WO2023284039A1 - 锂电池组内部短路异常诊断方法及系统 - Google Patents

锂电池组内部短路异常诊断方法及系统 Download PDF

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WO2023284039A1
WO2023284039A1 PCT/CN2021/110888 CN2021110888W WO2023284039A1 WO 2023284039 A1 WO2023284039 A1 WO 2023284039A1 CN 2021110888 W CN2021110888 W CN 2021110888W WO 2023284039 A1 WO2023284039 A1 WO 2023284039A1
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voltage
cell
battery pack
internal short
short circuit
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PCT/CN2021/110888
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English (en)
French (fr)
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朱广焱
张鹏博
施璐
谈文
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上海派能能源科技股份有限公司
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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
    • 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
    • H01M10/05—Accumulators with non-aqueous electrolyte
    • H01M10/052—Li-accumulators
    • H01M10/0525—Rocking-chair batteries, i.e. batteries with lithium insertion or intercalation in both electrodes; Lithium-ion batteries
    • 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
    • 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
    • H01M10/482—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte for several batteries or cells simultaneously or sequentially
    • 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

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  • the invention relates to the technical field of internal short circuit detection of a lithium battery pack, in particular to a method and system for diagnosing an internal short circuit abnormality of a lithium battery pack.
  • the internal short circuit in the middle and late stage has obvious thermal and electrical characteristics, which is very easy to identify, but the probability of developing into thermal runaway also increases sharply.
  • the internal short circuit has a long development cycle, the early effect is not significant, and it is difficult to identify and monitor, but it also provides a sufficient time window for the discovery and early warning of internal short circuits, and provides the possibility for early prevention of thermal runaway.
  • it is necessary to monitor the development of the internal short circuit of the battery pack.
  • the object of the present invention is to provide a method and system for diagnosing abnormalities in the internal short circuit of a lithium battery pack to solve the problem in the prior art that it is difficult to identify and monitor the internal short circuit of the battery pack at an early stage.
  • the present invention provides a method for diagnosing an internal short circuit abnormality of a lithium battery pack.
  • the method for diagnosing an internal short circuit abnormality of a lithium battery pack at least includes:
  • S2 Calculate the residual of each cell voltage and the average voltage of the battery pack, and calculate the cumulative residual of each cell voltage
  • S3 Set the threshold range of the current diagnosis cycle, and judge the cell whose cumulative residual error of the cell voltage exceeds the threshold range of the current diagnostic cycle as an abnormal cell, and the cumulative residual error of the cell voltage is within the threshold range of the current diagnostic cycle
  • the batteries are judged as normal batteries
  • step S4 Return to step S1 after eliminating abnormal cells, until no abnormal cells are detected within the preset time period, the diagnosis is completed and the diagnosis result is output.
  • step S1 after obtaining the voltages of each cell, smoothing processing is performed on each cell voltage.
  • the smoothing process is implemented using a filtering algorithm
  • the filtering algorithm includes any one of a median filtering algorithm, a moving average filtering algorithm, and a fast Fourier transform filtering algorithm.
  • the threshold range of the current diagnosis cycle is obtained based on the statistical characteristics of the cumulative residual error of each cell voltage in the previous step.
  • the threshold range of the current diagnosis cycle is a 95% confidence interval of the cumulative residual error of each cell voltage.
  • the diagnosis result includes whether there is an abnormal battery cell.
  • the diagnosis result also includes a serial number corresponding to the abnormal cell.
  • the present invention provides a lithium battery pack internal short circuit abnormality diagnosis system
  • the lithium battery pack internal short circuit abnormality diagnosis system at least includes:
  • Input module voltage processing module, voltage residual extraction module, threshold generation module, internal short circuit identification module and output module;
  • the input module obtains the voltage of each cell, and inputs the voltage of each cell to the voltage processing module and the voltage residual extraction module;
  • the voltage processing module is connected to the input module and the output terminal of the voltage processing module, and calculates the average voltage of the battery pack when the internal short circuit identification module outputs a normal signal of the battery pack; when the internal short circuit identification module outputs an abnormal Calculate the average voltage of the battery pack after excluding the corresponding cell voltage input by the input module when the cell is numbered;
  • the voltage residual extraction module is connected to the output terminal of the input module and the voltage processing module, and extracts the cumulative residual of the cell voltage in the current diagnosis cycle;
  • the threshold generation module is connected to the output terminal of the voltage residual extraction module, and updates and generates the threshold range of the current diagnosis cycle;
  • the internal short circuit identification module is connected to the output terminal of the threshold generation module, and judges whether there is an abnormal cell based on the threshold range of the current diagnosis cycle;
  • the output module is connected to the output terminal of the internal short circuit identification module, and outputs a diagnosis result.
  • the voltage processing module further includes a smoothing processing unit, and the smoothing processing unit performs smoothing processing on the cell voltage input by the input module.
  • the smoothing processing unit includes, but is not limited to, a median filter, a moving average filter or a fast Fourier transform filter.
  • the threshold generation module generates the threshold range of the current diagnosis cycle based on the statistical characteristics of the cumulative residual of each cell voltage in the current diagnosis cycle.
  • the threshold range of the current diagnosis cycle is a 95% confidence interval of the cumulative residual error of each cell voltage.
  • the method and system for diagnosing the internal short circuit abnormality of the lithium battery pack of the present invention have the following beneficial effects:
  • the abnormal diagnosis method and system of the lithium battery pack of the present invention have a short detection period, and can diagnose abnormal batteries with serious internal short circuits within a few hours.
  • the abnormal diagnosis method and system of the lithium battery pack of the present invention have strong diagnostic and identification capabilities, and can locate the position of the abnormal cell, and can detect the abnormal cell in advance in the early stage of the internal short circuit, preventing the development and deterioration of the internal short circuit from causing Serious accidents such as thermal runaway improve the safety level of battery packs.
  • the internal short-circuit anomaly diagnosis method of the lithium battery pack and the algorithm of the system of the present invention have low space-time complexity, simple diagnostic technical process, and low requirements on data accuracy, and have great application value in the safe operation of lithium-ion battery packs.
  • Fig. 1 shows a schematic diagram of the curve of the cell voltage residual of the present invention.
  • FIG. 2 is a schematic flowchart of a method for diagnosing an internal short circuit abnormality of a lithium battery pack according to the present invention.
  • Fig. 3 is a schematic diagram showing the principle of diagnosing the abnormal battery No.
  • Fig. 4 is a schematic diagram showing the principle of diagnosing the abnormal cell No.
  • FIG. 5 is a schematic structural diagram of the internal short-circuit abnormality diagnosis system of the lithium battery pack according to the present invention.
  • each battery cell in the battery pack is measured, and the measurement time is 2 hours, and the curve of the residual voltage of each battery cell is obtained.
  • the residual error of abnormal battery No. 1 and abnormal battery No. 2 is obvious It deviates from the residual curve of the remaining cells in the battery pack; it can be seen that the residual voltage of the cell voltage is a signal indicator that can effectively distinguish abnormal cells.
  • the present invention is based on the detection of the residual voltage of the battery cell, realizes the early identification of the short circuit of the battery pack in the internal short circuit, and then improves the safety of the battery pack, and the implementation method is as follows.
  • this embodiment provides a method for diagnosing an internal short circuit abnormality of a lithium battery pack.
  • the method for diagnosing an internal short circuit abnormality of a lithium battery pack includes:
  • S1 battery pack voltage processing step obtain the voltage of each cell, and calculate the average voltage of the battery pack.
  • the collected voltages of each cell are obtained, and the sum of the voltages of each cell is divided by the number of cells to obtain the average voltage of the battery pack.
  • the battery pack to be diagnosed in order to improve the accuracy of diagnosis, is placed in an environment with a stable ambient temperature, so as to avoid the influence of temperature on the voltage of each battery cell in the battery pack.
  • the influence of the ambient temperature on the final abnormal diagnosis result of the internal short circuit of the battery pack can be ignored, and the present embodiment is not limited thereto.
  • the voltage of each battery cell is smoothed to eliminate the random error of a single measurement.
  • a filtering algorithm is used to achieve smoothing.
  • the filtering algorithm includes but is not limited to a median filtering algorithm, a moving average filtering algorithm, and a fast Fourier transform filtering algorithm. Any filtering algorithm that can smooth the cell voltage is applicable to this application. Invention, not repeat them one by one here.
  • S2 The step of obtaining the cumulative residual of the voltage of each battery cell: calculating the residual of the voltage of each battery cell and the average voltage of the battery pack, and calculating the cumulative residual of the voltage of each battery cell respectively.
  • the difference between the voltage of each cell and the average voltage of the battery pack is obtained, which is recorded as the residual of each cell voltage, and the residuals of the same cell voltage are accumulated to obtain the cumulative residual of the cell voltage , where the cumulative residual error of each cell voltage in the first diagnostic cycle is the current residual error of each cell voltage.
  • the voltages of the cells in step S2 are the voltage values after the smoothing process. It can be seen from Figure 1 that there is a certain fluctuation in the residual error of the cell voltage. In order to obtain a more stable identification index signal, the present invention accumulates the residual error to amplify the residual signal to obtain a more stable criterion. Therefore, the accuracy of the diagnosis result is Accuracy is higher.
  • S3 Threshold range acquisition and judging whether the battery cell is abnormal set the threshold range of the current diagnosis cycle, and judge the cell whose cumulative residual error of the cell voltage exceeds the threshold range of the current diagnosis cycle as an abnormal cell, and the accumulation of cell voltage The cells whose residual error is within the threshold range of the current diagnosis cycle are judged as normal cells.
  • the distribution status of the cumulative residual error of each cell voltage in the current diagnosis cycle is obtained, and the threshold range of the current diagnosis cycle is set according to the statistical characteristics.
  • the specific threshold range can be set according to actual needs.
  • the threshold range of the current diagnosis cycle is set as the 95% confidence interval of the cumulative residual error of each cell voltage (that is, according to statistical laws, 95% of the cell voltage values should be distributed within the threshold range), further Generally, the upper limit of the 95% confidence interval is the mean value of the cumulative residual error of each cell voltage plus 1.96 times the standard deviation, and the lower limit is the mean value of the cumulative residual error of each cell voltage minus 1.96 times the standard deviation.
  • the threshold range of the current diagnosis cycle may be a preset value, and does not need to be obtained based on the distribution of the cumulative residual error of each battery cell voltage in the current diagnosis cycle.
  • setting the threshold range of the current diagnosis cycle as the 95% confidence interval of the cumulative residual error of each battery cell voltage can greatly speed up the diagnosis speed on the basis of ensuring accuracy.
  • the cell is a normal cell; if the accumulated residual error of the cell voltage exceeds the threshold range of the current diagnosis cycle, it is determined that the cell The cell is an abnormal cell.
  • S4 cycle diagnosis step return to step S1 after eliminating abnormal cells, until no abnormal cells are detected within the preset time period, the diagnosis is completed and the diagnosis result is output.
  • the abnormal cells are excluded, and then the cell voltage is reacquired based on the remaining cells and the next diagnostic cycle is performed.
  • the threshold range of each diagnostic cycle needs to be updated; in this embodiment, the threshold range of each diagnostic cycle is based on The statistical characteristics of the cumulative residual of each cell voltage in the previous step are obtained. If no abnormal battery cell is detected within the preset time period, the diagnosis is considered to be completed, and the diagnosis result is output.
  • the output result includes whether there is an abnormal battery cell, and if there is an abnormal battery cell, the number corresponding to the abnormal battery cell is output.
  • the duration of the preset time period (multiple diagnostic cycles can be executed within the preset time period) can be set according to actual needs, so as to ensure that accurate diagnosis results can be obtained, and details will not be repeated here.
  • each cell As shown in Figure 3, it is the cumulative residual distribution of each cell voltage, and each cell (distinguished by different cell numbers) has a corresponding cumulative residual error, where the dotted lines represent the upper and lower limits of the threshold range, between the upper limit Between the upper limit and the lower limit, it is within the threshold range, and if it is greater than the upper limit or smaller than the lower limit, it is out of the threshold range. Assume that there are abnormal cell No. 1 and abnormal cell No. 2, other cells are normal, and the cumulative residual error distribution of normal cells is even and within the threshold range; and in the current diagnosis cycle, the cell of abnormal cell No. If the cumulative residual error of the voltage is lower than the lower limit of the threshold range (beyond the threshold range), cell No.
  • the cumulative residual error of the cell voltage of the abnormal battery No. 2 is lower than the threshold range of the current diagnostic cycle (new The lower limit of the threshold range) (beyond the threshold range), the cell No. 2 is diagnosed as an abnormal cell. After eliminating the abnormal battery No. 2, continue to perform the steps of cyclic diagnosis. If no abnormal battery is detected after the preset time, the diagnosis ends, and the diagnosis result is output: there are abnormal batteries, and the abnormal battery numbers are V102 and V103.
  • the invention uses the cumulative residual error of the cell voltage as an intuitive quantification judgment index to identify and diagnose abnormal cells, and can effectively distinguish abnormal cells. It is worth noting that in this embodiment, the abnormal cells in the battery pack can be judged after only 2 hours of measurement, which proves that this method can quickly diagnose the short-circuit abnormal cells inside the battery pack.
  • this embodiment provides a system for diagnosing an internal short circuit abnormality of a lithium battery pack.
  • the system for diagnosing an internal short circuit abnormality of a lithium battery pack includes:
  • An input module 1 a voltage processing module 2 , a voltage residual extraction module 3 , a threshold generation module 4 , an internal short circuit identification module 5 and an output module 6 .
  • the input module 1 acquires the voltage of each battery cell, and inputs the voltage of each battery cell to the voltage processing module 2 and the voltage residual extraction module 3 .
  • the voltage processing module 2 is connected to the output terminal of the input module 1 and the voltage processing module 2, and calculates the average voltage of the battery pack when the internal short circuit identification module 5 outputs a normal signal of the battery pack ; When the internal short-circuit identification module 5 outputs an abnormal cell number, calculate the average voltage of the battery pack after excluding the corresponding cell voltage input by the input module 1 .
  • the voltage processing module 2 includes an average value calculation unit, and any hardware circuit or software code that can select an input signal and perform average value calculation on the selected signal is applicable to the present invention.
  • the voltage processing module 2 further includes a smoothing processing unit, and the smoothing processing unit performs smoothing processing on the cell voltage input by the input module 1 .
  • the smoothing processing unit includes but is not limited to a median filter, a moving average filter or a fast Fourier transform filter. In this embodiment, a moving average filter is used, and the moving average filter can be adjusted as required. The length of the sliding window; any filtering algorithm unit capable of smoothing the cell voltage is applicable to the present invention, and will not be repeated here.
  • the voltage residual extraction module 3 is connected to the output terminals of the input module 1 and the voltage processing module 2 to extract the accumulated residual of the cell voltage in the current diagnosis cycle.
  • the voltage residual extraction module 3 receives the voltage of each battery cell and the average voltage of the battery pack in the current diagnosis cycle, and calculates the difference between the voltage of each cell and the average voltage of the battery pack, and calculates the difference value corresponding to each cell It is accumulated with the residual accumulated in the previous diagnostic cycle to obtain the accumulated residual of each cell voltage.
  • the threshold generation module 4 is connected to the output terminal of the voltage residual extraction module 3 to update and generate the threshold range of the current diagnosis period.
  • the threshold generation module 4 generates the threshold range of the current diagnosis cycle based on the statistical characteristics of the cumulative residual of each cell voltage in the current diagnosis cycle.
  • the threshold range for the current diagnostic cycle is a 95% confidence interval of the cumulative residual for each cell voltage.
  • the threshold range can be set as required, and is not limited to this embodiment.
  • the internal short circuit identification module 5 is connected to the output terminal of the threshold generation module 4 , and judges whether there is an abnormal cell based on the diagnosis threshold range.
  • the cumulative residual error of the cell voltage is within the threshold range of the current diagnosis cycle, it is determined that the cell is a normal cell and the output battery pack is normal; if the cumulative residual error of the cell voltage exceeds the threshold range of the current diagnostic cycle , then it is determined that the cell is an abnormal cell, and the abnormal cell label is further output.
  • the output module 6 is connected to the output terminal of the internal short circuit identification module 5 to output a diagnosis result.
  • the system for diagnosing an internal short-circuit abnormality of a lithium battery pack in this embodiment can be used to implement the method for diagnosing an internal short-circuit abnormality of a lithium battery pack in Embodiment 1, and specific principles will not be repeated here.
  • the present invention amplifies the voltage difference caused by the internal short circuit of the battery by accumulating the residual voltage of each cell in the battery pack, especially the voltage difference caused by the early internal micro-short circuit, and then according to the statistical information of the accumulated residual of each cell in the battery pack, By horizontal comparison, the consistency of the cumulative residual voltage of each battery cell is used to diagnose and identify abnormal cells in the battery pack. There is no need to disassemble the battery, only the static battery voltage collected by the onboard battery management system of the battery, and the difference in battery manufacturing and capacity will lead to differences in the voltage of each battery. Therefore, the present invention uses the residual voltage of the battery, This is a relative quantity, which can avoid the voltage difference between the cells, so that the cells with serious internal short circuits can be extracted in a short measurement time range.
  • the present invention provides a method and system for diagnosing abnormalities in the internal short circuit of a lithium battery pack, including: S1: obtaining the voltage of each cell, and calculating the average voltage of the battery pack; S2: calculating the voltage of each cell and the voltage of the battery pack The residual of the average voltage, and calculate the cumulative residual of each cell voltage separately; S3: Set the threshold range of the current diagnosis cycle, and judge the cells whose cumulative residual error of the cell voltage exceeds the threshold range of the current diagnosis cycle as abnormal Cells, cells whose accumulated residual voltage of the cell voltage is within the threshold range of the current diagnosis cycle are judged to be normal cells; S4: return to step S1 after excluding abnormal cells, until no abnormal cells are detected within the preset time period , the diagnosis ends and the diagnosis result is output.
  • the internal short-circuit abnormality diagnosis method and system of the lithium battery pack of the present invention amplify the voltage difference caused by the internal short-circuit, especially the early internal micro-short circuit by accumulating voltage residuals, and then according to the distribution of the accumulated residuals of each cell in the battery pack And the 95% confidence interval is used as the threshold, and the horizontal comparison is used to diagnose and identify the abnormal cells in the battery pack by using the consistency of the cumulative residual voltage of each cell voltage; the detection speed is fast and the detection accuracy is high, and the internal detection of the battery pack can be quickly and accurately realized.
  • Abnormal short-circuit diagnosis find cells with rapid internal short-circuit development, give early warning, and improve the overall operation safety of the battery pack. Therefore, the present invention effectively overcomes various shortcomings in the prior art and has high industrial application value.

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Abstract

本发明提供一种锂电池组内部短路异常诊断方法及系统，包括：S1：获取各电芯电压，并计算电池组平均电压；S2：计算各电芯电压与所述电池组平均电压的残差，并分别计算各电芯电压的累积残差；S3：设定当前诊断周期的阈值范围，将电芯电压的累积残差超出当前诊断周期的阈值范围的电芯判定为异常电芯，电芯电压的累积残差在当前诊断周期的阈值范围内的电芯判定为正常电芯；S4：排除异常电芯后返回步骤S1，直至预设时间段内无异常电芯检出，诊断结束并输出诊断结果。本发明检测速度快，检出精度高，可快速准确地实现电池组内部短路异常诊断，发现内部短路发展较快的电芯，提前预警，提升电池组的整体运行安全性。

Description

锂电池组内部短路异常诊断方法及系统 技术领域
本发明涉及锂电池组内部短路检测技术领域，特别是涉及一种锂电池组内部短路异常诊断方法及系统。
背景技术
近年来，大多数锂电池的起火事故是由其热失控引起的，热失控对锂电池的运行安全威胁已成为亟待解决的突出问题。在实际应用中，通常由几十上百乃至数千个锂电池串联和并联组成锂电池组，当其中一个电池出现热失控时，该现象可能会在电池组内扩散，从而引起火灾，爆炸或其他严重后果。通常，热失控的主要诱因是机械、电气和热等方面的滥用引起的锂电池内部严重短路。电池内部短路电流产生的焦耳热会导致电池温度升高，如果局部热量积聚到足以触发热失控，就会发生起火和爆炸等灾难性事故。内部短路已成为威胁电池整体安全性的重要原因。
中后期的内部短路具有明显的热特性与电特性，非常容易识别，但是相应发展为热失控的几率也剧增。内部短路在发展周期较长，早期效应不显著，难以辨识监测，但是这也为内部短路的发现和预警提供了足够的时间窗口，并为热失控的早期预防提供了可能。为增加电池组的安全可靠性能，有必要监测电池组内部短路的发展情况，然而，在内部短路早期，对电池组内各电芯的电压和压差、温度和温差变化影响很小，且容易受到噪声、负载电流和环境温度波动的干扰，很难识别早期的微弱内部短路引起的电池异常信号。因此，如何识别电池组在内部短路早期异常行为，提高电池组安全性，已成为本领域技术人员亟待解决的问题之一。
发明内容
鉴于以上所述现有技术的缺点，本发明的目的在于提供一种锂电池组内部短路异常诊断方法及系统，用于解决现有技术中电池组内部短路在早期难以辨识监测的问题。
为实现上述目的及其他相关目的，本发明提供一种锂电池组内部短路异常诊断方法，所述锂电池组内部短路异常诊断方法至少包括：
S1：获取各电芯电压，并计算电池组平均电压；
S2：计算各电芯电压与所述电池组平均电压的残差，并分别计算各电芯电压的累积残差；
S3：设定当前诊断周期的阈值范围，将电芯电压的累积残差超出当前诊断周期的阈值范 围的电芯判定为异常电芯，电芯电压的累积残差在当前诊断周期的阈值范围内的电芯判定为正常电芯；
S4：排除异常电芯后返回步骤S1，直至预设时间段内无异常电芯检出，诊断结束并输出诊断结果。
可选地，在步骤S1中，获取各电芯电压后，对各电芯电压进行平滑处理。
更可选地，所述平滑处理采用滤波算法实现，滤波算法包括中值滤波算法、滑动平均滤波算法、快速傅里叶变换滤波算法中任意一种。
可选地，当前诊断周期的阈值范围基于上一步各电芯电压的累积残差的统计学特征获取。
更可选地，当前诊断周期的阈值范围为各电芯电压的累积残差的95％置信区间。
可选地，所述诊断结果包括是否存在异常电芯。
更可选地，所述诊断结果还包括异常电芯对应的编号。
为实现上述目的及其他相关目的，本发明提供一种锂电池组内部短路异常诊断系统，所述锂电池组内部短路异常诊断系统至少包括：
输入模块，电压处理模块，电压残差提取模块，阈值生成模块，内部短路识别模块及输出模块；
所述输入模块获取各电芯电压，并将各电芯电压输入到所述电压处理模块及所述电压残差提取模块；
所述电压处理模块连接所述输入模块及所述电压处理模块的输出端，当所述内部短路识别模块输出电池组正常信号时计算所述电池组平均电压；当所述内部短路识别模块输出异常电芯编号时排除所述输入模块输入的对应电芯电压后计算电池组平均电压；
所述电压残差提取模块连接于所述输入模块及所述电压处理模块的输出端，提取当前诊断周期内电芯电压的累积残差；
所述阈值生成模块连接于所述电压残差提取模块的输出端，更新并产生当前诊断周期的阈值范围；
所述内部短路识别模块连接于所述阈值生成模块的输出端，基于当前诊断周期的阈值范围判断是否存在异常电芯；
所述输出模块连接于所述内部短路识别模块的输出端，输出诊断结果。
可选地，所述电压处理模块还包括平滑处理单元，所述平滑处理单元对所述输入模块输入的电芯电压进行平滑处理。
更可选地，所述平滑处理单元包括但不限于中值滤波器、滑动平均滤波器或快速傅里叶 变换滤波器。
可选地，所述阈值生成模块基于当前诊断周期内各电芯电压的累积残差的统计学特征产生当前诊断周期的阈值范围。
更可选地，当前诊断周期的阈值范围为各电芯电压的累积残差的95％置信区间。
如上所述，本发明的锂电池组内部短路异常诊断方法及系统，具有以下有益效果：
1、本发明的锂电池组内部短路异常诊断方法及系统的检测周期短，可在数小时内诊断出电池组内部短路较严重的异常电芯。
2、本发明的锂电池组内部短路异常诊断方法及系统的诊断识别能力强，并可定位异常电芯的位置，可在内部短路早期提前发现异常的电芯，防止由内部短路发展恶化，造成热失控等严重事故，提升电池组安全水平。
3、本发明的锂电池组内部短路异常诊断方法及系统的算法时空复杂度低，诊断技术流程简单，对数据精度的要求也不高，对锂离子电池组安全运行方面极具应用价值。
附图说明
图1显示为本发明的电芯电压残差的曲线示意图。
图2显示为本发明的锂电池组内部短路异常诊断方法的流程示意图。
图3显示为本发明的锂电池组内部短路异常诊断方法诊断出异常电芯1号的原理示意图。
图4显示为本发明的锂电池组内部短路异常诊断方法诊断出异常电芯2号的原理示意图。
图5显示为本发明的锂电池组内部短路异常诊断系统的结构示意图。
元件标号说明
1                      输入模块
2                      电压处理模块
3                      电压残差提取模块
4                      阈值生成模块
5                      内部短路识别模块
6                      输出模块
S1～S4                 步骤
具体实施方式
以下通过特定的具体实例说明本发明的实施方式，本领域技术人员可由本说明书所揭露 的内容轻易地了解本发明的其他优点与功效。本发明还可以通过另外不同的具体实施方式加以实施或应用，本说明书中的各项细节也可以基于不同观点与应用，在没有背离本发明的精神下进行各种修饰或改变。
请参阅图1～图5。需要说明的是，本实施例中所提供的图示仅以示意方式说明本发明的基本构想，遂图式中仅显示与本发明中有关的组件而非按照实际实施时的组件数目、形状及尺寸绘制，其实际实施时各组件的型态、数量及比例可为一种随意的改变，且其组件布局型态也可能更为复杂。
如图1所示，对电池组中各电芯进行测量，测量时间为2小时，获得的各电芯电压残差的曲线，其中，异常电芯1号和异常电芯2号的残差明显偏离电池组其余电芯的残差曲线；由此可见，电芯电压的残差是一个能够有效分辨异常电芯的信号指标。本发明正是基于对电芯电压的残差的检测，实现在内部短路早期识别电池组短路情况，进而提高电池组安全性，实现方式如下。
实施例一
如图2所示，本实施例提供一种锂电池组内部短路异常诊断方法，所述锂电池组内部短路异常诊断方法包括：
S1电池组电压处理步骤：获取各电芯电压，并计算电池组平均电压。
具体地，获取采集到的各电芯电压，将各电芯电压求和后除以电芯的数量，以此获得所述电池组平均电压。
具体地，在本实施例中，为了提高诊断的准确性，将待诊断的电池组静置在环境温度稳定的环境中，以避免温度对电池组中各电芯电压的影响。在实际使用中，可忽略环境温度对最终的电池组内部短路异常诊断结果的影响，不以本实施例为限。
具体地，由于采集的电芯电压包含较大的随机误差，在内部短路早期，内部短路引起的电压变化微弱，与随机误差量级相当，难以分辨，因此在本实施例中，为了提高诊断的准确性，在计算所述电池组平均电压之前，先对各电芯电压进行平滑处理，以消除单次测量的随机误差。作为示例，采用滤波算法实现平滑处理，滤波算法包括但不限于中值滤波算法、滑动平均滤波算法、快速傅里叶变换滤波算法，任意可对电芯电压进行平滑处理的滤波算法均适用于本发明，在此不一一赘述。
S2各电芯电压累积残差获取步骤：计算各电芯电压与所述电池组平均电压的残差，并分别计算各电芯电压的累积残差。
具体地，求取各电芯电压与所述电池组平均电压的差值，分别记为各电芯电压的残差，将同一电芯电压的残差进行累加得到该电芯电压的累积残差，其中，第一个诊断周期中各电芯电压的累积残差即为当前各电芯电压的残差。
需要说明的是，当步骤S1对各电芯电压进行平滑处理后，步骤S2中各电芯电压即为平滑处理后的电压值。由图1可知，电芯电压的残差存在一定波动，本发明为获得更稳定的识别指标信号，将残差进行累积，以对残差信号放大，获得更稳定的判据，因此诊断结果的准确性更高。
S3阈值范围获取并判断电芯是否异常步骤：设定当前诊断周期的阈值范围，将电芯电压的累积残差超出当前诊断周期的阈值范围的电芯判定为异常电芯，电芯电压的累积残差在当前诊断周期的阈值范围内的电芯判定为正常电芯。
具体地，在本实施例中，获取当前诊断周期中各电芯电压的累积残差的分布状况，根据统计学特征，设置当前诊断周期的阈值范围，具体的阈值范围可根据实际需要进行设置。作为示例，当前诊断周期的阈值范围设定为各电芯电压的累积残差的95％置信区间(即按照统计学规律，应该有95％的电芯电压数值分布在该阈值范围内)，进一步地，95％置信区间的上限为各电芯电压的累积残差的平均值加1.96倍标准差，下限为各电芯电压的累积残差的平均值减1.96倍标准差。
需要说明的是，在实际使用中，当前诊断周期的阈值范围可以是预设值，无需基于当前诊断周期中各电芯电压的累积残差的分布状况获取。本实施例中，将当前诊断周期的阈值范围设定为各电芯电压的累积残差的95％置信区间可在确保准确性的基础上大大加快诊断速度。
具体地，若电芯电压的累积残差在当前诊断周期的阈值范围内，则判定该电芯为正常电芯；若电芯电压的累积残差超出当前诊断周期的阈值范围，则判定该电芯为异常电芯。
S4循环诊断步骤：排除异常电芯后返回步骤S1，直至预设时间段内无异常电芯检出，诊断结束并输出诊断结果。
具体地，将异常电芯排除出去，然后基于剩余的电芯重新获取电芯电压并进行下一诊断周期，各诊断周期的阈值范围需要更新；在本实施例中，各诊断周期的阈值范围基于上一步各电芯电压的累积残差的统计学特征获取。若预设时间段内未检出异常电芯，则认为诊断结束，输出诊断结果，输出结果包括是否存在异常电芯，若存在异常电芯则输出异常电芯对应的编号。其中，所述预设时间段(在所述预设时间段内可执行多个诊断周期)的时长可根据实际需要设置，确保能获得准确的诊断结果即可，在此不一一赘述。
如图3所示，为各电芯电压的累积残差分布，各电芯(通过不同电芯编号区分)具有对应的累积残差，其中，虚线分别表示阈值范围的上限和下限，介于上限和下限之间即在阈值范围内，大于上限或小于下限即超出阈值范围。假设存在异常电芯1号及异常电芯2号，其它电芯正常，正常电芯的累积残差分布均匀，且位于阈值范围内；而在当前诊断周期内，异常电芯1号的电芯电压的累积残差低于阈值范围的下限(超出阈值范围)，诊断出电芯1号为异常电芯；异常电芯2号的电芯电压的累积残差在当前诊断周期的阈值范围内，因此，虽然其累积残差明显偏离正常电芯的累积残差分布，但是并未诊断为异常电芯。排除异常电芯1号后，继续执行循环诊断的步骤。
如图4所示，在下一诊断周期或多个诊断周期后(未超出所述预设时间段)，异常电芯2号的电芯电压的累积残差低于当前诊断周期的阈值范围(新的阈值范围)的下限(超出阈值范围)，诊断出电芯2号为异常电芯。排除异常电芯2号后，继续执行循环诊断的步骤，经过预设时间后没有再检出异常电芯，则诊断结束，输出诊断结果：存在异常电芯，异常电芯编号为V102及V103。
本发明将电芯电压的累积残差作为直观量化判定指标，对异常电芯进行识别诊断，能够有效分辨出异常电芯。值得注意的是，在本实施例中，仅仅测量2小时即可判断出电池组内异常电芯，证明该方法可对电池组内部短路异常电芯快速诊断。
实施例二
如图5所示，本实施例提供一种锂电池组内部短路异常诊断系统，所述锂电池组内部短路异常诊断系统包括：
输入模块1，电压处理模块2，电压残差提取模块3，阈值生成模块4，内部短路识别模块5及输出模块6。
如图5所示，所述输入模块1获取各电芯电压，并将各电芯电压输入到所述电压处理模块2及所述电压残差提取模块3。
如图5所示，所述电压处理模块2连接所述输入模块1及所述电压处理模块2的输出端，当所述内部短路识别模块5输出电池组正常信号时计算所述电池组平均电压；当所述内部短路识别模块5输出异常电芯编号时排除所述输入模块1输入的对应电芯电压后计算电池组平均电压。
具体地，所述电压处理模块2包括平均值计算单元，任意可选择输入信号并对选中的信号进行平均值计算的硬件电路或软件代码均适用于本发明。
具体地，作为本发明的另一种实现方式，所述电压处理模块2还包括平滑处理单元，所述平滑处理单元对所述输入模块1输入的电芯电压进行平滑处理。所述平滑处理单元包括但不限于中值滤波器、滑动平均滤波器或快速傅里叶变换滤波器，在本实施例中，采用滑动平均滤波器实现，并可根据需要调整滑动平均滤波器的滑动窗口长度；任意可对电芯电压进行平滑处理的滤波算法单元均适用于本发明，在此不一一赘述。
如图5所示，所述电压残差提取模块3连接于所述输入模块1及所述电压处理模块2的输出端，提取当前诊断周期内电芯电压的累积残差。
具体地，所述电压残差提取模块3接收当前诊断周期的各电芯电压及电池组平均电压，并计算各电芯电压与电池组平均电压的差值，并将各电芯对应的差值与之前诊断周期累积的残差累加，得到各电芯电压的累积残差。
如图5所示，所述阈值生成模块4连接于所述电压残差提取模块3的输出端，更新并产生当前诊断周期的阈值范围。
具体地，在本实施例中，所述阈值生成模块4基于当前诊断周期内各电芯电压的累积残差的统计学特征产生当前诊断周期的阈值范围。作为示例，当前诊断周期的阈值范围为各电芯电压的累积残差的95％置信区间。在实际使用中可根据需要设定阈值范围，不以本实施例为限。
如图5所示，所述内部短路识别模块5连接于所述阈值生成模块4的输出端，基于所述诊断阈值范围判断是否存在异常电芯。
具体地，若电芯电压的累积残差在当前诊断周期的阈值范围内，则判定该电芯为正常电芯，输出电池组正常；若电芯电压的累积残差超出当前诊断周期的阈值范围，则判定该电芯为异常电芯，进一步输出异常电芯标号。
如图5所示，所述输出模块6连接于所述内部短路识别模块5的输出端，输出诊断结果。
本实施例的锂电池组内部短路异常诊断系统可用于实现实施例一的锂电池组内部短路异常诊断方法，具体原理在此不一一赘述。
本发明通过对电池组内各电芯电压残差进行累积，将电池内部短路，特别是早期内部微短路引起的电压差异进行放大，然后根据电池组内各电芯的累积残差的统计信息，横向对比，利用各个电芯电压累积残差的一致性对电池组内异常电芯进行诊断识别。不需要拆解电芯，仅通过电池板载电池管理系统收集的静态电芯电压，而电芯制造及容量差异会导致各电芯电压存在差异，因此，本发明使用电芯电压的残差，这是一个相对量，可以避免电芯之间的电 压差异，从而能够在较短测量时间范围提取内部短路严重的电芯。
综上所述，本发明提供一种锂电池组内部短路异常诊断方法及系统，包括：S1：获取各电芯电压，并计算电池组平均电压；S2：计算各电芯电压与所述电池组平均电压的残差，并分别计算各电芯电压的累积残差；S3：设定当前诊断周期的阈值范围，将电芯电压的累积残差超出当前诊断周期的阈值范围的电芯判定为异常电芯，电芯电压的累积残差在当前诊断周期的阈值范围内的电芯判定为正常电芯；S4：排除异常电芯后返回步骤S1，直至预设时间段内无异常电芯检出，诊断结束并输出诊断结果。本发明的锂电池组内部短路异常诊断方法及系统通过积累电压残差，对内部短路，特别是早期内部微短路引起的电压差异进行放大，然后根据电池组内各电芯的累积残差的分布以及95％置信区间作为阈值，横向对比，利用各个电芯电压累积残差的一致性对电池组内异常电芯进行诊断识别；检测速度快，检出精度高，可快速准确地实现电池组内部短路异常诊断，发现内部短路发展较快的电芯，提前预警，提升电池组的整体运行安全性。所以，本发明有效克服了现有技术中的种种缺点而具高度产业利用价值。
上述实施例仅例示性说明本发明的原理及其功效，而非用于限制本发明。任何熟悉此技术的人士皆可在不违背本发明的精神及范畴下，对上述实施例进行修饰或改变。因此，举凡所属技术领域中具有通常知识者在未脱离本发明所揭示的精神与技术思想下所完成的一切等效修饰或改变，仍应由本发明的权利要求所涵盖。

Claims (11)

  1. 一种锂电池组内部短路异常诊断方法，其特征在于，所述锂电池组内部短路异常诊断方法至少包括：
    S1：获取各电芯电压，并计算电池组平均电压；
    S2：计算各电芯电压与所述电池组平均电压的残差，并分别计算各电芯电压的累积残差；
    S3：设定当前诊断周期的阈值范围，将电芯电压的累积残差超出当前诊断周期的阈值范围的电芯判定为异常电芯，电芯电压的累积残差在当前诊断周期的阈值范围内的电芯判定为正常电芯；
    S4：排除异常电芯后返回步骤S1，直至预设时间段内无异常电芯检出，诊断结束并输出诊断结果。
  2. 根据权利要求1所述的锂电池组内部短路异常诊断方法，其特征在于：在步骤S1中，获取各电芯电压后，对各电芯电压进行平滑处理。
  3. 根据权利要求2所述的锂电池组内部短路异常诊断方法，其特征在于：所述平滑处理采用滤波算法实现，滤波算法包括中值滤波算法、滑动平均滤波算法、快速傅里叶变换滤波算法中任意一种。
  4. 根据权利要求1所述的锂电池组内部短路异常诊断方法，其特征在于：当前诊断周期的阈值范围基于上一步各电芯电压的累积残差的统计学特征获取。
  5. 根据权利要求1或4所述的锂电池组内部短路异常诊断方法，其特征在于：当前诊断周期的阈值范围为各电芯电压的累积残差的95％置信区间。
  6. 根据权利要求1所述的锂电池组内部短路异常诊断方法，其特征在于：所述诊断结果包括是否存在异常电芯及异常电芯编号。
  7. 一种锂电池组内部短路异常诊断系统，其特征在于，所述锂电池组内部短路异常诊断系统至少包括：
    输入模块，电压处理模块，电压残差提取模块，阈值生成模块，内部短路识别模块及输出模块；
    所述输入模块获取各电芯电压，并将各电芯电压输入到所述电压处理模块及所述电压残差提取模块；
    所述电压处理模块连接所述输入模块及所述电压处理模块的输出端，当所述内部短路识别模块输出电池组正常信号时计算所述电池组平均电压；当所述内部短路识别模块输出异常电芯编号时排除所述输入模块输入的对应电芯电压后计算电池组平均电压；
    所述电压残差提取模块连接于所述输入模块及所述电压处理模块的输出端，提取当前诊断周期内电芯电压的累积残差；
    所述阈值生成模块连接于所述电压残差提取模块的输出端，更新并产生当前诊断周期的阈值范围；
    所述内部短路识别模块连接于所述阈值生成模块的输出端，基于当前诊断周期的阈值范围判断是否存在异常电芯；
    所述输出模块连接于所述内部短路识别模块的输出端，输出诊断结果。
  8. 根据权利要求7所述的锂电池组内部短路异常诊断系统，其特征在于：所述电压处理模块还包括平滑处理单元，所述平滑处理单元对所述输入模块输入的电芯电压进行平滑处理。
  9. 根据权利要求8所述的锂电池组内部短路异常诊断系统，其特征在于：所述平滑处理单元包括中值滤波器、滑动平均滤波器或快速傅里叶变换滤波器。
  10. 根据权利要求7所述的锂电池组内部短路异常诊断系统，其特征在于：所述阈值生成模块基于当前诊断周期内各电芯电压的累积残差的统计学特征产生当前诊断周期的阈值范围。
  11. 根据权利要求10所述的锂电池组内部短路异常诊断系统，其特征在于：当前诊断周期的阈值范围为各电芯电压的累积残差的95％置信区间。
PCT/CN2021/110888 2021-07-13 2021-08-05 锂电池组内部短路异常诊断方法及系统 WO2023284039A1 (zh)

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