CN-122025923-A - Water-in-water spray cooling system for energy storage battery

CN122025923ACN 122025923 ACN122025923 ACN 122025923ACN-122025923-A

Abstract

The invention provides a water-in-water spray cooling system for an energy storage battery, which relates to the technical field of spray cooling and comprises a data acquisition and processing module, a dynamic risk quantization module, an intelligent spray control module and a spray effect evaluation module. The invention adopts water as fire extinguishing medium, can rapidly extinguish open fire, can continuously cool down to prevent re-combustion, can decontaminate toxic substances generated by electrolyte decomposition, fundamentally eliminates secondary disaster risks, can generate a thermal state three-dimensional dynamic grid map with risk level color marks and thermal runaway propagation path marks and update in real time, enables operators to intuitively grasp the situation of full cabin risk and the spreading trend of fire, can perform differential spraying operation and pre-scheduling preparation on the spraying cooling units of each three-dimensional grid area, realizes accurate on-demand spraying and prospective response, and can quantitatively evaluate the spraying effect and generate an evaluation report.

Inventors

ZHANG XIAOLONG
REN YONGFENG
LIU PENG
HE BIN

Assignees

三峡新能源四子王旗有限公司
内蒙古工业大学

Dates

Publication Date: 20260512
Application Date: 20260409

Claims (9)

1. The water-in-water spray cooling system for the energy storage battery is characterized by comprising the following components: The data acquisition processing module is used for constructing a thermal state three-dimensional dynamic grid map of the battery compartment based on the physical structure of the battery compartment and multi-source sensing data of each three-dimensional grid area of the battery compartment, wherein the thermal state three-dimensional dynamic grid map comprises a battery thermal state development stage of the current monitoring period of each three-dimensional grid area and a corresponding stage quantization coefficient; The dynamic risk quantization module is used for calculating a fixed risk coefficient, an associated risk increment coefficient and a final propagation threat coefficient of the current monitoring period of each three-dimensional grid region based on the data acquired by the data acquisition and processing module, obtaining a comprehensive risk coefficient of the current monitoring period of each three-dimensional grid region based on the fixed risk coefficient, the associated risk increment coefficient and the final propagation threat coefficient, and generating a thermal state three-dimensional dynamic grid map with risk level color identifications and thermal runaway propagation path identifications; the intelligent spray control module is used for controlling the spray cooling units of the three-dimensional grid areas to perform differential spray operation and pre-scheduling preparation based on RGB color values and brightness of the three-dimensional grid areas in each historical monitoring period and the line width and arrow direction of a streamline corresponding to each thermal runaway propagation path on the thermal state three-dimensional dynamic grid map; The spray effect evaluation module is used for inputting the thermal state three-dimensional dynamic grid map corresponding to each three-dimensional grid region with the non-zero comprehensive risk coefficient before and after spraying into the trained spray effect evaluation model, obtaining the spray effect evaluation value of each three-dimensional grid region with the non-zero comprehensive risk coefficient, and generating an evaluation report containing the spray effect evaluation value and the distribution of the three-dimensional grid regions.
2. The water spray cooling system of an energy storage battery pack according to claim 1, wherein the data acquisition and processing module comprises: the three-dimensional grid dividing sub-module is used for dividing the battery compartment into a plurality of three-dimensional grid areas in the horizontal direction and the vertical direction according to the physical structure of the battery compartment and the arrangement mode of each battery pack in the battery compartment; the multi-source sensor data acquisition sub-module is used for acquiring real-time data of a temperature sensor, a smoke sensor, a characteristic gas sensor and a battery pack voltage monitoring sensor which are arranged in each three-dimensional grid area; The battery thermal state development stage analysis submodule is used for determining a battery thermal state development stage and a corresponding stage quantization coefficient of each three-dimensional grid region based on data acquired by the multi-source sensor data acquisition submodule; the dynamic map generation sub-module is used for mapping the battery thermal state development stage and the corresponding stage quantization coefficient of each three-dimensional grid region to the corresponding three-dimensional grid region to generate a thermal state three-dimensional dynamic grid map; The battery thermal state development stage comprises a normal stage, an early warning stage, a local fire extinguishing stage, a total submerged fire extinguishing stage, a re-burning throttling stage and a device re-returning stage, wherein stage quantization coefficients corresponding to the normal stage, the early warning stage, the local fire extinguishing stage, the total submerged fire extinguishing stage, the re-burning throttling stage and the device re-returning stage are respectively a safety stability coefficient, a thermal runaway risk coefficient, a fire intensity coefficient, a disaster spreading coefficient, a re-burning risk coefficient and a system recovery coefficient.
3. The water spray cooling system of an energy storage battery pack of claim 2, wherein the battery thermal state development stage analysis sub-module comprises: The thermal state vector generation unit is used for arranging real-time data of the temperature sensor, the smoke sensor, the characteristic gas sensor and the battery pack voltage monitoring sensor at each acquisition time of the current monitoring period of each three-dimensional grid area according to a fixed sequence to generate a thermal state vector at each acquisition time of the current monitoring period of each three-dimensional grid area; The thermal state matrix generation unit is used for arranging the thermal state vectors of each acquisition time of the current monitoring period of each three-dimensional grid area according to the time sequence to generate a thermal state matrix of the current monitoring period of each three-dimensional grid area; The development stage determining unit is used for inputting the thermal state matrix of the current monitoring period of each three-dimensional grid region into the trained thermal state development stage identification model and outputting the battery thermal state development stage of the current monitoring period of each three-dimensional grid region and the corresponding stage quantization coefficients thereof.
4. The water-in-energy storage battery spray cooling system of claim 1, wherein the dynamic risk quantification module comprises: The fixed risk calculation sub-module is used for calculating the fixed risk coefficient of the current monitoring period of each three-dimensional grid region based on the battery thermal state development stage of the current monitoring period of each three-dimensional grid region and the corresponding stage quantization coefficient; The regional association analysis sub-module is used for acquiring the spatial position of each three-dimensional grid region on the thermal state three-dimensional dynamic grid map, the battery thermal state development stage of the current monitoring period and the corresponding stage quantization coefficients thereof, analyzing the mutual influence between adjacent three-dimensional grid regions and calculating the association risk increment coefficient of the current monitoring period of each three-dimensional grid region; The propagation path prediction submodule is used for acquiring real-time data of the temperature sensor at each acquisition time in the current monitoring period of each three-dimensional grid region, identifying a thermal runaway propagation source region of the current monitoring period, carrying out spatial clustering on each thermal runaway propagation source region to obtain at least one propagation source cluster, acquiring a geometric center coordinate of each propagation source cluster, an equivalent temperature gradient vector of the current monitoring period and an equivalent temperature rise rate of the current monitoring period, generating a thermal runaway propagation path of each propagation source cluster, calculating a propagation threat coefficient of the current monitoring period of each three-dimensional grid region relative to each thermal runaway propagation path, taking the maximum value of the propagation threat coefficient as a final propagation threat coefficient of the current monitoring period of the three-dimensional grid region, rendering each thermal runaway propagation path to a thermal state three-dimensional dynamic grid map, and adjusting brightness of the three-dimensional grid region through which each thermal runaway propagation path passes on the thermal state three-dimensional dynamic grid map; the linkage risk accumulation sub-module is used for carrying out weighted fusion on the fixed risk coefficient, the associated risk increment coefficient and the final propagation threat coefficient of the current monitoring period of each three-dimensional grid region, and calculating to obtain the comprehensive risk coefficient of the current monitoring period of each three-dimensional grid region; The dynamic map rendering sub-module is used for converting the comprehensive risk coefficient into a corresponding RGB color value based on the comprehensive risk coefficient of the current monitoring period of each three-dimensional grid region, and coloring the corresponding three-dimensional grid region in the thermal state three-dimensional dynamic grid map in real time based on the RGB color value corresponding to each three-dimensional grid region to generate the thermal state three-dimensional dynamic grid map with the risk level color identification and the thermal runaway propagation path identification.
5. The water-in-energy storage battery spray cooling system of claim 4, wherein the area correlation analysis submodule comprises: The space neighborhood determining unit is used for determining a set of adjacent three-dimensional grid areas of each three-dimensional grid area based on the central space coordinates of each three-dimensional grid area; The neighborhood influence calculation unit is used for calculating the space influence contribution value of each adjacent three-dimensional grid region to the three-dimensional grid region according to the fixed risk coefficient of the current monitoring period of the adjacent three-dimensional grid region of each three-dimensional grid region and the space distance between each adjacent three-dimensional grid region of each three-dimensional grid region and the three-dimensional grid region; The sensing factor determining unit is used for determining the sensing factor of the three-dimensional grid area on the influence of the neighborhood based on the battery thermal state development stage of the current monitoring period of each three-dimensional grid area; The associated risk increment accumulation unit is used for accumulating the space influence contribution values of all adjacent three-dimensional grid areas of each three-dimensional grid area to the three-dimensional grid area, and multiplying the sensitivity factors of each corresponding three-dimensional grid area to the neighborhood influence to obtain the associated risk increment coefficient of the current monitoring period of each three-dimensional grid area.
6. The water-in-energy storage battery spray cooling system of claim 4, wherein the propagation path prediction submodule comprises: The propagation source clustering unit is used for identifying the thermal runaway propagation source regions in the current monitoring period according to the fixed risk coefficient of the current monitoring period of each three-dimensional grid region to obtain a propagation source region set, clustering according to the spatial positions of each thermal runaway propagation source region, and dividing the thermal runaway propagation source regions with the spatial distance smaller than a preset clustering threshold value into the same propagation source cluster to obtain at least one propagation source cluster; The path characteristic calculation unit is used for determining the geometric center coordinates of each propagation source cluster, the equivalent temperature gradient vector of the current monitoring period and the equivalent temperature rise rate of the current monitoring period; The propagation speed determining unit is used for calculating the thermal runaway propagation speed of the current monitoring period of each propagation source cluster according to the equivalent temperature rise rate of the current monitoring period of each propagation source cluster and the modular length of the equivalent temperature gradient vector; A propagation path determining unit, configured to track a streamline along an equivalent temperature gradient vector direction of each propagation source cluster with a geometric center of the propagation source cluster as a starting point, generate a streamline composed of a series of continuous space points, and use the streamline as a thermal runaway propagation path corresponding to the propagation source cluster; The threat coefficient calculation unit is used for calculating the shortest distance from the central space coordinate of the three-dimensional grid area to each thermal runaway propagation path for each three-dimensional grid area, calculating the propagation threat coefficient of the current monitoring period of the three-dimensional grid area relative to each thermal runaway propagation path based on the shortest distance and the direction relation of the three-dimensional grid area relative to each thermal runaway propagation path, and taking the maximum value in the propagation threat coefficients corresponding to all the thermal runaway propagation paths as the final propagation threat coefficient of the current monitoring period of the three-dimensional grid area; And the path visualization unit is used for rendering each thermal runaway propagation path to the thermal state three-dimensional dynamic grid map.
7. The water spray cooling system of an energy storage battery pack according to claim 6, wherein the path characteristic calculation unit comprises: A cluster geometric center acquisition subunit, configured to determine geometric center coordinates of each propagation source cluster based on center space coordinates of each thermal runaway propagation source region in each propagation source cluster; The cluster temperature gradient acquisition subunit is used for acquiring a temperature gradient vector of each thermal runaway propagation source region in the current monitoring period based on the real-time data of the temperature sensor at each acquisition time in the current monitoring period of each thermal runaway propagation source region, and determining an equivalent temperature gradient vector of each propagation source cluster based on the temperature gradient vector and a fixed risk coefficient of each thermal runaway propagation source region in each propagation source cluster in the current monitoring period; And the cluster temperature rise rate acquisition subunit is used for acquiring the average temperature rise rate of each thermal runaway propagation source region in the current monitoring period based on the real-time data of the temperature sensor at each acquisition time in the current monitoring period of each thermal runaway propagation source region, and determining the equivalent temperature rise rate of each propagation source cluster based on the average temperature rise rate and the fixed risk coefficient of each thermal runaway propagation source region in each propagation source cluster in the current monitoring period.
8. The water-in-energy storage battery spray cooling system as set forth in claim 1, wherein the intelligent spray control module comprises: The risk situation mapping sub-module is used for extracting state parameters of the current monitoring period of each three-dimensional grid region from the thermal state three-dimensional dynamic grid map, fusing and mapping the state parameters into situation points of each three-dimensional grid region in a preset two-dimensional risk situation space, wherein the state parameters comprise RGB color values and brightness values of the current monitoring period of each three-dimensional grid region and thermal runaway propagation path linewidths of positions of the three-dimensional grid region; The spray parameter geometric decision sub-module is used for determining the spray flow, the spray pressure and the spray water temperature of the spray cooling unit of the three-dimensional grid area in the current monitoring period based on the geometric attribute of the situation point of each three-dimensional grid area in the preset two-dimensional risk situation space; The historical track prediction sub-module is used for storing a situation point sequence of each three-dimensional grid area for continuously multiple historical monitoring periods, forming a situation point historical track in a two-dimensional risk situation space by the situation point sequence, predicting the situation point of the three-dimensional grid area in the next monitoring period based on a track extrapolation algorithm, and generating a spraying pre-scheduling parameter in the next monitoring period based on the predicted situation point; The partition execution control sub-module is used for converting the spraying flow, the spraying pressure and the spraying water temperature of the spraying cooling unit of the three-dimensional grid area in the current monitoring period and the spraying pre-dispatching parameters of the next monitoring period into control instructions and respectively sending the control instructions to the spraying cooling units of the corresponding three-dimensional grid area so as to drive the spraying cooling units to execute differentiated spraying operation and pre-dispatching preparation.
9. The water-in-energy storage battery spray cooling system as set forth in claim 8, wherein the spray parameter geometry decision submodule comprises: The flow decision unit is used for determining the spraying flow of the spraying cooling unit of the three-dimensional grid area in the current monitoring period based on Euclidean distance from the situation point of the current monitoring period of each three-dimensional grid area to the original point of the preset two-dimensional risk situation space; The pressure decision unit is used for determining the spray pressure of the spray cooling unit of the three-dimensional grid area in the current monitoring period based on the included angle between the situation point of the current monitoring period of each three-dimensional grid area and the positive direction of the X axis of the preset two-dimensional risk situation space; the water temperature decision unit is used for determining the spray water temperature of the spray cooling unit of the three-dimensional grid area in the current monitoring period based on the projection of the situation point of the current monitoring period of each three-dimensional grid area on the X axis and the Y axis of the preset two-dimensional risk situation space and the current environment temperature of the three-dimensional grid area.

Description

Water-in-water spray cooling system for energy storage battery Technical Field The invention belongs to the technical field of spray cooling, and particularly relates to a water-packaged spray cooling system for an energy storage battery. Background The energy storage system is used as a key technology for supporting new energy consumption, and plays an irreplaceable role in the construction of a novel electric power system. The lithium ion battery has become a mainstream technical route in the electrochemical energy storage field by virtue of the advantages of high energy density, long cycle life, low self-discharge rate and the like. However, lithium ion batteries are prone to causing internal side reactions to run away under abnormal working conditions such as overcharging, overdischarging, short circuit, mechanical abuse and the like, so that the temperature is rapidly increased, the thermal runaway is triggered, and further fire and even explosion accidents are caused. Particularly, the energy storage system is generally arranged in a container type intensive mode, the quantity of batteries is large, the energy density is extremely high, once thermal runaway occurs, the fire is extremely easy to rapidly spread between modules and battery packs, a large quantity of toxic and harmful gas is generated, and the life safety of personnel and the asset safety of a power grid are seriously threatened. The existing battery water-packing spray cooling system mainly has the following technical defects: Firstly, the fire extinguishing medium is selected to have fundamental limitation, the mainstream gas fire extinguishing technology such as perfluoro-hexanone, heptafluoropropane and the like can only extinguish open fire, the thermal runaway battery cannot be effectively cooled, the high temperature inside the battery is continuously accumulated to cause extremely high re-combustion rate, the capability of washing and extinguishing highly toxic gases such as hydrogen fluoride and the like generated by electrolyte decomposition cannot be achieved, the risk of secondary disasters cannot be fundamentally eliminated, secondly, the effect evaluation closed loop is lacking in the visual perception capability of fire conditions, the existing system only depends on scattered sensor threshold value for alarming, the real-time risk distribution map of the whole battery compartment cannot be constructed, the fire spreading path and speed cannot be predicted, operators can only see scattered alarm signals and cannot grasp the global situation, thirdly, the spraying control strategy is rough, the existing system adopts a unified starting mode of the whole compartment, the differential spraying cannot be carried out according to the actual risk level of each area, the water resource is wasted, the capability of predicting future and pre-scheduling preparation based on historical data is not achieved, and fourth, the effect evaluation closed loop is lacking in the effect evaluation, the existing system only can not really evaluate whether the hidden danger exists after the spraying is finished, and whether the hidden danger is really and the hidden danger can not be quantitatively treated after the hidden danger is completely. Disclosure of Invention The invention provides a water-in-water spray cooling system for an energy storage battery, which is used for solving at least one technical problem. In order to solve the technical problems, the invention discloses a water spray cooling system for an energy storage battery pack, which comprises the following components: The data acquisition processing module is used for constructing a thermal state three-dimensional dynamic grid map of the battery compartment based on the physical structure of the battery compartment and multi-source sensing data of each three-dimensional grid area of the battery compartment, wherein the thermal state three-dimensional dynamic grid map comprises a battery thermal state development stage of the current monitoring period of each three-dimensional grid area and a corresponding stage quantization coefficient; The dynamic risk quantization module is used for calculating a fixed risk coefficient, an associated risk increment coefficient and a final propagation threat coefficient of the current monitoring period of each three-dimensional grid region based on the data acquired by the data acquisition and processing module, obtaining a comprehensive risk coefficient of the current monitoring period of each three-dimensional grid region based on the fixed risk coefficient, the associated risk increment coefficient and the final propagation threat coefficient, and generating a thermal state three-dimensional dynamic grid map with risk level color identifications and thermal runaway propagation path identifications; the intelligent spray control module is used for controlling the spray cooling units of the three-dimensional grid areas to perform differential s