WO2019114238A1 - 一种电池分类方法和系统 - Google Patents

一种电池分类方法和系统 Download PDF

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Publication number
WO2019114238A1
WO2019114238A1 PCT/CN2018/092389 CN2018092389W WO2019114238A1 WO 2019114238 A1 WO2019114238 A1 WO 2019114238A1 CN 2018092389 W CN2018092389 W CN 2018092389W WO 2019114238 A1 WO2019114238 A1 WO 2019114238A1
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Prior art keywords
feature data
data set
battery
discharge
battery pack
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PCT/CN2018/092389
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English (en)
French (fr)
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韩燕川
赵昂
贾亚沛
刘松利
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北京创昱科技有限公司
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Publication of WO2019114238A1 publication Critical patent/WO2019114238A1/zh

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/378—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC] specially adapted for the type of battery or accumulator
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/385—Arrangements for measuring battery or accumulator variables
    • G01R31/387—Determining ampere-hour charge capacity or SoC
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/389—Measuring internal impedance, internal conductance or related variables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/425—Structural combination with electronic components, e.g. electronic circuits integrated to the outside of the casing
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/48—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M2220/00—Batteries for particular applications
    • H01M2220/20—Batteries in motive systems, e.g. vehicle, ship, plane
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M4/00—Electrodes
    • H01M4/02—Electrodes composed of, or comprising, active material
    • H01M4/36—Selection of substances as active materials, active masses, active liquids
    • H01M4/58—Selection of substances as active materials, active masses, active liquids of inorganic compounds other than oxides or hydroxides, e.g. sulfides, selenides, tellurides, halogenides or LiCoFy; of polyanionic structures, e.g. phosphates, silicates or borates
    • H01M4/5825—Oxygenated metallic salts or polyanionic structures, e.g. borates, phosphates, silicates, olivines
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/10—Energy storage using batteries

Definitions

  • Embodiments of the present invention relate to the field of battery management technologies, and in particular, to a battery classification method and system.
  • battery energy storage Since China proposed the construction of smart grid in 2009, battery energy storage has played an important role in all aspects of power generation, transmission, substation, power distribution and power consumption, and has become an indispensable component of smart grid construction. section. At present, the investment cost of the battery energy storage system is relatively high, accounting for more than 70% of the total investment, which has become a constraint factor restricting the large-scale application of battery energy storage.
  • the use of the vehicle power battery for efficient ladders in the battery energy storage system can not only reduce the investment cost of the energy storage system, but also effectively reduce the use cost of the vehicle power battery, save energy, and promote the development of the entire energy storage industry chain. There is a positive driving effect.
  • the vehicle power battery used in the ladder needs to be disassembled, detected, classified, and reorganized to be utilized.
  • the consistency is worse than that of the newly loaded battery.
  • the single battery in the battery box has characteristics such as capacity, internal resistance, and power. More tend to be discretized. If the batteries are classified according to the above characteristics, it takes a lot of time, manpower, and material resources.
  • embodiments of the present invention provide a battery classification method and system.
  • an embodiment of the present invention provides a battery classification method, where the method includes:
  • the feature data set is reduced to obtain a reduction result feature data set of the battery pack
  • the battery cells in the battery group are classified by using a fuzzy clustering algorithm.
  • an embodiment of the present invention provides a battery classification system, where the system includes:
  • An acquisition module configured to acquire cyclic charge and discharge data of the battery pack to be classified, and extract a feature data set of the battery pack from the charge and discharge data;
  • a reduction module configured to reduce the feature data set by using a rough set theory, to obtain a reduction result feature data set of the battery set;
  • a clustering module configured to classify battery cells in the battery pack according to the reduced result feature data set by using a fuzzy clustering algorithm.
  • an embodiment of the present invention provides an electronic device, where the device includes a memory and a processor, where the processor and the memory complete communication with each other through a bus; the memory is stored by the processor Executing program instructions, the processor invoking the program instructions to perform the battery classification method described above.
  • an embodiment of the present invention provides a computer readable storage medium, where a computer program is stored thereon, and the computer program is implemented by a processor to implement the battery classification method.
  • the battery classification method and system provided by the embodiments of the present invention obtain the cycle data of the battery pack to be classified, extract the feature data set of the battery pack from the charge and discharge data, and use the rough set theory to reduce the feature data set.
  • the reduction result characteristic data set of the battery pack is obtained; according to the reduction result feature data set, the fuzzy single clustering algorithm is used to classify the battery cells in the battery pack.
  • the battery classification method and system provided by the embodiments of the present invention can be applied to a repellent battery, and the efficiency of classifying the repellent battery is improved.
  • FIG. 1 is a flowchart of a battery classification method according to an embodiment of the present invention
  • FIG. 2 is a schematic structural diagram of a battery classification system according to an embodiment of the present invention.
  • FIG. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.
  • FIG. 4 is a schematic diagram of a charge and discharge curve of a lithium iron phosphate battery pack according to an embodiment of the present invention
  • FIG. 5 is a diagram showing a battery attribute weight distribution diagram of a lithium iron phosphate battery pack according to an embodiment of the present invention
  • FIG. 6 is a fuzzy clustering distribution diagram of a lithium iron phosphate battery pack according to an embodiment of the present invention.
  • FIG. 1 is a flowchart of a battery classification method according to an embodiment of the present invention. As shown in FIG. 1, the method includes:
  • Step 10 acquiring cyclic charge and discharge data of the battery pack to be classified, and extracting a feature data set of the battery pack from the charge and discharge data;
  • Step 11 Using a rough set theory, reducing the feature data set to obtain a reduction result feature data set of the battery pack;
  • Step 12 According to the reduction result feature data set, use a fuzzy clustering algorithm to classify battery cells in the battery pack.
  • FIG. 4 is a schematic diagram of a charge and discharge curve of a lithium iron phosphate battery pack according to an embodiment of the present invention.
  • lithium iron phosphate batteries widely used in the field of electric vehicles are mostly formed by a series of modules in series or in parallel through dozens of batteries. Among them, several battery cells can reach higher voltage through series connection to drive the motor to work. Take a section of the domestic 100Ah return battery pack as an example, and conduct a 0.3C rate charge and discharge test. The test curve is shown in Figure 4.
  • the server may first acquire the cyclic charge and discharge data of the battery pack to be classified, and extract the feature data set of the battery pack from the charge and discharge data.
  • the battery pack to be classified includes 24 lithium iron phosphate batteries
  • the server may first obtain 0.3C cycle charge and discharge data of the 24 lithium iron phosphate battery, and then, from the 0.3C cycle charge and discharge data, The index data for characterizing the battery characteristics of the 24-cell lithium iron phosphate battery is extracted to constitute a feature data set.
  • the server may adopt a rough set theory to reduce the feature data set, and exclude the indicator data that is not important in the feature data set to obtain a reduction result feature data set of the battery group to be classified.
  • the server may perform cluster analysis on the reduction result feature data set by using an existing fuzzy clustering algorithm, and classify the battery cells in the battery group.
  • the battery classification method provided by the embodiment of the invention obtains the cyclic charge and discharge data of the battery pack to be classified, extracts the characteristic data set of the battery pack from the charging and discharging data, and uses the rough set theory to reduce the feature data set.
  • the reduction result characteristic data set of the battery pack, according to the reduction result feature data set adopts the fuzzy clustering algorithm to classify the battery cells in the battery pack, and can be applied to the retreat battery, and the battery of the retreat force is improved. The efficiency of classification.
  • the feature data set includes any combination of the following feature data: charging ohmic internal resistance, discharge ohmic internal resistance, energy efficiency, and charging of each battery cell in the battery pack. Average power, average discharge power, polarization voltage, temperature, maximum charging power, and maximum discharge power.
  • the feature data set described in the foregoing embodiment may include any combination of the following feature data: charging ohmic internal resistance, discharge ohmic internal resistance, energy efficiency, charging average power, and discharge average power of each battery cell in the battery group to be classified. , polarization voltage, temperature, maximum charging power and maximum discharge power.
  • a battery pack composed of 24 lithium iron phosphate batteries is used as a battery pack to be classified, and the technical solution provided by the embodiment of the present invention is described in detail.
  • the server may extract the feature data set of the 24 lithium iron phosphate battery from the 0.3 C cycle charge and discharge data of the battery pack composed of the 24 lithium iron phosphate battery, wherein the feature data set may include:
  • the eight indicators of each lithium iron phosphate battery in the 24 lithium iron phosphate battery are: charging ohm internal resistance, discharge ohmic internal resistance, energy efficiency, charging average power, discharge average power, polarization voltage, maximum charging Power and maximum discharge power.
  • the charging ohmic internal resistance may be denoted as Rc
  • the discharge ohmic internal resistance may be denoted as Rd
  • the energy efficiency may be denoted as ⁇
  • the charging average power may be denoted as Pca
  • the discharge average power may be recorded
  • Pda the polarization voltage can be written as Up
  • the maximum charging power can be recorded as Pch
  • the maximum discharge power can be recorded as Pdh.
  • the characteristic data set of the battery pack composed of the 24 lithium iron phosphate battery is as shown in Table 1, wherein the number of each battery cell in the 24 lithium iron phosphate battery is sequentially ranked from 1 to 24.
  • the server may use the existing rough set theory to reduce the feature data set, and remove the unimportant indicator data to obtain the reduction result feature data set of the battery group to be classified. For example, when the server uses the rough set theory to reduce the feature data set, it finds that the energy efficiency, the average charging power, and the average discharge power have little or no effect on the battery classification, and the server The three indicator data of the energy efficiency, the charging average power and the discharge average power of the battery to be classified may be eliminated to obtain a reduced result feature data set, and then the reduced clustering feature data set is aggregated by using a fuzzy clustering algorithm. Class analysis to achieve classification of the battery pack.
  • the battery classification method provided by the embodiment of the present invention includes any combination of the following characteristic data by the composition of the feature data set of the battery group to be classified: charging ohmic internal resistance, discharge ohmic internal resistance, energy of each battery cell in the battery group Efficiency, charge average power, discharge average power, polarization voltage, temperature, maximum charge power, and maximum discharge power make the method more scientific.
  • the roughening set theory is used to reduce the feature data set to obtain a reduction result feature data set of the battery group, including:
  • the process described in the foregoing embodiment uses the rough set theory to reduce the feature data set of the battery pack to be reduced, and the process of obtaining the reduction result data set of the battery pack to be classified may specifically include the following process.
  • the server may use the rough set theory to process the feature data set to obtain the weight of each feature data in the feature data set.
  • the server may use the rough set theory to process the feature data set to obtain the weight of each feature data in the feature data set.
  • FIG. 5 is a diagram showing battery attribute weight distribution of a lithium iron phosphate battery pack according to an embodiment of the present invention.
  • the server uses the rough set theory to process the feature data set of the battery pack composed of the 24 lithium iron phosphate batteries, and the weight of each feature data in the feature data set can be obtained.
  • the energy efficiency ⁇ has the highest weight, and secondly, the polarization voltage Up and the maximum discharge power Pdh, and the weight of other characteristic data is 0, which indicates the energy efficiency ⁇ , the polarization voltage Up, and the maximum discharge power Pdh, More important than other feature data.
  • the server may remove the other feature data, and only retain the feature data corresponding to the energy efficiency ⁇ , the polarization voltage Up, and the maximum discharge power Pdh of the 24-cell lithium iron phosphate battery, to obtain a reduction result feature data set.
  • FIG. 6 is a fuzzy clustering distribution diagram of a lithium iron phosphate battery pack according to an embodiment of the present invention.
  • the first, second, third, fourth, fifth, and sixth, 7, 11, 13, 14, and 15 batteries are classified into the first category, and the 21st battery is classified into the second category.
  • the 8th and 22nd batteries are classified into the third category, the 9, 10, 12, 16, and 17, The 18, 19, 20, 23, and 24 batteries were classified into the fourth category.
  • the 21st battery, the 8th battery, and the 22nd battery are highly polarized and are sorted separately.
  • the battery classification system processes the feature data set of the battery pack to be classified according to the rough set theory, obtains the weight of each feature data in the feature data set, and then, according to the weight of each feature data in the feature data set, The feature data set is filtered to obtain a reduction result feature data set of the battery group to be classified, so that the method is more scientific.
  • FIG. 2 is a schematic structural diagram of a battery classification system according to an embodiment of the present invention. As shown in FIG. 2, the system includes: an acquisition module 20, a reduction module 21, and a clustering module 22, where:
  • the obtaining module 20 is configured to acquire cyclic charge and discharge data of the battery pack to be classified, and extract a feature data set of the battery pack from the charge and discharge data;
  • the reduction module 21 is configured to adopt the rough set theory to perform the feature data The set is reduced to obtain a reduction result feature data set of the battery pack;
  • the clustering module 22 is configured to use the fuzzy clustering algorithm to select a battery cell in the battery pack according to the reduction result feature data set sort.
  • lithium iron phosphate batteries widely used in the field of electric vehicles are mostly formed by a series of modules in series or in parallel through dozens of batteries. Among them, several battery cells can reach higher voltage through series connection to drive the motor to work. Taking a battery pack of 100Ah in a domestic model as an example, a 0.3C rate charge and discharge test is carried out, and the test curve is shown in Fig. 4.
  • the battery classification system may include: an acquisition module 20, a reduction module 21, and a clustering module 22.
  • the acquiring module 20 may acquire cyclic charging and discharging data of the battery group to be classified, and extract a feature data set of the battery group from the charging and discharging data.
  • the battery pack to be classified includes 24 lithium iron phosphate batteries.
  • the obtaining module 20 may acquire 0.3C cycle charge and discharge data of the 24 lithium iron phosphate battery, and then extract a battery for characterizing the 24 lithium iron phosphate battery from the 0.3C cycle charge and discharge data.
  • the indicator data of the characteristics constitutes a feature data set.
  • the reduction module 21 may adopt an existing rough set theory, reduce the feature data set, and exclude the indicator data that is not important in the feature data set to obtain a reduction result of the battery group to be classified. Feature data set.
  • the clustering module 22 may perform cluster analysis on the reduced result feature data set by using an existing fuzzy clustering algorithm, and classify the battery cells in the battery pack.
  • the battery classification system provided by the embodiment of the present invention is specifically referred to the foregoing method embodiment, and details are not described herein again.
  • the battery classification system obtained by the embodiment of the invention obtains the cyclic charge and discharge data of the battery pack to be classified, extracts the characteristic data set of the battery pack from the charging and discharging data, and uses the rough set theory to reduce the feature data set.
  • the reduction result characteristic data set of the battery pack according to the reduction result feature data set, adopts the fuzzy clustering algorithm to classify the battery cells in the battery pack, and can be applied to the retreat battery, and the battery of the retreat force is improved. The efficiency of classification.
  • the acquiring module is specifically configured to:
  • charge ohmic internal resistance charge ohmic internal resistance
  • discharge ohmic internal resistance energy efficiency
  • charge average power charge average power
  • discharge average power polarization of each battery cell in the battery pack Voltage, temperature, maximum charging power and maximum discharge power.
  • the obtaining module described in the foregoing embodiment may extract any combination of the following characteristic data from the charging and discharging data of the battery group to be classified: charging ohmic internal resistance, discharge ohmic internal resistance of each battery cell in the battery group, Energy efficiency, average charging power, average power of discharge, polarization voltage, temperature, maximum charging power, and maximum discharging power.
  • a battery pack composed of 24 lithium iron phosphate batteries is used as a battery pack to be classified, and the technical solution provided by the embodiment of the present invention is described in detail.
  • the acquiring module may extract, from the 0.3 C cycle charge and discharge data of the battery pack composed of the 24 lithium iron phosphate battery, a feature data set of the 24 lithium iron phosphate battery, wherein the feature data set may include : 8 kinds of index data of each lithium iron phosphate battery in the 24 lithium iron phosphate battery are: charging ohm internal resistance, discharge ohmic internal resistance, energy efficiency, charging average power, discharge average power, polarization voltage , maximum charging power and maximum discharge power.
  • the charging ohmic internal resistance may be denoted as Rc
  • the discharge ohmic internal resistance may be denoted as Rd
  • the energy efficiency may be denoted as ⁇
  • the charging average power may be denoted as Pca
  • the discharge average power may be recorded
  • Pda the polarization voltage can be written as Up
  • the maximum charging power can be recorded as Pch
  • the maximum discharge power can be recorded as Pdh.
  • the characteristic data set of the battery pack composed of the 24 lithium iron phosphate battery is as shown in Table 1, wherein the number of each battery cell in the 24 lithium iron phosphate battery is sequentially ranked from 1 to 24.
  • the reduction module may use the existing rough set theory to reduce the feature data set, and reject the unimportant index data to obtain the The reduction result feature data set of the battery pack to be classified. For example, when the reduction module uses the rough set theory to reduce the feature data set, it finds that the energy efficiency, the average charging power, and the average discharge power have little or no effect on the battery classification.
  • the reduction module may exclude the three indicator data of the energy efficiency, the charging average power and the discharge average power of the battery to be classified, and obtain a reduction result feature data set, and then the clustering module may adopt a fuzzy clustering algorithm according to the The result data set is reduced, and the battery pack to be classified is classified.
  • the battery classification system includes: the charging ohmic internal resistance, the discharge ohmic internal resistance, the energy efficiency, the charging average power, and the charging ohm internal resistance of each battery cell in the battery group,
  • the average discharge power, polarization voltage, maximum charging power, and maximum discharge power make the system more scientific.
  • the reduction module includes: a weight sub-module and a reduction sub-module, where:
  • the weight sub-module is configured to process the feature data set according to the rough set theory to obtain weights of each feature data in the feature data set; and the reduction sub-module is configured to calculate weights of each feature data according to the feature data set.
  • the feature data set is screened to obtain a reduction result feature data set of the battery pack.
  • the reduction module described in the foregoing embodiment may include: a weight sub-module and a reduction sub-module.
  • the weight sub-module may use a rough set theory to process the feature data set of the classified battery pack to obtain the weight of each feature data in the feature data set. Taking the battery pack composed of the 24 lithium iron phosphate battery described in the above embodiments as an example, the technical solution provided by the implementation of the present invention will be described in detail.
  • the weight sub-module may use a rough set theory to process a feature data set of the battery pack composed of the 24 lithium iron phosphate batteries to obtain weights of each feature data in the feature data set.
  • the energy efficiency ⁇ has the highest weight, and secondly, the polarization voltage Up and the maximum discharge power Pdh, and the weight of other characteristic data is 0, which indicates the energy efficiency ⁇ , the polarization voltage Up, and the maximum discharge power Pdh, More important than other feature data.
  • the reduction sub-module may remove the other characteristic data, and only retain the characteristic data corresponding to the energy efficiency ⁇ , the polarization voltage Up, and the maximum discharge power Pdh of the 24-cell lithium iron phosphate battery, and obtain the battery group to be classified.
  • the reduction result data set may be removed.
  • the first, second, third, fourth, fifth, sixth, seventh, eleventh, thirteenth, fourteenth, and fifteenth batteries are classified into the first type, the twenty-first battery.
  • the 8th and 22nd batteries are classified into the third category, and the 9th, 10th, 12th, 16th, 17th, 18th, 19th, 20th, 23rd, and 24th batteries are classified into the fourth category.
  • the 21st battery, the 8th battery, and the 22nd battery are highly polarized and are sorted separately.
  • the battery classification system processes the feature data set of the battery pack to be classified according to the rough set theory, obtains the weight of each feature data in the feature data set, and then, according to the weight of each feature data in the feature data set, The feature data set is filtered to obtain a reduction result feature data set of the battery group to be classified, so that the method is more scientific.
  • FIG. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. As shown in FIG. 4, the device includes: a processor 31, a memory 32, and a bus 33, where:
  • the processor 31 and the memory 32 complete communication with each other through the bus 33; the processor 31 is configured to invoke program instructions in the memory 32 to perform the methods provided by the foregoing method embodiments, For example, comprising: acquiring cyclic charge and discharge data of the battery pack to be classified, extracting a feature data set of the battery pack from the charge and discharge data; and using a rough set theory to reduce the feature data set to obtain the The reduction result characteristic data set of the battery pack; according to the reduction result feature data set, the battery cells in the battery group are classified by using a fuzzy clustering algorithm.
  • Embodiments of the present invention disclose a computer program product, the computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions, when the program instructions are executed by a computer,
  • the computer can perform the method provided by the foregoing method embodiments, for example, including: acquiring cyclic charge and discharge data of the battery pack to be classified, extracting a feature data set of the battery pack from the charge and discharge data; using a rough set theory, The feature data set is reduced to obtain a reduction result feature data set of the battery group; according to the reduction result feature data set, a fuzzy clustering algorithm is used to perform battery cells in the battery pack classification.
  • An embodiment of the present invention provides a non-transitory computer readable storage medium storing computer instructions, the computer instructions causing the computer to perform the methods provided by the foregoing method embodiments, for example
  • the method includes: acquiring cyclic charge and discharge data of a battery pack to be classified, extracting a feature data set of the battery pack from the charge and discharge data, and reducing the feature data set by using a rough set theory to obtain the battery
  • the reduction result data set of the group according to the reduction result feature data set, the battery cells in the battery group are classified by using a fuzzy clustering algorithm.

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Abstract

本发明实施例提供一种电池分类方法和系统，所述方法包括：获取待分类的电池组的循环充放电数据，从所述充放电数据中提取所述电池组的特征数据集；采用粗糙集理论，对所述特征数据集进行约简，得到所述电池组的约简结果特征数据集；根据所述约简结果特征数据集，采用模糊聚类算法，对所述电池组中的电池单体进行分类。本发明实施例提供的电池分类方法和系统，可以适用于退运动力电池，提高了对退运动力电池进行分类的效率。

Description

一种电池分类方法和系统
交叉引用
本申请引用于2017年12月13日提交的专利名称为“一种电池分类方法和系统”的第2017113288576号中国专利申请，其通过引用被全部并入本申请。
技术领域
本发明实施例涉及电池管理技术领域，尤其涉及一种电池分类方法和系统。
背景技术
自2009年我国提出建设智能电网的规划以来，电池储能在电力系统的发电、输电、变电、配电和用电的各个环节都发挥了重要作用，已成为智能电网建设不可或缺的组成部分。目前电池储能系统的建设成本中电池投资成本较高，约占总投入的70％以上，已成为制约电池储能大规模应用的制约因素。
根据公安部交管局的相关数据统计，2015年纯电动汽车的保有量已经达到了33.2万辆，国家工信部预计在2020年废旧动力电池数量将达到12-17万吨。车用动力电池的容量低于80％就做退役处理，但是从汽车上退运的电池仍有很高的利用价值，若直接报废回收处理，会造成资源的严重浪费。
将车用动力电池进行有效的梯次利用于电池储能系统，不仅能降低储能系统投资成本，还能有效降低车用动力电池的使用成本，节约了能源，对促进整个储能产业链的发展有积极的推动作用。
然而，梯次利用的车用动力电池需要进行拆解、检测、分类、重组才能加以利用，一致性相比新装车电池更加恶化，电池箱中的单体电池在容量、内阻、功率等特性上更趋于离散化。若根据上述特性来分类电池，需要耗费巨大的时间、人力和物力。
发明内容
针对现有技术中存在的问题，本发明实施例提供一种电池分类方法和系统。
第一方面，本发明实施例提供一种电池分类方法，所述方法包括：
获取待分类的电池组的循环充放电数据，从所述充放电数据中提取所述电池组的特征数据集；
采用粗糙集理论，对所述特征数据集进行约简，得到所述电池组的约简结果特征数据集；
根据所述约简结果特征数据集，采用模糊聚类算法，对所述电池组中的电池单体进行分类。
第二方面，本发明实施例提供一种电池分类系统，所述系统包括：
获取模块，用于获取待分类的电池组的循环充放电数据，从所述充放电数据中提取所述电池组的特征数据集；
约简模块，用于采用粗糙集理论，对所述特征数据集进行约简，得到所述电池组的约简结果特征数据集；
聚类模块，用于根据所述约简结果特征数据集，采用模糊聚类算法，对所述电池组中的电池单体进行分类。
第三方面，本发明实施例提供一种电子设备，所述设备包括存储器和处理器，所述处理器和所述存储器通过总线完成相互间的通信；所述存储器存储有可被所述处理器执行的程序指令，所述处理器调用所述程序指令能够执行上述电池分类方法。
第四方面，本发明实施例提供一种计算机可读存储介质，其上存储有计算机程序，该计算机程序被处理器执行时实现上述电池分类方法。
本发明实施例提供的电池分类方法和系统，通过获取待分类的电池组的循环充放电数据，从充放电数据中提取电池组的特征数据集，采用粗糙集理论，对特征数据集进行约简，得到电池组的约简结果特征数据集；根据约简结果特征数据集，采用模糊聚类算法，对电池组中的电池单体进行分类。本发明实施例提供的电池分类方法和系统，可以适用于退运动力电池，提高了对退运动力电池进行分类的效率。
附图说明
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作一简单地介绍，显而易见地，下面描述中的附图是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1为本发明实施例提供的电池分类方法流程图；
图2为本发明实施例提供的电池分类系统的结构示意图；
图3为本发明实施例提供的电子设备的结构示意图；
图4为本发明实施例提供的磷酸铁锂电池组的充放电曲线示意图；
图5为本发明实施例提供的磷酸铁锂电池组的电池属性权重分布图；
图6为本发明实施例提供的磷酸铁锂电池组的模糊聚类分布图。
具体实施方式
为使本发明实施例的目的、技术方案和优点更加清楚，下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚地描述，显然，所描述的实施例是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
图1为本发明实施例提供的电池分类方法流程图，如图1所示，所述方法包括：
步骤10、获取待分类的电池组的循环充放电数据，从所述充放电数据中提取所述电池组的特征数据集；
步骤11、采用粗糙集理论，对所述特征数据集进行约简，得到所述电池组的约简结果特征数据集；
步骤12、根据所述约简结果特征数据集，采用模糊聚类算法，对所述电池组中的电池单体进行分类。
图4为本发明实施例提供的磷酸铁锂电池组的充放电曲线示意图。目前广泛应用于电动汽车领域的磷酸铁锂电池，大多是数十节电池通过串联或并联，形成一个单元模组。其中，若干节电池通过串联可以达到较高电压，以驱动电机工作。现以24节国内某型号100Ah退运电池组为例，对其进行0.3C倍率充放电测试，测试曲线如图4所示。
从图4可以看出，在充放电过程中，24节磷酸铁锂电池的电压离散性较大。其中，健康状态最差的磷酸铁锂电池最先达到充放电截止电压阈值，并且，有若干节磷酸铁锂电池的充放电曲线比较接近。如果不经过特殊处理，很难仅凭充放电曲线，一次将上述24节磷酸铁锂电池进行合理分类。
本发明实施例提供的电池分类方法中，服务器可以首先获取待分类的电池组的循环充放电数据，从所述充放电数据中提取所述电池组的特征数据集。比如，待分类的电池组包括24节磷酸铁锂电池，所述服务器可以首先获取所述24节磷酸铁锂电池的0.3C循环充放电数据，然后，从所述0.3C循环充放电数据中，提取用于表征所述24节磷酸铁锂电池的电池特性的指标数据，构成特征数据集。
然后，所述服务器可以采用粗糙集理论，对所述特征数据集进行约简，将所述特征数据集中不重要的指标数据进行剔除，得到所述待分类电池组的约简结果特征数据集。
所述服务器可以采用现有的模糊聚类算法，对所述约简结果特征数据集进行聚类分析，对所述电池组中的电池单体进行分类。
本发明实施例提供的电池分类方法，通过获取待分类的电池组的循环充放电数据，从充放电数据中提取电池组的特征数据集，采用粗糙集理论，对特征数据集进行约简，得到电池组的约简结果特征数据集，根据约简结果特征数据集，采用模糊聚类算法，对电池组中的电池单体进行分类，可以适用于退运动力电池，提高了对退运动力电池进行分类的效率。
可选的，在上述实施例的基础上，所述特征数据集包括如下特征数据的任意组合：所述电池组中每个电池单体的充电欧姆内阻、放电欧姆内阻、能量效率、充电平均功率、放电平均功率、极化电压、温度、最大充电功率和最大放电功率。
上述实施例中所述的特征数据集可以包括如下特征数据的任意组合：待分类电池组中每个电池单体的充电欧姆内阻、放电欧姆内阻、能量效率、充电平均功率、放电平均功率、极化电压、温度、最大充电功率和最大放电功率。
下面以24节磷酸铁锂电池组成的电池组作为待分类电池组，详细说明一下本发明实施例提供的技术方案。
服务器可以从所述24节磷酸铁锂电池组成的电池组的0.3C循环充放电数据中，提取所述24节磷酸铁锂电池的特征数据集，其中，所述特征数据集可以包括：所述24节磷酸铁锂电池中的每节磷酸铁锂电池的8种指标数据，分别为：充电欧姆内阻、放电欧姆内阻、能量效率、充电平均功率、放电平均功率、极化电压、最大充电功率和最大放电功率。
其中，所述充电欧姆内阻可以记为Rc，所述放电欧姆内阻可以记为Rd，所述能量效率可以记为η，所述充电平均功率可以记为Pca，所述放电平均功率可以记为Pda，所述极化电压可以记为Up，所述最大充电功率可以记为Pch，所述最大放电功率可以记为Pdh。所述24节磷酸铁锂电池组成的电池组的特征数据集，如表一所示，其中，所述24节磷酸铁锂电池中的每节电池单体的编号从1按顺序排到24。
表一
Figure PCTCN2018092389-appb-000001
所述服务器可以采用现有的粗糙集理论，对上述特征数据集进行约简，将其中不重要的指标数据进行剔除，得到所述待分类电池组的约简结果特征数据集。比如，所述服务器采用粗糙集理论对特征数据集进行约简处理时，发现能量效率、充电平均功率和放电平均功率三种指标数据，对电池分类的影响不大或没有影响，则所述服务器可以将所述待分类电池组的能量效率、充电平均功率和放电平均功率三种指标数据进行剔除，得到约简结果特征数据集，然后，采用模糊聚类算法对约简结果特征数据集进行聚类分析，从而实现对所述电池组进行分类。
本发明实施例提供的电池分类方法，通过将待分类电池组的特征数据集的组成包括如下特征数据的任意组合：电池组中每个电池单体的充电欧姆内阻、放电欧姆内阻、能量效率、充电平均功率、放电平均功率、极化电压、温度、最大充电功率和最大放电功率，使得所述方法更加科学。
可选的，在上述实施例的基础上，所述采用粗糙集理论，对所述特征数据集进行约简，得到所述电池组的约简结果特征数据集，包括：
根据粗糙集理论对所述特征数据集进行处理，得到所述特征数据集中每个特征数据的权重；
根据所述特征数据集中每个特征数据的权重，对所述特征数据集进行筛选，得到所述电池组的约简结果特征数据集。
上述实施例中所述的服务器采用粗糙集理论，对待分类电池组的特征数据集进行约简，得到所述待分类电池组的约简结果数据集的过程，可以具体包括如下过程。
首先，所述服务器可以采用粗糙集理论，对特征数据集进行处理，得到所述特征数据集中每个特征数据的权重。以上述实施例中所述的24节磷酸铁锂电池组成的电池组为例，详细说明本发明实施所提供的技术方案。
图5为本发明实施例提供的磷酸铁锂电池组的电池属性权重分布图。服务器采用粗糙集理论，对所述24节磷酸铁锂电池组成的电池组的特征数据集进行处理，可以得到所述特征数据集中每个特征数据的权重。
如图5所示，能量效率η的权重最高，其次，是极化电压Up和最大放电功率Pdh，其他特征数据的权重为0，这说明能量效率η、极化电压 Up和最大放电功率Pdh，相对其他特征数据更加重要。
所述服务器可以将所述其他特征数据进行剔除，只保留所述24节磷酸铁锂电池的能量效率η、极化电压Up和最大放电功率Pdh对应的特征数据，得到约简结果特征数据集。
然后，所述服务器可以采用现有的模糊聚类算法，对所述约简结果特征数据集进行分类。比如，在初始化时设定电池分类数C＝4，模糊权重指数m＝2，终止条件为迭代次数为100或阈值ε＝0.00001。则达到终止条件之后，所述服务器可以将所述24节磷酸铁锂电池分为4类。
图6为本发明实施例提供的磷酸铁锂电池组的模糊聚类分布图，如图6所示，所述24节磷酸铁锂电池中，第1、2、3、4、5、6、7、11、13、14、15节电池被分第1类，第21节电池被分第2类，第8、22节电池被分第3类，第9、10、12、16、17、18、19、20、23、24节电池被分第4类。第21节电池、第8节电池、第22节电池极化较大，被单独分类筛选出来。
这种分类结果与图4所示磷酸铁锂电池组的充放电电压变化相一致，这说明，将模糊聚类算法应用于对电池进行分类，是非常有效的。
本发明实施例提供的电池分类系统，通过根据粗糙集理论对待分类电池组的特征数据集进行处理，得到特征数据集中每个特征数据的权重，然后，根据特征数据集中每个特征数据的权重，对特征数据集进行筛选，得到待分类电池组的约简结果特征数据集，使得所述方法更加科学。
图2为本发明实施例提供的电池分类系统的结构示意图，如图2所示，所述系统包括：获取模块20、约简模块21和聚类模块22，其中：
获取模块20用于获取待分类的电池组的循环充放电数据，从所述充放电数据中提取所述电池组的特征数据集；约简模块21用于采用粗糙集理论，对所述特征数据集进行约简，得到所述电池组的约简结果特征数据集；聚类模块22用于根据所述约简结果特征数据集，采用模糊聚类算法，对所述电池组中的电池单体进行分类。
目前广泛应用于电动汽车领域的磷酸铁锂电池，大多是数十节电池通过串联或并联，形成一个单元模组。其中，若干节电池通过串联可以达到较高电压，以驱动电机工作。现以24节国内某型号100Ah退运电池组为 例，对其进行0.3C倍率充放电测试，测试曲线如图4所示。
从图4可以看出，在充放电过程中，24节磷酸铁锂电池的电压离散性较大。其中，健康状态最差的磷酸铁锂电池最先达到充放电截止电压阈值，并且，有若干节磷酸铁锂电池的充放电曲线比较接近。如果不经过特殊处理，很难仅凭充放电曲线，一次将上述24节磷酸铁锂电池进行合理分类。
本发明实施例提供的电池分类系统可以包括：获取模块20、约简模块21和聚类模块22。
所述获取模块20可以获取待分类的电池组的循环充放电数据，从所述充放电数据中提取所述电池组的特征数据集。比如，待分类的电池组包括24节磷酸铁锂电池。所述获取模块20可以获取所述24节磷酸铁锂电池的0.3C循环充放电数据，然后，从所述0.3C循环充放电数据中，提取用于表征所述24节磷酸铁锂电池的电池特性的指标数据，构成特征数据集。
所述约简模块21可以采用现有的粗糙集理论，对所述特征数据集进行约简，将所述特征数据集中不重要的指标数据进行剔除，得到所述待分类电池组的约简结果特征数据集。
所述聚类模块22可以采用现有的模糊聚类算法，对所述约简结果特征数据集进行聚类分析，对所述电池组中的电池单体进行分类。
本发明实施例提供的电池分类系统，其功能具体参照上述方法实施例，此处不再赘述。
本发明实施例提供的电池分类系统，通过获取待分类的电池组的循环充放电数据，从充放电数据中提取电池组的特征数据集，采用粗糙集理论，对特征数据集进行约简，得到电池组的约简结果特征数据集，根据约简结果特征数据集，采用模糊聚类算法，对电池组中的电池单体进行分类，可以适用于退运动力电池，提高了对退运动力电池进行分类的效率。
可选的，在上述实施例的基础上，所述获取模块具体用于：
从所述充放电数据中，提取如下特征数据的任意组合：所述电池组中每个电池单体的充电欧姆内阻、放电欧姆内阻、能量效率、充电平均功率、放电平均功率、极化电压、温度、最大充电功率和最大放电功率。
上述实施例中所述的获取模块可以从待分类电池组的充放电数据中， 提取如下特征数据的任意组合：所述电池组中每个电池单体的充电欧姆内阻、放电欧姆内阻、能量效率、充电平均功率、放电平均功率、极化电压、温度、最大充电功率和最大放电功率。
下面以24节磷酸铁锂电池组成的电池组作为待分类电池组，详细说明一下本发明实施例提供的技术方案。
所述获取模块可以从所述24节磷酸铁锂电池组成的电池组的0.3C循环充放电数据中，提取所述24节磷酸铁锂电池的特征数据集，其中，所述特征数据集可以包括：所述24节磷酸铁锂电池中的每节磷酸铁锂电池的8种指标数据，分别为：充电欧姆内阻、放电欧姆内阻、能量效率、充电平均功率、放电平均功率、极化电压、最大充电功率和最大放电功率。
其中，所述充电欧姆内阻可以记为Rc，所述放电欧姆内阻可以记为Rd，所述能量效率可以记为η，所述充电平均功率可以记为Pca，所述放电平均功率可以记为Pda，所述极化电压可以记为Up，所述最大充电功率可以记为Pch，所述最大放电功率可以记为Pdh。所述24节磷酸铁锂电池组成的电池组的特征数据集，如表一所示，其中，所述24节磷酸铁锂电池中的每节电池单体的编号从1按照顺序排到24。
所述获取模块提取出待分类电池组的特征数据集之后，约简模块可以采用现有的粗糙集理论，对上述特征数据集进行约简，将其中不重要的指标数据进行剔除，得到所述待分类电池组的约简结果特征数据集。比如，所述约简模块采用粗糙集理论对特征数据集进行约简处理时，发现能量效率、充电平均功率和放电平均功率三种指标数据，对电池分类的影响不大或没有影响，则所述约简模块可以将所述待分类电池组的能量效率、充电平均功率和放电平均功率三种指标数据进行剔除，得约简结果特征数据集，然后，聚类模块可以采用模糊聚类算法根据约简结果特征数据集，对所述待分类电池组进行分类。
本发明实施例提供的电池分类系统，通过将待分类电池组的特征数据集的组成包括：电池组中每个电池单体的充电欧姆内阻、放电欧姆内阻、能量效率、充电平均功率、放电平均功率、极化电压、最大充电功率和最大放电功率，使得所述系统更加科学。
可选的，在上述实施例的基础上，所述约简模块包括：权重子模块和 约简子模块，其中：
权重子模块用于根据粗糙集理论对所述特征数据集进行处理，得到所述特征数据集中每个特征数据的权重；约简子模块用于根据所述特征数据集中每个特征数据的权重，对所述特征数据集进行筛选，得到所述电池组的约简结果特征数据集。
具体地，上述实施例中所述的约简模块可以包括：权重子模块和约简子模块。
其中，所述权重子模块可以采用粗糙集理论，对待分类电池组的特征数据集进行处理，得到所述特征数据集中每个特征数据的权重。以上述实施例中所述的24节磷酸铁锂电池组成的电池组为例，详细说明本发明实施所提供的技术方案。所述权重子模块可以采用粗糙集理论，对所述24节磷酸铁锂电池组成的电池组的特征数据集进行处理，得到所述特征数据集中每个特征数据的权重。
如图5所示，能量效率η的权重最高，其次，是极化电压Up和最大放电功率Pdh，其他特征数据的权重为0，这说明能量效率η、极化电压Up和最大放电功率Pdh，相对其他特征数据更加重要。
所述约简子模块可以将所述其他特征数据进行剔除，只保留所述24节磷酸铁锂电池的能量效率η、极化电压Up和最大放电功率Pdh对应的特征数据，得到待分类电池组的约简结果特征数据集。
然后，聚类模块可以采用现有的模糊聚类算法，对所述约简结果特征数据集进行分类。比如，在初始化时设定电池分类数C＝4，模糊权重指数m＝2，终止条件为迭代次数为100或阈值ε＝0.00001。则达到终止条件之后，所述聚类模块可以将所述24节磷酸铁锂电池分为4类。
如图6所示，所述24节磷酸铁锂电池中，第1、2、3、4、5、6、7、11、13、14、15节电池被分第1类，第21节电池被分第2类，第8、22节电池被分第3类，第9、10、12、16、17、18、19、20、23、24节电池被分第4类。第21节电池、第8节电池、第22节电池极化较大，被单独分类筛选出来。
本发明实施例提供的电池分类系统，通过根据粗糙集理论对待分类电池组的特征数据集进行处理，得到特征数据集中每个特征数据的权重，然 后，根据特征数据集中每个特征数据的权重，对特征数据集进行筛选，得到待分类电池组的约简结果特征数据集，使得所述方法更加科学。
图3为本发明实施例提供的电子设备的结构示意图，如图4所示，所述设备包括：处理器(processor)31、存储器(memory)32和总线33，其中：
所述处理器31和所述存储器32通过所述总线33完成相互间的通信；所述处理器31用于调用所述存储器32中的程序指令，以执行上述各方法实施例所提供的方法，例如包括：获取待分类的电池组的循环充放电数据，从所述充放电数据中提取所述电池组的特征数据集；采用粗糙集理论，对所述特征数据集进行约简，得到所述电池组的约简结果特征数据集；根据所述约简结果特征数据集，采用模糊聚类算法，对所述电池组中的电池单体进行分类。
本发明实施例公开一种计算机程序产品，所述计算机程序产品包括存储在非暂态计算机可读存储介质上的计算机程序，所述计算机程序包括程序指令，当所述程序指令被计算机执行时，计算机能够执行上述各方法实施例所提供的方法，例如包括：获取待分类的电池组的循环充放电数据，从所述充放电数据中提取所述电池组的特征数据集；采用粗糙集理论，对所述特征数据集进行约简，得到所述电池组的约简结果特征数据集；根据所述约简结果特征数据集，采用模糊聚类算法，对所述电池组中的电池单体进行分类。
本发明实施例提供一种非暂态计算机可读存储介质，所述非暂态计算机可读存储介质存储计算机指令，所述计算机指令使所述计算机执行上述各方法实施例所提供的方法，例如包括：获取待分类的电池组的循环充放电数据，从所述充放电数据中提取所述电池组的特征数据集；采用粗糙集理论，对所述特征数据集进行约简，得到所述电池组的约简结果特征数据集；根据所述约简结果特征数据集，采用模糊聚类算法，对所述电池组中的电池单体进行分类。
以上所描述的电子设备等实施例仅仅是示意性的，其中所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部模块 来实现本实施例方案的目的。本领域普通技术人员在不付出创造性的劳动的情况下，即可以理解并实施。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到各实施方式可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件。基于这样的理解，上述技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品可以存储在计算机可读存储介质中，如ROM/RAM、磁碟、光盘等，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行各个实施例或者实施例的某些部分所述的方法。
最后应说明的是：以上各实施例仅用以说明本发明的实施例的技术方案，而非对其限制；尽管参照前述各实施例对本发明的实施例进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分或者全部技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本发明的实施例各实施例技术方案的范围。

Claims (8)

  1. 一种电池分类方法，其特征在于，包括：
    获取待分类的电池组的循环充放电数据，从所述充放电数据中提取所述电池组的特征数据集；
    采用粗糙集理论，对所述特征数据集进行约简，得到所述电池组的约简结果特征数据集；
    根据所述约简结果特征数据集，采用模糊聚类算法，对所述电池组中的电池单体进行分类。
  2. 根据权利要求1所述的方法，其特征在于，所述特征数据集包括如下特征数据的任意组合：所述电池组中每个电池单体的充电欧姆内阻、放电欧姆内阻、能量效率、充电平均功率、放电平均功率、极化电压、温度、最大充电功率和最大放电功率。
  3. 根据权利要求1或2所述的方法，其特征在于，所述采用粗糙集理论，对所述特征数据集进行约简，得到所述电池组的约简结果特征数据集，包括：
    根据粗糙集理论对所述特征数据集进行处理，得到所述特征数据集中每个特征数据的权重；
    根据所述特征数据集中每个特征数据的权重，对所述特征数据集进行筛选，得到所述电池组的约简结果特征数据集。
  4. 一种电池分类系统，其特征在于，包括：
    获取模块，用于获取待分类的电池组的循环充放电数据，从所述充放电数据中提取所述电池组的特征数据集；
    约简模块，用于采用粗糙集理论，对所述特征数据集进行约简，得到所述电池组的约简结果特征数据集；
    聚类模块，用于根据所述约简结果特征数据集，采用模糊聚类算法，对所述电池组中的电池单体进行分类。
  5. 根据权利要求4所述的系统，其特征在于，所述获取模块具体用于：
    从所述充放电数据中，提取如下特征数据的任意组合：所述电池组中每个电池单体的充电欧姆内阻、放电欧姆内阻、能量效率、充电平均功率、 放电平均功率、极化电压、温度、最大充电功率和最大放电功率。
  6. 根据权利要求4或5所述的系统，其特征在于，所述约简模块包括：
    权重子模块，用于根据粗糙集理论对所述特征数据集进行处理，得到所述特征数据集中每个特征数据的权重；
    约简子模块，用于根据所述特征数据集中每个特征数据的权重，对所述特征数据集进行筛选，得到所述电池组的约简结果特征数据集。
  7. 一种电子设备，其特征在于，包括存储器和处理器，所述处理器和所述存储器通过总线完成相互间的通信；所述存储器存储有可被所述处理器执行的程序指令，所述处理器调用所述程序指令能够执行如权利要求1至3任一所述的方法。
  8. 一种计算机可读存储介质，其上存储有计算机程序，其特征在于，该计算机程序被处理器执行时实现如权利要求1至3任一所述的方法。
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CN111915600B (zh) * 2020-08-12 2023-09-26 南京工程学院 一种退役动力锂电池热特征一致性筛选方法
CN112791997A (zh) * 2020-12-16 2021-05-14 北方工业大学 一种退役电池梯次利用筛选的方法

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