CN117671876B - Fire early warning and monitoring system and method for electrochemical energy storage station - Google Patents
Fire early warning and monitoring system and method for electrochemical energy storage station Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000012544 monitoring process Methods 0.000 title claims abstract description 30
- 238000012983 electrochemical energy storage Methods 0.000 title claims abstract description 22
- 238000013461 design Methods 0.000 claims abstract description 11
- 239000000779 smoke Substances 0.000 claims description 24
- 238000010438 heat treatment Methods 0.000 claims description 9
- 238000004781 supercooling Methods 0.000 claims description 9
- 230000007613 environmental effect Effects 0.000 claims description 3
- 238000005562 fading Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 3
- 238000011156 evaluation Methods 0.000 abstract description 3
- 210000004027 cell Anatomy 0.000 description 82
- 230000007547 defect Effects 0.000 description 4
- 238000004146 energy storage Methods 0.000 description 4
- 238000010248 power generation Methods 0.000 description 4
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 3
- 229910052744 lithium Inorganic materials 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- 230000008030 elimination Effects 0.000 description 2
- 238000003379 elimination reaction Methods 0.000 description 2
- 238000011835 investigation Methods 0.000 description 2
- GELKBWJHTRAYNV-UHFFFAOYSA-K lithium iron phosphate Chemical compound [Li+].[Fe+2].[O-]P([O-])([O-])=O GELKBWJHTRAYNV-UHFFFAOYSA-K 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 239000000178 monomer Substances 0.000 description 2
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 1
- 239000002253 acid Substances 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 230000002238 attenuated effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 210000001787 dendrite Anatomy 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000003792 electrolyte Substances 0.000 description 1
- 229910001416 lithium ion Inorganic materials 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000013021 overheating Methods 0.000 description 1
- 238000003908 quality control method Methods 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 238000002791 soaking Methods 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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- Y02E60/10—Energy storage using batteries
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Abstract
The invention discloses a fire disaster early warning and monitoring system and method for an electrochemical energy storage station, wherein the monitoring method comprises the following steps: collecting design parameters of each cell module in a battery cluster; calculating a deformation coefficient, a capacity attenuation coefficient and a performance coefficient of the battery cell module in the use process; calculating the gradient fluctuation coefficient of the battery cluster; calculating the influence coefficient of the working environment of the battery cell module on fire; and calculating a fire disaster early warning value by using the echelon coefficient and the influence coefficient, and comprehensively evaluating the fire disaster risk level. The monitoring system comprises: the system comprises an early warning module, a control module and a monitoring module. The invention is used for carrying out fire early warning evaluation and monitoring in the electrochemical energy storage station, and avoids the increase of fire risk due to overlarge difference between the battery cells. Meanwhile, the current fire risk level is comprehensively evaluated by combining the parameters of the working environment of the cell module, and the occurrence of fire is timely prevented and monitored.
Description
Technical Field
The invention relates to the field of fire risk management and control of an electrochemical energy storage station, in particular to a fire early warning and monitoring system and method of the electrochemical energy storage station.
Background
Currently, in electrochemical energy storage power stations operated commercially in China, the types of batteries are mainly ternary lithium batteries, lithium iron phosphate batteries and lead-acid batteries. Even the lithium iron phosphate battery with the best safety performance cannot completely avoid the risk of short circuit, the short circuit is a first killer of the safety of the energy storage battery, and the electrochemical energy storage power station battery has the characteristics of large series-parallel connection quantity, large scale, high running power and the like, and once the short circuit occurs, thermal runaway can occur, so that fire disaster is caused.
In general, a short circuit may be caused by both internal and external factors. From the internal point of view, in the manufacturing process of the battery, defects or hidden dangers may exist in the production and manufacturing of the battery core, or in the long-term use process of the battery, due to aging of the battery caused by factors such as a charge-discharge system, environment and the like, dendrite lithium is generated in the battery core, and internal short circuit of the battery is triggered. Externally, external impact and water soaking of the battery may also cause damage to the battery, thereby causing a short circuit.
The lithium ion battery energy storage power station can be divided into four layers: battery monomer, module, battery cluster and battery compartment. The battery monomers are arranged and integrated into a module, the module is electrically connected to form a battery cluster, and a plurality of battery clusters and devices such as a converter form a battery compartment.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a fire early warning and monitoring system and method for an electrochemical energy storage station.
In order to achieve the aim of the invention, the invention adopts the following technical scheme:
the fire disaster early warning and monitoring method for the electrochemical energy storage station comprises the following steps:
s1: collecting design parameters of each cell module in the battery cluster, wherein the design parameters comprise structural pressure threshold values of cell module designQ 0 Initial battery capacityD 0 And rated service temperature range；
S2: calculating deformation coefficient of battery cell module in useq 1 Capacity fade coefficientq 2 And coefficient of performanceq 3 ；
S3: according to the changeForm factorq 1 Attenuation coefficientq 2 And coefficient of performanceq 3 Calculating gradient coefficient of each cell module in battery clusterQAnd calculating the gradient fluctuation coefficient of the battery clusterf 1 ；
S4: the temperature sensor collects the temperature of the corresponding position in the battery clusteraThe smoke sensor collects the smoke concentration at the position above the battery clusterbThe harmful gas sensor collects the concentration of harmful gas at the position above the battery clustercHumidity sensor for collecting humidity of environment in battery compartmentdThe method comprises the steps of carrying out a first treatment on the surface of the Calculating influence coefficient of working environment of battery cell module on fire disaster；
S5: by means of gradient coefficientsQCoefficient of influenceCalculating fire disaster early warning valueUSetting fire early warning thresholdU Threshold value The method comprises the steps of carrying out a first treatment on the surface of the When (when)U＞U Threshold value At this time, the fire risk of the battery cluster is high; when (when)U≤U Threshold value At this time, the fire risk of the battery cluster is low.
Further, step S2 calculates a deformation coefficientq 1 The method of (1) is as follows:
based on structural pressure experienced by the cell module installed in the cell clusterFCalculating deformation coefficient of the battery cell module in useq 1 ：
；
Wherein,K 1 is the bulk modulus of elasticity of the cell module,K 2 as a derivative modulus of the cell module,sthe cell module is subjected to the area of the structural pressure surface.
Further, step S2 calculates a capacity fade coefficientq 2 The method of (1) is as follows:
；
；
wherein,D 1 for the detected capacity of the current cell module,k 0 the attenuation coefficient of the capacity of the battery cell module along with the working time under the rated condition,Tthe working time of the battery cell module is as long as the working time of the battery cell module.
Further, step S2 calculates a coefficient of performanceq 3 The method of (1) is as follows:
s21: when the cell module works, the working temperature of the working process is collected once at intervals of set timevForming a heating data set，nFor the number of times the operating temperature is acquired,v n is the firstnThe working temperature of secondary collection;
s22: data set of heatingEvery temperature value in the range of temperature +.>Comparing if->Judging that the cell module is in a normal heating range at the moment; if->Judging that the working temperature of the cell module is supercooled at the moment; if->Judging that the working temperature of the cell module at the moment is overheated;
s23: extracting supercooled temperature value of working temperature to form supercooled temperature data setExtracting the overheat temperature value of the working temperature to form overheat temperature data set +.>；m 1 Andm 2 the number of temperature values in the supercooling temperature data set and the superheating temperature data set are respectively;
s24: calculating the current coefficient of performance of the cell moduleq 3 ：
；
Wherein,Vfor an optimal operating temperature of the cell module,is the first in the supercooling temperature data seti 1 Temperature value->Is the first in the overheat temperature data groupi 2 The value of the temperature is set to be the same,i 1 、i 2 for the number of temperature values, +.>For the coefficient of influence of supercooling operating temperature on cell module performance +.>Is the coefficient of influence of the overheated operating temperature on the cell module performance.
Further, step S3 includes:
s31: according to the deformation coefficientq 1 Attenuation coefficientq 2 And coefficient of performanceq 3 Calculating gradient coefficient of each cell module in battery clusterQ：
；
Wherein,k 3 in the deformed conditionThe influence coefficient of the condition on the gradient coefficient,k 4 the coefficient of influence of the capacity fading condition of the cell module on the gradient coefficient,k 5 the influence coefficient of the performance condition of the battery cell module on the gradient coefficient is obtained;
s32: calculating gradient coefficient of each cell module in the battery cluster to obtain a data set of gradient coefficients，MThe number of cell modules in a cell cluster;
s33: from data setsThe gradient coefficient in the battery cluster is calculatedf 1 ：
；
Wherein,Q I for data setsInner firstIThe number of coefficients of the echelon,Inumbering of gradient coefficients>For data set->Maximum value in>For data set->Minimum in (c).
Further, step S4 includes:
s41: the temperature sensor collects the temperature of each position in the battery clusteraThe smoke sensor collects the smoke concentration at the position above the battery clusterbThe harmful gas sensor collects the concentration of harmful gas at the position above the battery clustercHumidity sensor for collecting humidity of environment in battery compartmentd；
S42: calculating influence coefficient of working environment of battery cell module on fire disaster：
；
Wherein,was the weight coefficient of the light-emitting diode,ein order to be a value of the fluctuation,fluctuation factors of temperature, smoke concentration, harmful gas concentration and environmental humidity on fire disaster, respectively, +.>Is a random proportionality coefficient>Taking a random number between 0 and 1, < + >>For the temperature standard value of each position in the battery cluster, < >>A smoke concentration standard value for a position above the battery cluster, < >>Standard value of harmful gas concentration for the position above the battery cluster,/-for>Is the standard value of the humidity of the environment in the battery compartment.
Further, in step S5, a fire early warning value is calculatedUThe method of (1) is as follows:
；
wherein,gradient coefficient calculated for smooth retirement of battery clustersQAnd influence coefficient->Is a range length of (a).
The fire disaster early-warning monitoring system of the electrochemical energy storage station is provided, and the fire disaster early-warning monitoring method of the electrochemical energy storage station is executed, and comprises an early-warning module, a control module and a monitoring module;
the monitoring module comprises temperature sensors arranged at corresponding positions in the battery cluster, smoke sensors and harmful gas sensors arranged at positions above the battery cluster, humidity sensors arranged in the battery compartment and temperature sensors arranged on the battery cell module;
the control module is used for processing the acquired temperature value, the harmful gas concentration value, the humidity value and the smoke concentration value to acquire the fire risk level of the battery cluster at the moment;
the fire risk level is sent to the early warning module, and the early warning module gives an alarm to staff in a short message and alarm mode.
The beneficial effects of the invention are as follows: the invention is used for carrying out fire early warning evaluation and monitoring in the electrochemical energy storage station, taking the battery cluster in the power generation cabin as a unit, and realizing the monitoring of the fire risk of each battery cluster, so as to obtain the fire high risk area or position in the power generation cabin, and further facilitate the staff to carry out targeted fire risk investigation or elimination on the fire high risk area or position. The difference of different cell modules is calculated by combining the structural change, the capacity change and the temperature change of the cell modules in the cell clusters during operation, so that the gradient fluctuation coefficient of the cell modules in the same cell cluster is obtained, the gradient fluctuation coefficient characterizes the difference between the cell modules, and the increase of fire risks due to overlarge difference between the cell modules is avoided. Meanwhile, the current fire risk level is comprehensively evaluated by combining the parameters of the working environment of the cell module, and the occurrence of fire is timely prevented and monitored.
Drawings
FIG. 1 is a flow chart of a method for fire early warning and monitoring in an electrochemical energy storage station.
FIG. 2 is a schematic block diagram of an electrochemical energy storage station fire hazard warning monitoring system.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and all the inventions which make use of the inventive concept are protected by the spirit and scope of the present invention as defined and defined in the appended claims to those skilled in the art.
As shown in fig. 1, the fire early warning and monitoring method for the electrochemical energy storage station comprises the following steps:
s1: collecting design parameters of each cell module in the battery cluster, wherein the design parameters comprise structural pressure threshold values of cell module designQ 0 Initial battery capacityD 0 And rated service temperature range；
S2: calculating deformation coefficient of battery cell module in useq 1 Capacity fade coefficientq 2 And coefficient of performanceq 3 ；
Calculating deformation coefficientq 1 The method of (1) is as follows:
based on structural pressure experienced by the cell module installed in the cell clusterFCalculating deformation coefficient of the battery cell module in useq 1 ：
；
Wherein,K 1 is the bulk modulus of elasticity of the cell module,K 2 as a derivative modulus of the cell module,sthe cell module is subjected to the area of the structural pressure surface.
The cell module is installed on the mount of battery cluster, realizes through stacking up and down the mode that the pressure of cell module that different positions gave is different, and the pressure of cell module is too big, will cause the deformation of cell module external structure, rupture or damage, further increases the risk that the conflagration takes place.
The fire hazard of lithium batteries is mainly derived from the construction thereof, and is directly related to the material composition of the battery, and under the abuse conditions, such as overheating of the battery, overcharge and discharge, short circuit caused by defects of the battery design and raw material defects, etc., chemical reaction occurs between the materials of the battery inside, and the electrolyte is decomposed to generate a large amount of heat and gas, causing thermal runaway of the battery.
Calculating capacity fade coefficientsq 2 The method of (1) is as follows:
；
；
wherein,D 1 for the detected capacity of the current cell module,k 0 the attenuation coefficient of the capacity of the battery cell module along with the working time under the rated condition,Tthe working time of the battery cell module is as long as the working time of the battery cell module.
For the service conditions of different battery core modules, the capacity of the battery core module can be attenuated differently along with the service time, the quality control detection problem when the battery is removed from the factory and installed is solved, the service life of the battery in the energy storage power station is considered, the service life of one energy storage power station reaches 10-15 years, and even if the battery in the same batch is used for several years, the consistency is reduced.
Calculating coefficient of performanceq 3 The method of (1) is as follows:
s21: when the cell module works, the working temperature of the working process is collected once at intervals of set timevForming a heating data set，nFor collectingIs used for the number of times of the working temperature of the (c),v n is the firstnThe working temperature of secondary collection;
s22: data set of heatingEvery temperature value in the range of temperature +.>Comparing if->Judging that the cell module is in a normal heating range at the moment; if->Judging that the working temperature of the cell module is supercooled at the moment; if->Judging that the working temperature of the cell module at the moment is overheated;
s23: extracting supercooled temperature value of working temperature to form supercooled temperature data setExtracting the overheat temperature value of the working temperature to form overheat temperature data set +.>；m 1 Andm 2 the number of temperature values in the supercooling temperature data set and the superheating temperature data set are respectively;
s24: calculating the current coefficient of performance of the cell moduleq 3 ：
；
Wherein,Vfor an optimal operating temperature of the cell module,is the first in the supercooling temperature data seti 1 Temperature value->Is the first in the overheat temperature data groupi 2 The value of the temperature is set to be the same,i 1 、i 2 for the number of temperature values, +.>For the coefficient of influence of supercooling operating temperature on cell module performance +.>Is the coefficient of influence of the overheated operating temperature on the cell module performance.
S3: according to the deformation coefficientq 1 Attenuation coefficientq 2 And coefficient of performanceq 3 Calculating gradient coefficient of each cell module in battery clusterQAnd calculating the gradient fluctuation coefficient of the battery clusterf 1 。
The step S3 comprises the following steps:
s31: according to the deformation coefficientq 1 Attenuation coefficientq 2 And coefficient of performanceq 3 Calculating gradient coefficient of each cell module in battery clusterQ：
；
Wherein,k 3 to influence the gradient coefficient by the deformation,k 4 the coefficient of influence of the capacity fading condition of the cell module on the gradient coefficient,k 5 the influence coefficient of the performance condition of the battery cell module on the gradient coefficient is obtained;
s32: calculating gradient coefficient of each cell module in the battery cluster to obtain a data set of gradient coefficients，MThe number of cell modules in a cell cluster;
s33: from data setsThe gradient coefficient in the battery cluster is calculatedf 1 ：
；
Wherein,Q I for data setsInner firstIThe number of coefficients of the echelon,Inumbering of gradient coefficients>For data set->Maximum value in>For data set->Minimum in (c).
S4: the temperature sensor collects the temperature of the corresponding position in the battery clusteraThe smoke sensor collects the smoke concentration at the position above the battery clusterbThe harmful gas sensor collects the concentration of harmful gas at the position above the battery clustercHumidity sensor for collecting humidity of environment in battery compartmentdThe method comprises the steps of carrying out a first treatment on the surface of the Calculating influence coefficient of working environment of battery cell module on fire disaster；
The step S4 includes:
s41: the temperature sensor collects the temperature of each position in the battery clusteraThe smoke sensor collects the smoke concentration at the position above the battery clusterbThe harmful gas sensor collects the concentration of harmful gas at the position above the battery clustercHumidity sensor for collecting humidity of environment in battery compartmentd；
S42: computing the working environment of the cell module against fireInfluence coefficient of (2)：
；
Wherein,was the weight coefficient of the light-emitting diode,ein order to be a value of the fluctuation,fluctuation factors of temperature, smoke concentration, harmful gas concentration and environmental humidity on fire disaster, respectively, +.>Is a random proportionality coefficient>Taking a random number between 0 and 1, < + >>For the temperature standard value of each position in the battery cluster, < >>A smoke concentration standard value for a position above the battery cluster, < >>Standard value of harmful gas concentration for the position above the battery cluster,/-for>Is the standard value of the humidity of the environment in the battery compartment.
S5: by means of gradient coefficientsQCoefficient of influenceCalculating fire disaster early warning valueUSetting fire early warning thresholdU Threshold value The method comprises the steps of carrying out a first treatment on the surface of the When (when)U＞U Threshold value At this time, the fire risk of the battery cluster is high; when (when)U≤U Threshold value At this time, the fire risk of the battery cluster is low.
Step S5, calculating fire disaster early warning valueUThe method of (1) is as follows:
；
wherein,gradient coefficient calculated for smooth retirement of battery clustersQAnd influence coefficient->Is a range length of (2);
the fire early-warning monitoring system of the electrochemical energy storage station executes the fire early-warning monitoring method of the electrochemical energy storage station, and comprises an early-warning module, a control module and a monitoring module;
the monitoring module comprises temperature sensors arranged at corresponding positions in the battery cluster, smoke sensors and harmful gas sensors arranged at positions above the battery cluster, humidity sensors arranged in the battery compartment and temperature sensors arranged on the battery cell module;
the control module is used for processing the acquired temperature value, the harmful gas concentration value, the humidity value and the smoke concentration value to acquire the fire risk level of the battery cluster at the moment;
the fire risk level is sent to the early warning module, and the early warning module gives an alarm to staff in a short message and alarm mode.
The invention is used for carrying out fire early warning evaluation and monitoring in the electrochemical energy storage station, taking the battery cluster in the power generation cabin as a unit, and realizing the monitoring of the fire risk of each battery cluster, so as to obtain the fire high risk area or position in the power generation cabin, and further facilitate the staff to carry out targeted fire risk investigation or elimination on the fire high risk area or position. The difference of different cell modules is calculated by combining the structural change, the capacity change and the temperature change of the cell modules in the cell clusters during operation, so that the gradient fluctuation coefficient of the cell modules in the same cell cluster is obtained, the gradient fluctuation coefficient characterizes the difference between the cell modules, and the increase of fire risks due to overlarge difference between the cell modules is avoided. Meanwhile, the current fire risk level is comprehensively evaluated by combining the parameters of the working environment of the cell module, and the occurrence of fire is timely prevented and monitored.
Claims (2)
1. The fire disaster early warning and monitoring method for the electrochemical energy storage station is characterized by comprising the following steps of:
s1: collecting design parameters of each cell module in the battery cluster, wherein the design parameters comprise structural pressure threshold values of cell module designQ 0 Initial battery capacityD 0 And rated service temperature range；
S2: calculating deformation coefficient of battery cell module in useq 1 Capacity fade coefficientq 2 And coefficient of performanceq 3 ；
S3: according to the deformation coefficientq 1 Attenuation coefficientq 2 And coefficient of performanceq 3 Calculating gradient coefficient of each cell module in battery clusterQ；
S4: the temperature sensor collects the temperature of the corresponding position in the battery clusteraThe smoke sensor collects the smoke concentration at the position above the battery clusterbThe harmful gas sensor collects the concentration of harmful gas at the position above the battery clustercHumidity sensor for collecting humidity of environment in battery compartmentdThe method comprises the steps of carrying out a first treatment on the surface of the Calculating influence coefficient of working environment of battery cell module on fire disaster；
S5: by means of gradient coefficientsQCoefficient of influenceCalculating fire disaster early warning valueUSetting fire early warning thresholdU Threshold value The method comprises the steps of carrying out a first treatment on the surface of the When (when)U＞U Threshold value At this time, the fire risk of the battery cluster is high;when (when)U≤U Threshold value At this time, the fire risk of the battery cluster is low;
the step S2 calculates the deformation coefficientq 1 The method of (1) is as follows:
based on structural pressure experienced by the cell module installed in the cell clusterFCalculating deformation coefficient of the battery cell module in useq 1 ：
If it isThen->；
If it isThen->；
Wherein,K 1 is the bulk modulus of elasticity of the cell module,K 2 as a derivative modulus of the cell module,sthe area of the pressure surface bearing the structure for the cell module;
the step S2 calculates a capacity fade coefficientq 2 The method of (1) is as follows:
if it isThen->；
If it isThen->；
Wherein,D 1 for the detected capacity of the current cell module,k 0 the attenuation coefficient of the capacity of the battery cell module along with the working time under the rated condition,Tthe working time of the battery cell module is;
step S2, calculating the coefficient of performanceq 3 The method of (1) is as follows:
s21: when the cell module works, the working temperature of the working process is collected once at intervals of set timevForming a heating data set，nFor the number of times the operating temperature is acquired,v n is the firstnThe working temperature of secondary collection;
s22: data set of heatingEvery temperature value in the range of temperature +.>Comparing ifJudging that the cell module is in a normal heating range at the moment; if->Judging that the working temperature of the cell module is supercooled at the moment; if->Judging that the working temperature of the cell module at the moment is overheated;
s23: extracting supercooled temperature value of working temperature to form supercooled temperature data setExtracting the overheat temperature value of the working temperature to form overheat temperature data set +.>；m 1 Andm 2 the number of temperature values in the supercooling temperature data set and the superheating temperature data set are respectively;
s24: calculating the current coefficient of performance of the cell moduleq 3 ：
；
Wherein,Vfor an optimal operating temperature of the cell module,is the first in the supercooling temperature data seti 1 Temperature value->Is the first in the overheat temperature data groupi 2 The value of the temperature is set to be the same,i 1 、i 2 for the number of temperature values, +.>For the coefficient of influence of supercooling operating temperature on cell module performance +.>Is the influence coefficient of the overheat working temperature to the cell module performance;
the step S3 includes:
s31: according to the deformation coefficientq 1 Attenuation coefficientq 2 And coefficient of performanceq 3 Calculating gradient coefficient of each cell module in battery clusterQ：
；
Wherein,k 3 to influence the gradient coefficient by the deformation,k 4 the coefficient of influence of the capacity fading condition of the cell module on the gradient coefficient,k 5 for the gradient coefficient of the performance of the cell moduleInfluence coefficient;
s32: calculating gradient coefficient of each cell module in the battery cluster to obtain a data set of gradient coefficients，MThe number of cell modules in a cell cluster;
the step S4 includes:
s41: the temperature sensor collects the temperature of each position in the battery clusteraThe smoke sensor collects the smoke concentration at the position above the battery clusterbThe harmful gas sensor collects the concentration of harmful gas at the position above the battery clustercHumidity sensor for collecting humidity of environment in battery compartmentd；
S42: calculating influence coefficient of working environment of battery cell module on fire disaster：
；
Wherein,was the weight coefficient of the light-emitting diode,ein order to be a value of the fluctuation,fluctuation factors of temperature, smoke concentration, harmful gas concentration and environmental humidity on fire disaster, respectively, +.>Is a random proportionality coefficient>Taking a random number between 0 and 1, < + >>For the temperature standard value of each position in the battery cluster, < >>A smoke concentration standard value for a position above the battery cluster, < >>Standard value of harmful gas concentration for the position above the battery cluster,/-for>The humidity standard value of the environment in the battery compartment;
step S5, calculating fire disaster early warning valueUThe method of (1) is as follows:
；
wherein,gradient coefficient calculated for smooth retirement of battery clustersQAnd influence coefficient->Is a range length of (a).
2. An electrochemical energy storage station fire early warning monitoring system for executing the electrochemical energy storage station fire early warning monitoring method of claim 1, which is characterized in that:
the system comprises an early warning module, a control module and a monitoring module;
the monitoring module comprises a temperature sensor arranged at a corresponding position in the battery cluster, a smoke sensor and a harmful gas sensor arranged at a position above the battery cluster, a humidity sensor arranged in the battery compartment and a temperature sensor arranged on the battery cell module;
the control module is used for processing the acquired temperature value, the harmful gas concentration value, the humidity value and the smoke concentration value to acquire the fire risk level of the battery cluster at the moment;
the fire risk level is sent to the early warning module, and the early warning module gives an alarm to staff in a short message and alarm mode.
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