Intelligent liquid cooling temperature control system of energy storage bin and control method of intelligent liquid cooling temperature control system

Abstract

The embodiment of the invention provides an intelligent liquid cooling temperature control system of an energy storage bin and a control method thereof, and belongs to the technical field of energy storage bin temperature control. The system comprises a data acquisition module which comprises a plurality of sensors and is used for acquiring data of battery state parameters, cooling liquid parameters and environment parameters; the edge control module is used for carrying out same-stroke balance liquid cooling pipeline control, PACK-level directional fire-fighting linkage control and dynamic switching of the cooling mode of the energy storage bin; the analysis module dynamically distributes the power of the energy storage bin battery cluster and predicts the fault of the energy storage bin intelligent liquid cooling temperature control system; and the cloud module is used for dynamically optimizing the energy efficiency of the energy storage bin by adopting a multi-objective optimization algorithm and adjusting the monitoring frequency of the energy storage bin by adopting a health state evaluation model so as to adjust PACK-level directional fire-fighting linkage control. The aims of reducing the temperature difference, prolonging the service life of the battery, orienting the fire-fighting fault PACK, not needing to stop the whole bin, being high in safety, reducing the auxiliary power consumption, early warning and reducing the fault rate can be achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage bin temperature control, and in particular to an intelligent liquid cooling temperature control system for an energy storage bin and a control method thereof. Background Art

[0002] Energy storage silos are specialized facilities for the centralized storage and management of electrical energy. They transform the traditional electrical system layout, civil engineering design, and construction methods of substations. They primarily utilize containerized or commercial cabinet structures and are widely used in grid-side, industrial and commercial energy storage, and other fields. Their core functions include battery storage, thermal management, safety protection, and system integration.

[0003] Existing energy storage warehouses typically use fans and air conditioners to achieve air cooling to address heat dissipation issues. However, air cooling temperature control relies on air convection to dissipate heat, which cannot quickly remove the heat generated by high-density batteries. Uneven airflow distribution accelerates battery life degradation. Air cooling temperature control has high energy consumption and high operating costs. In high-temperature environments, the heat dissipation efficiency drops sharply, and power outages or power outages cause high losses. In addition, air heat transfer is slow, and the characteristic gases of thermal runaway are difficult to suppress in time after diffusion. Extinguishing a fire in the entire warehouse will cause the entire warehouse to stop operating, resulting in poor safety. The performance in harsh environments is poor, and the failure rate is increased. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent liquid cooling temperature control system for an energy storage bin and a control method thereof, so as to solve the problems in the prior art of uneven air flow distribution during air cooling and heat dissipation in the energy storage bin, which accelerates the attenuation of battery life, has high energy consumption and high operating costs, and the diffusion of thermal runaway characteristic gases can cause the entire bin to stop operating, resulting in a high failure rate.

[0005] In order to achieve the above-mentioned objectives, the present invention provides an intelligent liquid cooling temperature control system for an energy storage bin, comprising: a data acquisition module, comprising a plurality of sensors, for collecting data on battery status parameters, coolant parameters, and environmental parameters; an edge control module, for performing in-process balanced liquid cooling pipeline control, PACK-level directional fire linkage control, and dynamic switching of the cooling mode of the energy storage bin based on the collected data on battery status parameters, coolant parameters, and environmental parameters; an analysis module, for dynamically allocating the power of the battery cluster of the energy storage bin based on the data on the battery status parameters, and predicting faults of the intelligent liquid cooling temperature control system of the energy storage bin based on the data on the coolant parameters and the environmental parameters; a cloud module, for dynamically optimizing the energy efficiency of the energy storage bin by adopting a multi-objective optimization algorithm in combination with the cooling mode of the energy storage bin, and adjusting the monitoring frequency of the energy storage bin by adopting a health status assessment model to adjust the PACK-level directional fire linkage control.

[0006] Optionally, the control of the same-process balanced liquid cooling pipeline includes: reading the temperature difference of multiple branches according to the structure of the same-process balanced liquid cooling pipeline; when the maximum temperature difference of any branch exceeds a threshold, using a flow compensation algorithm to calculate the compensation flow of the branch with the maximum temperature difference, and dynamically adjusting the opening of the corresponding valve of the branch to adjust the temperature difference of each branch.

[0007] Optionally, the PACK-level directional fire linkage control includes: when the aerosol concentration of a single PACK exceeds the limit, responding to the first-level early warning mechanism and increasing the temperature sampling frequency of the PACK; when two PACKs alarm at the same time, responding to the second-level alarm mechanism, triggering the sound and light alarm and starting the pressurization of the adjacent liquid cooling plate; when thermal runaway is confirmed, responding to the third-level fire extinguishing mechanism, and cutting off the faulty PACK circuit through the BMS, using perfluorohexanone nozzles for directional spraying to extinguish the fire, and automatically increasing the flow of the adjacent PACK liquid cooling plate after the fire is extinguished to form a low-temperature isolation zone to isolate heat diffusion.

[0008] Optionally, the dynamic switching of the cooling mode of the energy storage bin includes: when the temperature of all PACKs is lower than the heating threshold, the energy storage bin is in heating mode, and the PTC heater is started to adjust the liquid temperature of the coolant; when the temperature of any PACK is higher than the cooling threshold, the energy storage bin is in cooling mode, and the compressor is started to adjust the liquid temperature of the coolant; when the temperature of all PACKs is higher than the first preset value and lower than the second preset value, the energy storage bin is in self-circulation mode, and the water pump is run to naturally dissipate heat through the ambient cold source.

[0009] Optionally, the power of the energy storage compartment battery cluster is dynamically allocated based on the data of the battery status parameters, including: when the data of the battery status parameters show a first health state, the corresponding energy storage compartment battery cluster takes priority in high-rate charging and discharging tasks; when the data of the battery status parameters show a second health state, the power of the corresponding energy storage compartment battery cluster is limited to slow down the attenuation rate.

[0010] Optionally, the data based on the coolant parameters and the environmental parameters predicts the failure of the intelligent liquid cooling temperature control system of the energy storage warehouse, including: predicting the leakage or blockage risk of the intelligent liquid cooling temperature control system of the energy storage warehouse based on the historical trends of the coolant flow, pressure, and conductivity; when the maximum temperature difference of the intelligent liquid cooling temperature control system of the energy storage warehouse exceeds the dynamic threshold, triggering the pipeline air tightness detection.

[0011] Optionally, the historical trends of coolant flow, pressure, and conductivity are used to predict the leakage or blockage risk of the intelligent liquid cooling temperature control system of the energy storage warehouse, including: using a leakage prediction model to calculate the pressure change rate of the coolant and trigger a leakage alarm; using a blockage prediction model to calculate the flow attenuation coefficient of the coolant and prompt local blockage; using a degradation monitoring model to calculate the temperature-corrected conductivity of the coolant and prompt coolant dilution.

[0012] Optionally, the multi-objective optimization algorithm is used in combination with the cooling mode of the energy storage warehouse to dynamically optimize the energy efficiency of the energy storage warehouse, including: based on the data of the battery status parameters, the data of the environmental parameters and the electricity price signal data, the multi-objective optimization algorithm is used to calculate the optimal cooling power consumption of the intelligent liquid cooling temperature control system of the energy storage warehouse; when the electricity price signal data reaches a peak, the liquid temperature of the coolant is increased to reduce the power consumption of the compressor; during the low temperature period, the energy storage warehouse is switched to the self-circulation mode to dissipate heat naturally through the environmental cold source.

[0013] Optionally, the health status assessment model is used to adjust the monitoring frequency of the energy storage bin to adjust the PACK-level directional fire linkage control, including: using the health status assessment model to calculate the health score of the battery cluster of the energy storage bin; when the health score is lower than the health threshold, the monitoring frequency of the PACK is automatically increased, and the high-risk PACK is pre-filled with fire-fighting agents.

[0014] On the other hand, the present invention provides a control method for intelligent liquid cooling temperature control of an energy storage bin, which includes: using multiple sensors to collect data on battery status parameters, coolant parameters, and environmental parameters; based on the collected data on battery status parameters, coolant parameters, and environmental parameters, performing in-process balanced liquid cooling pipeline control, PACK-level directional fire linkage control, and dynamically switching the cooling mode of the energy storage bin; based on the data on the battery status parameters, dynamically allocating the power of the energy storage bin battery cluster, and based on the data on the coolant parameters and the environmental parameters, predicting the failure of the intelligent liquid cooling temperature control system of the energy storage bin; using a multi-objective optimization algorithm, combined with the cooling mode of the energy storage bin, to dynamically optimize the energy efficiency of the energy storage bin, and using a health status assessment model to adjust the monitoring frequency of the energy storage bin to adjust the PACK-level directional fire linkage control.

[0015] Through the above technical solution, through the same-process balanced liquid cooling pipeline control and flow compensation algorithm, the coolant flow of each branch can be dynamically adjusted to eliminate the uneven flow distribution of parallel pipelines, reduce the temperature difference between battery cells, extend battery life, and reduce the capacity attenuation caused by thermal stress; through PACK-level directional fire linkage control, the fire fault PACK can be targeted without the need to shut down the entire warehouse, the availability of the entire warehouse is improved, and the safety is high. In addition, liquid cooling boost suppresses heat diffusion and shortens the thermal runaway response time; by dynamically switching the cooling mode of the energy storage warehouse, the auxiliary power consumption can be reduced and operating costs can be saved; through the fault prediction model, various faults of the intelligent liquid cooling temperature control system of the energy storage warehouse can be predicted, and the health status assessment model can be used to provide very early warning to reduce the failure rate of the system.

[0016] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following detailed description, they are used to explain the embodiments of the present invention, but do not constitute a limitation of the embodiments of the present invention. In the accompanying drawings: Figure 1 This is a schematic diagram of the process of an intelligent liquid cooling temperature control system for an energy storage bin of the present invention. Figure 1 ; Figure 2 This is a schematic diagram of the process of an intelligent liquid cooling temperature control system for an energy storage bin of the present invention. Figure 2 ; Figure 3 It is a schematic diagram of the process of controlling the same-process balanced liquid cooling pipeline in the present invention; Figure 4 It is a flow chart of a control method for intelligent liquid cooling temperature control of an energy storage bin according to the present invention. DETAILED DESCRIPTION

[0018] The following describes the specific implementation of the embodiment of the present invention in detail with reference to the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the embodiment of the present invention and is not used to limit the embodiment of the present invention.

[0019] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application are in compliance with the relevant provisions of national laws and regulations. In the embodiments of this application, certain software, components, models, and other existing solutions in the industry may be mentioned. These should be considered as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of this application, but it does not mean that the applicant has or will necessarily use such solutions.

[0020] First embodiment: Please refer to Figure 1 and Figure 2 An embodiment of the present invention provides an intelligent liquid cooling temperature control system for an energy storage bin. The intelligent liquid cooling temperature control system may include: a data acquisition module, an edge control module, an analysis module, and a cloud module. The data acquisition module includes a variety of sensors for collecting data on battery status parameters, coolant parameters, and environmental parameters; the edge control module is used to perform in-process balanced liquid cooling pipeline control, PACK-level directional fire linkage control, and dynamically switch the cooling mode of the energy storage bin based on the collected data on battery status parameters, coolant parameters, and environmental parameters; the analysis module is used to dynamically allocate the power of the energy storage bin battery cluster based on the data on battery status parameters, and predict the failure of the intelligent liquid cooling temperature control system of the energy storage bin based on the data on coolant parameters and environmental parameters; the cloud module is used to dynamically optimize the energy efficiency of the energy storage bin by using a multi-objective optimization algorithm in combination with the cooling mode of the energy storage bin, and to adjust the monitoring frequency of the energy storage bin by using a health status assessment model to adjust the PACK-level directional fire linkage control.

[0021] According to the above embodiment, the edge control module can dynamically adjust the coolant flow of each branch, eliminate the uneven flow distribution of parallel pipelines, reduce the temperature difference between battery cells, extend battery life, reduce capacity attenuation caused by thermal stress, and can target fire fault PACK without shutting down the entire warehouse. The availability of the entire warehouse is improved, and the safety is high. The cooling mode of the energy storage warehouse can also be dynamically switched to reduce auxiliary power consumption and save operating costs. Through the analysis module and the cloud module, various faults of the intelligent liquid cooling temperature control system of the energy storage warehouse can be predicted, and through the health status assessment model, very early warning can be given to reduce the failure rate of the system.

[0022] In an embodiment of the present invention, the data acquisition module may include multiple sensors for collecting data on battery status parameters, coolant parameters, and environmental parameters.

[0023] Among them, battery status parameters can include temperature parameters, electrical performance parameters, and fire signal parameters. When collecting temperature parameters, NTC temperature sensors can be deployed on the surface of each battery cell to collect single-cell temperature in real time. Water temperature sensors can be installed at the inlet and outlet of the liquid cooling plate to monitor the coolant temperature difference. Infrared thermal imagers can be configured in each battery pack to assist in identifying local hot spots. When collecting electrical performance parameters, Hall sensors can be used to collect real-time charge and discharge current or voltage to calculate the battery's state of charge. The AC injection method can be used to periodically measure the battery's internal resistance to assess the battery's health status. When collecting fire signal parameters, an aerosol concentration sensor can be installed on the top of the PACK to monitor thermal runaway characteristic gases such as CO and H2 in real time.

[0024] Furthermore, when collecting coolant parameters, the branch flow can be monitored by an electromagnetic flowmeter, the pipeline pressure can be monitored by a pressure sensor, and the coolant purity can be detected by a conductivity sensor; when collecting environmental parameters, temperature and humidity sensors can be deployed at the four corners of the energy storage cabin, and a light intensity sensor can be installed on the top.

[0025] In an embodiment of the present invention, the edge control module can be used to perform in-process balanced liquid cooling pipeline control, PACK-level directional fire linkage control, and dynamically switch the cooling mode of the energy storage bin based on the collected data of battery status parameters, coolant parameters, and environmental parameters.

[0026] Please refer to Figure 3 In a preferred embodiment of the present invention, performing the same-process balanced liquid cooling pipeline control may include: Step S1: Read the temperature difference of multiple branches according to the structure of the same-process balanced liquid cooling pipeline; Among them, the structure of the same-process balanced liquid cooling pipeline control can be designed to branch out N equal-length branches from the main liquid inlet pipe, with the branch length deviation not exceeding 2%. An electric proportional control valve is installed at the branch inlet, and a parallel circuit can be used to avoid the accumulation of temperature differences at the ends of the series pipelines.

[0027] Step S2: When the maximum temperature difference of any branch exceeds the threshold, a flow compensation algorithm is used to calculate the compensation flow of the branch with the maximum temperature difference, and dynamically adjust the opening of the valve corresponding to the branch to adjust the temperature difference of each branch.

[0028] Furthermore, the compensation flow can be expressed by the following formula: :

[0029] in, Indicates the total system flow, Indicates the maximum temperature difference of the branch battery pack, represents the temperature difference correlation function, Indicates the dynamically adjusted flow distribution coefficient.

[0030] For example, for the same-flow balanced liquid cooling pipeline of a certain energy storage warehouse, the surface temperature of the battery cells in each battery pack of the energy storage warehouse can be collected in real time through the NTC temperature sensor, the coolant flow of each branch can be monitored through the electromagnetic flowmeter, and the maximum temperature difference of each battery pack can be calculated through the edge controller; if the maximum temperature difference of a branch exceeds the threshold, the flow compensation algorithm is started to calculate the compensation flow required by the branch, and the opening of the electric proportional control valve is adjusted to increase the flow supply of the branch; then the temperature difference of each battery pack after adjustment is monitored in real time until the maximum temperature difference of the entire warehouse is ≤3°C. In this way, the branch flow can be dynamically redistributed to eliminate the uneven flow distribution of parallel pipelines, and the temperature difference between battery cells can be achieved to ≤3°C, thereby extending the battery life and reducing the capacity degradation caused by thermal stress.

[0031] In a preferred embodiment of the present invention, the PACK-level directional fire linkage control may include: when the aerosol concentration of a single PACK exceeds the limit, responding to the first-level early warning mechanism and increasing the temperature sampling frequency of the PACK; when two PACKs alarm at the same time, responding to the second-level alarm mechanism, triggering the sound and light alarm and starting the pressurization of the adjacent liquid cooling plate; when thermal runaway is confirmed, responding to the third-level fire extinguishing mechanism, and cutting off the faulty PACK circuit through the BMS, using perfluorohexanone nozzles for directional spraying to extinguish the fire, and automatically increasing the flow of the adjacent PACK liquid cooling plate after the fire is extinguished to form a low-temperature isolation zone to isolate heat diffusion.

[0032] Among them, PACK-level directional firefighting can accurately extinguish fires in battery packs, prevent the fire from spreading to other surrounding battery packs or equipment, and reduce losses. It can use specially designed firefighting equipment and fire extinguishing agents to accurately deliver the fire extinguishing agent to the location of the battery pack where the fire occurred according to the characteristics of the battery pack and the characteristics of the fire, thereby achieving the purpose of efficient fire extinguishing and fire control.

[0033] For example, for a certain energy storage warehouse, an aerosol concentration sensor can be used to detect characteristic gases (such as CO or H2) in the PACK in real time. When the concentration continuously exceeds the first-level threshold, a first-level warning is triggered, and the temperature sampling frequency of the PACK is increased, and the flow rate of the liquid cooling plate in the adjacent area is increased by a percentage of the basic value; when multiple PACKs alarm at the same time, a second-level warning is triggered. At this time, the BMS limits the charging and discharging power of the faulty PACK and starts the liquid cooling system boost mode; when thermal runaway is confirmed, the faulty PACK circuit is cut off, and the perfluorohexanone nozzle is triggered for directional spraying to extinguish the fire; after the fire is extinguished, the flow rate of the liquid cooling plate of the adjacent battery pack is automatically increased to form a low-temperature isolation zone. In this way, the directional firefighting only affects the faulty PACK, without the need to shut down the entire warehouse, and the availability of the entire warehouse is improved by more than 50%. The liquid cooling boost suppresses heat diffusion and shortens the response time to thermal runaway.

[0034] In a preferred embodiment of the present invention, the dynamic switching of the cooling mode of the energy storage bin includes: when all PACK temperatures are lower than the heating threshold, the energy storage bin is in the heating mode, and the PTC heater is started to adjust the liquid temperature of the coolant; when any PACK temperature is higher than the cooling threshold, the energy storage bin is in the cooling mode, and the compressor is started to adjust the liquid temperature of the coolant; when all PACK temperatures are higher than the first preset value and lower than the second preset value, the energy storage bin is in the self-circulation mode, and the water pump is run to naturally dissipate heat through the ambient cold source, thereby achieving the purpose of reducing auxiliary power consumption and saving operating costs by dynamically switching the cooling mode of the energy storage bin.

[0035] Furthermore, the power required for compressor cooling can be expressed by the following formula: :

[0036] in, represents the specific heat capacity of the coolant, Indicates the coolant density, Indicates the flow rate measured by the flow meter, Indicates the inlet water temperature of the liquid cooling plate. Indicates the outlet water temperature of the liquid cooling plate. Indicates the system energy efficiency ratio (for example, 3.0~4.5).

[0037] In an embodiment of the present invention, the analysis module can be used to dynamically allocate the power of the energy storage warehouse battery cluster based on the data of battery status parameters, and predict the failure of the energy storage warehouse intelligent liquid cooling temperature control system based on the data of coolant parameters and environmental parameters.

[0038] In a preferred embodiment of the present invention, the power of the energy storage bin battery cluster is dynamically allocated based on the data of the battery status parameters, which may include: when the data of the battery status parameters show a first health state, the corresponding energy storage bin battery cluster takes priority in high-rate charge and discharge tasks; when the data of the battery status parameters show a second health state, the power of the corresponding energy storage bin battery cluster is limited to slow down the attenuation rate.

[0039] Furthermore, the objective function of dynamic power allocation of battery cluster can be expressed as follows:

[0040] in, Indicates the The health of the cluster, Indicates allocation to The power of the cluster.

[0041] In a preferred embodiment of the present invention, based on the data of coolant parameters and environmental parameters, the failure of the intelligent liquid cooling temperature control system of the energy storage warehouse is predicted, which may include: predicting the leakage or blockage risk of the intelligent liquid cooling temperature control system of the energy storage warehouse based on the historical trends of coolant flow, pressure, and conductivity; when the maximum temperature difference of the intelligent liquid cooling temperature control system of the energy storage warehouse exceeds the dynamic threshold, triggering the pipeline air tightness detection.

[0042] In a preferred embodiment of the present invention, based on the historical trends of coolant flow, pressure, and conductivity, the risk of leakage or blockage of the intelligent liquid cooling temperature control system of the energy storage warehouse can be predicted, which can include: using a leakage prediction model to calculate the pressure change rate of the coolant and triggering a leakage alarm. The pressure change rate can be expressed by the following formula: :

[0043] in, Indicates the inlet pressure, Indicates the outlet pressure, represents the coolant volume, used to normalize the pressure change, Indicates the monitoring time interval. If (threshold), it means that leakage will cause the pipeline pressure to continue to drop. A positive increase indicates a leakage risk, thereby triggering a leakage alarm.

[0044] Furthermore, a blockage prediction model can be used to calculate the coolant flow attenuation coefficient and indicate local blockage. The flow attenuation coefficient can be expressed by the following formula: :

[0045] in, Indicates the calculated flow rate, Indicates the actual measured value of the flow meter. Indicates the design rated flow rate. If , it reflects that the flow resistance is abnormal, and the blockage causes the actual flow rate to be lower than the theoretical calculated value, indicating local blockage.

[0046] Furthermore, the degradation monitoring model can be used to calculate the temperature-corrected conductivity of the coolant and indicate coolant dilution. The temperature-corrected conductivity can be expressed as :

[0047] in, Indicates the conductivity measured by the sensor. Indicates the medium characteristic coefficient (for example, water ≈ 0.02, ethylene glycol solution ≈ 0.015), Indicates the reference temperature (usually 25°C), Indicates the actual temperature of the coolant. Conductivity is positively correlated with ion concentration. Temperature correction can more accurately reflect the liquid purity. If the value continues to rise, it indicates ionic contamination (for example, metal precipitation or additive decomposition). A sudden drop indicates coolant dilution (e.g., water infiltration).

[0048] In a preferred embodiment of the present invention, when the maximum temperature difference of the intelligent liquid cooling temperature control system of the energy storage warehouse exceeds the dynamic threshold, the pipeline air tightness detection is triggered. When the temperature difference suddenly changes, it indicates that there is gas accumulation or micro leakage in the pipeline, which will trigger the following actions: first turn off the circulation pump and let the system stand; then monitor the pressure decay rate. ;like , it is confirmed that the seal has failed. In this way, multiple groups of fault prediction models can be used to predict various faults in the intelligent liquid cooling temperature control system of the energy storage warehouse, thereby reducing the system failure rate.

[0049] In an embodiment of the present invention, the cloud module can be used to adopt a multi-objective optimization algorithm, combined with the cooling mode of the energy storage warehouse, to dynamically optimize the energy efficiency of the energy storage warehouse, and adopt a health status assessment model to adjust the monitoring frequency of the energy storage warehouse to adjust the PACK-level directional fire linkage control.

[0050] In a preferred embodiment of the present invention, a multi-objective optimization algorithm is used in combination with the cooling mode of the energy storage bin to dynamically optimize the energy efficiency of the energy storage bin, which may include: calculating the optimal cooling power consumption of the intelligent liquid cooling temperature control system of the energy storage bin based on the data of battery status parameters, environmental parameters and electricity price signal data; increasing the liquid temperature of the coolant during peak hours of electricity price signal data to reduce the power consumption of the compressor; switching the energy storage bin to self-circulation mode during low temperature periods to naturally dissipate heat through the environmental cold source.

[0051] Furthermore, the function of the multi-objective optimization algorithm can be expressed as follows:

[0052] in, Indicates the real-time power consumption of the cooling system. Indicates the real-time electricity price of the power grid, Indicates the maximum temperature difference of the entire warehouse. 、 and These are all configurable weight coefficients. Based on this, the optimal solution for cooling power consumption can be obtained by integrating battery SOC or SOH, environmental parameters, and electricity price signals.

[0053] For example, the intelligent liquid cooling temperature control system of a certain energy storage warehouse can first obtain real-time data on grid electricity price signals, ambient temperature, and average battery health status from the cloud center, and then calculate the optimal coolant temperature through the function of the multi-objective optimization algorithm. The optimal coolant temperature is increased during peak electricity prices, and the self-circulation mode of the energy storage warehouse is switched during low temperature periods. High-SOH battery clusters take priority over high-rate tasks, while low-SOH battery clusters operate at limited power, and the thermal management load is simultaneously reduced. In this way, the goal of integrating economic efficiency and thermal management requirements, reducing auxiliary power consumption, extending the system's full life cycle discharge, and improving energy utilization can be achieved.

[0054] In a preferred embodiment of the present invention, a health status assessment model is used to adjust the monitoring frequency of the energy storage bin to adjust the PACK-level directional fire linkage control, which may include: using the health status assessment model to calculate the health score of the energy storage bin battery cluster. When the health score is lower than the health threshold, the monitoring frequency of the PACK is automatically increased, and the high-risk PACK is pre-filled with fire-fighting agents. In this way, the purpose of providing extremely early warning and reducing the failure rate of the system can be achieved through the health status assessment model.

[0055] Furthermore, the health score can be expressed as follows: :

[0056] in, Indicates the battery charge status. Indicates the battery health status, calculated based on the internal resistance growth rate. Indicates the maximum temperature difference between PACKs, 、 and These are weight coefficients (the default values ​​are 0.4, 0.4, and 0.2).

[0057] In a preferred embodiment of the present invention, the charging cut-off voltage can be dynamically adjusted based on historical cloud data (for example, a high SOH battery can be allowed to be slightly overcharged), and the discharge depth is automatically limited in a low-temperature environment, thereby correcting the charge and discharge curve and extending the battery life.

[0058] Second embodiment: Please refer to Figure 4 The present invention also provides a method for controlling the intelligent liquid cooling temperature of an energy storage bin, the method comprising: Step S110: using a variety of sensors to collect data on battery status parameters, coolant parameters, and environmental parameters; Step S120: Based on the collected data of battery status parameters, coolant parameters, and environmental parameters, perform in-process balanced liquid cooling pipeline control, PACK-level directional fire linkage control, and dynamically switch the cooling mode of the energy storage bin; Step S130: Dynamically allocate power to the battery cluster in the energy storage compartment based on the battery status parameter data, and predict failures of the intelligent liquid cooling temperature control system in the energy storage compartment based on the coolant parameter and environmental parameter data; Step S140: Using a multi-objective optimization algorithm, combined with the cooling mode of the energy storage bin, dynamically optimize the energy efficiency of the energy storage bin, and using a health status assessment model to adjust the monitoring frequency of the energy storage bin to adjust the PACK-level directional fire linkage control.

[0059] Based on this, an embodiment of the present invention provides an intelligent liquid cooling temperature control system for an energy storage bin and a control method thereof. Through the same-process balanced liquid cooling pipeline control and flow compensation algorithm, the coolant flow of each branch can be dynamically adjusted to eliminate the uneven flow distribution of parallel pipelines, reduce the temperature difference between battery cells, extend the battery life, and reduce the capacity attenuation caused by thermal stress; through PACK-level directional fire linkage control, the fire fault PACK can be targeted without the need to shut down the entire bin, the availability of the entire bin is improved, and the safety is high. In addition, liquid cooling boost suppresses heat diffusion and shortens the thermal runaway response time; by dynamically switching the cooling mode of the energy storage bin, auxiliary power consumption can be reduced and operating costs can be saved; through the fault prediction model, various faults of the intelligent liquid cooling temperature control system of the energy storage bin can be predicted, and through the health status assessment model, very early warning can be performed to reduce the failure rate of the system.

[0060] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0061] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0062] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0063] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0064] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0065] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0066] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can be implemented using any method or technology for information storage. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change RAM (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media, such as modulated data signals and carrier waves.

[0067] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0068] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. An intelligent liquid cooling temperature control system for an energy storage bin, characterized in that: The intelligent liquid cooling temperature control system includes: The data acquisition module includes a variety of sensors for collecting data on battery status parameters, coolant parameters, and environmental parameters; The edge control module is used to perform on-line balanced liquid cooling pipe control, PACK-level directional fire linkage control, and dynamically switch the cooling mode of the energy storage bin based on the collected data of battery status parameters, coolant parameters, and environmental parameters; An analysis module, configured to dynamically allocate power to the battery cluster of the energy storage compartment based on the data of the battery status parameters, and to predict failures of the intelligent liquid cooling temperature control system of the energy storage compartment based on the data of the coolant parameters and the environmental parameters; The cloud module is used to dynamically optimize the energy efficiency of the energy storage warehouse by adopting a multi-objective optimization algorithm and combining the cooling mode of the energy storage warehouse. It also uses a health status assessment model to adjust the monitoring frequency of the energy storage warehouse to adjust the PACK-level directional fire linkage control.

2. The intelligent liquid cooling temperature control system according to claim 1, characterized in that: The same-process balanced liquid cooling pipeline control includes: According to the structure of the same-process balanced liquid cooling pipeline, the temperature difference of multiple branches is read; When the maximum temperature difference of any branch exceeds the threshold, the flow compensation algorithm is used to calculate the compensation flow of the branch with the maximum temperature difference, and dynamically adjust the opening of the corresponding valve of the branch to adjust the temperature difference of each branch.

3. The intelligent liquid cooling temperature control system according to claim 1, characterized in that: The PACK-level directional fire linkage control includes: When the aerosol concentration of a single PACK exceeds the limit, the first-level warning mechanism will be activated and the temperature sampling frequency of the PACK will be increased; When two PACKs alarm at the same time, the secondary alarm mechanism responds, triggering the sound and light alarm and starting the pressurization of the adjacent liquid cooling plate; When thermal runaway is confirmed, the three-level fire extinguishing mechanism responds, and the faulty PACK circuit is cut off through the BMS. The fire is extinguished by directional spraying of perfluorohexanone nozzles. After the fire is extinguished, the flow rate of the adjacent PACK liquid cooling plate is automatically increased to form a low-temperature isolation zone to isolate heat spread.

4. The intelligent liquid cooling temperature control system according to claim 1, characterized in that: The dynamic switching of the cooling mode of the energy storage bin includes: When the temperature of all packs is lower than the heating threshold, the energy storage compartment is in heating mode and the PTC heater is activated to adjust the coolant temperature. When the temperature of any PACK is greater than the cooling threshold, the energy storage tank is in cooling mode and the compressor is started to adjust the coolant temperature. When the temperature of all PACKs is greater than the first preset value and less than the second preset value, the energy storage bin is in self-circulation mode and the water pump is running to naturally dissipate heat through the ambient cold source.

5. The intelligent liquid cooling temperature control system according to claim 1, characterized in that: The method of dynamically allocating the power of the energy storage battery cluster based on the data of the battery status parameters includes: When the data of the battery status parameter shows a first health state, the corresponding energy storage battery cluster takes priority in high-rate charge and discharge tasks; When the data of the battery status parameter shows a second health state, the power of the corresponding energy storage compartment battery cluster is limited to slow down the decay rate.

6. The intelligent liquid cooling temperature control system according to claim 1, characterized in that: The predicting of a failure of the intelligent liquid cooling temperature control system of the energy storage warehouse based on the data of the coolant parameters and the environmental parameters includes: Predict leakage or blockage risks in the energy storage warehouse's intelligent liquid cooling temperature control system based on historical trends in coolant flow, pressure, and conductivity; When the maximum temperature difference of the energy storage warehouse's intelligent liquid cooling temperature control system exceeds the dynamic threshold, the pipeline air tightness detection is triggered.

7. The intelligent liquid cooling temperature control system according to claim 6, characterized in that: The prediction of leakage or blockage risk of the intelligent liquid cooling temperature control system of the energy storage warehouse based on the historical trends of coolant flow, pressure, and conductivity includes: Use a leakage prediction model to calculate the coolant pressure change rate and trigger a leakage alarm; Use the blockage prediction model to calculate the coolant flow attenuation coefficient and indicate local blockage; A degradation monitoring model is used to calculate the temperature-corrected conductivity of the coolant and provide a warning of coolant dilution.

8. The intelligent liquid cooling temperature control system according to claim 1, characterized in that: The multi-objective optimization algorithm is used to dynamically optimize the energy efficiency of the energy storage bin in combination with the cooling mode of the energy storage bin, including: Based on the battery status parameter data, environmental parameter data, and electricity price signal data, a multi-objective optimization algorithm is used to calculate the optimal cooling power consumption of the intelligent liquid cooling temperature control system of the energy storage warehouse; When electricity price signal data reaches a peak, the coolant temperature is increased to reduce compressor power consumption; During low temperature periods, the energy storage compartment switches to self-circulation mode and dissipates heat naturally through the ambient cold source.

9. The intelligent liquid cooling temperature control system according to claim 1, characterized in that: The health status assessment model is used to adjust the monitoring frequency of the energy storage bin to adjust the PACK-level directional fire linkage control, including: A health status assessment model is used to calculate the health score of the energy storage battery cluster. When the health score is lower than the health threshold, the monitoring frequency of the PACK is automatically increased, and the high-risk PACK is pre-filled with fire-fighting agents.

10. A method for controlling intelligent liquid cooling temperature control of an energy storage bin, characterized in that: The control method includes: Use multiple sensors to collect data on battery status parameters, coolant parameters, and environmental parameters; Based on the collected data of battery status parameters, coolant parameters and environmental parameters, it performs synchronous balanced liquid cooling pipeline control, PACK-level directional fire linkage control, and dynamically switches the cooling mode of the energy storage bin; Based on the data of the battery status parameters, the power of the energy storage warehouse battery cluster is dynamically allocated, and based on the data of the coolant parameters and the environmental parameters, the failure of the energy storage warehouse intelligent liquid cooling temperature control system is predicted; A multi-objective optimization algorithm is used, combined with the cooling mode of the energy storage warehouse, to dynamically optimize the energy efficiency of the energy storage warehouse. A health status assessment model is used to adjust the monitoring frequency of the energy storage warehouse to adjust the PACK-level directional fire linkage control.

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