WO2019166035A2 - 一种锂电池内短路电压电流异常检测方法及系统 - Google Patents

一种锂电池内短路电压电流异常检测方法及系统 Download PDF

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Publication number
WO2019166035A2
WO2019166035A2 PCT/CN2019/091557 CN2019091557W WO2019166035A2 WO 2019166035 A2 WO2019166035 A2 WO 2019166035A2 CN 2019091557 W CN2019091557 W CN 2019091557W WO 2019166035 A2 WO2019166035 A2 WO 2019166035A2
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voltage
current
lithium battery
data
abnormality
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PCT/CN2019/091557
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English (en)
French (fr)
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WO2019166035A3 (zh
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赵少华
王守模
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广东恒翼能科技有限公司
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Priority to PCT/CN2019/091557 priority Critical patent/WO2019166035A2/zh
Publication of WO2019166035A2 publication Critical patent/WO2019166035A2/zh
Publication of WO2019166035A3 publication Critical patent/WO2019166035A3/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
    • G01R31/50—Testing of electric apparatus, lines, cables or components for short-circuits, continuity, leakage current or incorrect line connections
    • G01R31/52—Testing for short-circuits, leakage current or ground faults
    • 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]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G01R31/3842—Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current measurements
    • 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]
    • G01R31/392—Determining battery ageing or deterioration, e.g. state of health

Definitions

  • the invention relates to the technical field of lithium batteries, and in particular to a method and a system for detecting an abnormality of a short circuit voltage and current in a lithium battery.
  • the present invention provides a method and system for detecting an abnormality of a short-circuit voltage and current in a lithium battery.
  • a method for detecting an abnormality of short-circuit voltage and current in a lithium battery comprises the following steps: S1: charging and discharging a lithium battery and determining that the lithium battery enters a charging and discharging state; S2: synchronously collecting voltage data and passing through the two ends of the lithium battery in real time Determining at least one of current data of the lithium battery and performing internal short-circuit indicator monitoring; S3: determining whether an internal short-circuit indicator of at least one of the voltage data and the current data is abnormal; when the voltage data and the When one of the current data is abnormal, the charging and discharging of the lithium battery are stopped.
  • determining whether the voltage data is abnormal comprises: filtering out noise of the voltage data, selecting the voltage data whose under-sampling rate reaches a set value, and dynamically updating a maximum recording voltage; the internal short-circuit indicator
  • the monitoring includes at least one of voltage rise abnormality detection, voltage abnormality drop detection, and voltage drop trend abnormality detection.
  • the lithium battery is in a state of constant current charging, constant voltage charging or constant current discharging, and the corresponding current abnormality is: constant current charging abnormality, constant voltage charging abnormality or constant current discharging abnormality.
  • determining whether the current data is abnormal comprises: filtering out noise of the current data and dynamically updating the buffer data; determining a state of the lithium battery and calculating a rate of change of the current; The set rate of change of the set current is compared, and if the rate of change of the current is greater than a rate of change rate of the current set in advance, the change in current is abnormal.
  • the current data, N is a time interval; the threshold rate of change of the current set in advance when the lithium battery is in the constant current state of charge is T 1 , and if d>T 1 , the constant current charging is abnormal; the lithium battery is at a constant voltage
  • the rate of change rate of the current set in the charging state is T 2 . If d>T 2 , the constant voltage charging is abnormal; when the lithium battery is in the constant current discharging state, the preset rate of change of the current is T 3 , if d>T 3 , the constant current discharge is abnormal.
  • the invention also provides a short-circuit voltage and current abnormality detecting system for a lithium battery, comprising: a charging and discharging unit for charging and discharging a lithium battery; a voltage collecting unit for collecting voltage data at both ends of the lithium battery in real time; and a current collecting unit For collecting current data through the lithium battery in real time; the processing unit is configured to determine whether the voltage data and the current data are abnormal; when the voltage and/or the current is abnormal, stopping charging and discharging.
  • the invention further provides a computer readable storage medium storing a computer program that, when executed by a processor, implements the method of any of the above.
  • the invention has the beneficial effects of providing a method and a system for detecting an abnormality of a short-circuit voltage and current in a lithium battery, by collecting voltage and current data of the charged and discharged lithium battery and determining whether the voltage data and the current data are abnormal; when the voltage and/or When the current is abnormal, the charging and discharging are stopped, and the short circuit in the lithium battery due to the abnormal voltage and current is effectively avoided, and the safety of the lithium battery is improved.
  • the various performance modes of the internal short circuit are summarized, and the characterization parameters in each mode are formulated. By monitoring these characterization parameters, the occurrence of an internal short circuit is monitored.
  • FIG. 1 is a schematic diagram of a thermal alarm early warning protection system for a lithium battery according to an embodiment of the present invention.
  • FIG. 2 is a schematic diagram of a method for detecting an abnormality of a short-circuit voltage and current in a lithium battery according to an embodiment of the present invention.
  • FIG. 3 is a schematic diagram of a method for determining whether the voltage data is abnormal in an embodiment of the present invention.
  • FIG. 4 is a schematic diagram of a method for detecting abnormal voltage rise in an embodiment of the present invention.
  • FIG. 5 is a schematic diagram of a method for detecting abnormal voltage drop in an embodiment of the present invention.
  • FIG. 6 is a schematic diagram of a method for detecting an abnormality of a voltage drop trend in an embodiment of the present invention.
  • FIG. 7 is a schematic diagram of a method for determining whether the current data is abnormal in an embodiment of the present invention.
  • FIG. 8 is a schematic diagram of a short circuit voltage and current abnormality detecting system in a lithium battery according to an embodiment of the present invention.
  • Figure 9 is a schematic diagram of a charging voltage in an embodiment of the present invention.
  • Figure 10 is a schematic diagram of voltage trends in an embodiment of the present invention.
  • Figure 11 is a schematic diagram of a charging current in an embodiment of the present invention.
  • Figure 12 is a schematic diagram of current trends in an embodiment of the present invention.
  • Figure 13 is a schematic view showing a method of calculating the surface temperature of a battery cell of a lithium battery in an embodiment of the present invention.
  • FIG. 14 is a schematic view showing a method of calculating the correlation between the surface temperature of the cell of the lithium battery and the measured temperature of the temperature sensor in the embodiment of the present invention.
  • Figure 15 is a block diagram showing the structure of a temperature abnormality detecting subsystem in the embodiment of the present invention.
  • Figure 16 is a graph showing the estimated error distribution between the surface temperature of the cell estimated by the temperature anomaly detection subsystem and the actual measured temperature in the embodiment of the present invention.
  • 1-temperature sensor 2-needle bed, 3-tray, 4-cell, 5-smoke sensor, 6-probe mounting module, 7-probe support module.
  • connection can be for a fixed effect or for circuit communication.
  • first and second are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated.
  • features defining “first” or “second” may include one or more of the features either explicitly or implicitly.
  • the meaning of "a plurality" is two or more, unless specifically defined otherwise.
  • the internal short circuit is divided into the following types: diaphragm defects or aging cracks; lithium dendrites, elemental iron ions reduction deposition pierce the diaphragm; foreign matter entrainment causes piercing of the diaphragm when making the cell; current collector copper foil or aluminum foil edge glitch.
  • the internal short circuit form of the battery is divided into the following types: short circuit of anode material and aluminum current collector; short circuit of copper current collector and aluminum current collector; short circuit of copper current collector and positive electrode material; positive electrode material and negative electrode material Short circuit
  • the performance of various internal short circuits is different: among them, the short circuit of the aluminum current collector and the charged negative electrode material has a large current through the small contact resistance, and it is easy to cause a thermal side reaction due to a rapid increase in the temperature of the internal short circuit portion in a short time, thereby generating thermal runaway;
  • the short circuit of the aluminum current collector and the copper current collector is similar to the external short circuit, and the temperature is uniformly conducted to the entire battery; the short circuit between the positive electrode material and the copper current collector and the positive and negative materials is minimized due to the large impedance of the positive electrode material.
  • the present invention analyzes the internal short circuit caused by various causes and locations, summarizes the typical mode, and finds the appropriate characterization parameters of each mode, so that the monitoring of the parameters can be realized to realize the monitoring of the internal short circuit.
  • these modes include:
  • the lithium iron phosphate (LiFeP04) as the secondary battery of the positive electrode material has low conductivity and the lithium ion diffusion rate is extremely slow, after puncture (simulated internal short circuit)
  • puncture simulated internal short circuit
  • the voltage drops from the initial 3.7V to 3.2V in 1 second, and then the voltage goes into a steady state and no longer drops.
  • the surface temperature of the casing gradually increases rapidly as the puncture progresses, and enters the thermal runaway state in 4 to 6 seconds.
  • the internal short circuit detection methods of lithium batteries include the following:
  • Voltage abnormality detection The internal short circuit of the lithium battery will cause the voltage to drop. By monitoring the voltage drop trend, the internal short circuit can be judged and early warning.
  • Thermal detection It is determined whether a short circuit has occurred by attaching a thermocouple to the side wall of the lithium battery to detect a temperature change.
  • the temperature display has a certain hysteresis, so that the short circuit condition cannot be fed back in the first time.
  • Capacity abnormality detection Since some internal energy is converted into heat energy loss due to internal short circuit, the charging capacity during charging will be higher than when no internal short circuit occurs, so when the charging capacity is higher than the reference capacity, the internal short circuit fault is reported. When the charging capacity is lower than or equal to the reference capacity, the lithium battery is in a normal state.
  • a lithium battery thermal runaway warning protection system includes:
  • a voltage and current abnormality detecting subsystem configured to collect voltage data at both ends of the lithium battery and current data of the lithium battery in real time during charging and discharging of the lithium battery, and determine whether the voltage data and the current data are abnormal; When the voltage and/or the current is abnormal, the charging and discharging are stopped;
  • a temperature abnormality detecting subsystem configured to collect the temperature of the lithium battery in real time during charging and discharging of the lithium battery, determine whether the temperature exceeds a preset temperature threshold, and when the temperature exceeds the temperature threshold, issue Abnormal temperature alarm;
  • the smoke sensing abnormality detecting subsystem is configured to detect whether there is smoke in real time during charging and discharging of the lithium battery, and when the smoke is detected, start an alarm and a fire spray.
  • the voltage change occurs first within 1 to 2 seconds of internal short circuit, and the rise of the shell problem can be clearly detected in the next 2 to 4 seconds, and most of the time after 4 to 6 seconds
  • the lithium battery will enter the thermal runaway state. Therefore, the accurate and safe thermal runaway protection strategy should be protected step by step according to the strategy of abnormal current current ⁇ temperature abnormality ⁇ smoke induction abnormality.
  • the voltage current abnormality detecting subsystem captures an abnormal signal before the temperature abnormality detecting subsystem and the smoke sensing abnormal detecting subsystem; when the smoke sensing abnormality detecting subsystem issues an early warning When the voltage current abnormality detecting subsystem and the temperature abnormality detecting subsystem fail to capture the abnormal signal, it is determined that the smoke sensing abnormality detecting subsystem is falsely reported.
  • the voltage current abnormality detecting subsystem synchronously collects at least one of voltage data at both ends of the lithium battery and current data passing through the lithium battery, and performs internal short-circuit index monitoring, and determines the voltage data and the current data. Whether the internal short-circuit indicator of at least one of them is abnormal.
  • the three subsystems effectively prevent the thermal runaway of the lithium battery by comprehensive monitoring of voltage, temperature and smoke sensing, and adopt strategies such as single channel stop, complete disk stop and start fire observation, start fire spray, and six-sided protection. Control and protect the thermal runaway step by step.
  • the voltage and current anomaly monitoring subsystem will detect the abnormal signal before the temperature anomaly detection subsystem and the smoke induction anomaly detection subsystem, and usually stop the single channel charging when the voltage and current abnormality monitoring subsystem is early warning.
  • the discharge operation prevents further increase of the side reaction; when the temperature abnormality detecting subsystem detects a significant rise in the temperature of the casing of the lithium battery, the whole disk is stopped according to the user configuration and the fire observation operation is started; when the smoke sensing abnormality detection When the subsystem issues an early warning, it will alarm and start the fire sprinkler.
  • the system will determine that the smoke-sensing anomaly detection subsystem has misreported according to the configuration.
  • the automatic needle bed location of the thermal runaway warning protection system in the automated production of lithium batteries is equipped with a 6-face protection device to prevent expansion to adjacent locations when thermal runaway occurs.
  • the system has independent step protection and global protection, eliminating the potential factors of thermal runaway caused by overcharge, overdischarge, poor crimping, voltage and current exceeding the upper and lower limits.
  • the voltage and current anomaly detection subsystem is the core of the thermal runaway warning protection system in the automatic production of lithium batteries. By monitoring and tracking the changes of voltage and current in real time, the law of voltage and current changes during short circuit in the battery is used to judge the short circuit of the internal battery. And early warning.
  • the voltage and current anomaly detection subsystem can usually stop the abnormal channel within 1 second of the occurrence of the internal short circuit, thereby preventing the internal short circuit negative reaction from further aggravating, thereby greatly reducing the probability of occurrence of thermal runaway.
  • the internal short circuit judgment and early warning are realized by monitoring the abnormality of the voltage during charging and discharging and the abnormal drop of the current during discharging. Because the voltage and current abnormalities of the internal short circuit are small instantaneous fluctuations, this places high demands on the accuracy and response speed of the detecting device.
  • High-precision charge and discharge equipment has the following conditions:
  • High sampling rate The sampling rate of the lower computer is up to 5ms.
  • the digital finite impulse response filter of the Kassel window is used to filter out the white noise and out-of-band interference while outputting each point, avoiding the length of the moving average filter. Delay
  • the high-performance lower position machine can realize the trend tracking algorithm in the lower position machine. Combined with high precision and high sampling rate, it can track the abnormality of voltage and current in real time.
  • the anomaly tracking algorithm (internally called an electron microscopy magnifier) tracks the following trends:
  • the anomaly tracking algorithm tracks the abnormally decreasing trend of the current, that is, the slope of ⁇ I/ ⁇ t is abnormal.
  • a method for detecting an abnormality of a short-circuit voltage current in a lithium battery includes the following steps:
  • S2 synchronously collecting at least one of voltage data at both ends of the lithium battery and current data passing through the lithium battery, and performing internal short-circuit indicator monitoring;
  • S3 determining whether an internal short circuit indicator of at least one of the voltage data and the current data is abnormal; when one of the voltage data and the current data is abnormal, stopping charging and discharging of the lithium battery.
  • determining whether the voltage data is abnormal includes the following steps:
  • the internal short-circuit indicator monitoring includes at least one of a voltage rise abnormality detection, a voltage abnormal drop detection, and a voltage drop trend abnormality detection.
  • the voltage rise anomaly detection includes:
  • the voltage drop anomaly detection includes:
  • the abnormality detection of the voltage drop trend includes:
  • Step 1 The device enters the chemical composition process, and starts the step of charging and discharging the lithium battery;
  • Step 2 Reset all variables
  • Step 3 Determine whether the lithium battery enters the charging state, if it enters the charging state, proceeds to step 4;
  • Step 4 The device collects the battery voltage data V n and filters the noise through a low-pass filter, and at the same time, the under-sampling counter is incremented;
  • Step 5 If the undersampling counter reaches the undersampling rate N, update the voltage buffer (remove the sampling point before the W time in the voltage buffer, and save the current sampling data V n into the buffer), otherwise return to step 4; Where W is the size of the observation window. Generally, the default setting is 128. N is adjusted according to the sampling rate, and is generally set to 1 or 2.
  • Step 7 Voltage rise abnormality detection: If the voltage rise abnormality detection switch is turned on, voltage rise abnormality detection is performed, otherwise this step is skipped.
  • the rising trend abnormal threshold that is, v ras ⁇ V rais_Thres , the system issues a voltage rise abnormal alarm and stops the channel from exiting the current working state;
  • Step 8 Voltage abnormality drop monitoring: If the voltage abnormality drop monitoring switch is turned on, the voltage abnormal drop detection is performed, otherwise skip this step.
  • Step 9 Voltage drop trend abnormal monitoring: If the voltage drop trend abnormality monitoring switch is turned on, the voltage drop trend abnormality detection is performed, otherwise this step is skipped.
  • the voltage drop trend anomaly detection algorithm is as follows:
  • Step 10 Repeat steps 4 through 9 for each sample point until the channel step is completed or exits abnormally.
  • an abnormality detecting method for internal short-circuit voltage and current in automatic production of a lithium battery is in a state of constant current charging, constant voltage charging or constant current discharging, and the corresponding current abnormality is: constant Abnormal flow charging, constant voltage charging abnormality or constant current discharge abnormality.
  • determining whether the current data is abnormal includes the following steps:
  • the rate of change of the current is compared with a threshold of a rate of change of the current set in advance, and if the rate of change of the current is greater than a threshold of a rate of change of the current set in advance, the change in current is abnormal.
  • the rate of change rate of the current set in advance when the lithium battery is in the constant current charging state is T 1 , and if d>T 1 , the constant current charging is abnormal;
  • the rate of change rate of the current set in advance when the lithium battery is in the constant voltage state of charge is T 2 , and if d>T 2 , the constant voltage charging is abnormal;
  • the rate of change rate of the current set in advance when the lithium battery is in the constant current discharge state is T 3 , and if d>T 3 , the constant current discharge is abnormal.
  • Step 1 The device enters the chemical composition process, and starts the step of charging and discharging the lithium battery;
  • Step 2 Reset all variables
  • Step 3 Determine whether the lithium battery enters the charge and discharge state, if it enters the charge and discharge state, proceeds to step 4;
  • Step 4 The device collects the current data C n and filters the noise through the low-pass filter to update the current observation buffer c_buff buffer: removes the sampling point before the N time in the buffer, and drops the current sampling data C n .
  • the device collects the current data C n and filters the noise through the low-pass filter to update the current observation buffer c_buff buffer: removes the sampling point before the N time in the buffer, and drops the current sampling data C n .
  • c_buff buffer removes the sampling point before the N time in the buffer, and drops the current sampling data C n .
  • the present invention further provides a short-circuit voltage and current abnormality detecting system for a lithium battery, comprising:
  • a charge and discharge unit for charging and discharging a lithium battery
  • a voltage collecting unit configured to collect voltage data at both ends of the lithium battery in real time
  • a current collecting unit configured to collect current data through the lithium battery in real time
  • the processing unit is configured to determine whether the voltage data and the current data are abnormal; when the voltage and/or the current is abnormal, the charging and discharging are stopped.
  • the processing unit may also determine whether the temperature is abnormal or the smoke is abnormal, and the corresponding operation as described above is taken.
  • the present invention implements all or part of the processes in the foregoing embodiments, and may also be completed by a computer program to instruct related hardware.
  • the computer program may be stored in a computer readable storage medium, and the computer program is in the processor. When executed, the steps of the various method embodiments described above can be implemented.
  • the computer program comprises computer program code, which may be in the form of source code, object code form, executable file or some intermediate form.
  • the computer readable medium may include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM).
  • RAM Random Access Memory
  • electrical carrier signals telecommunications signals
  • software distribution media any suitable distribution media.
  • the content contained in the computer readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in a jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, computer readable media Does not include electrical carrier signals and telecommunication signals.
  • FIG. 9 is a voltage curve of a cell during constant current constant voltage charging
  • FIG. 10 is a voltage trend calculated in real time by the system of the present invention
  • FIG. 11 is a current curve during constant current constant voltage charging
  • FIG. 12 is a system using the present invention.
  • Current trend calculated in real time As shown in Figure 10 and Figure 12, in the constant voltage charging phase, the voltage trend and current trend of three cells are shown in Figure 9-12.
  • the bad cells are different from the normal voltage trend and current trend.
  • the abnormal jump of the battery can detect and mark the bad battery.
  • the system detects and determines 23,000 bad cells, and verifies 150 false positives by offline test, and the false positive rate is less than 0.01 ⁇ .
  • the temperature abnormality detecting subsystem of this embodiment includes: the lithium battery is crimped to a temperature probe on the tab; it is understood that since each lithium battery has a temperature probe crimped to the ear, in the lithium During the charging and discharging process of the battery, the temperature of the lithium battery is monitored in real time. When the temperature of the ear ear continuously exceeds the temperature threshold, the temperature abnormality detecting subsystem will issue a temperature abnormality alarm.
  • the present embodiment 8 gives a method of calculating the surface temperature of the battery cell of the lithium battery by software.
  • a row of temperature sensor arrays are mounted on the front and back of the needle bed of the lithium battery, and the temperature sensor array includes L temperature sensors equally spaced to form a 2*L temperature sensor array.
  • the temperature of each battery pack can be calculated from 2*L temperature sensor measurement data.
  • the top and bottom rows of the needle bed 2 of the lithium battery thermal runaway warning protection system Four temperature sensors are installed, a total of eight temperature sensors form a temperature sensor array, and one tray 3 is provided with 32 soft-packed cells 4, and a surface of each cell 4 is mounted with a temperature sensor 1 for measuring the surface of the cell. temperature.
  • Each lithium battery is crimped to a temperature probe on the tab, and the probe mounting module 6 and the probe support module 7 are visible in the figure.
  • the smoke sensor 5 is disposed between the temperature sensor 1 and the battery core 4.
  • a method of calculating a cell surface temperature of the lithium battery includes the following steps:
  • T1 calculating a correlation between a cell surface temperature of the lithium battery and a measured temperature of the temperature sensor
  • T2 estimating a cell surface temperature of the lithium battery in real time according to the correlation and the measured temperature of the temperature sensor array.
  • step T1 includes:
  • T11 Calculate a cross-correlation matrix between the temperature sensors:
  • r n,l is the cross-correlation between the measured temperature of the nth temperature sensor and the measured temperature of the first temperature sensor, The covariance between the measured temperature of the nth temperature sensor and the measured temperature of the 1st temperature sensor, The variance of the measured temperature for the nth temperature sensor, The first temperature sensor measures the variance of the temperature;
  • T12 calculating a cross-correlation matrix between each of the lithium battery and the temperature sensor
  • c m,l is the cross-correlation between the surface temperature of the mth cell and the measured temperature of the first temperature sensor
  • the first temperature sensor measures the variance of the temperature.
  • Step T2 includes: setting the measured temperature vector of the temperature sensor at time k:
  • the temperature of the cell surface is:
  • the surface temperature of the battery tab or the surface temperature of the battery exceeds 100 degrees Celsius, it can be judged that the temperature is abnormal and an alarm is activated.
  • Figure 16 shows the estimated error distribution between the surface temperature of the cell estimated by the system and the actual measured temperature. From the results, it can be seen that most of the error distribution is in the range of [-0.1, 0.1], and the mean square error It is 6e-3. It can be seen that the result is very reliable and can meet the requirements of thermal runaway warning protection.

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Abstract

本发明提供一种锂电池内短路电压电流异常检测方法及系统，包括如下步骤：S1：对锂电池进行充放电并确定所述锂电池进入充放电状态；S2：同步实时采集所述锂电池两端的电压数据和通过所述锂电池的电流数据中的至少一者；S3：判断所述电压数据和所述电流数据是否异常；当所述电压和/或所述电流异常时，停止自动化生产。可以有效避免因锂电池内短路造成的化成分容过程中的热失控，提高锂电池自动化生产的安全性。

Description

一种锂电池内短路电压电流异常检测方法及系统 技术领域
本发明涉及锂电池技术领域，尤其涉及一种锂电池内短路电压电流异常检测方法及系统。
背景技术
在锂电池的后段自动化生产工艺(化成、分容、静置、DCIR检测等)中，引起锂电池发热甚至爆炸的主要原因可以归于以下两大类：1.由于过充或过放导致锂电池固体电解质界面膜SEI分解、脱落甚至逆反应，伴随发热和气体等剧烈反应最终引起热失控。由于化成分容和检测系统中有专门针对过充和过放的保护策略，由该原因引起的热失控可以避免。2.由于短路引起的锂电池热失控。短路分为外部短路和内部短路，充放电系统中的双电压监测系统可以有效检测和预防外部短路，因此主要目前需要针对锂电池的内部短路做有效的预警和控制，避免内部短路引发的热失控状态。
由于内短路难以直接监测，目前现有技术中缺乏一种电池内部短路检测途径。
发明内容
本发明为了解决现有的问题，提供一种锂电池内短路电压电流异常检测方法及系统。
为了解决上述问题，本发明采用的技术方案如下所述：
一种锂电池内短路电压电流异常检测方法，包括如下步骤：S1：对锂电池进行充放电并确定所述锂电池进入充放电状态；S2：同步实时采集所述锂电池两端的电压数据和通过所述锂电池的电流数据中的至少一者，并进行内短路指标监测；S3：判断所述电压数据和所述电流数据中的至少一者的内短路指标是否异常；当所述电压数据和所述电流数据中的一者异常时，停止对锂电池进行充放电。
优选地，判断所述电压数据是否异常包括如下步骤：滤除所述电压数据的噪音，实时选取欠采样率达到设定数值的所述电压数据，并动态更新最大记录电压；所述内短路指标监测包括电压上升异常检测、电压异常下降检测、电压下降趋势异常检测中的至少一者。
优选地，所述电压上升异常检测包括：计算实时的电压数据V n与充电起始 电压V start的差值V ras＝V n-V start；判断所述差值是否小于预先设置的电压上升阈值，若所述差值小于所述预先设置的电压上升阈值则电压上升异常。
优选地，所述电压异常下降检测包括：计算所述最大记录电压V max与实时的电压数据V n的差值ΔV＝V max-V n；判断所述差值是否超过预先设置的电压下降阈值，若所述差值超过所述预先设置的电压下降阈值则电压下降异常。
优选地，所述电压下降趋势异常检测包括：计算实时采集的所述锂电池两端的电压数据的差分：dn＝V n-V n-1并获取dn的正负的符号d sng-n；计算所述符号的和是否超过预先设置的电压下降趋势阈值，若所述符号的和小于所述预先设置的电压下降趋势阈值，则记录电压下降趋势起始点电压V ref；计算电压下降斜率：S＝(V ref-V n)/N，其中N为电压欠采样率；判断所述电压下降斜率是否超过预先设置的下降斜率阈值，如果所述电压下降斜率超过所述预先设置的下降斜率阈值则电压下降趋势异常。
优选地，所述锂电池处于恒流充电、恒压充电或恒流放电状态，对应的所述电流异常为：恒流充电异常、恒压充电异常或恒流放电异常。
优选地，判断所述电流数据是否异常包括如下步骤：滤除所述电流数据的噪音并动态更新缓存数据；确定所述锂电池的状态并计算电流的变化速率；所述电流的变化速率与预先设置的电流的变化速率阈值比较，若所述电流的变化速率大于预先设置的电流的变化速率阈值，则电流的变化异常。
优选地，计算电流的变化速率：d＝(C n-C n-N+1)/N，其中，C n是实时的电流数据，C n-N+1是在实时的时刻的N时刻之前的电流数据，N是时间间隔；所述锂电池处于恒流充电状态时预先设置的电流的变化速率阈值是T 1，若d>T 1，则恒流充电异常；所述锂电池处于恒压充电状态时预先设置的电流的变化速率阈值是T 2，若d>T 2，则恒压充电异常；所述锂电池处于恒流放电状态时预先设置的电流的变化速率阈值是T 3，若d>T 3，则恒流放电异常。
本发明还提供一种锂电池内短路电压电流异常检测系统，包括：充放电单元，用于对锂电池进行充放电；电压采集单元，用于实时采集所述锂电池两端的电压数据；电流采集单元，用于实时采集通过所述锂电池的电流数据；处理单元，用于判断所述电压数据和所述电流数据是否异常；当所述电压和/或所述电流异常时，停止充放电。
本发明又提供一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，所述计算机程序被处理器执行时实现如上任一所述的方法。
本发明的有益效果为：提供一种锂电池内短路电压电流异常检测方法及系统，通过采集充放电的锂电池的电压和电流数据并判断电压数据和电流数据是否异常；当所述电压和/或所述电流异常时，停止充放电，有效避免了因为电压电流异常导致的锂电池内短路，提高锂电池的安全性。
在本发明一些实施例中，针对内短路成因复杂、外部表现不明显、仅靠电压电路指标本身不易觉察的特点，总结出内短路的各种表现模式，并制订出各模式中的表征参数，通过对这些表征参数的监控，从而监控到内短路的发生。
附图说明
图1是本发明实施例中一种锂电池热失控预警保护系统的示意图。
图2是本发明实施例中锂电池内短路电压电流异常检测方法的示意图。
图3是本发明实施例中判断所述电压数据是否异常的方法示意图。
图4是本发明实施例中电压上升异常检测的方法示意图。
图5是本发明实施例中电压下降异常检测的方法示意图。
图6是本发明实施例中电压下降趋势异常检测的方法示意图。
图7是本发明实施例中判断所述电流数据是否异常的方法示意图。
图8是本发明实施例中一种锂电池内短路电压电流异常检测系统的示意图。
图9是本发明实施例中的充电电压示意图。
图10是本发明实施例中的电压趋势示意图。
图11是本发明实施例中的充电电流示意图。
图12是本发明实施例中的电流趋势示意图。
图13是本发明实施例中的计算锂电池的电芯表面温度的方法示意图。
图14是本发明实施例中的计算锂电池的电芯表面温度与所温度传感器的测量温度的相关性的方法示意图。
图15是本发明实施例中温度异常检测子系统的结构示意图。
图16是本发明实施例中温度异常检测子系统估计的电芯表面温度与实际测量温度之间的估计误差分布图。
其中，1-温度传感器，2-针床，3-托盘，4-电芯，5-烟雾传感器，6-探针安装 模组，7-探针支撑模组。
具体实施方式
为了使本发明实施例所要解决的技术问题、技术方案及有益效果更加清楚明白，以下结合附图及实施例，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。
需要说明的是，当元件被称为“固定于”或“设置于”另一个元件，它可以直接在另一个元件上或者间接在该另一个元件上。当一个元件被称为是“连接于”另一个元件，它可以是直接连接到另一个元件或间接连接至该另一个元件上。另外，连接即可以是用于固定作用也可以是用于电路连通作用。
需要理解的是，术语“长度”、“宽度”、“上”、“下”、“前”、“后”、“左”、“右”、“竖直”、“水平”、“顶”、“底”“内”、“外”等指示的方位或位置关系为基于附图所示的方位或位置关系，仅是为了便于描述本发明实施例和简化描述，而不是指示或暗示所指的装置或元件必须具有特定的方位、以特定的方位构造和操作，因此不能理解为对本发明的限制。
此外，术语“第一”、“第二”仅用于描述目的，而不能理解为指示或暗示相对重要性或者隐含指明所指示的技术特征的数量。由此，限定有“第一”、“第二”的特征可以明示或者隐含地包括一个或者更多该特征。在本发明实施例的描述中，“多个”的含义是两个或两个以上，除非另有明确具体的限定。
本发明下述实施例中的一部分是基于如下认识：
1、导致内部短路的情况分为以下几种：隔膜缺陷或老化破裂；锂枝晶、单质铁离子还原沉积刺穿隔膜；制作电芯时异物夹带导致刺穿隔膜；集流体铜箔或铝箔边缘毛刺。
2、根据内部短路的部位分类，电池内部短路形式又分为以下几种：负极材料和铝集流体短路；铜集流体和铝集流体短路；铜集流体和正极材料短路；正极材料和负极材料短路；
3、各种内短路的表现不同：其中，铝集流体与荷电的负极材料短路由于接触电阻小通过电流大，容易短时间内部短路部位温度急速升高引发热副反应，从而产生热失控；铝集流体与铜集流体的短路与外部短路相似，温度会均匀传导到整个电池；正极材料与铜集流体和正负极材料的短路由于正极材料阻抗大，影响 最小。
由此可见，导致内部短路的原因多样、部位也多变，其电压、电压表现也必然各不相同。为此，本发明对各种原因及部位所导致的内短路进行了分析，总结出典型模式，并找到各模式的合适的表征参数，从而可以通过对该参数的监测，实现对内短路的监测。比如，这些模式包括：
1、不具有多孔性保护膜结构的锂电池在发生内部短路时，电池电压迅速降低，之后电压不再恢复。
2、三元锂离子动力电池发生内部短路时，电压在2秒内迅速降低到0，同时伴随着温度的升高，温度在5秒附近升高到200度左右，随后处于热失控状态。
3、带多孔保护膜的锂电池发生电池内部短路时，内部短路初期出现电压下降，随后由于短路被保护隔膜阻止进入微短路状态，电压有所回升，随着温度的逐步升高电压又逐步下降。
4、磷酸铁锂电池单体内部短路时，由于磷酸铁锂(LiFeP04)作为该正极材料的二次电池其导电性低，而且锂离子的扩散速度也极慢，在穿刺(模拟内部短路)后1秒内电压由最初的3.7V下降到3.2V，随后电压进入稳定状态不再下降。壳体表面温度随着穿刺的进行而逐步急速升高，在4～6秒进入热失控状态。
综合上述不同类型锂电池的实验数据我们发现，内部短路1～2秒内首先发生电压的变化，随后的2～4秒内可以明显检测到壳体温度的上升，之后的4～6秒的时间大部分电池会进入热失控状态。因此，准确和安全的热失控的保护策略应该按照电压电流参数监测、温度监测、雾监测的策略进行逐级保护。
基于此认识，本发明提了如下具体实施例：
实施例1
在锂电池的自动化生产中，锂电池内部短路检测途径包括如下：
电压异常检测：锂电池的内部短路会造成电压的下降，通过对电压下降趋势的监测可以对内部短路做出判断和预警。
热检测：通过在锂电池侧壁贴附热电偶检测温度变化的方式判定短路是否发生。现有技术中，由于短路发生时热量由内而外传导，温度显示存在一定的滞后，从而不能第一时间将短路情况反馈出来。
容量异常检测：由于内部短路发生时会有一部分电能转换为热能散失，这样 充电过程中充入的容量会比未发生内部短路时要高，因此当充电容量高于基准容量时上报内部短路故障，当充电容量低于或等于基准容量时，锂电池状态正常。
如图1所示，一种锂电池热失控预警保护系统，包括：
电压电流异常检测子系统，用于在锂电池充放电过程中实时采集所述锂电池两端的电压数据和通过所述锂电池的电流数据，判断所述电压数据和所述电流数据是否异常；当所述电压和/或所述电流异常时，停止充放电；
温度异常检测子系统，用于在所述锂电池充放电过程中实时采集所述锂电池的温度，判断所述温度是否超过预先设置的温度阈值，当所述温度超过所述温度阈值时，发出温度异常报警；
烟雾感应异常检测子系统，用于在所述锂电池充放电过程中实时探测是否有烟雾，当探测到烟雾时，启动报警和消防喷淋。
综合不同类型锂电池的实验数据得到，内部短路1～2秒内首先发生电压的变化，随后的2～4秒内可以明显检测到壳体问题的上升，之后的4～6秒的时间大部分锂电池会进入热失控状态，因此，准确和安全的热失控的保护策略应该按照：压电流异常→温度异常→烟雾感应异常的策略进行逐级保护。
可以理解的是，在本发明的一种实施例中，电压电流异常检测子系统会先于温度异常检测子系统和烟雾感应异常检测子系统捕捉到异常信号；当烟雾感应异常检测子系统发出预警而电压电流异常检测子系统和温度异常检测子系统没有捕捉到异常信号时，判断为烟雾感应异常检测子系统误报。电压电流异常检测子系统同步实时采集所述锂电池两端的电压数据和通过所述锂电池的电流数据中的至少一者，并进行内短路指标监测，并判断所述电压数据和所述电流数据中的至少一者的内短路指标是否异常。
三个子系统通过综合电压、温度和烟雾感应三方面的异常监测对锂电池的热失控进行有效预防，并采取单通道停止、整盘停止并启动消防观察、启动消防喷淋、六面防护等策略对热失控进行逐级控制和保护。
在发生短路时，电压电流异常监测子系统将会先于温度异常检测子系统和烟雾感应异常检测子系统捕捉到异常信号发出预警，通常会在电压电流异常监测子系统预警时停止单通道的充放电操作，防止副反应的进一步加剧；当温度异常检测子系统检测到锂电池的壳体的温度的显著上升时，会根据用户配置进行整盘停 止并启动消防观察的动作；当烟雾感应异常检测子系统发出预警时，报警并启动消防喷淋。当烟雾感应异常检测子系统发出预警而其他两个子系统未预警时，系统将会根据配置判断为烟雾感应异常检测子系统误报。
锂电池自动化生产中热失控预警保护系统的自动化针床库位配有6面防护装置，防止在热失控发生时扩展到邻近库位。系统具有独立工步保护和全局保护，消除了过充、过放、压接不良、电压和电流超过上下限等引起热失控的潜在因素。
实施例2
电压电流异常检测子系统是锂电池自动化生产中热失控预警保护系统的核心，通过实时监测和跟踪电压、电流的变化，利用电池内短路时电压、电流变化的规律，对内电池短路做出判断和预警。电压电流异常检测子系统通常可以在内短路发生的1秒内停止异常通道，防止内短路负反应的进一步加剧，从而大大降低热失控发生的概率。在化成分容工艺中，通过对充放电时电压的趋势异常监控以及放电时电流的异常下降监控实现对内短路的判断与预警。因为内短路的电压与电流异常都是微小的瞬时波动，这对检测设备的精度和响应速度提出了很高的要求。高精度充放电设备具备了以下条件：
(1)高精度：0.02％的精度设定可分辨2mv的电压波动；
(2)高采样率：下位机最高5ms的采样速率，采用卡塞尔窗数字有限冲激响应滤波器，滤除白噪声和带外干扰的同时每点输出，避免了移动平均滤波器的长时延；
(3)高性能的下位机可以让趋势跟踪算法在下位机实现，结合高精度和高采样率，对电压与电流的趋势异常做实时跟踪处理。在充电与静止环节，异常跟踪算法(内部称为电子显微放大镜)跟踪如下几种趋势：
a.电压突升或突降
b.电压迅速降低到另一个平台并缓慢逐步降低
c.电压迅速大幅降低后又逐步回升，随后缓慢逐步降低
放电阶段，异常跟踪算法跟踪电流的异常下降趋势，即ΔI/Δt的斜率异常。
如图2所示，在本发明的一种实施例中，锂电池内短路电压电流异常检测方法包括如下步骤：
S1：对锂电池进行充放电并确定所述锂电池进入充放电状态；
S2：同步实时采集所述锂电池两端的电压数据和通过所述锂电池的电流数据中的至少一者，并进行内短路指标监测；
S3：判断所述电压数据和所述电流数据中的至少一者的内短路指标是否异常；当所述电压数据和所述电流数据中的一者异常时，停止对锂电池进行充放电。
如图3所示，判断所述电压数据是否异常包括如下步骤：
滤除所述电压数据的噪音，实时选取欠采样率达到设定数值的所述电压数据，并动态更新最大记录电压；
所述内短路指标监测包括电压上升异常检测、电压异常下降检测、电压下降趋势异常检测中的至少一者。
如图4所示，电压上升异常检测包括：
计算实时的电压数据V n与充电起始电压V start的差值V ras＝V n-V start；
判断所述差值是否小于预先设置的电压上升阈值，若所述差值小于所述预先设置的电压上升阈值则电压上升异常。
如图5所示，电压下降异常检测包括：
计算所述最大记录电压V max与实时的电压数据V n的差值ΔV＝V max-V n；
判断所述差值是否超过预先设置的电压下降阈值，若所述差值超过所述预先设置的电压下降阈值则电压下降异常。
如图6所示，电压下降趋势异常检测包括：
计算实时采集的所述锂电池两端的电压数据的差分：dn＝V n-V n-1并获取dn的正负的符号d sng-n，
计算所述符号的和是否超过预先设置的电压下降趋势阈值，若所述符号的和小于所述预先设置的电压下降趋势阈值，则记录电压下降趋势起始点电压V ref；
计算电压下降斜率：S＝(V ref-V n)/N，其中N为电压欠采样率；
判断所述电压下降斜率是否超过预先设置的下降斜率阈值，如果所述电压下降斜率超过所述预先设置的下降斜率阈值则电压下降趋势异常。
具体的，实际自动化生产中可按照如下操作进行：
步骤1:设备进入化成分容工艺，启动工步对锂电池进行充放电；
步骤2：复位所有变量；
步骤3：判断锂电池是否进入充电状态，如果进入充电状态，进入步骤4；
步骤4：设备采集电池电压数据V n，并经过低通滤波器滤波滤除噪声，同时，欠采样计数器递增；
步骤5：如果欠采样计数器达到了欠采样率N，更新电压缓存器(移除电压缓存器中W时刻之前的采样点，并降将当前采样数据V n存入缓存)，否则返回步骤4；其中，W是观察窗口的大小，一般缺省设置为128，N是根据采样率的大小进行调整，一般设置为1或2。
步骤6：比较V n与最大记录电压V max，如果V n大于V max，将V n赋予V max，并清零计数器n_max＝0；否则记录Vmax的相对偏移n_max递增n_max＝n_max+1；
步骤7：电压上升异常检测：如果电压上升异常检测开关打开，则进行电压上升异常检测，否则跳过本步骤。电压上升异常检测算法如下：计算当前采样数据V n与充电起始电压V start的差值v ras＝V n-V star，如果经过判断电压上升异常的时间期限(Time_Rais_Thres)后上升差值小于电压上升趋势异常阈值，即v ras<V rais_Thres，系统发出电压上升异常报警并停止通道退出当前工作状态；
步骤8：电压异常下降监控：如果电压异常下降监控开关打开，则进行电压异常下降检测，否则跳过本步骤。电压异常下降检测算法如下：计算最大记录电压V max与当前采样数据V n的差值ΔV＝V max-v(n)。如果在判断电压异常下降的时间期限(Time_Drop_Thres)：记录Vmax的相对偏移n_max<Time_Drop_Thres内下降差值大于阈值，即ΔV>ΔV d系统发出电压下降异常报警并停止通道退出当前工作状态；
步骤9：电压下降趋势异常监控：如果电压下降趋势异常监控开关打开，则进行电压下降趋势异常检测，否则跳过本步骤。电压下降趋势异常检测算法如下：
(1)计算电压采样的差分d(n)＝V(n)-V(n-1)并获取dn的正负的d sng(n)；
(2)通过计算符号的和判断是否出现下降趋势：如果和d sng(n)小于阈值，即Σd sng＜D t,则出现下降趋势，执行(3)；
(3)如趋势跟踪未启动，即跟踪标志trace_flg＝0，启动趋势跟踪，记录下降趋势起始点电压Vref＝v(n)；如趋势跟踪已经启动，递增下降趋势计数器的记录趋势N_trend：N_trend＝N_trend+1；
(4)计算下降斜率Slope＝[Vref-v(n)]/N_trend；
(5)判断斜率是否超过下降斜率阈值Slope>Slop_Thres，如超过下降斜率阈值则发出电池内短路预警；
步骤10：对每一个采样点重复步骤4至步骤9直到通道工步完成或者异常退出。
实施例3
在本发明的一种实施例中，锂电池自动化生产中内短路电压电流异常检测方法，所述锂电池处于恒流充电、恒压充电或恒流放电状态，对应的所述电流异常为：恒流充电异常、恒压充电异常或恒流放电异常。
如图7所示，判断所述电流数据是否异常包括如下步骤：
滤除所述电流数据的噪音并动态更新缓存数据；
确定所述锂电池的状态并计算电流的变化速率；
所述电流的变化速率与预先设置的电流的变化速率阈值比较，若所述电流的变化速率大于预先设置的电流的变化速率阈值，则电流的变化异常。
计算电流的变化速率：d＝(C n-C n-N+1)/N，其中，C n是实时的电流数据，C n-N+1是在实时的时刻的N时刻之前的电流数据，N是时间间隔；
所述锂电池处于恒流充电状态时预先设置的电流的变化速率阈值是T 1，若d>T 1，则恒流充电异常；
所述锂电池处于恒压充电状态时预先设置的电流的变化速率阈值是T 2，若d>T 2，则恒压充电异常；
所述锂电池处于恒流放电状态时预先设置的电流的变化速率阈值是T 3，若d>T 3，则恒流放电异常。
具体的，实际自动化生产中可按照如下操作进行：
步骤1:设备进入化成分容工艺，启动工步对锂电池进行充放电；
步骤2：复位所有变量；
步骤3：判断锂电池是否进入充放电状态，如果进入充放电状态，进入步骤4；
步骤4：设备采集电流数据C n，并经过低通滤波器滤波滤除噪声，更新电流观察缓存c_buff缓存器：移除缓存器中N时刻之前的采样点，并降将当前采样数据C n存入缓存；
步骤5：计算电流变化速率：d＝(C n-C n-N+1)/N，并与阈值作比较，其中，C n是实时的电流数据，C n-N+1是在实时的时刻的N时刻之前的电流数据，N是时间间隔；
(1)设定阈值T：如果目前处于恒流充电状态，恒流充电异常阈值T＝Tccc，如果目前处于恒压充电状态，恒压充电异常阈值T＝Tcvc，如果目前处于恒流放电状态，恒流放电异常阈值T＝Tccd；
(2)比较：如果d大于T，判定电流出现异常，停止通道退出当前工步，否则重复步骤4。
实施例4
如图8所示，本发明还提供一种锂电池内短路电压电流异常检测系统，包括：
充放电单元，用于对锂电池进行充放电；
电压采集单元，用于实时采集所述锂电池两端的电压数据；
电流采集单元，用于实时采集通过所述锂电池的电流数据；
处理单元，用于判断所述电压数据和所述电流数据是否异常；当所述电压和/或所述电流异常时，停止充放电。
可以理解的是，上述对于各个单元的限定仅仅是功能性的，实际上任何可以实现本发明的系统都可以。在本发明的另一种实施例中，处理单元还可以判断温度是否异常或烟雾是否异常，都会采取如前所述的对应操作。
实施例5
本发明实现上述实施例方法中的全部或部分流程，也可以通过计算机程序来指令相关的硬件来完成，所述的计算机程序可存储于一计算机可读存储介质中，该计算机程序在被处理器执行时，可实现上述各个方法实施例的步骤。其中，所述计算机程序包括计算机程序代码，所述计算机程序代码可以为源代码形式、对象代码形式、可执行文件或某些中间形式等。所述计算机可读介质可以包括：能够携带所述计算机程序代码的任何实体或装置、记录介质、U盘、移动硬盘、磁碟、光盘、计算机存储器、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、电载波信号、电信信号以及软件分发介质等。需要说明的是，所述计算机可读介质包含的内容可以根据司法管辖区内 立法和专利实践的要求进行适当的增减，例如在某些司法管辖区，根据立法和专利实践，计算机可读介质不包括电载波信号和电信信号。
实施例6
采用本发明的系统和方法应用到实际生产中，对12只镍钴锰NCM配比为6：2：2的53Ah三元电芯进行充放电循环测试，其中在12只电芯中有3只为不良电芯但未作标识，实验目的是通过对锂电池的充放电循环测试验证本系统是否能够检测出不良电芯。
图9为恒流恒压充电时电芯的电压曲线，图10为采用本发明的系统实时计算的电压趋势，图11为恒流恒压充电时的电流曲线，图12为采用本发明的系统实时计算的电流趋势。如图10与图12所示，在恒压充电阶段，有三颗电芯的电压趋势与电流趋势如图9-图12所示，不良电芯在电压趋势和电流趋势同时出现出有别于正常电芯的异常跳变，可将不良电芯检测出来并标记。
在本发明的另一种实施例中，本系统检测判断不良电芯数量2.3万只，通过离线测试验证误判150只，误判率小于0.01‰。
实施例7
本实施例的温度异常检测子系统包括：所述锂电池在极耳压接一个温度探针；可以理解的是，由于每一只锂电池都有一个温度探针压接在极耳，在锂电池的充放电过程中，锂电池的温度被实时监控，当极耳温度连续超过温度阈值时，温度异常检测子系统会发出温度异常报警。
实施例8
对于软包电池，因为软包电池铝塑封导热性能不佳，因此软包电芯本体温度与极片温度差异较大。针对此种情况，本实施例8给出通过软件对锂电池的电芯表面温度进行计算的方法。本实施例中，在所述锂电池的针床顶部前后各安装一排温度传感器阵列，所述温度传感器阵列包括等间距的L个温度传感器，构成2*L的温度传感器阵列。可根据2*L个温度传感器测量数据计算每个电池包的温度。下面举例说明：
如图15所示，是在实际生产中应用的一个本发明的系统和方法测量电芯表面的温度的具体实例，锂电池热失控预警保护系统的分容的针床2的顶部前后两排各安装了4个温度传感器，共8个温度传感器组成了温度传感器阵列，一个 托盘3放置32只软包电芯4，每个电芯4表面都安装放置了温度传感器1用于测量电芯表面的温度。每个锂电池在极耳压接一个温度探针，图中可见探针安装模组6和探针支撑模组7。烟雾传感器5设置在温度传感器1和电芯4之间。
如图13所示，在实施例中，计算所述锂电池的电芯表面温度的方法包括如下步骤：
T1：计算所述锂电池的电芯表面温度与所述温度传感器的测量温度的相关性；
T2：根据所述相关性和所述温度传感器阵列的测量温度实时估计所述锂电池的电芯表面温度。
如图14所示，步骤T1包括：
T11：计算所述温度传感器之间的互相关矩阵：
Figure PCTCN2019091557-appb-000001
Figure PCTCN2019091557-appb-000002
其中，r n，l为第n个温度传感器的测量温度与第l个温度传感器的测量温度之间的互相关，
Figure PCTCN2019091557-appb-000003
为第n个温度传感器的测量温度与第l个温度传感器的测量温度之间的协方差，
Figure PCTCN2019091557-appb-000004
为第n个温度传感器的测量温度的方差，
Figure PCTCN2019091557-appb-000005
第l个温度传感器测量温度的方差；
T12：计算每个所述锂电池与所述温度传感器之间的互相关矩阵，
Figure PCTCN2019091557-appb-000006
Figure PCTCN2019091557-appb-000007
其中，c m，l为第m个电芯表面温度与第l个温度传感器测量温度之间的互相关，
Figure PCTCN2019091557-appb-000008
为第m个温度传感器表面温度与第l个温度传感器测量温度之间的协方差，
Figure PCTCN2019091557-appb-000009
为第m个电芯表面温度的方差，
Figure PCTCN2019091557-appb-000010
第l个温度传感器 测量温度的方差。
步骤T2包括：设在k时刻所述温度传感器的测量温度向量为：
Figure PCTCN2019091557-appb-000011
根据2D-MMSE准则电芯表面的温度为：
Figure PCTCN2019091557-appb-000012
通常，当电池极耳表面温度或者电芯表面温度超过100摄氏度，即可判断为温度异常，启动报警。
图16显示了采用本系统估计的电芯表面温度与实际测量温度之间的估计误差分布，从结果可以看到，绝大部分的误差分布在[-0.1，0.1]的范围内,均方误差为6e-3。由此可见，其结果十分可靠，能满足热失控预警保护的要求。
以上内容是结合具体的优选实施方式对本发明所作的进一步详细说明，不能认定本发明的具体实施只局限于这些说明。对于本发明所属技术领域的技术人员来说，在不脱离本发明构思的前提下，还可以做出若干等同替代或明显变型，而且性能或用途相同，都应当视为属于本发明的保护范围。

Claims (10)

  1. 一种锂电池内短路电压电流异常检测方法，其特征在于，包括如下步骤：
    S1：对锂电池进行充放电并确定所述锂电池进入充放电状态；
    S2：同步实时采集所述锂电池两端的电压数据和通过所述锂电池的电流数据中的至少一者，并进行内短路指标监测；
    S3：判断所述电压数据和所述电流数据中的至少一者的内短路指标是否异常；当所述电压数据和所述电流数据中的一者异常时，停止对锂电池进行充放电。
  2. 如权利要求1所述的锂电池内短路电压电流异常检测方法，其特征在于，判断所述电压数据是否异常包括如下步骤：
    滤除所述电压数据的噪音，实时选取欠采样率达到设定数值的所述电压数据，并动态更新最大记录电压；
    进行所述内短路指标监测，所述内短路指标监测包括同步进行电压上升异常检测、电压异常下降检测、电压下降趋势异常检测中的至少一者。
  3. 如权利要求2所述的锂电池内短路电压电流异常检测方法，其特征在于，所述电压上升异常检测包括：
    计算实时的电压数据V n与充电起始电压V start的差值V ras＝V n-V start；
    判断所述差值是否小于预先设置的电压上升阈值，若所述差值小于所述预先设置的电压上升阈值则电压上升异常。
  4. 如权利要求2所述的锂电池内短路电压电流异常检测方法，其特征在于，所述电压异常下降检测包括：
    计算所述最大记录电压V max与实时的电压数据V n的差值ΔV＝V max-V n；
    判断所述差值是否超过预先设置的电压下降阈值，若所述差值超过所述预先设置的电压下降阈值则电压下降异常。
  5. 如权利要求2所述的锂电池内短路电压电流异常检测方法，其特征在于，所述电压下降趋势异常检测包括：
    计算实时采集的所述锂电池两端的电压数据的差分：dn＝V n-V n-1并获取dn的正负的符号d sng-n；
    计算所述符号的和是否超过预先设置的电压下降趋势阈值，若所述符号的和小于所述预先设置的电压下降趋势阈值，则记录电压下降趋势起始点电压V ref；
    计算电压下降斜率：S＝(V ref-V n)/N，其中N为电压欠采样率；
    判断所述电压下降斜率是否超过预先设置的下降斜率阈值，如果所述电压下降斜率超过所述预先设置的下降斜率阈值则电压下降趋势异常。
  6. 如权利要求1所述的锂电池内短路电压电流异常检测方法，其特征在于，所述锂电池处于恒流充电、恒压充电或恒流放电状态，对应的所述电流异常为：恒流充电异常、恒压充电异常或恒流放电异常。
  7. 如权利要求6所述的锂电池内短路电压电流异常检测方法，其特征在于，判断所述电流数据是否异常包括如下步骤：
    滤除所述电流数据的噪音并动态更新缓存数据；
    确定所述锂电池的状态并计算电流的变化速率；
    所述电流的变化速率与预先设置的电流的变化速率阈值比较，若所述电流的变化速率大于预先设置的电流的变化速率阈值，则电流的变化异常。
  8. 如权利要求7所述的锂电池内短路电压电流异常检测方法，其特征在于，计算电流的变化速率：d＝(C n-C n-N+1)/N，其中，C n是实时的电流数据，C n-N+1是在实时的时刻的N时刻之前的电流数据，N是时间间隔；
    所述锂电池处于恒流充电状态时预先设置的电流的变化速率阈值是T 1，若d>T 1，则恒流充电异常；
    所述锂电池处于恒压充电状态时预先设置的电流的变化速率阈值是T 2，若d>T 2，则恒压充电异常；
    所述锂电池处于恒流放电状态时预先设置的电流的变化速率阈值是T 3，若d>T 3，则恒流放电异常。
  9. 一种锂电池内短路电压电流异常检测系统，其特征在于，包括：
    充放电单元，用于对锂电池进行充放电；
    电压采集单元，用于实时采集所述锂电池两端的电压数据；
    电流采集单元，用于实时采集通过所述锂电池的电流数据；
    处理单元，用于判断所述电压数据和所述电流数据是否异常；当所述电压和/或所述电流异常时，停止充放电。
  10. 一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实现如权利要求1-8任一所述的方法。
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CN112763919A (zh) * 2021-02-02 2021-05-07 江苏塔菲尔新能源科技股份有限公司 一种动力电池內短路异常的检测方法及系统
EP3951412A1 (en) * 2020-08-07 2022-02-09 Prime Planet Energy & Solutions, Inc. Method for inspecting insulation of a secondary battery

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CN101071160B (zh) * 2006-05-10 2010-05-26 盈正豫顺电子股份有限公司 电池健康状态的诊断方法
JP2009032506A (ja) * 2007-07-26 2009-02-12 Panasonic Corp 非水系電解質二次電池の内部短絡検知方法および装置
CN108107364B (zh) * 2016-11-24 2020-07-14 华为技术有限公司 一种检测电池的方法及装置

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EP3951412A1 (en) * 2020-08-07 2022-02-09 Prime Planet Energy & Solutions, Inc. Method for inspecting insulation of a secondary battery
CN112763919A (zh) * 2021-02-02 2021-05-07 江苏塔菲尔新能源科技股份有限公司 一种动力电池內短路异常的检测方法及系统
CN112763919B (zh) * 2021-02-02 2024-01-09 江苏正力新能电池技术有限公司 一种动力电池內短路异常的检测方法及系统

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