation; the operating loads of different batteries are different, and the inconsistency of the service lives of the batteries is directly influenced. Therefore, a full understanding of the evolution of the battery voltage inconsistency rule and the reasons behind it is the key to alleviate the battery inconsistency problem and the battery state estimation and life prediction problem caused by the inconsistency.
The SOC and SOH state estimation methods of the battery pack at present select the batteries with the maximum and minimum capacities as the basis, and directly convert the single battery state estimation model into the battery pack estimation model. The battery state can be accurately estimated in a short period in such a mode, but the actual working condition information of each battery cannot be captured, and the future state of each battery cannot be predicted.
The method completely depends on a Data-drive mathematical method, the accuracy of a battery simulation result can be ensured at the initial stage of battery use, but the method can predict the residual life of the battery after sufficient Data learning is carried out on a researched battery pack, and the learned parameters cannot be popularized to other batteries, namely the method has no universality. The neglected inconsistency information becomes an uncontrollable factor, so that the inconsistency evolution path of the battery pack in the future work deviates from an expected value, and the precision of the original state estimation method is not applicable. However, in order to reduce the amount of calculation, the battery pack monitoring of the actual BMS selects only two batteries having the largest and smallest capacities, and thus data of inconsistency of battery parameters cannot be acquired.
In the actual production and battery grouping stage, the inconsistency of the batteries is reduced by screening the battery capacity and the internal resistance, and the difference between the grouped single batteries can be reduced to a certain extent. The selection of the parameters can only ensure the consistency of the static parameters of the battery, but cannot ensure the consistency of the charging and discharging processes, namely the inconsistency of the voltage. However, the inconsistency of the voltage, although alleviated by the balancing module of the BMS, cannot be completely eliminated, and the inconsistency between the batteries is gradually increased as the performance of the batteries is degraded. Therefore, not only is there a need for the most possible exclusion of solutions to the problem of battery inconsistencies, but accurate monitoring and prediction is also needed.
At present, the state monitoring method of the battery pack and the research on the inconsistency problem of the grouped batteries are relatively independent. The problem of the inconsistency of the batteries caused by the production process can be solved to a certain extent by a screening method, but the problem of the inconsistency of the batteries after being grouped still lacks of comprehensive cognition. This will affect the long-term detection accuracy and condition control of the battery pack, making individual batteries become monitoring dead angles in the battery pack, unable to work under normal conditions, causing accelerated aging or safety problems.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a method and a device for estimating voltage inconsistency in a battery discharge process.
Specifically, the invention provides the following technical scheme:
in a first aspect, the present invention provides a method for estimating voltage inconsistency in a battery discharge process, including:
acquiring a probability distribution cloud chart of the discharge voltage of the battery changing along with the capacity before the battery is grouped;
carrying out statistical analysis on the probability distribution cloud picture by using a Weibull probability model to obtain the variation trend of the three parameters along with the discharge process of the battery;
and acquiring the variation rule of the voltage distribution discreteness and symmetry of different single batteries along with the discharge process according to the variation trend of the three parameters along with the discharge process of the batteries.
Further, the method further comprises:
and judging the similarity degree of the obtained variation rule of the voltage distribution discreteness of different single batteries along with the discharge process and the variation rule of the voltage distribution discreteness of different single batteries along with the discharge process according to the variation trend of the three parameters along with the battery discharge process, and obtaining the variation rule of the voltage distribution discreteness of different single batteries along with the discharge process in different discharge time periods by adopting a mode of obtaining the first derivative of the voltage to the capacity from the probability distribution cloud picture in the discharge time period when the similarity degree of the two reaches a preset standard.
Further, the method further comprises:
and obtaining a second derivative related to the voltage to the capacity of the probability distribution cloud picture, obtaining a change rule of the voltage distribution symmetry of different single batteries along with the discharge process according to the second derivative, judging the similarity degree of the obtained change rule of the voltage distribution symmetry of different single batteries along with the discharge process and the change rule of the voltage distribution symmetry of different single batteries along with the discharge process, which is obtained according to the change trend of three parameters along with the discharge process of the batteries, in different discharge time periods, and obtaining the change rule of the voltage distribution symmetry of different single batteries along with the discharge process by adopting a mode of obtaining the second derivative related to the voltage to the capacity of the probability distribution cloud picture in the discharge time period when the similarity degree of the two reaches a preset standard.
Further, the obtaining of the change rule of the voltage distribution symmetry of different single batteries along with the discharge process in the discharge time period when the similarity between the two reaches the preset standard by using a mode of solving a second derivative of the voltage to the capacity from a probability distribution cloud picture comprises:
and in the discharge time period when the similarity degree of the two reaches a preset standard, judging the concave-convex change condition of the voltage distribution symmetry of different single batteries along with the discharge change according to the sign of the second derivative value of the voltage to the capacity, which is obtained from the probability distribution cloud picture, so as to obtain the change rule of the voltage distribution symmetry of different single batteries along with the discharge process.
In a second aspect, the present invention further provides a device for estimating voltage inconsistency during battery discharge, including:
the acquisition module is used for acquiring a probability distribution curve of the discharge voltage of the battery changing along with the capacity before the battery is grouped;
the statistical analysis module is used for performing statistical analysis on the probability distribution cloud picture by using a Weibull probability model to obtain the variation trend of the three parameters along with the battery discharge process;
and the first estimation module is used for acquiring the variation rule of the voltage distribution discreteness and symmetry of different single batteries along with the discharge process according to the variation trend of the three parameters along with the discharge process of the batteries.
Further, the apparatus further comprises:
the second estimation module is used for solving a first derivative of the voltage to the capacity of the probability distribution cloud picture and acquiring a change rule of the voltage distribution discreteness of different single batteries along with a discharge process according to the first derivative;
the first judgment module is used for judging the similarity degree of the variation rule of the voltage distribution discreteness of different single batteries obtained by the second estimation module along with the discharge process and the variation rule of the voltage distribution discreteness of different single batteries obtained by the first estimation module according to the variation trend of the three parameters along with the discharge process of the batteries in different discharge time periods;
and the first selection module is used for selecting a mode of solving a first derivative related to the voltage to the capacity from the probability distribution cloud picture in the discharging time period when the similarity degree of the first judgment module and the second judgment module reaches a preset standard so as to obtain the change rule of the voltage distribution discreteness of different single batteries along with the discharging process.
Further, the apparatus further comprises:
the third estimation module is used for solving a second derivative related to the voltage to the capacity of the probability distribution cloud picture and acquiring a change rule of the voltage distribution symmetry of different single batteries along with a discharge process according to the second derivative;
the second judgment module is used for judging the similarity degree of the change rule of the voltage distribution symmetry of different single batteries obtained by the third estimation module along with the discharge process and the change rule of the voltage distribution symmetry of different single batteries obtained by the first estimation module according to the change trend of the three parameters along with the discharge process of the batteries in different discharge time periods;
and the second selection module is used for selecting a mode of solving a second derivative related to the voltage to the capacity from the probability distribution cloud picture in a discharge time period when the similarity degree of the two determined by the second judgment module reaches a preset standard so as to obtain a change rule of the voltage distribution symmetry of different single batteries along with the discharge process.
Further, the second selecting module is specifically configured to:
and in the discharge time period when the similarity degree of the two reaches a preset standard, judging the concave-convex change condition of the voltage distribution symmetry of different single batteries along with the discharge change according to the sign of the second derivative value of the voltage to the capacity, which is obtained from the probability distribution cloud picture, so as to obtain the change rule of the voltage distribution symmetry of different single batteries along with the discharge process.
In a third aspect, the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for estimating the voltage inconsistency during the battery discharge process according to the first aspect when executing the computer program.
In a fourth aspect, the present invention also provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for estimating a voltage inconsistency during a discharge process of a battery according to the first aspect.
According to the technical scheme, the method for estimating the voltage inconsistency in the battery discharging process comprises the following steps: acquiring a probability distribution cloud chart of the discharge voltage of the battery changing along with the capacity before the battery is grouped; carrying out statistical analysis on the probability distribution cloud picture by using a Weibull probability model to obtain the variation trend of the three parameters along with the discharge process of the battery; and acquiring the variation rule of the voltage distribution discreteness and symmetry of different single batteries along with the discharge process according to the variation trend of the three parameters along with the discharge process of the batteries. The method can clearly describe the change process of the voltage distribution in the battery discharge interval by adopting a three-parameter Weibull probability model. Compared with the traditional inconsistency estimation method, the method can describe not only the discrete change process of the voltage, but also the symmetrical change process of the voltage, and the statistical result is used as one of the performance indexes of the battery pack, so that the establishment of a battery pack state estimation algorithm can be effectively guided, each single battery in the battery pack is ensured to be in a monitoring range, and the monitoring dead angle is avoided. In addition, in other embodiments of the invention, the first-order derivative and the second-order derivative of the voltage obtained by the numerical derivation method with respect to the capacity replace the values a and b of the Weibull probability model, so that the voltage discreteness and symmetry can be directly obtained, the sampling cost and the test period of the battery are reduced, and the efficient and rapid prediction of the discharge voltage inconsistency is realized.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flowchart of a method for estimating voltage inconsistency during battery discharge according to an embodiment of the present invention;
FIG. 2 is a cloud diagram of simulated battery voltage probability distributions according to an embodiment of the present invention;
FIG. 3 is a cloud (X-Y view) of simulated battery voltage probability distributions according to an embodiment of the present invention;
FIG. 4 is a cloud (X-Z view) of a simulated battery voltage probability distribution provided by an embodiment of the present invention;
FIG. 5 is a Weibull probability model parameter curve provided by an embodiment of the present invention;
FIG. 6 shows a numerical derivative a' and a statistical estimate a according to an embodiment of the present inventionWeibullThe curve is compared with a schematic diagram;
FIG. 7 shows numerical derivation b' and statistical estimation b according to an embodiment of the present inventionWeibullThe curve is compared with a schematic diagram;
FIG. 8 is a schematic structural diagram of a device for estimating voltage inconsistency during battery discharge according to another embodiment of the present invention;
fig. 9 is a schematic structural diagram of an electronic device according to yet another embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a flowchart illustrating a method for estimating voltage inconsistency during battery discharge according to an embodiment of the present invention. As shown in fig. 1, the method for estimating voltage inconsistency in a battery discharge process according to this embodiment includes:
step 101: and obtaining a probability distribution cloud chart of the discharge voltage of the battery changing along with the capacity before the battery is grouped.
Step 102: and carrying out statistical analysis on the probability distribution cloud picture by using a Weibull probability model to obtain the variation trend of the three parameters along with the discharge process of the battery.
Step 103: and acquiring the variation rule of the voltage distribution discreteness and symmetry of different single batteries along with the discharge process according to the variation trend of the three parameters along with the discharge process of the batteries.
Known from the above technical solution, the method for estimating voltage inconsistency in the battery discharge process provided by this embodiment includes: acquiring a probability distribution cloud chart of the discharge voltage of the battery changing along with the capacity before the battery is grouped; carrying out statistical analysis on the probability distribution cloud picture by using a Weibull probability model to obtain the variation trend of the three parameters along with the discharge process of the battery; and acquiring the variation rule of the voltage distribution discreteness and symmetry of different single batteries along with the discharge process according to the variation trend of the three parameters along with the discharge process of the batteries. The three-parameter Weibull probability model can be used for clearly describing the change process of the voltage distribution in the battery discharge interval. Compared with the traditional inconsistency estimation method, the method can describe not only the discrete change process of the voltage, but also the symmetrical change process of the voltage, and the statistical result is used as one of the performance indexes of the battery pack, so that the establishment of a battery pack state estimation algorithm can be effectively guided, each single battery in the battery pack is ensured to be in a monitoring range, and the monitoring dead angle is avoided. In addition, in other embodiments of this embodiment, the first derivative and the second derivative of the voltage with respect to the capacity obtained by the numerical derivation method are used to replace the values a and b of the Weibull probability model, so that the voltage dispersion and symmetry can be directly obtained, thereby reducing the sampling cost and the test period of the battery, and realizing efficient and rapid prediction of the discharge voltage inconsistency.
Based on the above description of the embodiments, in an optional implementation, the method further includes:
and judging the similarity degree of the obtained variation rule of the voltage distribution discreteness of different single batteries along with the discharge process and the variation rule of the voltage distribution discreteness of different single batteries along with the discharge process according to the variation trend of the three parameters along with the battery discharge process, and obtaining the variation rule of the voltage distribution discreteness of different single batteries along with the discharge process in different discharge time periods by adopting a mode of obtaining the first derivative of the voltage to the capacity from the probability distribution cloud picture in the discharge time period when the similarity degree of the two reaches a preset standard.
In the embodiment, besides the change rule of the voltage distribution dispersion and the symmetry of different single batteries along with the discharge process is obtained by adopting a three-parameter Weibull statistical analysis mode, the reason for forming the change trend of the voltage inconsistency is analyzed by adopting a numerical derivation method, and the voltage dispersion is highly related to the first derivative of the voltage to the capacity, and the voltage symmetry is related to the second derivative of the voltage to the capacity. Therefore, the embodiment makes full use of the characteristic, the change process of the discharge voltage inconsistency caused by the capacity inconsistency is directly obtained by deriving the voltage curve, so that the change rule of the voltage distribution in the discharge process of the battery is predicted, the discharge voltage inconsistency analysis result obtained by using the derivation result is compared with the three-parameter Weibull statistical analysis result, and if the comparison result shows that the two are similar in a certain discharge time period (such as the later discharge period) (if the similarity reaches the preset standard of 80%), the estimation of the discharge voltage inconsistency can be obtained in the discharge time period by adopting the derivation method. It should be noted that, in the actual production and group matching process of the battery, the first derivative and the second derivative of the voltage with respect to the capacity are obtained by a numerical derivation method to replace the values a and b of the Weibull probability model, so that the voltage discreteness and symmetry can be directly obtained, the sampling cost and the test period of the battery are reduced, and the discharge voltage inconsistency can be efficiently and quickly predicted.
Based on the above description of the embodiments, in an optional implementation, the method further includes:
and obtaining a second derivative related to the voltage to the capacity of the probability distribution cloud picture, obtaining a change rule of the voltage distribution symmetry of different single batteries along with the discharge process according to the second derivative, judging the similarity degree of the obtained change rule of the voltage distribution symmetry of different single batteries along with the discharge process and the change rule of the voltage distribution symmetry of different single batteries along with the discharge process, which is obtained according to the change trend of three parameters along with the discharge process of the batteries, in different discharge time periods, and obtaining the change rule of the voltage distribution symmetry of different single batteries along with the discharge process by adopting a mode of obtaining the second derivative related to the voltage to the capacity of the probability distribution cloud picture in the discharge time period when the similarity degree of the two reaches a preset standard.
Based on the content of the foregoing embodiment, in an optional implementation manner, the obtaining a change rule of voltage distribution symmetry of different single batteries along with a discharge process in a manner of solving a second derivative of voltage with respect to capacity from a probability distribution cloud map in a discharge time period in which a similarity between the two reaches a preset standard includes:
and in the discharge time period when the similarity degree of the two reaches a preset standard, judging the concave-convex change condition of the voltage distribution symmetry of different single batteries along with the discharge change according to the sign of the second derivative value of the voltage to the capacity, which is obtained from the probability distribution cloud picture, so as to obtain the change rule of the voltage distribution symmetry of different single batteries along with the discharge process.
It should be noted that, for the voltage symmetry, since the voltage symmetry analysis result obtained by solving the second derivative value of the voltage with respect to the capacity from the probability distribution cloud chart is different from the voltage symmetry analysis result obtained by the three-parameter Weibull statistical analysis, b cannot be directly inferred from the second derivative valueWeibullThe value of (b) can be determined by the sign of the second derivative valueWeibullAnd performing qualitative speculation to further obtain the change rule of the voltage distribution symmetry of different single batteries along with the discharge process.
As can be seen from the above, in the embodiment, in order to improve the recognition of the inconsistency problem by the battery pack detection algorithm, ensure the long-term accuracy of the battery pack state estimation and life prediction algorithm, and simultaneously meet the requirement of the healthy working environment of each battery as much as possible, the battery discharge behavior before grouping is statistically analyzed, and the reliability of the statistical result is verified by using a numerical derivation method, so that the change process of the voltage inconsistency behavior in the battery discharge process is obtained, and the change process is used for predicting the voltage inconsistency of the batteries after grouping.
The method for estimating the voltage inconsistency during the battery discharge process according to the present embodiment will be described in detail with reference to the drawings and the principles.
The first step is as follows: probability distribution of voltage curve. The probability distribution of the discharge voltage of 100 batteries was made into a 4-dimensional image with the change of the discharge capacity, as shown in fig. 2-4, the four dimensions were respectively: the x axis is as follows: discharge capacity; y-axis normalized voltage; and a z-axis: a voltage; color axis: and (4) distributing the probability.
The second step is that: statistical analysis of voltage non-uniformity.
The Weibull probability model Probability Density Function (PDF) is shown in equation one below:
the function is determined by three parameters, wherein a is a size parameter, b is a shape parameter, and c is a position parameter;
three-parameter Weibull statistical analysis is carried out on the discharge voltage curve of 100 batteries, and the obtained variation trend of the three parameters along with the discharge process is shown in FIG. 5.
The third step: numerical derivation of voltage non-uniformity
According to the formula two V ═ V (SOC) voltage-capacity curve and the formula three
And deducing the inconsistent behavior change rule of the battery according to the relation between the capacity and the SOC. And respectively solving a first derivative and a second derivative related to the capacity of the voltage-capacity curve to obtain a variation rule of the discreteness and the symmetry of the voltage caused by the capacity. Wherein C represents the discharge capacity of the battery, CtRepresenting battery capacity, SOC representing battery state of charge; SOC (C)tAnd C) represents the state of charge of the battery as a function of capacity and discharged charge.
The relationship between the cell voltage dispersion and the capacity dispersion is as follows:
the relationship between the battery voltage symmetry and the discharge capacity is as follows:
let expressions four and five be a 'and b', respectively, and the calculated results of expressions four and five are compared with the statistical results, as shown in fig. 6 and 7.
And the a 'value and the b' value obtained by deriving the numerical value of the voltage curve are used for analyzing the influence of the dispersion and the concave-convex property of the voltage curve on the voltage distribution. Wherein, the calculated result shows better similarity with the trends of the value a and the value b of the Weibull probability model, and the value a' and the value aWeibullThe value curves are highly consistent in trend, and the values in the later discharging period are also highly consistent, so that a' can be used for the discreteness prediction of the voltage discreteness in the later discharging period. b' andbWeibullthe trends are quite similar, but the calculation results of the two methods have magnitude difference, so that b cannot be directly presumed from the b' valueWeibullSo that the irregularity of the function can be judged by the sign of the value of b', andWeibullqualitative speculation is made.
From the above description, the present embodiment can clearly describe the variation process of the voltage distribution characteristic in the battery discharge region by using the three-parameter Weibull probability model. Compared with the traditional inconsistency estimation method, the method can describe not only the discrete change process of the voltage, but also the symmetrical change process of the voltage. The statistical result is used as one of the performance indexes of the battery pack, so that the establishment of a battery pack state estimation algorithm can be effectively guided, each single battery in the battery pack is ensured to be in a monitoring range, and monitoring dead angles are avoided. In addition, in the actual production and group matching process of the batteries, the first-order derivative and the second-order derivative of the voltage obtained by the numerical derivation method are used for replacing the values a and b of the Weibull probability model, and the voltage discreteness and symmetry can be directly obtained, so that the sampling cost and the test period of the batteries are reduced, and the discharge voltage inconsistency is efficiently and quickly predicted.
Fig. 8 is a schematic structural diagram of a battery discharge process voltage inconsistency estimation apparatus according to another embodiment of the present invention. As shown in fig. 8, the present embodiment provides a battery discharge process voltage inconsistency estimation apparatus including: an acquisition module 21, a statistical analysis module 22 and a first estimation module 23, wherein:
the acquiring module 21 is configured to acquire a probability distribution cloud chart of the change of the battery discharge voltage with the capacity before the battery is grouped;
the statistical analysis module 22 is used for performing statistical analysis on the probability distribution cloud chart by using a Weibull probability model to obtain the variation trend of the three parameters along with the battery discharge process;
the first estimation module 23 is configured to obtain a variation rule of the voltage distribution discreteness and symmetry of different single batteries along with the discharge process according to a variation trend of the three parameters along with the discharge process of the battery.
Based on the content of the foregoing embodiment, in an optional implementation manner, the apparatus further includes:
the second estimation module is used for solving a first derivative of the voltage to the capacity of the probability distribution cloud picture and acquiring a change rule of the voltage distribution discreteness of different single batteries along with a discharge process according to the first derivative;
the first judgment module is used for judging the similarity degree of the variation rule of the voltage distribution discreteness of different single batteries obtained by the second estimation module along with the discharge process and the variation rule of the voltage distribution discreteness of different single batteries obtained by the first estimation module according to the variation trend of the three parameters along with the discharge process of the batteries in different discharge time periods;
and the first selection module is used for selecting a mode of solving a first derivative related to the voltage to the capacity from the probability distribution cloud picture in the discharging time period when the similarity degree of the first judgment module and the second judgment module reaches a preset standard so as to obtain the change rule of the voltage distribution discreteness of different single batteries along with the discharging process.
Based on the content of the foregoing embodiment, in an optional implementation manner, the apparatus further includes:
the third estimation module is used for solving a second derivative related to the voltage to the capacity of the probability distribution cloud picture and acquiring a change rule of the voltage distribution symmetry of different single batteries along with a discharge process according to the second derivative;
the second judgment module is used for judging the similarity degree of the change rule of the voltage distribution symmetry of different single batteries obtained by the third estimation module along with the discharge process and the change rule of the voltage distribution symmetry of different single batteries obtained by the first estimation module according to the change trend of the three parameters along with the discharge process of the batteries in different discharge time periods;
and the second selection module is used for selecting a mode of solving a second derivative related to the voltage to the capacity from the probability distribution cloud picture in a discharge time period when the similarity degree of the two determined by the second judgment module reaches a preset standard so as to obtain a change rule of the voltage distribution symmetry of different single batteries along with the discharge process.
Based on the content of the foregoing embodiment, in an optional implementation manner, the second selecting module is specifically configured to:
and in the discharge time period when the similarity degree of the two reaches a preset standard, judging the concave-convex change condition of the voltage distribution symmetry of different single batteries along with the discharge change according to the sign of the second derivative value of the voltage to the capacity, which is obtained from the probability distribution cloud picture, so as to obtain the change rule of the voltage distribution symmetry of different single batteries along with the discharge process.
Since the device for estimating the voltage inconsistency in the battery discharging process provided by the embodiment can be used for executing the method for estimating the voltage inconsistency in the battery discharging process in the above embodiment, and the operation principle and the beneficial effects are similar, detailed descriptions are omitted here, and specific contents can be referred to the description of the above embodiment.
Fig. 9 shows a schematic structural diagram of an electronic device according to still another embodiment of the present invention. As shown in fig. 9, the electronic device provided in this embodiment specifically includes the following contents: a processor 601, a memory 602, a communication interface 603, and a bus 604;
the processor 601, the memory 602 and the communication interface 603 complete mutual communication through the bus 604; the communication interface 603 is used for realizing information transmission among related devices such as modeling software, an intelligent manufacturing equipment module library and the like;
the processor 601 is configured to call the computer program in the memory 602, and the processor implements all the steps in the first embodiment when executing the computer program, for example, the processor implements the following steps when executing the computer program:
step 101: and obtaining a probability distribution cloud chart of the discharge voltage of the battery changing along with the capacity before the battery is grouped.
Step 102: and carrying out statistical analysis on the probability distribution cloud picture by using a Weibull probability model to obtain the variation trend of the three parameters along with the discharge process of the battery.
Step 103: and acquiring the variation rule of the voltage distribution discreteness and symmetry of different single batteries along with the discharge process according to the variation trend of the three parameters along with the discharge process of the batteries.
In addition, the logic instructions in the memory may be implemented in the form of software functional units and may be stored in a computer readable storage medium when sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Based on the same inventive concept, another embodiment of the present invention provides a computer-readable storage medium, having a computer program stored thereon, where the computer program is executed by a processor to implement all the steps of the first embodiment, for example, when the processor executes the computer program, the processor implements the following steps:
step 101: and obtaining a probability distribution cloud chart of the discharge voltage of the battery changing along with the capacity before the battery is grouped.
Step 102: and carrying out statistical analysis on the probability distribution cloud picture by using a Weibull probability model to obtain the variation trend of the three parameters along with the discharge process of the battery.
Step 103: and acquiring the variation rule of the voltage distribution discreteness and symmetry of different single batteries along with the discharge process according to the variation trend of the three parameters along with the discharge process of the batteries.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
It should be noted that the above-described device embodiments are merely illustrative, where the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present 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 solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for estimating voltage inconsistency in a battery discharge process is characterized by comprising the following steps:
acquiring a probability distribution cloud chart of the discharge voltage of the battery changing along with the capacity before the battery is grouped;
carrying out statistical analysis on the probability distribution cloud picture by using a Weibull probability model to obtain the variation trend of the three parameters along with the discharge process of the battery;
acquiring the variation rule of the voltage distribution discreteness and symmetry of different single batteries along with the discharge process according to the variation trend of the three parameters along with the discharge process of the batteries;
wherein, the probability distribution cloud chart is subjected to statistical analysis by using a Weibull probability model, and the variation trend of the three parameters along with the battery discharge process is obtained; and acquiring the variation rule of the voltage distribution discreteness and symmetry of different single batteries along with the discharge process according to the variation trend of the three parameters along with the discharge process of the batteries, and specifically comprising the following steps:
the first step is as follows: probability distribution of voltage curves; the probability distribution of the discharge voltage of 100 batteries is made into a 4-dimensional image along with the change of the discharge capacity, and the four dimensions are respectively as follows: the x axis is as follows: discharge capacity; y-axis normalized voltage; and a z-axis: a voltage; color axis: distributing probability;
the second step is that: statistical analysis of voltage inconsistency;
the Weibull probability model probability density function is shown in equation one below:
the function is determined by three parameters, wherein a is a size parameter, b is a shape parameter, and c is a position parameter;
carrying out three-parameter Weibull statistical analysis on the discharge voltage curve of 100 batteries to obtain the variation trend of the three parameters along with the discharge process;
the third step: numerical derivation of voltage non-uniformity
According to the formula two V ═ V (SOC) voltage-capacity curve and the formula three
Deducing a change rule of inconsistent behavior of the battery according to the relation between the capacity and the SOC, and respectively solving a first derivative and a second derivative related to the capacity of a voltage-capacity curve to obtain a change rule of discreteness and symmetry of the voltage caused by the capacity, wherein C represents the discharge capacity of the battery, and C represents the discharge capacity of the batterytRepresenting battery capacity, SOC representing battery state of charge; SOC (C)tC) represents the state of charge of the battery as a function of capacity and discharge capacity;
the relationship between the cell voltage dispersion and the capacity dispersion is as follows:
the relationship between the battery voltage symmetry and the discharge capacity is as follows:
respectively setting the formula IV and the formula V as a 'and b', and comparing the calculation results of the formula IV and the formula V with the statistical results;
and the a 'value and the b' value obtained by deriving the numerical value of the voltage curve are used for analyzing the influence of the dispersion and the concave-convex property of the voltage curve on the voltage distribution.
2. The method of claim 1, further comprising:
and judging the similarity degree of the obtained variation rule of the voltage distribution discreteness of different single batteries along with the discharge process and the variation rule of the voltage distribution discreteness of different single batteries along with the discharge process according to the variation trend of the three parameters along with the battery discharge process, and obtaining the variation rule of the voltage distribution discreteness of different single batteries along with the discharge process in different discharge time periods by adopting a mode of obtaining the first derivative of the voltage to the capacity from the probability distribution cloud picture in the discharge time period when the similarity degree of the two reaches a preset standard.
3. The method of claim 1, further comprising:
and obtaining a second derivative related to the voltage to the capacity of the probability distribution cloud picture, obtaining a change rule of the voltage distribution symmetry of different single batteries along with the discharge process according to the second derivative, judging the similarity degree of the obtained change rule of the voltage distribution symmetry of different single batteries along with the discharge process and the change rule of the voltage distribution symmetry of different single batteries along with the discharge process, which is obtained according to the change trend of three parameters along with the discharge process of the batteries, in different discharge time periods, and obtaining the change rule of the voltage distribution symmetry of different single batteries along with the discharge process by adopting a mode of obtaining the second derivative related to the voltage to the capacity of the probability distribution cloud picture in the discharge time period when the similarity degree of the two reaches a preset standard.
4. The method according to claim 3, wherein the obtaining of the change rule of the voltage distribution symmetry of different single batteries with the discharge process in a manner of solving a second derivative of voltage with respect to capacity from a probability distribution cloud map in the discharge time period in which the similarity between the two reaches a preset standard comprises:
and in the discharge time period when the similarity degree of the two reaches a preset standard, judging the concave-convex change condition of the voltage distribution symmetry of different single batteries along with the discharge change according to the sign of the second derivative value of the voltage to the capacity, which is obtained from the probability distribution cloud picture, so as to obtain the change rule of the voltage distribution symmetry of different single batteries along with the discharge process.
5. An apparatus for estimating voltage inconsistency in a discharge process of a battery, comprising:
the acquisition module is used for acquiring a probability distribution cloud picture of the discharge voltage of the battery changing along with the capacity before the battery is grouped;
the statistical analysis module is used for performing statistical analysis on the probability distribution cloud picture by using a Weibull probability model to obtain the variation trend of the three parameters along with the battery discharge process;
the first estimation module is used for acquiring the variation rules of the voltage distribution discreteness and symmetry of different single batteries along the discharge process according to the variation trends of the three parameters along the discharge process of the batteries;
the statistical analysis module and the first estimation module are specifically configured to:
the first step is as follows: probability distribution of voltage curves; the probability distribution of the discharge voltage of 100 batteries is made into a 4-dimensional image along with the change of the discharge capacity, and the four dimensions are respectively as follows: the x axis is as follows: discharge capacity; y-axis normalized voltage; and a z-axis: a voltage; color axis: distributing probability;
the second step is that: statistical analysis of voltage inconsistency;
the Weibull probability model probability density function is shown in equation one below:
the function is determined by three parameters, wherein a is a size parameter, b is a shape parameter, and c is a position parameter;
carrying out three-parameter Weibull statistical analysis on the discharge voltage curve of 100 batteries to obtain the variation trend of the three parameters along with the discharge process;
the third step: numerical derivation of voltage non-uniformity
According to the formula two V ═ V (SOC) voltage-capacity curve and the formula three
Deducing a change rule of inconsistent behavior of the battery according to the relation between the capacity and the SOC, and respectively solving a first derivative and a second derivative related to the capacity of a voltage-capacity curve to obtain a change rule of discreteness and symmetry of the voltage caused by the capacity, wherein C represents the discharge capacity of the battery, and C represents the discharge capacity of the batterytTo representBattery capacity, SOC, represents battery state of charge; SOC (C)tC) represents the state of charge of the battery as a function of capacity and discharge capacity;
the relationship between the cell voltage dispersion and the capacity dispersion is as follows:
the relationship between the battery voltage symmetry and the discharge capacity is as follows:
respectively setting the formula IV and the formula V as a 'and b', and comparing the calculation results of the formula IV and the formula V with the statistical results;
and the a 'value and the b' value obtained by deriving the numerical value of the voltage curve are used for analyzing the influence of the dispersion and the concave-convex property of the voltage curve on the voltage distribution.
6. The apparatus of claim 5, further comprising:
the second estimation module is used for solving a first derivative of the voltage to the capacity of the probability distribution cloud picture and acquiring a change rule of the voltage distribution discreteness of different single batteries along with a discharge process according to the first derivative;
the first judgment module is used for judging the similarity degree of the variation rule of the voltage distribution discreteness of different single batteries obtained by the second estimation module along with the discharge process and the variation rule of the voltage distribution discreteness of different single batteries obtained by the first estimation module according to the variation trend of the three parameters along with the discharge process of the batteries in different discharge time periods;
and the first selection module is used for selecting a mode of solving a first derivative related to the voltage to the capacity from the probability distribution cloud picture in the discharging time period when the similarity degree of the first judgment module and the second judgment module reaches a preset standard so as to obtain the change rule of the voltage distribution discreteness of different single batteries along with the discharging process.
7. The apparatus of claim 5, further comprising:
the third estimation module is used for solving a second derivative related to the voltage to the capacity of the probability distribution cloud picture and acquiring a change rule of the voltage distribution symmetry of different single batteries along with a discharge process according to the second derivative;
the second judgment module is used for judging the similarity degree of the change rule of the voltage distribution symmetry of different single batteries obtained by the third estimation module along with the discharge process and the change rule of the voltage distribution symmetry of different single batteries obtained by the first estimation module according to the change trend of the three parameters along with the discharge process of the batteries in different discharge time periods;
and the second selection module is used for selecting a mode of solving a second derivative related to the voltage to the capacity from the probability distribution cloud picture in a discharge time period when the similarity degree of the two determined by the second judgment module reaches a preset standard so as to obtain a change rule of the voltage distribution symmetry of different single batteries along with the discharge process.
8. The apparatus of claim 7, wherein the second selection module is specifically configured to:
and in the discharge time period when the similarity degree of the two reaches a preset standard, judging the concave-convex change condition of the voltage distribution symmetry of different single batteries along with the discharge change according to the sign of the second derivative value of the voltage to the capacity, which is obtained from the probability distribution cloud picture, so as to obtain the change rule of the voltage distribution symmetry of different single batteries along with the discharge process.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the method for estimating a battery discharge process voltage inconsistency according to any of claims 1 to 4.
10. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor implements the steps of the battery discharge process voltage inconsistency estimation method according to any of claims 1 to 4.
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| CN115166563B (en) * | 2022-08-17 | 2024-06-18 | 山东大学 | Power battery aging state evaluation and retirement screening method and system |
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