CN110350258B - Lithium battery thermal runaway early warning protection system and method - Google Patents

Lithium battery thermal runaway early warning protection system and method Download PDF

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CN110350258B
CN110350258B CN201910522647.3A CN201910522647A CN110350258B CN 110350258 B CN110350258 B CN 110350258B CN 201910522647 A CN201910522647 A CN 201910522647A CN 110350258 B CN110350258 B CN 110350258B
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lithium battery
temperature
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CN110350258A (en
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赵少华
王守模
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Guangdong Hengyineng Technology Co ltd
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Guangdong Hengyi Energy Technology Co ltd
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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]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/389—Measuring internal impedance, internal conductance or related variables
    • 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
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    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M2200/00—Safety devices for primary or secondary batteries
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M2200/00—Safety devices for primary or secondary batteries
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Abstract

The invention provides a lithium battery thermal runaway early warning protection system, which comprises: the voltage and current abnormity detection subsystem is used for acquiring voltage data at two ends of the lithium battery and current data passing through the lithium battery in real time in the charging and discharging processes of the lithium battery and judging whether the voltage data and the current data are abnormal or not; when the voltage and/or the current are abnormal, stopping charging and discharging; the temperature anomaly detection subsystem is used for acquiring the temperature of the lithium battery in real time in the charging and discharging processes of the lithium battery, judging whether the temperature exceeds a preset temperature threshold value or not, and sending out a temperature anomaly alarm when the temperature exceeds the temperature threshold value; the smoke induction abnormity detection subsystem is used for detecting whether smoke exists in the lithium battery charging and discharging process in real time, and when the smoke is detected, alarming and fire-fighting spraying are started. Through the synergistic effect of the three subsystems, the early warning of the thermal runaway of the lithium battery is realized, and the adverse effect caused by the thermal runaway is effectively avoided.

Description

Lithium battery thermal runaway early warning protection system and method
Technical Field
The invention relates to the technical field of lithium batteries, in particular to a thermal runaway early warning protection system for a lithium battery.
Background
In the latter-stage automatic production process (formation, capacity grading, standing, DCIR detection, etc.) of lithium batteries, the main causes of lithium battery heating and even explosion can be classified into the following two categories: 1. the SEI of the solid electrolyte interface film of the lithium battery is decomposed, falls off and even reacts reversely due to overcharge or overdischarge, and thermal runaway is finally caused along with violent reactions such as heating, gas and the like. Due to the fact that the component capacitance and detection system has a protection strategy specially aiming at overcharge and overdischarge, thermal runaway caused by the reason can be avoided. 2. Thermal runaway of lithium batteries due to short circuits. The short circuit is divided into an external short circuit and an internal short circuit, and the double-voltage monitoring system in the charging and discharging system can effectively detect and prevent the external short circuit, so that effective early warning and control are mainly needed for the internal short circuit of the lithium battery at present, and a thermal runaway state caused by the internal short circuit is avoided.
Due to the fact that internal short circuit is not easy to monitor visually, and the internal temperature of the lithium battery is difficult to measure directly, an accurate and effective lithium battery thermal runaway early warning protection system and method are lacked in the prior art.
Disclosure of Invention
The invention provides a lithium battery thermal runaway early warning protection system and method for solving the existing problems.
In order to solve the problems, the technical scheme of the thermal runaway early warning protection system for the lithium battery is as follows:
a lithium battery thermal runaway early warning protection system comprises: the voltage and current abnormity detection subsystem is used for acquiring voltage data at two ends of the lithium battery and current data passing through the lithium battery in real time in the charging and discharging processes of the lithium battery and judging whether the voltage data and the current data are abnormal or not; when the voltage and/or the current are abnormal, stopping charging and discharging; the temperature anomaly detection subsystem is used for acquiring the temperature of the lithium battery in real time in the charging and discharging processes of the lithium battery, judging whether the temperature exceeds a preset temperature threshold value or not, and sending out a temperature anomaly alarm when the temperature exceeds the temperature threshold value; and the smoke induction abnormity detection subsystem is used for detecting whether smoke exists in the lithium battery charging and discharging process in real time, and starting alarm and fire-fighting spraying when the smoke is detected.
Preferably, the voltage and current abnormality detection subsystem captures an abnormal signal before the temperature abnormality detection subsystem and the smoke sensing abnormality detection subsystem capture an abnormal signal; the voltage and current abnormity detection subsystem synchronously collects at least one of voltage data at two ends of the lithium battery and current data passing through the lithium battery in real time, carries out internal short circuit index monitoring, and judges whether the internal short circuit index of at least one of the voltage data and the current data is abnormal.
Preferably, the judging whether the voltage data is abnormal includes the steps of: filtering noise of the voltage data, selecting the voltage data with an undersampling rate reaching a set value in real time, and dynamically updating the maximum recorded voltage; and monitoring the internal short circuit index, wherein the monitoring of the internal short circuit index comprises synchronous voltage rise abnormity detection, voltage abnormity drop detection and voltage drop trend abnormity detection.
Preferably, the voltage rise abnormality detection includes: calculating real-time voltage data VnAnd a charge starting voltage VstartDifference value V ofras＝Vn-Vstart(ii) a Judging whether the difference value is smaller than a preset voltage rising threshold value or not, and if the difference value is smaller than the preset voltageRaising the threshold value causes abnormal voltage rise; the voltage drop abnormality detection includes: calculating the maximum recording voltage VmaxWith real-time voltage data VnIs equal to Vmax-Vn(ii) a Judging whether the difference value exceeds a preset voltage drop threshold value or not, and if the difference value exceeds the preset voltage drop threshold value, judging that the voltage drop is abnormal; the voltage falling tendency abnormality detection includes: calculating the difference of the voltage data of the two ends of the lithium battery collected in real time: dn ═ Vn-Vn-1And obtaining the sign d of the sign of dnsng-nCalculating whether the sum of the symbols exceeds a preset voltage descending trend threshold value or not, and recording a voltage descending trend starting point voltage V if the sum of the symbols is smaller than the preset voltage descending trend threshold valuerefCalculating the voltage drop slope: s ═ Vref-Vn) N, wherein N is the voltage undersampling rate; and judging whether the voltage falling slope exceeds a preset falling slope threshold value or not, wherein if the voltage falling slope exceeds the preset falling slope threshold value, the voltage falling trend is abnormal.
Preferably, the lithium battery is in a constant current charging, constant voltage charging or constant current discharging state, and the corresponding current abnormality is: constant current charging is abnormal, constant voltage charging is abnormal or constant current discharging is abnormal.
Preferably, the judging whether the current data is abnormal includes the steps of: filtering noise of the current data and dynamically updating the cache data; determining the state of the lithium battery and calculating the rate of change of current: d ═ Cn-Cn-N+1) N, wherein CnIs real-time current data, Cn-N+1Is current data prior to time N, which is a time interval, of the real-time; comparing the change rate of the current with a preset change rate threshold value of the current: the preset current change rate threshold value is T when the lithium battery is in a constant current charging state1If d > T1If the charging is abnormal, the constant current charging is abnormal; the preset current change rate threshold value is T when the lithium battery is in a constant-voltage charging state2If d > T2If so, the constant voltage charging is abnormal;the preset current change rate threshold value is T when the lithium battery is in a constant current discharge state3If d > T3The constant current discharge is abnormal.
Preferably, the temperature abnormality detection subsystem includes: the temperature probe is in pressure joint with the lithium battery pole ear pressure; or, a row of temperature sensor arrays are respectively arranged at the front and the back of the top of a needle bed of the lithium battery, and the temperature sensor arrays comprise L temperature sensors at equal intervals; and the calculating device is used for calculating the surface temperature of the battery core of the lithium battery according to the detection result of the temperature sensor array.
Preferably, the method for calculating the cell surface temperature of the lithium battery comprises the following steps: t1: calculating the correlation between the cell surface temperature of the lithium battery and the measurement temperature of the temperature sensor; t2: and estimating the surface temperature of the battery core of the lithium battery in real time according to the correlation and the measured temperature of the temperature sensor array.
Preferably, step T1 includes: t11: calculating a cross-correlation matrix between the temperature sensors:
Figure GDA0002685218310000031
Figure GDA0002685218310000032
wherein r isn，lIs the cross-correlation between the measured temperature of the nth temperature sensor and the measured temperature of the 1 st temperature sensor,
Figure GDA0002685218310000033
is the covariance between the measured temperature of the nth temperature sensor and the measured temperature of the 1 st temperature sensor,
Figure GDA0002685218310000034
is the variance of the measured temperature of the nth temperature sensor,
Figure GDA0002685218310000035
measuring a variance of temperature for the 1 st temperature sensor;
t12: calculating a cross-correlation matrix between each of the lithium batteries and the temperature sensor,
Figure GDA0002685218310000036
Figure GDA0002685218310000037
wherein, cm，lFor the cross-correlation between the mth cell surface temperature and the 1 st temperature sensor measurement temperature,
Figure GDA0002685218310000038
is the covariance between the surface temperature of the mth temperature sensor and the measured temperature of the lth temperature sensor,
Figure GDA0002685218310000039
is the variance of the surface temperature of the mth cell,
Figure GDA00026852183100000310
the 1 st temperature sensor measures the variance of the temperature.
Preferably, step T2 includes: and setting the measurement temperature vector of the temperature sensor at the moment k as follows:
Figure GDA00026852183100000311
the cell surface temperature according to the 2D-MMSE criterion was:
Figure GDA00026852183100000312
the invention also comprises a lithium battery thermal runaway early warning protection method, which comprises the following steps: s1: charging and discharging a lithium battery and determining that the lithium battery enters a charging and discharging state; s2: performing voltage and current abnormity detection, temperature abnormity detection and smoke induction abnormity detection on the lithium battery; wherein the voltage and currentThe abnormality detection comprises the steps of synchronously acquiring at least one of voltage data at two ends of the lithium battery and current data passing through the lithium battery in real time, and carrying out internal short circuit index monitoring; judging whether an internal short circuit index of at least one of the voltage data and the current data is abnormal; stopping charging and discharging the lithium battery when one of the voltage data and the current data is abnormal; the temperature detection includes: the method comprises the steps of collecting the temperature of the lithium battery in real time in the charging and discharging process of the lithium battery, judging whether the temperature exceeds a preset temperature threshold value, and sending out a temperature abnormity alarm when the temperature exceeds the temperature threshold value; the smoke induction abnormity detection comprises the steps of detecting whether smoke exists in the lithium battery charging and discharging process in real time, and starting alarming and fire-fighting spraying when the smoke is detected.
Preferably, the embodiment of the present invention further includes the following features:
judging whether the voltage data is abnormal comprises the following steps: filtering noise of the voltage data, selecting the voltage data with an undersampling rate reaching a set value in real time, and dynamically updating the maximum recorded voltage; the internal short circuit index monitoring comprises at least one of voltage rising abnormity detection, voltage abnormity falling detection and voltage falling trend abnormity detection.
The voltage rise abnormality detection includes: calculating a difference value Vras between the real-time voltage data Vn and the charging starting voltage Vstart, which is Vn-Vstart; and judging whether the difference value is smaller than a preset voltage rising threshold value or not, and if the difference value is smaller than the preset voltage rising threshold value, judging that the voltage rises abnormally.
The voltage abnormal drop detection includes: calculating a difference value delta V between the maximum recording voltage Vmax and the real-time voltage data Vn, wherein the difference value delta V is Vmax-Vn; and judging whether the difference value exceeds a preset voltage drop threshold value or not, and if the difference value exceeds the preset voltage drop threshold value, judging that the voltage drop is abnormal.
The voltage falling tendency abnormality detection includes: calculating the difference of the voltage data of the two ends of the lithium battery collected in real time: the dn is Vn-Vn-1, and the positive and negative signs dsng-n of the dn are obtained; calculating whether the sum of the symbols exceeds a preset voltage descending trend threshold value or not, and recording a voltage descending trend starting point voltage Vref if the sum of the symbols is smaller than the preset voltage descending trend threshold value; calculating the voltage drop slope: (Vref-Vn)/N, where N is the voltage undersampling rate; and judging whether the voltage falling slope exceeds a preset falling slope threshold value or not, wherein if the voltage falling slope exceeds the preset falling slope threshold value, the voltage falling trend is abnormal.
The lithium battery is in a constant current charging state, a constant voltage charging state or a constant current discharging state, and the corresponding current abnormity is as follows: constant current charging is abnormal, constant voltage charging is abnormal or constant current discharging is abnormal.
Judging whether the current data is abnormal comprises the following steps: filtering noise of the current data and dynamically updating the cache data; determining the state of the lithium battery and calculating the change rate of current; and comparing the change rate of the current with a preset change rate threshold of the current, and if the change rate of the current is greater than the preset change rate threshold of the current, judging that the change of the current is abnormal.
Calculating the rate of change of current: d ═ Cn-Cn-N+1) N, where Cn is the current data in real time, Cn-N+1Is current data prior to time N, which is a time interval, of the real-time; the preset current change rate threshold value is T1 when the lithium battery is in a constant current charging state, and if d is larger than T1, the constant current charging is abnormal; the preset current change rate threshold value is T2 when the lithium battery is in a constant-voltage charging state, and if d is larger than T2, the constant-voltage charging is abnormal; the preset current change rate threshold value is T3 when the lithium battery is in a constant current discharge state, and if d is larger than T3, the constant current discharge is abnormal.
The lithium battery comprises a lithium battery, a needle bed and a temperature sensor array, wherein the lithium battery is characterized in that the front part and the back part of the needle bed top of the lithium battery are respectively provided with a row of temperature sensor arrays, and the temperature sensor arrays comprise L temperature sensors at equal intervals; calculating the surface temperature of the battery cell of the lithium battery according to the detection result of the temperature sensor array by using a calculating device; the method for calculating the surface temperature of the battery core of the lithium battery comprises the following steps: t1: calculating the correlation between the cell surface temperature of the lithium battery and the measurement temperature of the temperature sensor; t2: and estimating the surface temperature of the battery core of the lithium battery in real time according to the correlation and the measured temperature of the temperature sensor array.
Step T1 includes: t11: calculating a cross-correlation matrix between the temperature sensors:
Figure GDA0002685218310000051
Figure GDA0002685218310000052
wherein r isn，lIs the cross-correlation between the measured temperature of the nth temperature sensor and the measured temperature of the ith temperature sensor,
Figure GDA0002685218310000053
is the covariance between the measured temperature of the nth temperature sensor and the measured temperature of the ith temperature sensor,
Figure GDA0002685218310000054
is the variance of the measured temperature of the nth temperature sensor,
Figure GDA0002685218310000055
measuring a variance of temperature for the ith temperature sensor; t12: calculating a cross-correlation matrix between each of the lithium batteries and the temperature sensor,
Figure GDA0002685218310000061
Figure GDA0002685218310000062
wherein, cm，lFor the mth cell surface temperature and the lth temperature sensingThe cross-correlation between the temperatures is measured by the instrument,
Figure GDA0002685218310000063
is the covariance between the surface temperature of the mth temperature sensor and the measured temperature of the lth temperature sensor,
Figure GDA0002685218310000064
is the variance of the surface temperature of the mth cell,
Figure GDA0002685218310000065
the ith temperature sensor measures the variance of the temperature.
The invention also concerns a computer-readable storage medium, in which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the method as described above.
The invention has the beneficial effects that: the lithium battery thermal runaway early warning protection system is provided, and through the synergistic effect of the voltage and current abnormality detection subsystem, the temperature abnormality detection subsystem and the smoke induction abnormality detection subsystem, the lithium battery thermal runaway early warning is realized, and the adverse consequences caused by the thermal runaway are effectively avoided.
In some embodiments of the invention, aiming at the characteristics that the cause of the internal short circuit is complex, the external performance is not obvious, and the voltage circuit index is not easily perceived, various performance modes of the internal short circuit are summarized, characterization parameters in each mode are formulated, and the occurrence of the internal short circuit is monitored by monitoring the characterization parameters.
In some embodiments of the invention, a unique method for setting a proper temperature sensor array and calculating the battery cell surface temperature of the lithium battery through software is formulated according to the characteristic that the short circuit condition cannot be fed back at the first time due to certain lag of temperature display when the heat is conducted from inside to outside when the short circuit occurs, especially for a soft package battery because the heat conduction performance of aluminum plastic package of the soft package battery is poor, so that the difference between the body temperature of the soft package battery cell and the temperature of a pole piece is large.
Drawings
Fig. 1 is a schematic diagram of a thermal runaway early warning protection system for a lithium battery in an embodiment of the invention.
Fig. 2 is a schematic diagram of a method for detecting abnormal 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 according to an embodiment of the present invention.
FIG. 4 is a schematic diagram of a method for detecting abnormal voltage rise according to an embodiment of the present invention.
FIG. 5 is a schematic diagram of a method for detecting abnormal voltage drop according to an embodiment of the present invention.
Fig. 6 is a schematic diagram of a method for detecting an abnormal voltage-decreasing 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 according to an embodiment of the present invention.
Fig. 8 is a schematic diagram of a system for detecting an abnormal short-circuit voltage and current in a lithium battery according to an embodiment of the present invention.
Fig. 9 is a schematic diagram of the charging voltage in the embodiment of the invention.
Fig. 10 is a schematic diagram of voltage trend in an embodiment of the invention.
Fig. 11 is a schematic diagram of a charging current in an embodiment of the invention.
Fig. 12 is a schematic view of current trend in the embodiment of the present invention.
Fig. 13 is a schematic diagram of a method for calculating a cell surface temperature of a lithium battery in an embodiment of the present invention.
Fig. 14 is a schematic diagram of a method for calculating a correlation between a cell surface temperature of a lithium battery and a measured temperature of a temperature sensor in an embodiment of the present invention.
Fig. 15 is a schematic structural diagram of a temperature anomaly detection subsystem in an embodiment of the present invention.
Fig. 16 is an estimation error distribution diagram between the cell surface temperature estimated by the temperature abnormality detection subsystem and the actually measured temperature in the embodiment of the present invention.
The probe mounting device comprises a temperature sensor 1, a needle bed 2, a tray 3, an electric core 4, a smoke sensor 5, a probe mounting module 6 and a probe supporting module 7.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects to be solved by the embodiments of the present invention more clearly apparent, the present invention is further described in detail below with reference to the accompanying drawings and the embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
It will be understood that when an element is referred to as being "secured to" or "disposed on" another element, it can be directly on the other element or be indirectly on the other element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or be indirectly connected to the other element. The connection may be for fixation or for circuit connection.
It is to be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like are used in an orientation or positional relationship indicated in the drawings for convenience in describing the embodiments of the present invention and to simplify the description, and are not intended to indicate or imply that the referenced device or element must have a particular orientation, be constructed in a particular orientation, and be in any way limiting of the present invention.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the embodiments of the present invention, "a plurality" means two or more unless specifically limited otherwise.
Some of the following embodiments of the invention are based on the recognition that:
1. the cases causing the internal short circuit are classified into the following ones: membrane defects or age cracking; penetrating the diaphragm through lithium dendrite and simple substance iron ion reduction deposition; when the battery core is manufactured, the membrane is pierced due to the entrainment of foreign matters; and burrs are formed on the edge of the copper foil or the aluminum foil of the current collector.
2. According to the classification of the internal short circuit position, the internal short circuit form of the battery is divided into the following types: short-circuiting the negative electrode material and the aluminum current collector; short circuiting the copper current collector and the aluminum current collector; short circuit of copper current collector and anode material; the positive electrode material and the negative electrode material are short-circuited;
3. the various internal shorts behave differently: the short circuit between the aluminum current collector and the charged negative electrode material is easy to cause thermal side reaction due to rapid temperature rise of the short circuit part in a short time because of small contact resistance and large passing current, so that thermal runaway is generated; the short circuit of the aluminum current collector and the copper current collector is similar to the external short circuit, and the temperature can be uniformly transmitted to the whole battery; the short circuit of the anode material, the copper current collector and the anode and cathode materials has the minimum influence because the resistance of the anode material is large.
As a result, the causes of the internal short circuit are various, the locations are also varied, and the voltage expression are inevitably different from each other. Therefore, the invention analyzes the internal short circuit caused by various reasons and parts, summarizes typical modes, and finds out proper characteristic parameters of each mode, thereby realizing the monitoring of the internal short circuit through monitoring the parameters. For example, these modes include:
1. when an internal short circuit occurs in a lithium battery having no porous protective film structure, the battery voltage rapidly decreases, and thereafter the voltage does not recover.
2. When the ternary lithium ion power battery is subjected to internal short circuit, the voltage rapidly decreases to 0 within 2 seconds, and simultaneously, the temperature rises to about 200 ℃ in the vicinity of 5 seconds along with the rise of the temperature, and then the ternary lithium ion power battery is in a thermal runaway state.
3. When the internal short circuit of the lithium battery with the porous protective film occurs, the voltage drops in the initial stage of the internal short circuit, and then the voltage rises to some extent because the short circuit is prevented by the protective diaphragm from entering a micro-short circuit state, and the voltage drops gradually along with the gradual rise of the temperature.
4. When the lithium iron phosphate battery monomer is in an internal short circuit, the secondary battery using lithium iron phosphate (LiFePO4) as the anode material has low conductivity and extremely low lithium ion diffusion speed, the voltage is reduced from the initial 3.7V to 3.2V within 1 second after puncture (simulating internal short circuit), and then the voltage is not reduced after entering a stable state. The surface temperature of the shell gradually and rapidly rises along with the progress of the puncture, and the shell enters a thermal runaway state within 4-6 seconds.
According to experimental data of the lithium batteries of different types, the voltage change firstly occurs in 1-2 seconds of internal short circuit, the temperature rise of the shell can be obviously detected in the following 2-4 seconds, and most batteries can enter a thermal runaway state in the following 4-6 seconds. Therefore, an accurate and safe thermal runaway protection strategy should be protected step by step according to the strategies of voltage and current parameter monitoring, temperature monitoring and fog monitoring.
Based on this recognition, the present invention proposes the following specific examples:
example 1
In the automatic production of lithium batteries, the internal short circuit detection approach of lithium batteries comprises the following steps:
and (3) voltage abnormity detection: the internal short circuit of the lithium battery can cause the voltage to be reduced, and judgment and early warning can be made on the internal short circuit through monitoring the voltage reduction trend.
Thermal detection: whether short circuit occurs or not is judged in a mode of attaching a thermocouple to the side wall of the lithium battery to detect temperature change. In the prior art, because heat is conducted from inside to outside when a short circuit occurs, temperature display has certain lag, and therefore the short circuit condition cannot be fed back at the first time.
Capacity anomaly detection: because a part of electric energy is converted into heat energy to be dissipated when the internal short circuit occurs, the charging capacity in the charging process is higher than that when the internal short circuit does not occur, the internal short circuit fault is reported when the charging capacity is higher than the reference capacity, and the state of the lithium battery is normal when the charging capacity is lower than or equal to the reference capacity.
As shown in fig. 1, a thermal runaway early warning protection system for a lithium battery includes:
the voltage and current abnormity detection subsystem is used for acquiring voltage data at two ends of the lithium battery and current data passing through the lithium battery in real time in the charging and discharging processes of the lithium battery and judging whether the voltage data and the current data are abnormal or not; when the voltage and/or the current are abnormal, stopping charging and discharging;
the temperature anomaly detection subsystem is used for acquiring the temperature of the lithium battery in real time in the charging and discharging processes of the lithium battery, judging whether the temperature exceeds a preset temperature threshold value or not, and sending out a temperature anomaly alarm when the temperature exceeds the temperature threshold value;
and the smoke induction abnormity detection subsystem is used for detecting whether smoke exists in the lithium battery charging and discharging process in real time, and starting alarm and fire-fighting spraying when the smoke is detected.
Synthesize the experimental data of different grade type lithium cell and obtain, the change of at first taking place voltage in inside short circuit 1 ~ 2 seconds, can obviously detect the rising of casing problem in 2 ~ 4 seconds afterwards, and most lithium cells of time 4 ~ 6 seconds later can get into the thermal runaway state, and consequently, accurate and safe thermal runaway's protection strategy should be according to: piezoelectric flow abnormity → temperature abnormity → smoke induction abnormity is protected step by step.
It is understood that in one embodiment of the present invention, the voltage and current anomaly detection subsystem may capture the anomaly signal before the temperature anomaly detection subsystem and the smoke sensing anomaly detection subsystem; when the smoke induction abnormity detection subsystem sends out early warning and the voltage and current abnormity detection subsystem and the temperature abnormity detection subsystem do not capture abnormal signals, the smoke induction abnormity detection subsystem is judged to report by mistake. The voltage and current abnormity detection subsystem synchronously collects at least one of voltage data at two ends of the lithium battery and current data passing through the lithium battery in real time, carries out internal short circuit index monitoring, and judges whether the internal short circuit index of at least one of the voltage data and the current data is abnormal.
The three subsystems effectively prevent the thermal runaway of the lithium battery through abnormal monitoring of three aspects of comprehensive voltage, temperature and smoke induction, and adopt strategies of single-channel stopping, whole-disk stopping, starting fire observation, starting fire spraying, six-surface protection and the like to control and protect the thermal runaway step by step.
When short circuit occurs, the voltage and current abnormity monitoring subsystem captures abnormal signals before the temperature abnormity detection subsystem and the smoke induction abnormity detection subsystem to send early warning, and the single-channel charging and discharging operation is usually stopped when the voltage and current abnormity monitoring subsystem gives early warning to prevent the side reaction from being further aggravated; when the temperature anomaly detection subsystem detects that the temperature of the shell of the lithium battery is remarkably increased, the whole lithium battery can be stopped and fire-fighting observation can be started according to user configuration; when the smoke induction abnormity detection subsystem sends out early warning, the system gives an alarm and starts fire-fighting spraying. When the smoke induction abnormity detection subsystem sends out early warning and the other two subsystems do not send early warning, the system judges that the smoke induction abnormity detection subsystem misrereports according to the configuration.
The automatic needle bed storehouse position of the thermal runaway early warning protection system in the automatic production of the lithium battery is provided with 6 protective devices, so that the thermal runaway is prevented from expanding to an adjacent storehouse position. The system has independent process step protection and global protection, and eliminates potential factors causing thermal runaway caused by overcharge, overdischarge, poor crimping, voltage and current exceeding upper and lower limits and the like.
Example 2
The voltage and current abnormity detection subsystem is the core of a thermal runaway early warning protection system in automatic production of the lithium battery, and judges and early warns the short circuit of the internal battery by monitoring and tracking the change of voltage and current in real time and utilizing the rule of the change of voltage and current in the short circuit of the internal battery. The voltage and current abnormality detection subsystem can usually stop an abnormal channel within 1 second of occurrence of an internal short circuit, so that further aggravation of negative reaction of the internal short circuit is prevented, and the probability of occurrence of thermal runaway is greatly reduced. In the formation and partial capacitance process, judgment and early warning of internal short circuit are realized by monitoring abnormal trend of voltage during charging and discharging and monitoring abnormal reduction of current during discharging. Since the voltage and current anomalies of the internal short circuit are small instantaneous fluctuations, this places high demands on the accuracy and response speed of the detection device. The high-precision charging and discharging equipment has the following conditions:
(1) high precision: a voltage fluctuation of 2mv is resolvable with an accuracy setting of 0.02%;
(2) high sampling rate: the sampling rate of the lower computer is up to 5ms, a Cassel window digital finite impulse response filter is adopted, white noise and out-of-band interference are filtered out, and meanwhile, each point outputs, and long time delay of a moving average filter is avoided;
(3) the high-performance lower computer can realize a trend tracking algorithm on the lower computer, and the trend abnormity of the voltage and the current is tracked and processed in real time by combining high precision and high sampling rate. In the charging and static links, an anomaly tracking algorithm (internally called an electron microscope magnifier) tracks the following trends:
a. sudden rise or fall of voltage
b. The voltage is rapidly reduced to another platform and slowly and gradually reduced
c. The voltage is rapidly and greatly reduced and then gradually increased again, and then slowly and gradually reduced
In the discharging stage, the abnormity tracking algorithm tracks the abnormity descending trend of the current, namely the slope abnormity of delta I/delta t.
As shown in fig. 2, in an embodiment of the present invention, a method for detecting an abnormal short-circuit voltage and current in a lithium battery includes the following steps:
s1: charging and discharging a lithium battery and determining that the lithium battery enters a charging and discharging state;
s2: synchronously acquiring at least one of voltage data at two ends of the lithium battery and current data passing through the lithium battery in real time, and carrying out internal short circuit index monitoring;
s3: judging whether an internal short circuit index of at least one of the voltage data and the current data is abnormal; and stopping charging and discharging the lithium battery when one of the voltage data and the current data is abnormal.
As shown in fig. 3, determining whether the voltage data is abnormal includes the following steps:
filtering noise of the voltage data, selecting the voltage data with an undersampling rate reaching a set value in real time, and dynamically updating the maximum recorded voltage;
and monitoring the internal short circuit index, wherein the monitoring of the internal short circuit index comprises at least one of synchronous voltage rise abnormity detection, voltage abnormity drop detection and voltage drop trend abnormity detection.
As shown in fig. 4, the voltage rise abnormality detection includes:
calculating real-time voltage data VnAnd a charge starting voltage VstartDifference value V ofras＝Vn-Vstart；
And judging whether the difference value is smaller than a preset voltage rising threshold value or not, and if the difference value is smaller than the preset voltage rising threshold value, judging that the voltage rises abnormally.
As shown in fig. 5, the voltage drop abnormality detection includes:
calculating the maximum recording voltage VmaxWith real-time voltage data VnIs equal to Vmax-Vn；
And judging whether the difference value exceeds a preset voltage drop threshold value or not, and if the difference value exceeds the preset voltage drop threshold value, judging that the voltage drop is abnormal.
As shown in fig. 6, the voltage-falling tendency abnormality detection includes:
calculating the difference of the voltage data of the two ends of the lithium battery collected in real time: dn ═ Vn-Vn-1And obtaining the sign d of the sign of dnsng-n，
Calculating whether the sum of the symbols exceeds a preset voltage descending trend threshold value or not, and recording a voltage descending trend starting point voltage V if the sum of the symbols is smaller than the preset voltage descending trend threshold valueref；
Calculating the voltage drop slope: s ═ Vref-Vn) N, wherein N is the voltage undersampling rate;
and judging whether the voltage falling slope exceeds a preset falling slope threshold value or not, wherein if the voltage falling slope exceeds the preset falling slope threshold value, the voltage falling trend is abnormal.
Specifically, the actual automatic production can be performed according to the following operations:
step 1: the equipment enters a formation and grading process, and a working step is started to charge and discharge the lithium battery;
step 2: resetting all variables;
and step 3: judging whether the lithium battery enters a charging state, and if the lithium battery enters the charging state, entering a step 4;
and 4, step 4: device for collecting battery voltage data VnFiltering noise by a low-pass filter, and meanwhile, incrementing an under-sampling counter;
and 5: if the undersampling counter reaches the undersampling rate N, the voltage buffer is updated (the sampling point before the W moment in the voltage buffer is removed, and the current sampling data V is reducednStoring into a cache), otherwise, returning to the step 4; where W is the size of the observation window, typically set by default to 128, and N is adjusted according to the size of the sampling rate, typically set to 1 or 2.
Step 6: comparison VnAnd a maximum recording voltage VmaxIf V isnGreater than VmaxWill VnImpartation of VmaxAnd zero clearing counter n _ max is 0; otherwise, recording the relative offset n _ max of the Vmax and increasing by n _ max to n _ max + 1;
and 7: detecting abnormal voltage rise: and if the voltage rise abnormity detection switch is turned on, performing voltage rise abnormity detection, otherwise, skipping the step. The voltage rise anomaly detection algorithm is as follows: calculating the current sample data VnAnd a charge starting voltage VstartIs a difference v ofras＝Vn-VstarIf the rising difference value is smaller than the voltage rising trend abnormal threshold value after the Time limit (Time _ Rais _ Thres) for judging the voltage rising abnormality, namely vras＜Vrais_ThresThe system sends out a voltage rise abnormity alarm and stops the channel to exit the current working state;
and 8: monitoring abnormal voltage drop: and if the voltage abnormal drop monitoring switch is turned on, performing voltage abnormal drop detection, and otherwise, skipping the step. The voltage abnormal drop detection algorithm is as follows: calculating the maximum recording voltage VmaxWith the current sample data VnIs equal to Vmax-v (n). If it is judged thatTime limit (Time _ Drop _ Thres) of abnormal Drop of off voltage: the difference in the Drop is greater than a threshold value, i.e., Δ V > Δ V, within a relative offset of Vmax, n _ max < Time _ Drop _ Thres, recordeddThe system sends out a voltage drop abnormal alarm and stops the channel from exiting the current working state;
and step 9: monitoring the voltage drop trend abnormity: and if the voltage falling trend abnormity monitoring switch is turned on, carrying out voltage falling trend abnormity detection, otherwise, skipping the step. The voltage drop trend anomaly detection algorithm is as follows:
(1) calculating the difference d (n) of voltage samples V (n) V (n-1) and obtaining the positive and negative d of dnsng(n)；
(2) Judging whether the descending trend occurs by calculating the sum of the signs: if and dsng(n) less than a threshold, i.e., ∑ dsng＜DtIf so, executing (3) if a descending trend appears;
(3) if the trend tracking is not started, namely the tracking mark trace _ flg is 0, starting the trend tracking, and recording the descending trend starting point voltage Vref v (n); if trend tracking has been enabled, the recording trend of the down trend counter, N _ trend, is incremented: n _ trend + 1;
(4) calculating a falling Slope [ Vref-v (N) ]/N _ trend;
(5) judging whether the Slope exceeds a falling Slope threshold Slope which is more than Slope _ Thres, and if the Slope exceeds the falling Slope threshold Slope, sending out an early warning of short circuit in the battery;
step 10: and repeating the steps 4 to 9 for each sampling point until the channel working step is completed or the abnormal exit is realized.
Example 3
In an embodiment of the present invention, in an internal short-circuit voltage and current abnormality detection method in automatic production of a lithium battery, the lithium battery is in a constant-current charging state, a constant-voltage charging state or a constant-current discharging state, and the corresponding current abnormality is: constant current charging is abnormal, constant voltage charging is abnormal or constant current discharging is abnormal.
As shown in fig. 7, determining whether the current data is abnormal includes the following steps:
filtering noise of the current data and dynamically updating the cache data;
determining the state of the lithium battery and calculating the change rate of current;
and comparing the change rate of the current with a preset change rate threshold of the current, and if the change rate of the current is greater than the preset change rate threshold of the current, judging that the change of the current is abnormal.
Calculating the rate of change of current: d ═ Cn-Cn-N+1) N, wherein CnIs real-time current data, Cn-N+1Is current data prior to time N, which is a time interval, of the real-time;
the preset current change rate threshold value is T when the lithium battery is in a constant current charging state1If d > T1If the charging is abnormal, the constant current charging is abnormal;
the preset current change rate threshold value is T when the lithium battery is in a constant-voltage charging state2If d > T2If so, the constant voltage charging is abnormal;
the preset current change rate threshold value is T when the lithium battery is in a constant current discharge state3If d > T3The constant current discharge is abnormal.
Specifically, the actual automatic production can be performed according to the following operations:
step 1: the equipment enters a formation and grading process, and a working step is started to charge and discharge the lithium battery;
step 2: resetting all variables;
and step 3: judging whether the lithium battery enters a charge-discharge state, and entering a step 4 if the lithium battery enters the charge-discharge state;
and 4, step 4: device acquisition current data CnAnd filtering noise through a low-pass filter, and updating a current observation buffer c _ buff buffer: removing the sampling point before the N time in the buffer and reducing the current sampling data CnStoring the data into a cache;
and 5: calculating the current change rate: d ═ Cn-Cn-N+1) N and compared with a threshold value, wherein CnIs real-time current data, Cn-N+1At real-time instantsCurrent data before time N, N being the time interval;
(1) setting a threshold value T: if the current state is in the constant-current charging state, the constant-current charging abnormal threshold value T is Tccc, if the current state is in the constant-voltage charging state, the constant-voltage charging abnormal threshold value T is Tcvc, and if the current state is in the constant-current discharging state, the constant-current discharging abnormal threshold value T is Tccd;
(2) and (3) comparison: if d is larger than T, judging that the current is abnormal, stopping the channel to exit the current working step, and otherwise, repeating the step 4.
Example 4
As shown in fig. 8, the present invention further provides a system for detecting an abnormality of a short-circuit voltage and current in a lithium battery, including:
the charging and discharging unit is used for charging and discharging the lithium battery;
the voltage acquisition unit is used for acquiring voltage data of two ends of the lithium battery in real time;
the current acquisition unit is used for acquiring current data passing through the lithium battery in real time;
the processing unit is used for judging whether the voltage data and the current data are abnormal or not; and when the voltage and/or the current are abnormal, stopping charging and discharging.
It will be appreciated that the above definitions of the various elements are merely functional and that virtually any system capable of implementing the present invention is possible. In another embodiment of the present invention, the processing unit may further determine whether the temperature is abnormal or the smoke is abnormal, and then take the corresponding operations as described above.
Example 5
All or part of the flow of the method of the embodiments may be implemented by a computer program, which may be stored in a computer readable storage medium and executed by a processor, to instruct related hardware to implement the steps of the embodiments of the methods. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
Example 6
The system and the method are applied to actual production, and the proportion of 12 nickel-cobalt-manganese NCMs is 6: 2: 2, performing charge-discharge cycle test on the 53Ah ternary battery cell, wherein 3 of 12 battery cells are bad battery cells but are not marked, and the purpose of the experiment is to verify whether the system can detect the bad battery cells through the charge-discharge cycle test of the lithium battery.
Fig. 9 is a voltage curve of a battery 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, and fig. 12 is a current trend calculated in real time by the system of the present invention. As shown in fig. 10 and 12, in the constant voltage charging stage, voltage trends and current trends of three cells are shown in fig. 9 to 12, and abnormal jumps different from normal cells appear in a bad cell at the same time of the voltage trends and the current trends, so that the bad cell can be detected and marked.
In another embodiment of the invention, the system detects and judges 2.3 thousands of bad cells, 150 cells are judged by mistake through off-line test verification, and the judgment mistake rate is less than 0.01 per thousand.
Example 7
The temperature abnormality detection subsystem of the present embodiment includes: the lithium battery is characterized in that a temperature probe is crimped on a tab of the lithium battery; it can be understood that, because each lithium battery has a temperature probe pressed on the tab, the temperature of the lithium battery is monitored in real time in the charging and discharging process of the lithium battery, and when the temperature of the tab continuously exceeds a temperature threshold value, the temperature abnormity detection subsystem can send out temperature abnormity alarm.
Example 8
For laminate polymer battery, because laminate polymer battery aluminium plastic envelope heat conductivility is not good, consequently laminate polymer battery core body temperature and pole piece temperature difference are great. For such a situation, this embodiment 8 provides a method for calculating the cell surface temperature of the lithium battery through software. In the embodiment, a row of temperature sensor arrays are respectively arranged at the front and the back of the top of a needle bed of the lithium battery, and the temperature sensor arrays comprise L temperature sensors with equal intervals to form a 2L temperature sensor array. The temperature of each battery pack can be calculated according to the measured data of 2X L temperature sensors. The following examples illustrate:
as shown in fig. 15, which is a specific example of the system and method applied in actual production for measuring the temperature of the surface of the battery cell according to the present invention, 4 temperature sensors are respectively installed in the front row and the back row of the top of the needle bed 2 of the partial volume of the thermal runaway early warning protection system for the lithium battery, 8 temperature sensors form a temperature sensor array, 32 soft package battery cells 4 are placed on one tray 3, and a temperature sensor 1 is installed on the surface of each battery cell 4 for measuring the temperature of the surface of the battery cell. Each lithium battery is pressed with a temperature probe at a tab, and a probe installation module 6 and a probe supporting module 7 are visible in the figure. The smoke sensor 5 is arranged between the temperature sensor 1 and the battery cell 4.
As shown in fig. 13, in an embodiment, the method for calculating the cell surface temperature of the lithium battery includes the following steps:
t1: calculating the correlation between the cell surface temperature of the lithium battery and the measurement temperature of the temperature sensor;
t2: and estimating the surface temperature of the battery core of the lithium battery in real time according to the correlation and the measured temperature of the temperature sensor array.
As shown in fig. 14, step T1 includes:
t11: calculating a cross-correlation matrix between the temperature sensors:
Figure GDA0002685218310000161
Figure GDA0002685218310000162
wherein r isn，lIs the cross-correlation between the measured temperature of the nth temperature sensor and the measured temperature of the 1 st temperature sensor,
Figure GDA0002685218310000163
is the covariance between the measured temperature of the nth temperature sensor and the measured temperature of the 1 st temperature sensor,
Figure GDA0002685218310000178
is the variance of the measured temperature of the nth temperature sensor,
Figure GDA0002685218310000179
measuring a variance of temperature for the 1 st temperature sensor;
t12: calculating a cross-correlation matrix between each of the lithium batteries and the temperature sensor,
Figure GDA0002685218310000171
Figure GDA0002685218310000172
wherein, cm，lFor the cross-correlation between the mth cell surface temperature and the l-th temperature sensor measurement temperature,
Figure GDA0002685218310000173
is the covariance between the surface temperature of the mth temperature sensor and the measured temperature of the 1 st temperature sensor,
Figure GDA0002685218310000174
is as followsThe variance of the surface temperatures of the m cells,
Figure GDA0002685218310000175
the 1 st temperature sensor measures the variance of the temperature.
Step T2 includes: and setting the measurement temperature vector of the temperature sensor at the moment k as follows:
Figure GDA0002685218310000176
the cell surface temperature according to the 2D-MMSE criterion was:
Figure GDA0002685218310000177
generally, when the surface temperature of a battery tab or the surface temperature of a battery core exceeds 100 ℃, the temperature is judged to be abnormal, and an alarm is started.
Fig. 16 shows the estimated error distribution between the cell surface temperature estimated by the system and the actual measured temperature, and 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 is 6 e-3. Therefore, the result is very reliable, and the requirement of thermal runaway early warning protection can be met.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several equivalent substitutions or obvious modifications can be made without departing from the spirit of the invention, and all the properties or uses are considered to be within the scope of the invention.

Claims (20)

1. The utility model provides a lithium cell thermal runaway early warning protection system which characterized in that carries out the mode monitoring lithium cell internal short circuit that protects step by step according to voltage and current abnormity, temperature abnormity, the unusual strategy of smog response, includes:
the voltage and current abnormity detection subsystem is used for acquiring voltage data at two ends of the lithium battery and current data passing through the lithium battery in real time in the charging and discharging processes of the lithium battery and judging whether the voltage data and the current data are abnormal or not; when the voltage and/or the current are abnormal, stopping charging and discharging;
the temperature anomaly detection subsystem is used for acquiring the temperature of the lithium battery in real time in the charging and discharging processes of the lithium battery, judging whether the temperature exceeds a preset temperature threshold value or not, and sending out a temperature anomaly alarm when the temperature exceeds the temperature threshold value; stopping the whole fire control panel and starting fire control observation;
the smoke induction abnormity detection subsystem is used for detecting whether smoke exists in the charging and discharging processes of the lithium battery in real time, and starting an alarm and a fire-fighting spray when the smoke is detected;
the voltage and current abnormity detection subsystem captures an abnormal signal before the temperature abnormity detection subsystem and the smoke induction abnormity detection subsystem capture the abnormal signal;
and when the smoke induction abnormity detection subsystem gives out early warning and the voltage and current abnormity detection subsystem and the temperature abnormity detection subsystem do not give early warning, judging that the smoke induction abnormity detection subsystem gives false alarm.
2. The warning and protection system for thermal runaway of a lithium battery as claimed in claim 1, wherein the voltage and current anomaly detection subsystem synchronously collects at least one of voltage data at two ends of the lithium battery and current data passing through the lithium battery in real time, performs internal short circuit indicator monitoring, and determines whether an internal short circuit indicator of the at least one of the voltage data and the current data is anomalous.
3. The lithium battery thermal runaway early warning and protection system of claim 2, wherein judging whether the voltage data is abnormal comprises:
filtering noise of the voltage data, selecting the voltage data with an undersampling rate reaching a set value in real time, and dynamically updating the maximum recorded voltage;
and monitoring the internal short circuit index, wherein the monitoring of the internal short circuit index comprises at least one of synchronous voltage rise abnormity detection, voltage abnormity drop detection and voltage drop trend abnormity detection.
4. The lithium battery thermal runaway early warning protection system of claim 3, wherein the voltage rise anomaly detection comprises:
calculating real-time voltage data VnAnd a charge starting voltage VstartDifference value V ofras＝Vn-Vstart；
Judging whether the difference value is smaller than a preset voltage rising threshold value or not, and if the difference value is smaller than the preset voltage rising threshold value, judging that the voltage rises abnormally;
the voltage abnormal drop detection includes:
calculating the maximum recording voltage VmaxWith real-time voltage data VnIs equal to Vmax-Vn；
Judging whether the difference value exceeds a preset voltage drop threshold value or not, and if the difference value exceeds the preset voltage drop threshold value, judging that the voltage drop is abnormal;
the voltage falling tendency abnormality detection includes:
calculating the difference of the voltage data of the two ends of the lithium battery collected in real time: dn ═ Vn-Vn-1And obtaining the sign d of the sign of dnsng-n，
Calculating whether the sum of the symbols exceeds a preset voltage descending trend threshold value or not, and recording a voltage descending trend starting point voltage V if the sum of the symbols is smaller than the preset voltage descending trend threshold valueref，
Calculating the voltage drop slope: s ═ Vref-Vn) N, wherein N is the voltage undersampling rate;
and judging whether the voltage falling slope exceeds a preset falling slope threshold value or not, wherein if the voltage falling slope exceeds the preset falling slope threshold value, the voltage falling trend is abnormal.
5. The lithium battery thermal runaway early warning and protection system of claim 1, wherein the lithium battery is in a constant current charging, constant voltage charging, or constant current discharging state, and the corresponding current anomaly is: constant current charging is abnormal, constant voltage charging is abnormal or constant current discharging is abnormal.
6. The lithium battery thermal runaway early warning and protection system of claim 5, wherein judging whether the current data is abnormal comprises the following steps:
filtering noise of the current data and dynamically updating the cache data;
determining the state of the lithium battery and calculating the rate of change of current: d ═ Cn-Cn-N+1) N, wherein CnIs real-time current data, Cn-N+1Is current data prior to time N, which is a time interval, of the real-time;
comparing the change rate of the current with a preset change rate threshold value of the current: the preset current change rate threshold value is T when the lithium battery is in a constant current charging state1If d is>T1If the charging is abnormal, the constant current charging is abnormal; the preset current change rate threshold value is T when the lithium battery is in a constant-voltage charging state2If d is>T2If so, the constant voltage charging is abnormal; the preset current change rate threshold value is T when the lithium battery is in a constant current discharge state3If d is>T3The constant current discharge is abnormal.
7. The lithium battery thermal runaway early warning and protection system of claim 1, wherein the temperature anomaly detection subsystem comprises: the temperature probe is in compression joint with the lithium battery tab;
or, the temperature abnormality detection subsystem includes: the lithium battery comprises a lithium battery, a needle bed and a temperature sensor array, wherein the lithium battery is characterized in that the front part and the back part of the needle bed top of the lithium battery are respectively provided with a row of temperature sensor arrays, and the temperature sensor arrays comprise L temperature sensors at equal intervals; and the calculating device is used for calculating the surface temperature of the battery core of the lithium battery according to the detection result of the temperature sensor array.
8. The warning and protection system for thermal runaway of a lithium battery as claimed in claim 7, wherein the method for calculating the temperature of the surface of the cell of the lithium battery comprises the following steps:
t1: calculating the correlation between the cell surface temperature of the lithium battery and the measurement temperature of the temperature sensor; the correlation comprises a cross-correlation matrix between the temperature sensors, a cross-correlation matrix between each lithium battery and the temperature sensor;
t2: and estimating the surface temperature of the battery core of the lithium battery in real time according to the correlation and the measured temperature of the temperature sensor array.
9. The lithium battery thermal runaway early warning protection system of claim 8, wherein step T1 comprises:
t11: calculating a cross-correlation matrix between the temperature sensors:
Figure FDA0002685218300000031
Figure FDA0002685218300000032
wherein r isn，lIs the cross-correlation between the measured temperature of the nth temperature sensor and the measured temperature of the ith temperature sensor,
Figure FDA0002685218300000033
is the covariance between the measured temperature of the nth temperature sensor and the measured temperature of the ith temperature sensor,
Figure FDA0002685218300000034
is the variance of the measured temperature of the nth temperature sensor,
Figure FDA0002685218300000035
measuring a variance of temperature for the ith temperature sensor;
t12: calculating a cross-correlation matrix between each of the lithium batteries and the temperature sensor,
Figure FDA0002685218300000036
Figure FDA0002685218300000037
wherein, cm，lFor the cross-correlation between the mth cell surface temperature and the l-th temperature sensor measurement temperature,
Figure FDA0002685218300000038
is the covariance between the surface temperature of the mth temperature sensor and the measured temperature of the lth temperature sensor,
Figure FDA0002685218300000041
is the variance of the surface temperature of the mth cell,
Figure FDA0002685218300000042
the ith temperature sensor measures the variance of the temperature.
10. The lithium battery thermal runaway early warning protection system of claim 8, wherein step T2 comprises:
and setting the measurement temperature vector of the temperature sensor at the moment k as follows:
Figure FDA0002685218300000043
the cell surface temperature according to the 2D-MMSE criterion was:
Figure FDA0002685218300000044
11. a lithium battery thermal runaway early warning protection method is characterized by comprising the following steps:
s1: charging and discharging a lithium battery and determining that the lithium battery enters a charging and discharging state;
s2: monitoring the internal short circuit of the lithium battery in a step-by-step protection mode according to strategies of voltage and current abnormity, temperature abnormity and smoke induction abnormity, and performing voltage and current abnormity detection, temperature abnormity detection and smoke induction abnormity detection on the lithium battery;
wherein,
the voltage and current abnormity detection comprises the steps of synchronously acquiring at least one of voltage data at two ends of the lithium battery and current data passing through the lithium battery in real time, and monitoring an internal short circuit index; judging whether an internal short circuit index of at least one of the voltage data and the current data is abnormal; stopping charging and discharging the lithium battery when one of the voltage data and the current data is abnormal;
the temperature detection includes: the method comprises the steps of collecting the temperature of the lithium battery in real time in the charging and discharging process of the lithium battery, judging whether the temperature exceeds a preset temperature threshold value, and sending out a temperature abnormity alarm when the temperature exceeds the temperature threshold value; stopping the whole fire control panel and starting fire control observation;
the smoke induction abnormity detection comprises the steps of detecting whether smoke exists in the charging and discharging process of the lithium battery in real time, and starting an alarm and a fire-fighting spray when the smoke is detected;
the voltage and current abnormity detection subsystem captures an abnormal signal before the temperature abnormity detection subsystem and the smoke induction abnormity detection subsystem capture the abnormal signal;
and when the smoke induction abnormity detection subsystem gives out early warning and the voltage and current abnormity detection subsystem and the temperature abnormity detection subsystem do not give early warning, judging that the smoke induction abnormity detection subsystem gives false alarm.
12. The warning and protection method for thermal runaway of a lithium battery as claimed in claim 11, wherein judging whether the voltage data is abnormal comprises the steps of:
filtering noise of the voltage data, selecting the voltage data with an undersampling rate reaching a set value in real time, and dynamically updating the maximum recorded voltage;
and monitoring the internal short circuit index, wherein the monitoring of the internal short circuit index comprises at least one of synchronous voltage rise abnormity detection, voltage abnormity drop detection and voltage drop trend abnormity detection.
13. The lithium battery thermal runaway early warning protection method as claimed in claim 12, wherein the voltage rise anomaly detection comprises:
calculating real-time voltage data VnAnd a charge starting voltage VstartDifference value V ofras＝Vn-Vstart；
And judging whether the difference value is smaller than a preset voltage rising threshold value or not, and if the difference value is smaller than the preset voltage rising threshold value, judging that the voltage rises abnormally.
14. The lithium battery thermal runaway early warning protection method as claimed in claim 12, wherein the voltage abnormal drop detection comprises:
calculating the maximum recording voltage VmaxWith real-time voltage data VnIs equal to Vmax-Vn；
And judging whether the difference value exceeds a preset voltage drop threshold value or not, and if the difference value exceeds the preset voltage drop threshold value, judging that the voltage drop is abnormal.
15. The warning and protection method for thermal runaway of a lithium battery as claimed in claim 12, wherein the abnormal detection of the voltage drop trend comprises:
calculating the difference of the voltage data of the two ends of the lithium battery collected in real time: dn ═ Vn-Vn-1And obtaining the sign d of the sign of dnsng-n；
Calculating whether the sum of the symbols exceeds a preset voltage descending trend threshold value or not, and recording a voltage descending trend starting point voltage V if the sum of the symbols is smaller than the preset voltage descending trend threshold valueref；
Calculating the voltage drop slope: s ═ Vref-Vn) N, wherein N is the voltage undersampling rate;
and judging whether the voltage falling slope exceeds a preset falling slope threshold value or not, wherein if the voltage falling slope exceeds the preset falling slope threshold value, the voltage falling trend is abnormal.
16. The lithium battery thermal runaway early warning protection method as claimed in claim 11, wherein the lithium battery is in a constant current charging, constant voltage charging or constant current discharging state, and the corresponding current abnormality is: constant current charging is abnormal, constant voltage charging is abnormal or constant current discharging is abnormal.
17. The warning and protection method for thermal runaway of a lithium battery as claimed in claim 16, wherein judging whether the current data is abnormal comprises the steps of:
filtering noise of the current data and dynamically updating the cache data;
determining the state of the lithium battery and calculating the change rate of current;
and comparing the change rate of the current with a preset change rate threshold of the current, and if the change rate of the current is greater than the preset change rate threshold of the current, judging that the change of the current is abnormal.
18. The warning and protection method for thermal runaway of lithium battery as claimed in claim 17,
calculating the rate of change of current: d ═ Cn-Cn-N+1) N is, wherein，CnIs real-time current data, Cn-N+1Is current data prior to time N, which is a time interval, of the real-time;
the preset current change rate threshold value is T when the lithium battery is in a constant current charging state1If d is>T1If the charging is abnormal, the constant current charging is abnormal;
the preset current change rate threshold value is T when the lithium battery is in a constant-voltage charging state2If d is>T2If so, the constant voltage charging is abnormal;
the preset current change rate threshold value is T when the lithium battery is in a constant current discharge state3If d is>T3The constant current discharge is abnormal.
19. The lithium battery thermal runaway early warning protection method as claimed in claim 11, wherein a row of temperature sensor arrays are respectively mounted on the front and back of the top of a needle bed of the lithium battery, the temperature sensor arrays including L temperature sensors at equal intervals; calculating the surface temperature of the battery cell of the lithium battery according to the detection result of the temperature sensor array by using a calculating device; the method for calculating the surface temperature of the battery core of the lithium battery comprises the following steps:
t1: calculating the correlation between the cell surface temperature of the lithium battery and the measurement temperature of the temperature sensor;
t2: estimating the surface temperature of the battery cell of the lithium battery in real time according to the correlation and the measured temperature of the temperature sensor array;
step T1 includes:
t11: calculating a cross-correlation matrix between the temperature sensors:
Figure FDA0002685218300000061
Figure FDA0002685218300000062
wherein r isn，lIs the cross-correlation between the measured temperature of the nth temperature sensor and the measured temperature of the ith temperature sensor,
Figure FDA0002685218300000071
is the covariance between the measured temperature of the nth temperature sensor and the measured temperature of the ith temperature sensor,
Figure FDA0002685218300000072
is the variance of the measured temperature of the nth temperature sensor,
Figure FDA0002685218300000073
measuring a variance of temperature for the ith temperature sensor;
t12: calculating a cross-correlation matrix between each lithium battery and the temperature sensor
Figure FDA0002685218300000074
Figure FDA0002685218300000075
Wherein, cm，lFor the cross-correlation between the mth cell surface temperature and the l-th temperature sensor measurement temperature,
Figure FDA0002685218300000076
is the covariance between the surface temperature of the mth temperature sensor and the measured temperature of the lth temperature sensor,
Figure FDA0002685218300000077
is the variance of the surface temperature of the mth cell,
Figure FDA0002685218300000078
first temperatureThe sensor measures the variance of the temperature.
20. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 11-19.
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