WO2023246088A1 - 一种锂电池性能评分计算方法及系统 - Google Patents
一种锂电池性能评分计算方法及系统 Download PDFInfo
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- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 199
- 229910052744 lithium Inorganic materials 0.000 title claims abstract description 199
- 238000004364 calculation method Methods 0.000 title claims abstract description 66
- 238000011156 evaluation Methods 0.000 claims abstract description 46
- 238000000034 method Methods 0.000 claims abstract description 31
- 238000004590 computer program Methods 0.000 claims description 34
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- 239000011159 matrix material Substances 0.000 claims description 24
- 230000007613 environmental effect Effects 0.000 claims description 13
- 238000012417 linear regression Methods 0.000 claims description 8
- 230000000007 visual effect Effects 0.000 claims description 6
- 238000004140 cleaning Methods 0.000 claims description 5
- 238000007689 inspection Methods 0.000 claims description 5
- 230000008569 process Effects 0.000 claims description 5
- 238000012545 processing Methods 0.000 claims description 5
- 238000003860 storage Methods 0.000 claims description 5
- 230000003862 health status Effects 0.000 claims description 4
- 238000009826 distribution Methods 0.000 claims description 3
- 230000036541 health Effects 0.000 claims description 3
- 230000015572 biosynthetic process Effects 0.000 claims description 2
- 238000010219 correlation analysis Methods 0.000 claims description 2
- 238000013501 data transformation Methods 0.000 claims description 2
- 238000004519 manufacturing process Methods 0.000 claims description 2
- 238000003786 synthesis reaction Methods 0.000 claims description 2
- 238000010276 construction Methods 0.000 claims 1
- 238000004146 energy storage Methods 0.000 description 8
- 238000012423 maintenance Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 5
- 238000004458 analytical method Methods 0.000 description 4
- 230000008439 repair process Effects 0.000 description 4
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- 230000018109 developmental process Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000013210 evaluation model Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000012800 visualization Methods 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000002427 irreversible effect Effects 0.000 description 1
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- 238000012544 monitoring process Methods 0.000 description 1
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- 238000010248 power generation Methods 0.000 description 1
- 230000002459 sustained effect Effects 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/20—Administration of product repair or maintenance
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
Definitions
- the present disclosure relates to the field of energy storage, and specifically relates to a lithium battery performance score calculation method and system.
- Lithium batteries are widely used in energy storage due to their high energy density, stable electrochemical characteristics, less pollution, and long cycle life. They also promote the sustained and rapid economic development.
- lithium batteries will slowly undergo irreversible aging, which directly affects the practicality, economy, and safety of lithium batteries. Therefore, being able to accurately and quickly evaluate the real-time performance status of lithium batteries not only improves the safety of related fields, but also saves a lot of money and time in the energy storage field. Therefore, developing a method that can accurately evaluate the performance status of lithium batteries is of great significance for its practical application.
- the traditional lithium battery performance status evaluation method has great ambiguity, one-sided evaluation indicators, and human influence. It cannot accurately and comprehensively reflect the performance status of lithium batteries, and the evaluation results are less convincing.
- the present disclosure provides a lithium battery performance score calculation method and system to at least solve the problem of ambiguity in the lithium battery performance status evaluation method in related technologies, one-sided evaluation indicators, human influence, and inability to accurately and comprehensively reflect the performance status of lithium batteries. , the problem of poor persuasiveness of evaluation results.
- lithium battery performance rating database uses the lithium battery performance rating database to construct a lithium battery performance rating system from three dimensions: battery nameplate attributes, operating attributes, and environmental attributes;
- the fuzzy comprehensive evaluation method is used to construct a battery performance scoring calculation model.
- the second embodiment of the present disclosure proposes a lithium battery performance score calculation system, including:
- the acquisition module obtains data information during the operation of the lithium battery and uploads the data to the lithium battery performance rating database;
- the calculation module uses the fuzzy comprehensive evaluation method to construct a battery performance score calculation model and calculate the scores of various performance indicators of lithium batteries.
- the third embodiment of the present disclosure provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor.
- the processor executes the computer program, the first aspect of the present disclosure is implemented. Lithium battery performance rating calculation method.
- the fourth embodiment of the present disclosure provides a non-transitory computer-readable storage medium on which a computer program is stored.
- the computer program is executed by a processor, the lithium battery performance score calculation method of the first aspect of the present disclosure is implemented.
- the fifth embodiment of the present disclosure provides a computer program product, including a computer program, wherein the computer program implements the lithium battery performance score calculation method as described in the first embodiment of the present disclosure when executed by a processor.
- the sixth embodiment of the present disclosure provides a computer program, wherein the computer program includes computer program code.
- the computer program code When the computer program code is run on a computer, it causes the computer to execute the method described in the first embodiment of the present disclosure. Lithium battery performance rating calculation method.
- the present disclosure provides a lithium battery performance rating calculation method and system.
- the method includes obtaining data information during the operation of the lithium battery and uploading the data to a lithium battery performance rating database; using the lithium battery performance rating database to obtain data from the battery
- the lithium battery performance rating system is constructed from the three dimensions of nameplate attributes, operating attributes, and environmental attributes; the fuzzy comprehensive evaluation method is used to construct a battery performance rating calculation model and calculate the ratings of various performance indicators of lithium batteries, achieving real-time, fast, and accurate calculation of lithium batteries.
- the performance of the lithium battery can be used to evaluate the performance status of the lithium battery, improve the maintenance and repair efficiency of the lithium battery, and ensure the safe and stable operation of the lithium battery.
- Figure 1 is a flow chart of a lithium battery performance score calculation method provided according to an embodiment of the present disclosure
- Figure 2 is a specific flow chart of a lithium battery performance score calculation method provided according to an embodiment of the present disclosure
- Figure 3 is a schematic diagram of a fuzzy comprehensive evaluation model in a lithium battery performance score calculation method provided according to an embodiment of the present disclosure
- Figure 4 is a visual interface diagram for lithium battery performance status analysis in a lithium battery performance score calculation method provided according to an embodiment of the present disclosure
- FIG. 5 is a structural diagram of a lithium battery performance score calculation system provided according to an embodiment of the present disclosure.
- the present disclosure proposes a lithium battery performance rating calculation method and system.
- the method includes obtaining data information during the operation of the lithium battery and uploading the data to the lithium battery performance rating database; using the lithium battery performance rating database to obtain the data from the battery
- the lithium battery performance rating system is constructed from the three dimensions of nameplate attributes, operating attributes, and environmental attributes; the fuzzy comprehensive evaluation method is used to construct a battery performance rating calculation model and calculate the ratings of various performance indicators of lithium batteries, achieving real-time, fast, and accurate calculation of lithium batteries.
- the performance of the lithium battery can be used to evaluate the performance status of the lithium battery, improve the maintenance and repair efficiency of the lithium battery, and ensure the safe and stable operation of the lithium battery.
- Figure 1 is a flow chart of a lithium battery performance score calculation method provided by an embodiment of the present disclosure. As shown in Figure 1, the method includes: steps 1-3.
- Step 1 Obtain the data information during the operation of the lithium battery and upload the data to the lithium battery performance rating database.
- the data information during the operation of the lithium battery includes: lithium battery operating power, voltage, current, temperature, etc.
- FIG 2 is a specific flow chart of a lithium battery performance score calculation method provided according to an embodiment of the present disclosure. As shown in Figure 2, after uploading the data to the lithium battery performance score database, it also includes: Cleaning and processing of lithium battery performance data.
- the cleaning process of the lithium battery performance data in the lithium battery performance rating database includes: filling in missing values, processing outliers, etc.;
- missing value filling refers to removing the data of the day if the missing value exceeds 5 points, and filling in the missing value below 5 points using the average of the three data before and after; outlier processing refers to using statistics for outliers. analyze, Box plot method and other methods are used to construct indicator outlier identification methods, and delete or fill them as needed.
- Step 2 Use the lithium battery performance status evaluation method to construct a lithium battery performance scoring system from three dimensions: battery nameplate attributes, operating attributes, and environmental attributes.
- the lithium battery performance rating database before using the lithium battery performance rating database to construct the lithium battery performance rating system from the three dimensions of battery nameplate attributes, operating attributes, and environmental attributes, it also includes: constructing lithium battery indicator characteristics related to the lithium battery;
- methods for constructing lithium battery indicator characteristics related to lithium batteries include descriptive statistics, correlation analysis, data transformation, data coding, binning, feature combination, etc.
- nameplate attributes include battery model, battery capacity, battery manufacturing date, batch, manufacturer and location, etc.
- Operation attributes include total operating power, total voltage, total current, maximum and minimum voltage, maximum and minimum temperature, etc.
- Environmental attributes include external maximum and minimum temperatures, maximum and minimum humidity, weather data, etc.
- Step 3 Use fuzzy comprehensive evaluation method to build a battery performance scoring calculation model.
- FIG. 3 is a schematic diagram of a fuzzy comprehensive evaluation model in a lithium battery performance score calculation method provided according to an embodiment of the present disclosure.
- the use of the fuzzy comprehensive evaluation method to construct the battery performance score calculation model specifically includes:
- F1 Divide the lithium battery performance rating system into levels, including cell level, module level, battery cluster level and battery compartment level.
- F2 Divide the various index characteristics of different levels of lithium batteries into different types of membership functions.
- each individual indicator of lithium battery can be divided into different types of membership functions, among which membership functions include “parabolic”, “positive S-shaped” and “linear”.
- parabolic membership function is calculated as follows: In the formula: u1(x) is the parabolic membership function value, x 1 represents the numerical lower limit, x 2 represents the optimal numerical lower limit, x 3 represents the upper limit of the optimal value, and x 4 represents the upper limit of the value;
- u2(x) is the value of the positive S-line membership function
- x 1 represents the lower limit of the value
- x 4 represents the upper limit of the value
- u3(x) is the linear membership function value
- x 4 represents the upper limit of the value
- x 1 represents the lower limit of the value
- the measured value of the indicator is substituted into the corresponding membership function formula to calculate the membership degree, that is, the parabolic membership function, the positive S-line membership function and the linear membership function.
- the calculated values of the function form the single-factor evaluation matrix A:
- A is a matrix of m rows and n columns, m is the number of samples of lithium batteries, and n is the number of lithium battery indicators;
- ⁇ 11 is the membership degree of the first feature of the first battery sample;
- ⁇ 1n is the membership degree of the nth feature of the first battery sample;
- ⁇ mn is the membership degree of the nth feature of the mth battery sample .
- F4 Use the multiple linear regression method to calculate the weight coefficients of various index characteristics of lithium batteries, and form the weight coefficients that affect the performance of lithium batteries to construct the weight coefficient matrix R.
- F5 Use the fuzzy comprehensive evaluation method to multiply the single-factor evaluation matrix and the transposed weight coefficient matrix to calculate the comprehensive evaluation index, build a battery performance scoring calculation model, and obtain the scores of various performance indicators of lithium batteries through calculation, and obtain different levels of lithium batteries Comprehensive scoring results of various performance indicators to achieve accurate assessment of the performance status of lithium batteries.
- the multiple linear regression method is used to calculate the weight coefficients of various index characteristics of lithium batteries, the weight coefficients that affect the performance of lithium batteries are composed to construct the weight coefficient matrix R, and the fuzzy comprehensive evaluation method is used to combine the single factor evaluation matrix and the transposed weight coefficient matrix Multiply to calculate the comprehensive evaluation index and construct the battery performance score calculation model, which specifically includes:
- H1 Establish the kth indicator of lithium battery Linear regression equation with other indicators, the equation is as follows: In the formula: is a constant term, n is the number of lithium battery indicators, x 1 , x 2 & other indicators;
- the complex correlation coefficient Z k of the kth indicator is calculated as follows: In the formula, is the average value of x k ;
- the weight coefficient r k of each indicator is obtained, forming a weight coefficient matrix R;
- r 1 is the weight coefficient of the first lithium battery indicator
- r n is the weight coefficient of the nth lithium battery indicator
- H4 Use fuzzy matrix synthesis to calculate the comprehensive evaluation index B
- y 1 is the comprehensive score of the performance status of the first lithium battery sample
- y m is the comprehensive score of the m-th lithium battery sample
- m is the number of lithium battery samples.
- this method uses the multiple linear regression method to calculate the lithium battery indicator weight coefficient.
- the principle is based on the strong collinearity between each lithium battery indicator and other indicators. Weakness is used to determine the index weight, and there is no influence of human factors.
- the multiple linear regression method is used to calculate the weight coefficient of the lithium battery indicator, and the indicator weight is determined based on the strength of the collinearity between each indicator of the lithium battery and other indicators; that is, the greater the complex correlation coefficient Z between a certain indicator and other indicators, the greater the The stronger the collinear relationship between an indicator and other indicators, the easier it is to be represented by a linear combination of other indicators. The more repeated information, the smaller the weight of the indicator should be.
- the lithium battery performance score calculation method further includes:
- the performance health status of lithium batteries is classified into four levels: healthy (excellent), sub-health (good), unhealthy (poor), and seriously unhealthy (poor);
- the visual interface includes the number of lithium battery equipment, distribution topology, temperature, and performance status results, which is used to provide lithium battery non-health warnings for the transportation and inspection department.
- Table 1 shows the health status corresponding to the evaluation score, as shown in Table 1
- Figure 4 is a visual interface for lithium battery performance status analysis in a lithium battery performance score calculation method provided according to an embodiment of the present disclosure. As shown in Figure 4, the lithium battery performance status comprehensive score is connected to the visual interface for intuitive display. device performance status.
- the content includes but is not limited to the quantity, distribution, temperature, health status results of lithium battery equipment, etc., and provides the transportation inspection department with lithium battery non-health warnings.
- Score analysis is performed based on the comprehensive evaluation results, and those with low scores are included in the scope of equipment maintenance to assist in fault location and improve inspection efficiency. Big data technology is used to carry out real-time monitoring and early warning of lithium battery performance status, enabling comprehensive interaction between relevant staff and equipment to achieve holographic perception of lithium battery status.
- embodiments of the present disclosure provide a lithium battery performance score calculation method.
- the method includes obtaining data information during the operation of the lithium battery and uploading the data to the lithium battery performance score database; using the lithium battery
- the performance rating database constructs a lithium battery performance rating system from the three dimensions of battery nameplate attributes, operating attributes, and environmental attributes; it uses the fuzzy comprehensive evaluation method to build a battery performance rating calculation model and calculate the scores of various performance indicators of lithium batteries, achieving real-time, fast, and Accurately evaluate the performance status of lithium batteries, improve the efficiency of lithium battery maintenance and repair, and ensure the safe and stable operation of lithium batteries; achieve accurate assessment of the performance status of lithium batteries, and develop visualization scenarios of lithium battery performance status to intuitively display energy storage
- the real-time operation status of the lithium battery in the unit can be disclosed to support the formulation of lithium battery maintenance plan strategies and guide proactive repairs.
- the establishment module 200 uses the lithium battery performance rating database to select battery nameplate attributes, operating attributes, and environmental attributes. Construct a lithium battery performance rating system in three dimensions;
- the calculation module 300 uses the fuzzy comprehensive evaluation method to construct a battery performance score calculation model and calculates the scores of various performance indicators of the lithium battery.
- the lithium battery performance rating calculation system provided by the embodiments of the present disclosure, real-time, fast, and accurate calculation and evaluation of the performance status of the lithium battery are achieved.
- this embodiment also provides an electronic device.
- this embodiment also provides a non-transitory computer-readable storage medium.
- This embodiment provides a computer program, wherein the computer program includes computer program code.
- the computer program code When the computer program code is run on a computer, it causes the computer to execute the lithium battery performance score calculation method as in Embodiment 1.
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Abstract
公开了一种锂电池性能评分计算方法及系统，所述方法包括获取锂电池运行过程中的数据信息并将数据上传至锂电池性能评分数据库中；利用所述锂电池性能评分数据库从电池铭牌属性、运行属性、环境属性三个维度构建锂电池性能评分体系；采用模糊综合评价法构建电池性能评分计算模型并计算锂电池各项性能指标评分。
Description
相关申请的交叉引用
本申请基于申请号为2022107257073、申请日为2022年6月24日的中国专利申请提出，并要求该中国专利申请的优先权，该中国专利申请的全部内容在此引入本申请作为参考。
本公开涉及储能领域，具体涉及一种锂电池性能评分计算方法及系统。
随着储能行业的进一步发展，越来越多的风能、光伏等新能源发电选择将电能储存起来。锂电池由于具有能量密度高、电化学特性稳定以及污染较少、循环寿命较长等优点被储能广泛投入使用，同时也推动了经济的持续快速发展。但是随着储能充放电循环次数的增加，锂电池会性能缓慢的产生不可逆的老化现象，其直接影响到锂电池的实用性、经济性和安全性等方面。所以能够准确、快速的评价锂电池的实时性能状态不仅提高了相关领域的安全性，而且能够为储能领域节省大量的资金和时间。因此研究出能够准确评价锂电池的性能状态的方法对其实际应用有着重大意义。
传统的锂电池性能状态评价方法模糊性大、评价指标片面、存在人为影响，并不能准确全面地体现锂电池的性能状态，评价结果说服力较差。
发明内容
本公开提供的一种锂电池性能评分计算方法及系统，以至少解决相关技术中锂电池性能状态评价方法模糊性大、评价指标片面、存在人为影响且并不能准确全面地体现锂电池的性能状态、评价结果说服力较差的问题。
本公开第一方面实施例提出一种锂电池性能评分计算方法，包括：
获取锂电池运行过程中的数据信息并将数据上传至锂电池性能评分数据库中；
利用所述锂电池性能评分数据库从电池铭牌属性、运行属性、环境属性三个维度构建锂电池性能评分体系；
采用模糊综合评价法构建电池性能评分计算模型。
本公开第二方面实施例提出一种锂电池性能评分计算系统，包括：
获取模块，获取锂电池运行过程中的数据信息并将数据上传至锂电池性能评分数据库中；
建立模块，利用所述锂电池性能评分数据库从电池铭牌属性、运行属性、环境属性三个维度构建锂电池性能评分体系；
计算模块，采用模糊综合评价法构建电池性能评分计算模型并计算锂电池各项性能指标评分。
本公开第三方面实施例提出一种计算机设备，包括：存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，处理器执行计算机程序时，实现如本公开第一方面的锂电池性能评分计算方法。
本公开第四方面实施例提出一种非临时性计算机可读存储介质，其上存储有计算机程序，计算机程序被处理器执行时实现如本公开第一方面的锂电池性能评分计算方法。
本公开第五方面实施例提出一种计算机程序产品，包括计算机程序，其中所述计算机程序在被处理器执行时实现如本公开第一方面实施例所述的锂电池性能评分计算方法。
本公开第六方面实施例提出了一种计算机程序，其中所述计算机程序包括计算机程序代码，当所述计算机程序代码在计算机上运行时，使得计算机执行如本公开第一方面实施例所述的锂电池性能评分计算方法。
本公开的实施例提供的技术方案至少带来以下有益效果：
本公开提供了一种锂电池性能评分计算方法和系统，所述方法包括获取锂电池运行过程中的数据信息并将数据上传至锂电池性能评分数据库中；利用所述锂电池性能评分数据库从电池铭牌属性、运行属性、环境属性三个维度构建锂电池性能评分体系；采用模糊综合评价法构建电池性能评分计算模型并计算锂电池各项性能指标评分，实现了实时、快速、准确的计算锂电池的性能从而评价锂电池的性能状态，提高锂电池维护与检修效率，保证锂电池的安全稳定运行。
本公开附加的方面以及优点将在下面的描述中部分给出，部分将从下面的描述中变得明显，或通过本公开的实践了解到。
本公开上述的和/或附加的方面以及优点从下面结合附图对实施例的描述中将变得明显和容易理解，其中：
图1是根据本公开一个实施例提供的一种锂电池性能评分计算方法的流程图；
图2是根据本公开一个实施例提供的一种锂电池性能评分计算方法的具体流程图；
图3是根据本公开一个实施例提供的一种锂电池性能评分计算方法中模糊综合评价模型的原理图；
图4是根据本公开一个实施例提供的一种锂电池性能评分计算方法中锂电池性能状态分析可视化界面图；
图5为根据本公开一个实施例提供的本公开一个实施例提供的一种锂电池性能评分计算系统的结构图。
下面详细描述本公开的实施例，所述实施例的示例在附图中示出，其中自始至终相同或类似的标号表示相同或类似的元件或具有相同或类似功能的元件。下面通过参考附图描述的实施例是示例性的，旨在用于解释本公开，而不能理解为对本公开的限制。
本公开提出的一种锂电池性能评分计算方法和系统，所述方法包括获取锂电池运行过程中的数据信息并将数据上传至锂电池性能评分数据库中；利用所述锂电池性能评分数据库从电池铭牌属性、运行属性、环境属性三个维度构建锂电池性能评分体系；采用模糊综合评价法构建电池性能评分计算模型并计算锂电池各项性能指标评分，实现了实时、快速、准确的计算锂电池的性能从而从而评价锂电池的性能状态，提高锂电池维护与检修效率，保证锂电池的安全稳定运行。
下面参考附图描述本公开实施例的一种锂电池性能评分计算方法及系统。
实施例1
图1为本公开实施例提供的一个实施例提供的一种锂电池性能评分计算方法的流程图，如图1所述，所述方法包括：步骤1-3。
步骤1：获取锂电池运行过程中的数据信息并将数据上传至锂电池性能评分数据库中。
所述锂电池运行过程中的数据信息包括：锂电池运行功率、电压、电流、温度等。
图2是根据本公开一个实施例提供的一种锂电池性能评分计算方法的具体流程图,如图2所示，将数据上传至锂电池性能评分数据库中之后还包括：对锂电池性能评分数据库中的锂电池性能数据进行清洗处理。
所述对锂电池性能评分数据库中的锂电池性能数据进行清洗处理包括：缺失值填充、异常值处理等；
其中，缺失值填充指的是针对缺失值超过5个点以上的则剔除当日数据，缺失值在5个点以下的采用前后3次数据均值进行填充；异常值处理指的是针对异常值通过统计分析、
箱线图法等构建指标异常值识别方式，并按需求删除或填充。
步骤2：利用所述锂电池性能状态评所述利用所述锂电池性能评分数据库从电池铭牌属性、运行属性、环境属性三个维度构建锂电池性能评分体系。
在本公开实施例当中，所述利用锂电池性能评分数据库从电池铭牌属性、运行属性、环境属性三个维度构建锂电池性能评分体系之前还包括：构建与锂电池相关的锂电池指标特征；
其中，构建与锂电池相关的锂电池指标特征的方法包括描述性统计、关联分析、数据变换、数据编码、分箱、特征组合等。
进一步的，所述铭牌属性包括电池型号、电池容量、电池出厂日期、批次、生产厂家及位置等；
运行属性包括运行总功率、总电压、总电流、最高最低电压、最高最低温度等；
环境属性包括外接最高最低温度、最高最低湿度、天气数据等。
步骤3：采用模糊综合评价法构建电池性能评分计算模型。
图3是根据本公开一个实施例提供的一种锂电池性能评分计算方法中模糊综合评价模型的原理图,所述采用模糊综合评价法构建电池性能评分计算模型具体包括：
F1：将锂电池性能评分体系进行级别划分，划分为电芯级、模组级、电池簇级和电池舱级。
需要注意的是，传统的锂电池性能评价只是针对电芯级别的性能评价，而储能电站作为一个庞大的电力系统，其存在大量电芯的集成运行，所以本公开的方法不仅针对电芯级，更是对其上层的模组级、电池簇级和电池舱级也进行阶梯型评价，使得对储能电站的整体性能也有了综合性评价。
F2：将不同级别的锂电池各项指标特征划分为不同类型的隶属度函数。
需要注意的是，锂电池各单项指标可划分为不同类型的隶属度函数，其中隶属度函数包括“抛物线型”、“正S型”和“直线型”等。
F3：将各单项锂电池指标特征带入相应隶属度函数中，计算隶属度，组合得到各级别锂电池单因素评价矩阵A。
进一步的，抛物线型隶属度函数计算式如下：
式中：u1(x)为抛物线型隶属度函数值，x1表示数值下限，x2表示最优数值下限，
x3表示最优数值上限，x4表示数值上限；
式中：u1(x)为抛物线型隶属度函数值，x1表示数值下限，x2表示最优数值下限，
x3表示最优数值上限，x4表示数值上限；
正S线型隶属度函数计算式如下：
式中：u2(x)为正S线型隶属度函数值，x1表示数值下限，x4表示数值上限；
式中：u2(x)为正S线型隶属度函数值，x1表示数值下限，x4表示数值上限；
(3)直线型隶属度函数计算式如下：
u3(x)＝k*x+b
式中u3(x)为直线型隶属度函数值，其中,
x4表示数值上限，x1表示数值下限。
具体的，根据隶属度函数和设定的指标临界值，将指标的测定值代入相应隶属度函数公式，计算隶属度，即将抛物线型隶属度函数、正S线型隶属度函数和直线型隶属度函数计算值组成单因素评价矩阵A：
式中：式中：A为m行n列矩阵，m为锂电池的样本数量，n为锂电池指标的个数；
μ11为第一个电池样本的第一个特征的隶属度；μ1n为第一个电池样本的第n个特征的隶属度；μmn为第m个电池样本的第n个特征的隶属度。
式中：式中：A为m行n列矩阵，m为锂电池的样本数量，n为锂电池指标的个数；
μ11为第一个电池样本的第一个特征的隶属度；μ1n为第一个电池样本的第n个特征的隶属度；μmn为第m个电池样本的第n个特征的隶属度。
F4：利用多元线性回归法计算锂电池各项指标特征的权重系数，组成影响锂电池性能的权重系数来构建权重系数矩阵R。
F5：利用模糊综合评价法将单因素评价矩阵和转置的权重系数矩阵相乘计算综合评价指数，构建电池性能评分计算模型，通过计算得到锂电池各项性能指标的评分，得到不同级别锂电池各项性能指标状态综合评分结果，以实现对锂电池性能状态的精准评估。
所述利用多元线性回归法计算锂电池各项指标特征的权重系数，组成影响锂电池性能的权重系数来构建权重系数矩阵R以及利用模糊综合评价法将单因素评价矩阵和转置的权重系数矩阵相乘计算综合评价指数，构建电池性能评分计算模型具体包括：
H1：建立锂电池第k个指标与其他指标的线性回归方程，方程式如下：
式中：为常数项，n为锂电池指标的个数，x1、x2......xn为锂电池指标中除
了的其他指标；
式中：为常数项，n为锂电池指标的个数，x1、x2......xn为锂电池指标中除
了的其他指标；
H2：计算指标复相关系数
第k个指标的复相关系数Zk算式如下：
式中，为xk的平均值；
式中，为xk的平均值；
H3：构建指标权重系数矩阵
通过对每个指标的复相关系数的倒数(1/Zk)归一化后得到各项指标的权重系数rk，组成权重系数矩阵R；
R＝[r1 r2 … rn]
式中，r1为第一个锂电池指标的权重系数，rn为第n个锂电池指标的权重系数；
H4：利用模糊矩阵合成计算综合评价指数B
式中：y1为第一个锂电池样本性能状态的综合评分，ym为第m个锂电池样本的综
合评分，m为锂电池样本数。
式中：y1为第一个锂电池样本性能状态的综合评分，ym为第m个锂电池样本的综
合评分，m为锂电池样本数。
需要注意的是，由于层次分析法等权重获取方式存在人为因素影响，故本方法采用多元线性回归法计算锂电池指标权重系数，其原理是根据锂电池各指标与其他指标之间的共线性强弱来确定指标权重，不存在人为因素影响。
采用多元线性回归法计算锂电池指标权重系数，根据锂电池各指标与其他指标之间的共线性强弱来确定指标权重；即某一指标与其他指标的复相关系数Z越大，则说明该指标与其他指标之间的共线性关系越强，越容易由其他指标的线性组合表示，重复信息越多，该指标的权重也就应该越小。
在本公开实施例当中，所述锂电池性能评分计算方法还包括：
根据评价结果对锂电池性能健康状态进行分类，分为健康(优秀)、亚健康(良好)、不健康(较差)、严重不健康(差)四个等级；
绘制锂电池性能状态可视化界面。
其中，可视化界面包括锂电池设备数量、分布拓扑、温度、性能状态结果，用于为运检部门提供锂电池锂电池非健康预警。
具体的，表1为评价得分对应的健康状态，如表1所示
图4是根据本公开一个实施例提供的一种锂电池性能评分计算方法中锂电池性能状态分析可视化界面，如图4所示，将锂电池性能状态综合评分接入可视化界面，用以直观展示器性能状态。内容包括但不限于锂电池设备数量、分布、温度、健康状态结果等，为运检部门提供锂电池锂电池非健康预警。
需要注意的是，针对处于非健康状态的锂电池锂电池，进一步聚焦投运年限、负载容量比例、重过载次数等维度，从该维度进行得分分析，得分越低的维度纳入设备检修范围，辅助故障定位，提高检查效率。
从综合评价结果进行得分分析，得分低的纳入设备检修范围，辅助故障定位，提高检查效率。利用大数据技术开展锂电池性能状态实时监控预警，使相关工作人员与设备的全面交互，实现锂电池状态的全息感知。
综上所述，本公开实施例提供的一种锂电池性能评分计算方法，所述方法包括获取锂电池运行过程中的数据信息并将数据上传至锂电池性能评分数据库中；利用所述锂电池性能评分数据库从电池铭牌属性、运行属性、环境属性三个维度构建锂电池性能评分体系；采用模糊综合评价法构建电池性能评分计算模型并计算锂电池各项性能指标评分，实现了实时、快速、准确的评价锂电池的性能状态，提高锂电池维护与检修效率，保证锂电池的安全稳定运行；实现对锂电池性能状态的精准评估，并开发锂电池性能状态可视化场景，用以直观展示储能单元中锂电池的实时运行情况，该公开可用以支撑锂电池检修计划策略制定及指导主动抢修。
实施例2
图5为本公开实施例提供的一种锂电池性能评分计算系统的结构图，如图5所示，所述系统包括：
获取模块100，获取预锂电池运行过程中得数据信息并将数据上传至锂电池性能评分数据库中；
建立模块200，利用所述锂电池性能评分数据库从电池铭牌属性、运行属性、环境属性
三个维度构建锂电池性能评分体系；
计算模块300，采用模糊综合评价法构建电池性能评分计算模型并计算锂电池各项性能指标评分。
所述系统还包括清洗单元，所述清洗单元用于对上传至锂电池性能评分数据库中的锂电池性能数据进行清洗处理，具体包括缺失值填充、异常值处理等。
综上所述，根据本公开实施例提供的一种锂电池性能评分计算系统，实现了对锂电池的性能状态实时、快速、准确的计算和评价。
实施例3
为了实现上述实施例，本实施例还提出一种电子设备。
本实施例提供的电子设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，处理器执行计算机程序时，实现实施例1中的锂电池性能评分计算方法。
实施例4
为了实现上述实施例，本实施例还提出一种非临时性计算机可读存储介质。
本实施例提供的非临时性计算机可读存储介质，其上存储有计算机程序，计算机程序被处理器执行时实现实施例1中的锂电池性能评分计算方法。
实施例5
为了实现上述实施例，本实施例还提出一种计算机程序产品。
本实施例提供的计算机程序产品，包括计算机程序，其中所述计算机程序在被处理器执行时实现如实施例1中的锂电池性能评分计算方法。
实施例6
为了实现上述实施例，本实施例还提出了一种计算机程序。
本实施例提供的计算机程序，其中所述计算机程序包括计算机程序代码，当所述计算机程序代码在计算机上运行时，使得计算机执行如实施例1中的锂电池性能评分计算方法。
需要注意的是，前述对锂电池性能评分计算方法实施例的解释说明也适用于本公开实施例2-6中的锂电池性能评分计算系统、电子设备、非瞬时性计算机可读存储介质、计算机程序产品和计算机程序，此处不再赘述。
在本说明书的描述中，参考术语“一个实施例”、“一些实施例”、“示例”、“具体示例”、或“一些示例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或者特点包含于本公开的至少一个实施例或示例中。在本说明书中，对上述术语的示意性表述不必须针对的是相同的实施例或示例。而且，描述的具体特征、结构、材料或者特点可以在任一个或多个实施例或示例中以合适的方式结合。此外，在不相互矛盾的情况下，
本领域的技术人员可以将本说明书中描述的不同实施例或示例以及不同实施例或示例的特征进行结合和组合。
流程图中或在此以其他方式描述的任何过程或方法描述可以被理解为，表示包括一个或更多个用于实现定制逻辑功能或过程的步骤的可执行指令的代码的模块、片段或部分，并且本公开的优选实施方式的范围包括另外的实现，其中可以不按所示出或讨论的顺序，包括根据所涉及的功能按基本同时的方式或按相反的顺序，来执行功能，这应被本公开的实施例所属技术领域的技术人员所理解。
Claims (12)
- 一种锂电池性能评分计算方法，其特征在于，所述方法包括：获取锂电池运行过程中的数据信息并将数据上传至锂电池性能评分数据库中；利用所述锂电池性能评分数据库从电池铭牌属性、运行属性、环境属性三个维度构建锂电池性能评分体系；采用模糊综合评价法构建电池性能评分计算模型并计算锂电池各项性能指标评分。
- 如权利要求1所述的方法，其特征在于，将数据上传至锂电池性能评分数据库中之后还包括：对锂电池性能评分数据库中的锂电池性能数据进行清洗处理；所述对锂电池性能评分数据库中的锂电池性能数据进行清洗处理包括：缺失值填充、异常值处理。
- 如权利要求1或2所述的方法，其特征在于，利用所述锂电池性能评分数据库从电池铭牌属性、运行属性、环境属性三个维度构建锂电池性能评分体系之前还包括：构建与锂电池性能相关的锂电池指标特征；其中，所述构建与锂电池性能相关的锂电池指标特征的方法包括：描述性统计、关联分析、数据变换、数据编码、分箱、特征组合；所述铭牌属性包括电池型号、电池容量、电池出厂日期、批次、生产厂家及位置；所述运行属性包括运行总功率、总电压、总电流、最高最低电压、最高最低温度；所述环境属性包括外接最高最低温度、最高最低湿度、天气数据。
- 如权利要求1至3中任一项所述的方法，其特征在于，采用模糊综合评价法构建电池性能评分计算模型并计算锂电池各项性能指标评分包括：将锂电池性能评分体系进行级别划分，划分为电芯级、模组级、电池簇级和电池舱级；将不同级别的锂电池各项指标特征划分为不同类型的隶属度函数；将各单项锂电池指标特征带入相应隶属度函数中，计算隶属度，组合得到各级别锂电池单因素评价矩阵A；利用多元线性回归法计算锂电池各项指标特征的权重系数，组成影响锂电池性能的权重系数来构建权重系数矩阵R；利用模糊综合评价法将单因素评价矩阵和转置的权重系数矩阵相乘计算综合评价指数，构建电池性能评分计算模型，计算得到不同级别锂电池性能指标评分结果；其中，所述隶属度函数类型包括：抛物线型隶属度函数、正S线型隶属度函数和直线型隶属度函数。
- 如权利要求4所述的方法，其特征在于，所述抛物线型隶属度函数计算式如下：
式中：u1(x)为抛物线型隶属度函数值，x1表示数值下限，x2表示最优数值下限，x3表示最优数值上限，x4表示数值上限；所述正S线型隶属度函数计算式如下：
式中：u2(x)为正S线型隶属度函数值，x1表示数值下限，x4表示数值上限；所述直线型隶属度函数计算式如下：u3(x)＝k*x+b式中：u3(x)为直线型隶属度函数值，其中,
x4表示数值上限，x1表示数值下限；将抛物线型隶属度函数、正S线型隶属度函数和直线型隶属度函数计算值组成单因素评价矩阵A：
式中：A为m行n列矩阵，m为锂电池的样本数量，n为锂电池指标的个数；μ11为第一个电池样本的第一个特征的隶属度；μ1n为第一个电池样本的第n个特征的隶属度；μmn为第m个电池样本的第n个特征的隶属度。 - 如权利要求4所述的方法，其特征在于，所述利用多元线性回归法计算锂电池各项指标特征的权重系数，组成影响锂电池性能的权重系数来构建权重系数矩阵R以及利用模糊综合评价法将单因素评价矩阵和转置的权重系数矩阵相乘计算综合评价指数，构建电池性能评分计算模型具体包括：H1：建立锂电池第k个指标与其他指标的线性回归方程，的计算式如下：
式中：为常数项，n为锂电池指标的个数，x1、x2......xn为锂电池指标中除了的其他指标；H2：计算指标复相关系数第k个指标的复相关系数Zk计算式如下：
式中，为xk的平均值；H3：构建指标权重系数矩阵通过对每个指标的复相关系数的倒数(1/Zk)归一化后得到各项指标的权重系数r，组成权重系数矩阵R；R＝[r1 r2 … rn]式中，r1为第一个锂电池指标的权重系数，rn为第n个锂电池指标的权重系数；H4：利用模糊矩阵合成计算综合评价指数B
式中：y1为第一个锂电池样本性能状态的综合评分，ym为第m个锂电池样本的综合评分，m为锂电池样本数。 - 如权利要求1至6中任一项所述的方法，其特征在于，所述方法还包括：根据评价结果对锂电池性能健康状态进行分类，分为健康、亚健康、不健康、严重不健康四个等级；绘制锂电池性能状态可视化界面；其中，可视化界面包括锂电池设备数量、分布拓扑、温度、性能状态结果，用于为运检部门提供锂电池锂电池非健康预警。
- 一种锂电池性能评分计算系统，其特征在于，所述系统包括：获取模块，获取锂电池运行过程中的数据信息并将数据上传至锂电池性能评分数据库中；建立模块，利用所述锂电池性能评分数据库从电池铭牌属性、运行属性、环境属性三个维度构建锂电池性能评分体系；计算模块，采用模糊综合评价法构建电池性能评分计算模型并计算锂电池各项性能指标评分。
- 一种电子设备，其特征在于，包括：存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述程序时，实现如权利要求1至7中任一项所述的锂电池性能评分计算方法。
- 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，该程序被处理器执行时实现如权利要求1至7中任一项所述的锂电池性能评分计算方法。
- 一种计算机程序产品，包括计算机程序，其中所述计算机程序在被处理器执行时实现如权利要求1至7中任一项所述的锂电池性能评分计算方法。
- 一种计算机程序，其特征在于，所述计算机程序包括计算机程序代码，当所述计算机程序代码在计算机上运行时，使得计算机执行如权利要求1至7中任一项所述的锂电池性能评分计算方法。
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